From 34b0a082074b073eb14c2bd93c0c070e20ddcd16 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Fri, 10 Nov 2023 22:04:50 -0700 Subject: [PATCH 01/39] gguf-py: Refactor and allow reading/modifying existing GGUF files (#3981) * gguf-py: Refactor and add file reading support * Replay changes from #3871 Credit to @cebtenzzre for that pull * Various type annotation fixes. * sort imports with isort (again) * Fix missing return statement in add_tensor * style cleanup with flake8 * fix NamedTuple and Enum usage * Fix an issue with state init in GGUFReader Move examples to an examples/ directory Clean up examples Add an example of modifying keys in a GGUF file Update documentation with info on examples Try to support people importing gguf/gguf.py directly * Damagage is not a word. * Clean up gguf-py/examples/modify_gguf.py whitespace Co-authored-by: Jared Van Bortel * Update gguf-py/examples/modify_gguf.py formatting Co-authored-by: Jared Van Bortel * Update gguf-py/gguf/gguf_reader.py type hint Co-authored-by: Jared Van Bortel * Make examples executable, formatting changes * Add more information to GGUFReader and examples comments * Include a gguf Python package version bump * Add convert-gguf-endian.py script * cleanup * gguf-py : bump minor version * Reorganize scripts * Make GGUFReader endian detection less arbitrary * Add JSON dumping support to gguf-dump.py Which I kind of regret now * A few for gguf-dump.py cleanups * Murder accidental tuple in gguf-py/scripts/gguf-dump.py Co-authored-by: Jared Van Bortel * cleanup * constants : remove unneeded type annotations * fix python 3.8 compat * Set up gguf- scripts in pyproject.toml * And include scripts/__init__.py, derp * convert.py: We can't currently support Q8_0 on big endian. * gguf-py: SpecialVocab: Always try available sources for special token ids gguf-py: SpecialVocab: Try to load merges from merges.txt if not in tokenizer.json gguf-py: SpecialVocab: Add 'add_bos_token' type bools to GGUF metadata u * cleanup * Promote add_X_token to GGUF metadata for BOS and EOS --------- Co-authored-by: Jared Van Bortel Co-authored-by: Jared Van Bortel --- convert-baichuan-hf-to-gguf.py | 2 +- convert-llama-ggml-to-gguf.py | 24 +- convert-persimmon-to-gguf.py | 2 +- convert.py | 16 +- .../convert-train-checkpoint-to-gguf.py | 2 +- gguf-py/README.md | 10 + gguf-py/examples/writer.py | 40 + gguf-py/gguf/__init__.py | 6 +- gguf-py/gguf/constants.py | 470 +++++++ gguf-py/gguf/gguf.py | 1149 +---------------- gguf-py/gguf/gguf_reader.py | 264 ++++ gguf-py/gguf/gguf_writer.py | 409 ++++++ gguf-py/gguf/tensor_mapping.py | 257 ++++ gguf-py/gguf/vocab.py | 164 +++ gguf-py/pyproject.toml | 8 +- gguf-py/scripts/__init__.py | 12 + gguf-py/scripts/gguf-convert-endian.py | 113 ++ gguf-py/scripts/gguf-dump.py | 116 ++ gguf-py/scripts/gguf-set-metadata.py | 90 ++ gguf-py/tests/test_gguf.py | 4 +- 20 files changed, 1982 insertions(+), 1176 deletions(-) create mode 100755 gguf-py/examples/writer.py create mode 100644 gguf-py/gguf/constants.py create mode 100644 gguf-py/gguf/gguf_reader.py create mode 100644 gguf-py/gguf/gguf_writer.py create mode 100644 gguf-py/gguf/tensor_mapping.py create mode 100644 gguf-py/gguf/vocab.py create mode 100644 gguf-py/scripts/__init__.py create mode 100755 gguf-py/scripts/gguf-convert-endian.py create mode 100755 gguf-py/scripts/gguf-dump.py create mode 100755 gguf-py/scripts/gguf-set-metadata.py diff --git a/convert-baichuan-hf-to-gguf.py b/convert-baichuan-hf-to-gguf.py index 67ccbe99f132a..789602351ca9d 100755 --- a/convert-baichuan-hf-to-gguf.py +++ b/convert-baichuan-hf-to-gguf.py @@ -16,7 +16,7 @@ from sentencepiece import SentencePieceProcessor # type: ignore[import] if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf diff --git a/convert-llama-ggml-to-gguf.py b/convert-llama-ggml-to-gguf.py index 871add64d4ca7..d898d81c4c445 100755 --- a/convert-llama-ggml-to-gguf.py +++ b/convert-llama-ggml-to-gguf.py @@ -12,29 +12,9 @@ import os if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf -# Note: Does not support GGML_QKK_64 -QK_K = 256 -# Items here are (block size, type size) -GGML_QUANT_SIZES = { - gguf.GGMLQuantizationType.F32 : (1, 4), - gguf.GGMLQuantizationType.F16 : (1, 2), - gguf.GGMLQuantizationType.Q4_0 : (32, 2 + 16), - gguf.GGMLQuantizationType.Q4_1 : (32, 2 + 2 + 16), - gguf.GGMLQuantizationType.Q5_0 : (32, 2 + 4 + 16), - gguf.GGMLQuantizationType.Q5_1 : (32, 2 + 2 + 4 + 16), - gguf.GGMLQuantizationType.Q8_0 : (32, 2 + 32), - gguf.GGMLQuantizationType.Q8_1 : (32, 4 + 4 + 32), - gguf.GGMLQuantizationType.Q2_K : (256, 2 + 2 + QK_K // 16 + QK_K // 4), - gguf.GGMLQuantizationType.Q3_K : (256, 2 + QK_K // 4 + QK_K // 8 + 12), - gguf.GGMLQuantizationType.Q4_K : (256, 2 + 2 + QK_K // 2 + 12), - gguf.GGMLQuantizationType.Q5_K : (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12), - gguf.GGMLQuantizationType.Q6_K : (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16), - gguf.GGMLQuantizationType.Q8_K : (256, 4 + QK_K + QK_K // 8), -} - class GGMLFormat(IntEnum): GGML = 0 GGMF = 1 @@ -125,7 +105,7 @@ def load(self, data, offset): (n_dims, name_len, dtype) = struct.unpack('<3I', data[offset:offset + 12]) assert n_dims >= 0 and n_dims <= 4, f'Invalid tensor dimensions {n_dims}' assert name_len < 4096, 'Absurd tensor name length' - quant = GGML_QUANT_SIZES.get(dtype) + quant = gguf.GGML_QUANT_SIZES.get(dtype) assert quant is not None, 'Unknown tensor type' (blksize, tysize) = quant offset += 12 diff --git a/convert-persimmon-to-gguf.py b/convert-persimmon-to-gguf.py index e022ffe46189e..240f87306e578 100644 --- a/convert-persimmon-to-gguf.py +++ b/convert-persimmon-to-gguf.py @@ -6,7 +6,7 @@ from pathlib import Path from sentencepiece import SentencePieceProcessor if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf def _flatten_dict(dct, tensors, prefix=None): diff --git a/convert.py b/convert.py index b0f44dbef8332..a4b87e08849bc 100755 --- a/convert.py +++ b/convert.py @@ -3,11 +3,9 @@ import argparse import concurrent.futures -import copy import enum import faulthandler import functools -import io import itertools import json import math @@ -23,14 +21,14 @@ from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor from dataclasses import dataclass from pathlib import Path -from typing import IO, TYPE_CHECKING, Any, Callable, Generator, Iterable, Literal, Sequence, TypeVar +from typing import IO, TYPE_CHECKING, Any, Callable, Iterable, Literal, TypeVar import numpy as np from sentencepiece import SentencePieceProcessor import os if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf if TYPE_CHECKING: @@ -851,7 +849,7 @@ def add_meta_vocab(self, vocab: Vocab) -> None: elif isinstance(vocab, BpeVocab): self.gguf.add_tokenizer_model("gpt2") else: - raise ValueError(f'Unknown vocab type: Not BpeVocab or SentencePieceVocab') + raise ValueError('Unknown vocab type: Not BpeVocab or SentencePieceVocab') self.gguf.add_token_list(tokens) self.gguf.add_token_scores(scores) self.gguf.add_token_types(toktypes) @@ -905,7 +903,7 @@ def maybe_do_quantize(item: tuple[DataType, NDArray]) -> NDArray: return dt.quantize(arr) @staticmethod - def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY, endianess=gguf.GGUFEndian.LITTLE) -> None: + def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE) -> None: check_vocab_size(params, vocab) of = OutputFile(fname_out, endianess=endianess) @@ -1114,11 +1112,15 @@ def do_dump_model(model_plus: ModelPlus) -> None: def main(args_in: list[str] | None = None) -> None: + output_choices = ["f32", "f16"] + if np.uint32(1) == np.uint32(1).newbyteorder("<"): + # We currently only support Q8_0 output on little endian systems. + output_choices.append("q8_0") 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") parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") - parser.add_argument("--outtype", choices=["f32", "f16", "q8_0"], help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)") + parser.add_argument("--outtype", choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)") parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file") 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 (*.pth, *.pt, *.bin)") 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 887ed2e212786..ed93673bcf306 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 @@ -9,7 +9,7 @@ from pathlib import Path if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / '..' / '..' / 'gguf-py' / 'gguf')) + sys.path.insert(1, str(Path(__file__).parent / '..' / '..' / 'gguf-py')) import gguf # gguf constants diff --git a/gguf-py/README.md b/gguf-py/README.md index a28d8c57adc7d..502b6a510cc70 100644 --- a/gguf-py/README.md +++ b/gguf-py/README.md @@ -11,6 +11,16 @@ as an example for its usage. pip install gguf ``` +## API Examples/Simple Tools + +[examples/writer.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/examples/writer.py) — Generates `example.gguf` in the current directory to demonstrate generating a GGUF file. Note that this file cannot be used as a model. + +[scripts/gguf-dump.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-dump.py) — Dumps a GGUF file's metadata to the console. + +[scripts/gguf-set-metadata.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-set-metadata.py) — Allows changing simple metadata values in a GGUF file by key. + +[scripts/gguf-convert-endian.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-convert-endian.py) — Allows converting the endianness of GGUF files. + ## Development Maintainers who participate in development of this package are advised to install it in editable mode: diff --git a/gguf-py/examples/writer.py b/gguf-py/examples/writer.py new file mode 100755 index 0000000000000..f39eed1afe763 --- /dev/null +++ b/gguf-py/examples/writer.py @@ -0,0 +1,40 @@ +#!/usr/bin/env python3 +import sys +from pathlib import Path + +import numpy as np + +# Necessary to load the local gguf package +sys.path.insert(0, str(Path(__file__).parent.parent)) + +from gguf import GGUFWriter # noqa: E402 + + +# Example usage: +def writer_example() -> None: + # Example usage with a file + gguf_writer = GGUFWriter("example.gguf", "llama") + + gguf_writer.add_architecture() + gguf_writer.add_block_count(12) + gguf_writer.add_uint32("answer", 42) # Write a 32-bit integer + gguf_writer.add_float32("answer_in_float", 42.0) # Write a 32-bit float + gguf_writer.add_custom_alignment(64) + + tensor1 = np.ones((32,), dtype=np.float32) * 100.0 + tensor2 = np.ones((64,), dtype=np.float32) * 101.0 + tensor3 = np.ones((96,), dtype=np.float32) * 102.0 + + gguf_writer.add_tensor("tensor1", tensor1) + gguf_writer.add_tensor("tensor2", tensor2) + gguf_writer.add_tensor("tensor3", tensor3) + + gguf_writer.write_header_to_file() + gguf_writer.write_kv_data_to_file() + gguf_writer.write_tensors_to_file() + + gguf_writer.close() + + +if __name__ == '__main__': + writer_example() diff --git a/gguf-py/gguf/__init__.py b/gguf-py/gguf/__init__.py index f9b70a85b875e..110ab342ccd71 100644 --- a/gguf-py/gguf/__init__.py +++ b/gguf-py/gguf/__init__.py @@ -1 +1,5 @@ -from .gguf import * +from .constants import * +from .gguf_reader import * +from .gguf_writer import * +from .tensor_mapping import * +from .vocab import * diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py new file mode 100644 index 0000000000000..bf1ccf66922d0 --- /dev/null +++ b/gguf-py/gguf/constants.py @@ -0,0 +1,470 @@ +from __future__ import annotations + +import sys +from enum import Enum, IntEnum, auto +from typing import Any + +# +# constants +# + +GGUF_MAGIC = 0x46554747 # "GGUF" +GGUF_VERSION = 3 +GGUF_DEFAULT_ALIGNMENT = 32 + +# +# metadata keys +# + + +class Keys: + class General: + ARCHITECTURE = "general.architecture" + QUANTIZATION_VERSION = "general.quantization_version" + ALIGNMENT = "general.alignment" + NAME = "general.name" + AUTHOR = "general.author" + URL = "general.url" + DESCRIPTION = "general.description" + LICENSE = "general.license" + SOURCE_URL = "general.source.url" + SOURCE_HF_REPO = "general.source.huggingface.repository" + FILE_TYPE = "general.file_type" + + class LLM: + CONTEXT_LENGTH = "{arch}.context_length" + EMBEDDING_LENGTH = "{arch}.embedding_length" + BLOCK_COUNT = "{arch}.block_count" + FEED_FORWARD_LENGTH = "{arch}.feed_forward_length" + USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual" + TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout" + + class Attention: + HEAD_COUNT = "{arch}.attention.head_count" + HEAD_COUNT_KV = "{arch}.attention.head_count_kv" + MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias" + CLAMP_KQV = "{arch}.attention.clamp_kqv" + LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon" + LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon" + + class Rope: + DIMENSION_COUNT = "{arch}.rope.dimension_count" + FREQ_BASE = "{arch}.rope.freq_base" + SCALING_TYPE = "{arch}.rope.scaling.type" + SCALING_FACTOR = "{arch}.rope.scaling.factor" + SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length" + SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" + + class Tokenizer: + MODEL = "tokenizer.ggml.model" + LIST = "tokenizer.ggml.tokens" + TOKEN_TYPE = "tokenizer.ggml.token_type" + SCORES = "tokenizer.ggml.scores" + MERGES = "tokenizer.ggml.merges" + BOS_ID = "tokenizer.ggml.bos_token_id" + EOS_ID = "tokenizer.ggml.eos_token_id" + UNK_ID = "tokenizer.ggml.unknown_token_id" + SEP_ID = "tokenizer.ggml.seperator_token_id" + PAD_ID = "tokenizer.ggml.padding_token_id" + ADD_BOS = "tokenizer.ggml.add_bos_token" + ADD_EOS = "tokenizer.ggml.add_eos_token" + HF_JSON = "tokenizer.huggingface.json" + RWKV = "tokenizer.rwkv.world" + + +# +# recommended mapping of model tensor names for storage in gguf +# + + +class MODEL_ARCH(IntEnum): + LLAMA = auto() + FALCON = auto() + BAICHUAN = auto() + GPT2 = auto() + GPTJ = auto() + GPTNEOX = auto() + MPT = auto() + STARCODER = auto() + PERSIMMON = auto() + REFACT = auto() + BERT = auto() + BLOOM = auto() + + +class MODEL_TENSOR(IntEnum): + TOKEN_EMBD = auto() + TOKEN_EMBD_NORM = auto() + TOKEN_TYPES = auto() + POS_EMBD = auto() + OUTPUT = auto() + OUTPUT_NORM = auto() + ROPE_FREQS = auto() + ATTN_Q = auto() + ATTN_K = auto() + ATTN_V = auto() + ATTN_QKV = auto() + ATTN_OUT = auto() + ATTN_NORM = auto() + ATTN_NORM_2 = auto() + ATTN_ROT_EMBD = auto() + FFN_GATE = auto() + FFN_DOWN = auto() + FFN_UP = auto() + FFN_NORM = auto() + ATTN_Q_NORM = auto() + ATTN_K_NORM = auto() + + +MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { + MODEL_ARCH.LLAMA: "llama", + MODEL_ARCH.FALCON: "falcon", + MODEL_ARCH.BAICHUAN: "baichuan", + MODEL_ARCH.GPT2: "gpt2", + MODEL_ARCH.GPTJ: "gptj", + MODEL_ARCH.GPTNEOX: "gptneox", + MODEL_ARCH.MPT: "mpt", + MODEL_ARCH.STARCODER: "starcoder", + MODEL_ARCH.PERSIMMON: "persimmon", + MODEL_ARCH.REFACT: "refact", + MODEL_ARCH.BERT: "bert", + MODEL_ARCH.BLOOM: "bloom", +} + +TENSOR_NAMES: dict[MODEL_TENSOR, str] = { + MODEL_TENSOR.TOKEN_EMBD: "token_embd", + MODEL_TENSOR.TOKEN_EMBD_NORM: "token_embd_norm", + MODEL_TENSOR.TOKEN_TYPES: "token_types", + MODEL_TENSOR.POS_EMBD: "position_embd", + MODEL_TENSOR.OUTPUT_NORM: "output_norm", + MODEL_TENSOR.OUTPUT: "output", + MODEL_TENSOR.ROPE_FREQS: "rope_freqs", + MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm", + MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2", + MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv", + MODEL_TENSOR.ATTN_Q: "blk.{bid}.attn_q", + MODEL_TENSOR.ATTN_K: "blk.{bid}.attn_k", + MODEL_TENSOR.ATTN_V: "blk.{bid}.attn_v", + MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output", + MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd", + MODEL_TENSOR.ATTN_Q_NORM: "blk.{bid}.attn_q_norm", + MODEL_TENSOR.ATTN_K_NORM: "blk.{bid}.attn_k_norm", + MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm", + MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate", + MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down", + MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up", +} + +MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { + MODEL_ARCH.LLAMA: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.GPTNEOX: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.FALCON: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_NORM_2, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.BAICHUAN: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.STARCODER: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.POS_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.BERT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_TYPES, + MODEL_TENSOR.POS_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.MPT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.GPTJ: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.PERSIMMON: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.REFACT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.BLOOM: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.GPT2: [ + # TODO + ], + # TODO +} + +# tensors that will not be serialized +MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { + MODEL_ARCH.LLAMA: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.BAICHUAN: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.PERSIMMON: [ + MODEL_TENSOR.ROPE_FREQS, + ], +} + +# +# types +# + + +class TokenType(IntEnum): + NORMAL = 1 + UNKNOWN = 2 + CONTROL = 3 + USER_DEFINED = 4 + UNUSED = 5 + BYTE = 6 + + +class RopeScalingType(Enum): + NONE = 'none' + LINEAR = 'linear' + YARN = 'yarn' + + +class GGMLQuantizationType(IntEnum): + F32 = 0 + F16 = 1 + Q4_0 = 2 + Q4_1 = 3 + Q5_0 = 6 + Q5_1 = 7 + Q8_0 = 8 + Q8_1 = 9 + Q2_K = 10 + Q3_K = 11 + Q4_K = 12 + Q5_K = 13 + Q6_K = 14 + Q8_K = 15 + + +class GGUFEndian(IntEnum): + LITTLE = 0 + BIG = 1 + + +class GGUFValueType(IntEnum): + UINT8 = 0 + INT8 = 1 + UINT16 = 2 + INT16 = 3 + UINT32 = 4 + INT32 = 5 + FLOAT32 = 6 + BOOL = 7 + STRING = 8 + ARRAY = 9 + UINT64 = 10 + INT64 = 11 + FLOAT64 = 12 + + @staticmethod + def get_type(val: Any) -> GGUFValueType: + if isinstance(val, (str, bytes, bytearray)): + return GGUFValueType.STRING + elif isinstance(val, list): + return GGUFValueType.ARRAY + elif isinstance(val, float): + return GGUFValueType.FLOAT32 + elif isinstance(val, bool): + return GGUFValueType.BOOL + elif isinstance(val, int): + return GGUFValueType.INT32 + # TODO: need help with 64-bit types in Python + else: + print("Unknown type:", type(val)) + sys.exit() + + +# Note: Does not support GGML_QKK_64 +QK_K = 256 +# Items here are (block size, type size) +GGML_QUANT_SIZES = { + GGMLQuantizationType.F32: (1, 4), + GGMLQuantizationType.F16: (1, 2), + GGMLQuantizationType.Q4_0: (32, 2 + 16), + GGMLQuantizationType.Q4_1: (32, 2 + 2 + 16), + GGMLQuantizationType.Q5_0: (32, 2 + 4 + 16), + GGMLQuantizationType.Q5_1: (32, 2 + 2 + 4 + 16), + GGMLQuantizationType.Q8_0: (32, 2 + 32), + GGMLQuantizationType.Q8_1: (32, 4 + 4 + 32), + GGMLQuantizationType.Q2_K: (256, 2 + 2 + QK_K // 16 + QK_K // 4), + GGMLQuantizationType.Q3_K: (256, 2 + QK_K // 4 + QK_K // 8 + 12), + GGMLQuantizationType.Q4_K: (256, 2 + 2 + QK_K // 2 + 12), + GGMLQuantizationType.Q5_K: (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12), + GGMLQuantizationType.Q6_K: (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16), + GGMLQuantizationType.Q8_K: (256, 4 + QK_K + QK_K // 8), +} + + +# Aliases for backward compatibility. + +# general +KEY_GENERAL_ARCHITECTURE = Keys.General.ARCHITECTURE +KEY_GENERAL_QUANTIZATION_VERSION = Keys.General.QUANTIZATION_VERSION +KEY_GENERAL_ALIGNMENT = Keys.General.ALIGNMENT +KEY_GENERAL_NAME = Keys.General.NAME +KEY_GENERAL_AUTHOR = Keys.General.AUTHOR +KEY_GENERAL_URL = Keys.General.URL +KEY_GENERAL_DESCRIPTION = Keys.General.DESCRIPTION +KEY_GENERAL_LICENSE = Keys.General.LICENSE +KEY_GENERAL_SOURCE_URL = Keys.General.SOURCE_URL +KEY_GENERAL_SOURCE_HF_REPO = Keys.General.SOURCE_HF_REPO +KEY_GENERAL_FILE_TYPE = Keys.General.FILE_TYPE + +# LLM +KEY_CONTEXT_LENGTH = Keys.LLM.CONTEXT_LENGTH +KEY_EMBEDDING_LENGTH = Keys.LLM.EMBEDDING_LENGTH +KEY_BLOCK_COUNT = Keys.LLM.BLOCK_COUNT +KEY_FEED_FORWARD_LENGTH = Keys.LLM.FEED_FORWARD_LENGTH +KEY_USE_PARALLEL_RESIDUAL = Keys.LLM.USE_PARALLEL_RESIDUAL +KEY_TENSOR_DATA_LAYOUT = Keys.LLM.TENSOR_DATA_LAYOUT + +# attention +KEY_ATTENTION_HEAD_COUNT = Keys.Attention.HEAD_COUNT +KEY_ATTENTION_HEAD_COUNT_KV = Keys.Attention.HEAD_COUNT_KV +KEY_ATTENTION_MAX_ALIBI_BIAS = Keys.Attention.MAX_ALIBI_BIAS +KEY_ATTENTION_CLAMP_KQV = Keys.Attention.CLAMP_KQV +KEY_ATTENTION_LAYERNORM_EPS = Keys.Attention.LAYERNORM_EPS +KEY_ATTENTION_LAYERNORM_RMS_EPS = Keys.Attention.LAYERNORM_RMS_EPS + +# RoPE +KEY_ROPE_DIMENSION_COUNT = Keys.Rope.DIMENSION_COUNT +KEY_ROPE_FREQ_BASE = Keys.Rope.FREQ_BASE +KEY_ROPE_SCALING_TYPE = Keys.Rope.SCALING_TYPE +KEY_ROPE_SCALING_FACTOR = Keys.Rope.SCALING_FACTOR +KEY_ROPE_SCALING_ORIG_CTX_LEN = Keys.Rope.SCALING_ORIG_CTX_LEN +KEY_ROPE_SCALING_FINETUNED = Keys.Rope.SCALING_FINETUNED + +# tokenization +KEY_TOKENIZER_MODEL = Keys.Tokenizer.MODEL +KEY_TOKENIZER_LIST = Keys.Tokenizer.LIST +KEY_TOKENIZER_TOKEN_TYPE = Keys.Tokenizer.TOKEN_TYPE +KEY_TOKENIZER_SCORES = Keys.Tokenizer.SCORES +KEY_TOKENIZER_MERGES = Keys.Tokenizer.MERGES +KEY_TOKENIZER_BOS_ID = Keys.Tokenizer.BOS_ID +KEY_TOKENIZER_EOS_ID = Keys.Tokenizer.EOS_ID +KEY_TOKENIZER_UNK_ID = Keys.Tokenizer.UNK_ID +KEY_TOKENIZER_SEP_ID = Keys.Tokenizer.SEP_ID +KEY_TOKENIZER_PAD_ID = Keys.Tokenizer.PAD_ID +KEY_TOKENIZER_HF_JSON = Keys.Tokenizer.HF_JSON +KEY_TOKENIZER_RWKV = Keys.Tokenizer.RWKV diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py index 7e495cb19638d..651a81eb82824 100644 --- a/gguf-py/gguf/gguf.py +++ b/gguf-py/gguf/gguf.py @@ -1,1146 +1,15 @@ -#!/usr/bin/env python3 -from __future__ import annotations +# This file left for compatibility. If you want to use the GGUF API from Python +# then don't import gguf/gguf.py directly. If you're looking for examples, see the +# examples/ directory for gguf-py -import json -import os -import shutil -import struct +import importlib import sys -import tempfile -from enum import Enum, IntEnum, auto -from io import BufferedWriter from pathlib import Path -from typing import IO, Any, BinaryIO, Callable, Sequence -import numpy as np +sys.path.insert(0, str(Path(__file__).parent.parent)) -# -# constants -# +# Compatibility for people trying to import gguf/gguf.py directly instead of as a package. +importlib.invalidate_caches() +import gguf # noqa: E402 -GGUF_MAGIC = 0x46554747 -GGUF_VERSION = 3 -GGUF_DEFAULT_ALIGNMENT = 32 - - -# general -KEY_GENERAL_ARCHITECTURE = "general.architecture" -KEY_GENERAL_QUANTIZATION_VERSION = "general.quantization_version" -KEY_GENERAL_ALIGNMENT = "general.alignment" -KEY_GENERAL_NAME = "general.name" -KEY_GENERAL_AUTHOR = "general.author" -KEY_GENERAL_URL = "general.url" -KEY_GENERAL_DESCRIPTION = "general.description" -KEY_GENERAL_LICENSE = "general.license" -KEY_GENERAL_SOURCE_URL = "general.source.url" -KEY_GENERAL_SOURCE_HF_REPO = "general.source.huggingface.repository" -KEY_GENERAL_FILE_TYPE = "general.file_type" - -# LLM -KEY_CONTEXT_LENGTH = "{arch}.context_length" -KEY_EMBEDDING_LENGTH = "{arch}.embedding_length" -KEY_BLOCK_COUNT = "{arch}.block_count" -KEY_FEED_FORWARD_LENGTH = "{arch}.feed_forward_length" -KEY_USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual" -KEY_TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout" - -# attention -KEY_ATTENTION_HEAD_COUNT = "{arch}.attention.head_count" -KEY_ATTENTION_HEAD_COUNT_KV = "{arch}.attention.head_count_kv" -KEY_ATTENTION_MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias" -KEY_ATTENTION_CLAMP_KQV = "{arch}.attention.clamp_kqv" -KEY_ATTENTION_LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon" -KEY_ATTENTION_LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon" - -# RoPE -KEY_ROPE_DIMENSION_COUNT = "{arch}.rope.dimension_count" -KEY_ROPE_FREQ_BASE = "{arch}.rope.freq_base" -KEY_ROPE_SCALING_TYPE = "{arch}.rope.scaling.type" -KEY_ROPE_SCALING_FACTOR = "{arch}.rope.scaling.factor" -KEY_ROPE_SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length" -KEY_ROPE_SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" - -# tokenization -KEY_TOKENIZER_MODEL = "tokenizer.ggml.model" -KEY_TOKENIZER_LIST = "tokenizer.ggml.tokens" -KEY_TOKENIZER_TOKEN_TYPE = "tokenizer.ggml.token_type" -KEY_TOKENIZER_SCORES = "tokenizer.ggml.scores" -KEY_TOKENIZER_MERGES = "tokenizer.ggml.merges" -KEY_TOKENIZER_BOS_ID = "tokenizer.ggml.bos_token_id" -KEY_TOKENIZER_EOS_ID = "tokenizer.ggml.eos_token_id" -KEY_TOKENIZER_UNK_ID = "tokenizer.ggml.unknown_token_id" -KEY_TOKENIZER_SEP_ID = "tokenizer.ggml.seperator_token_id" -KEY_TOKENIZER_PAD_ID = "tokenizer.ggml.padding_token_id" -KEY_TOKENIZER_HF_JSON = "tokenizer.huggingface.json" -KEY_TOKENIZER_RWKV = "tokenizer.rwkv.world" - - -# -# recommended mapping of model tensor names for storage in gguf -# - - -class MODEL_ARCH(IntEnum): - LLAMA : int = auto() - FALCON : int = auto() - BAICHUAN : int = auto() - GPT2 : int = auto() - GPTJ : int = auto() - GPTNEOX : int = auto() - MPT : int = auto() - STARCODER : int = auto() - PERSIMMON : int = auto() - REFACT : int = auto() - BERT : int = auto() - BLOOM : int = auto() - - -class MODEL_TENSOR(IntEnum): - TOKEN_EMBD : int = auto() - TOKEN_EMBD_NORM : int = auto() - TOKEN_TYPES : int = auto() - POS_EMBD : int = auto() - OUTPUT : int = auto() - OUTPUT_NORM : int = auto() - ROPE_FREQS : int = auto() - ATTN_Q : int = auto() - ATTN_K : int = auto() - ATTN_V : int = auto() - ATTN_QKV : int = auto() - ATTN_OUT : int = auto() - ATTN_NORM : int = auto() - ATTN_NORM_2 : int = auto() - ATTN_ROT_EMBD : int = auto() - FFN_GATE : int = auto() - FFN_DOWN : int = auto() - FFN_UP : int = auto() - FFN_NORM : int = auto() - ATTN_Q_NORM : int = auto() - ATTN_K_NORM : int = auto() - - -MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { - MODEL_ARCH.LLAMA: "llama", - MODEL_ARCH.FALCON: "falcon", - MODEL_ARCH.BAICHUAN: "baichuan", - MODEL_ARCH.GPT2: "gpt2", - MODEL_ARCH.GPTJ: "gptj", - MODEL_ARCH.GPTNEOX: "gptneox", - MODEL_ARCH.MPT: "mpt", - MODEL_ARCH.STARCODER: "starcoder", - MODEL_ARCH.PERSIMMON: "persimmon", - MODEL_ARCH.REFACT: "refact", - MODEL_ARCH.BERT: "bert", - MODEL_ARCH.BLOOM: "bloom", -} - -TENSOR_NAMES: dict[MODEL_TENSOR, str] = { - MODEL_TENSOR.TOKEN_EMBD: "token_embd", - MODEL_TENSOR.TOKEN_EMBD_NORM: "token_embd_norm", - MODEL_TENSOR.TOKEN_TYPES: "token_types", - MODEL_TENSOR.POS_EMBD: "position_embd", - MODEL_TENSOR.OUTPUT_NORM: "output_norm", - MODEL_TENSOR.OUTPUT: "output", - MODEL_TENSOR.ROPE_FREQS: "rope_freqs", - MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm", - MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2", - MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv", - MODEL_TENSOR.ATTN_Q: "blk.{bid}.attn_q", - MODEL_TENSOR.ATTN_K: "blk.{bid}.attn_k", - MODEL_TENSOR.ATTN_V: "blk.{bid}.attn_v", - MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output", - MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd", - MODEL_TENSOR.ATTN_Q_NORM: "blk.{bid}.attn_q_norm", - MODEL_TENSOR.ATTN_K_NORM: "blk.{bid}.attn_k_norm", - MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm", - MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate", - MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down", - MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up", -} - -MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { - MODEL_ARCH.LLAMA: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ROPE_FREQS, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_Q, - MODEL_TENSOR.ATTN_K, - MODEL_TENSOR.ATTN_V, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.ATTN_ROT_EMBD, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_GATE, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.GPTNEOX: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_QKV, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.FALCON: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_NORM_2, - MODEL_TENSOR.ATTN_QKV, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.BAICHUAN: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ROPE_FREQS, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_Q, - MODEL_TENSOR.ATTN_K, - MODEL_TENSOR.ATTN_V, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.ATTN_ROT_EMBD, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_GATE, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.STARCODER: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.POS_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_QKV, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.BERT: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.TOKEN_TYPES, - MODEL_TENSOR.POS_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_Q, - MODEL_TENSOR.ATTN_K, - MODEL_TENSOR.ATTN_V, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.MPT: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_QKV, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.GPTJ: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_Q, - MODEL_TENSOR.ATTN_K, - MODEL_TENSOR.ATTN_V, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.PERSIMMON: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_QKV, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - MODEL_TENSOR.ATTN_Q_NORM, - MODEL_TENSOR.ATTN_K_NORM, - MODEL_TENSOR.ATTN_ROT_EMBD, - ], - MODEL_ARCH.REFACT: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_Q, - MODEL_TENSOR.ATTN_K, - MODEL_TENSOR.ATTN_V, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_GATE, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.BLOOM: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.TOKEN_EMBD_NORM, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_QKV, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - ], - MODEL_ARCH.GPT2: [ - # TODO - ], - # TODO -} - -# tensors that will not be serialized -MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { - MODEL_ARCH.LLAMA: [ - MODEL_TENSOR.ROPE_FREQS, - MODEL_TENSOR.ATTN_ROT_EMBD, - ], - MODEL_ARCH.BAICHUAN: [ - MODEL_TENSOR.ROPE_FREQS, - MODEL_TENSOR.ATTN_ROT_EMBD, - ], - MODEL_ARCH.PERSIMMON: [ - MODEL_TENSOR.ROPE_FREQS, - ] -} - - -class TensorNameMap: - mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { - # Token embeddings - MODEL_TENSOR.TOKEN_EMBD: ( - "gpt_neox.embed_in", # gptneox - "transformer.wte", # gpt2 gpt-j mpt refact - "transformer.word_embeddings", # falcon - "word_embeddings", # bloom - "model.embed_tokens", # llama-hf - "tok_embeddings", # llama-pth - "embeddings.word_embeddings", # bert - "language_model.embedding.word_embeddings", # persimmon - ), - - # Token type embeddings - MODEL_TENSOR.TOKEN_TYPES: ( - "embeddings.token_type_embeddings", # bert - ), - - # Normalization of token embeddings - MODEL_TENSOR.TOKEN_EMBD_NORM: ( - "word_embeddings_layernorm", # bloom - ), - - # Position embeddings - MODEL_TENSOR.POS_EMBD: ( - "transformer.wpe", # gpt2 - "embeddings.position_embeddings", # bert - ), - - # Output - MODEL_TENSOR.OUTPUT: ( - "embed_out", # gptneox - "lm_head", # gpt2 mpt falcon llama-hf baichuan - "output", # llama-pth bloom - "word_embeddings_for_head", # persimmon - ), - - # Output norm - MODEL_TENSOR.OUTPUT_NORM: ( - "gpt_neox.final_layer_norm", # gptneox - "transformer.ln_f", # gpt2 gpt-j falcon - "model.norm", # llama-hf baichuan - "norm", # llama-pth - "embeddings.LayerNorm", # bert - "transformer.norm_f", # mpt - "ln_f", # refact bloom - "language_model.encoder.final_layernorm", # persimmon - ), - - # Rope frequencies - MODEL_TENSOR.ROPE_FREQS: ( - "rope.freqs", # llama-pth - ), - } - - block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { - # Attention norm - MODEL_TENSOR.ATTN_NORM: ( - "gpt_neox.layers.{bid}.input_layernorm", # gptneox - "transformer.h.{bid}.ln_1", # gpt2 gpt-j refact - "transformer.blocks.{bid}.norm_1", # mpt - "transformer.h.{bid}.input_layernorm", # falcon7b - "h.{bid}.input_layernorm", # bloom - "transformer.h.{bid}.ln_mlp", # falcon40b - "model.layers.{bid}.input_layernorm", # llama-hf - "layers.{bid}.attention_norm", # llama-pth - "encoder.layer.{bid}.attention.output.LayerNorm", # bert - "language_model.encoder.layers.{bid}.input_layernorm", # persimmon - "model.layers.{bid}.ln1", # yi - ), - - # Attention norm 2 - MODEL_TENSOR.ATTN_NORM_2: ( - "transformer.h.{bid}.ln_attn", # falcon40b - ), - - # Attention query-key-value - MODEL_TENSOR.ATTN_QKV: ( - "gpt_neox.layers.{bid}.attention.query_key_value", # gptneox - "transformer.h.{bid}.attn.c_attn", # gpt2 - "transformer.blocks.{bid}.attn.Wqkv", # mpt - "transformer.h.{bid}.self_attention.query_key_value", # falcon - "h.{bid}.self_attention.query_key_value", # bloom - "language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon - ), - - # Attention query - MODEL_TENSOR.ATTN_Q: ( - "model.layers.{bid}.self_attn.q_proj", # llama-hf - "layers.{bid}.attention.wq", # llama-pth - "encoder.layer.{bid}.attention.self.query", # bert - "transformer.h.{bid}.attn.q_proj", # gpt-j - ), - - # Attention key - MODEL_TENSOR.ATTN_K: ( - "model.layers.{bid}.self_attn.k_proj", # llama-hf - "layers.{bid}.attention.wk", # llama-pth - "encoder.layer.{bid}.attention.self.key", # bert - "transformer.h.{bid}.attn.k_proj", # gpt-j - ), - - # Attention value - MODEL_TENSOR.ATTN_V: ( - "model.layers.{bid}.self_attn.v_proj", # llama-hf - "layers.{bid}.attention.wv", # llama-pth - "encoder.layer.{bid}.attention.self.value", # bert - "transformer.h.{bid}.attn.v_proj", # gpt-j - ), - - # Attention output - MODEL_TENSOR.ATTN_OUT: ( - "gpt_neox.layers.{bid}.attention.dense", # gptneox - "transformer.h.{bid}.attn.c_proj", # gpt2 refact - "transformer.blocks.{bid}.attn.out_proj", # mpt - "transformer.h.{bid}.self_attention.dense", # falcon - "h.{bid}.self_attention.dense", # bloom - "model.layers.{bid}.self_attn.o_proj", # llama-hf - "layers.{bid}.attention.wo", # llama-pth - "encoder.layer.{bid}.attention.output.dense", # bert - "transformer.h.{bid}.attn.out_proj", # gpt-j - "language_model.encoder.layers.{bid}.self_attention.dense" # persimmon - ), - - # Rotary embeddings - MODEL_TENSOR.ATTN_ROT_EMBD: ( - "model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf - "layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth - ), - - # Feed-forward norm - MODEL_TENSOR.FFN_NORM: ( - "gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox - "transformer.h.{bid}.ln_2", # gpt2 refact - "h.{bid}.post_attention_layernorm", # bloom - "transformer.blocks.{bid}.norm_2", # mpt - "model.layers.{bid}.post_attention_layernorm", # llama-hf - "layers.{bid}.ffn_norm", # llama-pth - "encoder.layer.{bid}.output.LayerNorm", # bert - "language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon - "model.layers.{bid}.ln2", # yi - ), - - # Feed-forward up - MODEL_TENSOR.FFN_UP: ( - "gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox - "transformer.h.{bid}.mlp.c_fc", # gpt2 - "transformer.blocks.{bid}.ffn.up_proj", # mpt - "transformer.h.{bid}.mlp.dense_h_to_4h", # falcon - "h.{bid}.mlp.dense_h_to_4h", # bloom - "model.layers.{bid}.mlp.up_proj", # llama-hf refact - "layers.{bid}.feed_forward.w3", # llama-pth - "encoder.layer.{bid}.intermediate.dense", # bert - "transformer.h.{bid}.mlp.fc_in", # gpt-j - "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon - ), - - # Feed-forward gate - MODEL_TENSOR.FFN_GATE: ( - "model.layers.{bid}.mlp.gate_proj", # llama-hf refact - "layers.{bid}.feed_forward.w1", # llama-pth - ), - - # Feed-forward down - MODEL_TENSOR.FFN_DOWN: ( - "gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox - "transformer.h.{bid}.mlp.c_proj", # gpt2 refact - "transformer.blocks.{bid}.ffn.down_proj", # mpt - "transformer.h.{bid}.mlp.dense_4h_to_h", # falcon - "h.{bid}.mlp.dense_4h_to_h", # bloom - "model.layers.{bid}.mlp.down_proj", # llama-hf - "layers.{bid}.feed_forward.w2", # llama-pth - "encoder.layer.{bid}.output.dense", # bert - "transformer.h.{bid}.mlp.fc_out", # gpt-j - "language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon - ), - - MODEL_TENSOR.ATTN_Q_NORM: ( - "language_model.encoder.layers.{bid}.self_attention.q_layernorm", - ), - - MODEL_TENSOR.ATTN_K_NORM: ( - "language_model.encoder.layers.{bid}.self_attention.k_layernorm", - ), - - MODEL_TENSOR.ROPE_FREQS: ( - "language_model.encoder.layers.{bid}.self_attention.rotary_emb.inv_freq", # persimmon - ) - } - - mapping: dict[str, tuple[MODEL_TENSOR, str]] - - def __init__(self, arch: MODEL_ARCH, n_blocks: int): - self.mapping = {} - for tensor, keys in self.mappings_cfg.items(): - if tensor not in MODEL_TENSORS[arch]: - continue - tensor_name = TENSOR_NAMES[tensor] - self.mapping[tensor_name] = (tensor, tensor_name) - for key in keys: - self.mapping[key] = (tensor, tensor_name) - for bid in range(n_blocks): - for tensor, keys in self.block_mappings_cfg.items(): - if tensor not in MODEL_TENSORS[arch]: - continue - tensor_name = TENSOR_NAMES[tensor].format(bid = bid) - self.mapping[tensor_name] = (tensor, tensor_name) - for key in keys: - key = key.format(bid = bid) - self.mapping[key] = (tensor, tensor_name) - - 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 - for suffix in try_suffixes: - if key.endswith(suffix): - result = self.mapping.get(key[:-len(suffix)]) - if result is not None: - return (result[0], result[1] + suffix) - return None - - 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] = ()) -> MODEL_TENSOR | None: - result = self.get_type_and_name(key, try_suffixes = try_suffixes) - if result is None: - return None - return result[0] - - def __getitem__(self, key: str) -> str: - try: - return self.mapping[key][1] - except KeyError: - raise KeyError(key) - - def __contains__(self, key: str) -> bool: - return key in self.mapping - - def __repr__(self) -> str: - return repr(self.mapping) - -def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> TensorNameMap: - return TensorNameMap(arch, n_blocks) - -class TokenType(IntEnum): - NORMAL = 1 - UNKNOWN = 2 - CONTROL = 3 - USER_DEFINED = 4 - UNUSED = 5 - BYTE = 6 - -class RopeScalingType(Enum): - NONE = 'none' - LINEAR = 'linear' - YARN = 'yarn' - -# -# implementation -# - - -class GGMLQuantizationType(IntEnum): - F32 = 0 - F16 = 1 - Q4_0 = 2 - Q4_1 = 3 - Q5_0 = 6 - Q5_1 = 7 - Q8_0 = 8 - Q8_1 = 9 - Q2_K = 10 - Q3_K = 11 - Q4_K = 12 - Q5_K = 13 - Q6_K = 14 - Q8_K = 15 - -class GGUFEndian(IntEnum): - LITTLE = 0 - BIG = 1 - - -class GGUFValueType(IntEnum): - UINT8 = 0 - INT8 = 1 - UINT16 = 2 - INT16 = 3 - UINT32 = 4 - INT32 = 5 - FLOAT32 = 6 - BOOL = 7 - STRING = 8 - ARRAY = 9 - UINT64 = 10 - INT64 = 11 - FLOAT64 = 12 - - @staticmethod - def get_type(val): - if isinstance(val, str) or isinstance(val, bytes) or isinstance(val, bytearray): - return GGUFValueType.STRING - elif isinstance(val, list): - return GGUFValueType.ARRAY - elif isinstance(val, float): - return GGUFValueType.FLOAT32 - elif isinstance(val, bool): - return GGUFValueType.BOOL - elif isinstance(val, int): - return GGUFValueType.INT32 - # TODO: need help with 64-bit types in Python - else: - print("Unknown type: "+str(type(val))) - sys.exit() - - -class WriterState(Enum): - EMPTY = auto() - HEADER = auto() - KV_DATA = auto() - TI_DATA = auto() - - -class GGUFWriter: - fout: BufferedWriter - temp_file: tempfile.SpooledTemporaryFile[bytes] | None - tensors: list[np.ndarray[Any, Any]] - - @property - def pack_prefix(self): - if self.endianess==GGUFEndian.LITTLE: - return "<" - else: - return ">" - - def __init__(self, path: os.PathLike[str] | str, arch: str, use_temp_file = True, endianess=GGUFEndian.LITTLE): - self.fout = open(path, "wb") - self.arch = arch - self.endianess = endianess - self._simple_value_packing = { - GGUFValueType.UINT8: f"{self.pack_prefix}B", - GGUFValueType.INT8: f"{self.pack_prefix}b", - GGUFValueType.UINT16: f"{self.pack_prefix}H", - GGUFValueType.INT16: f"{self.pack_prefix}h", - GGUFValueType.UINT32: f"{self.pack_prefix}I", - GGUFValueType.INT32: f"{self.pack_prefix}i", - GGUFValueType.FLOAT32: f"{self.pack_prefix}f", - GGUFValueType.UINT64: f"{self.pack_prefix}Q", - GGUFValueType.INT64: f"{self.pack_prefix}q", - GGUFValueType.FLOAT64: f"{self.pack_prefix}d", - GGUFValueType.BOOL: "?" , - } - self.offset_tensor = 0 - self.data_alignment = GGUF_DEFAULT_ALIGNMENT - self.kv_data = b"" - self.kv_data_count = 0 - self.ti_data = b"" - self.ti_data_count = 0 - self.use_temp_file = use_temp_file - self.temp_file = None - self.tensors = [] - endianess_str = "Big Endian" if self.endianess == GGUFEndian.BIG else "Little Endian" - print(f"This gguf file is for {endianess_str} only") - self.state = WriterState.EMPTY - - self.add_architecture() - - def write_header_to_file(self): - if self.state is not WriterState.EMPTY: - raise ValueError(f'Expected output file to be empty, got {self.state}') - - self.fout.write(struct.pack(" 0: - ltype = GGUFValueType.get_type(val[0]) - if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]): - raise ValueError("All items in a GGUF array should be of the same type") - self.kv_data += struct.pack(f"{self.pack_prefix}I", ltype) - self.kv_data += struct.pack(f"{self.pack_prefix}Q", len(val)) - for item in val: - self.add_val(item, add_vtype=False) - else: - raise ValueError("Invalid GGUF metadata value type or value") - - @staticmethod - def ggml_pad(x: int, n: int) -> int: - return ((x + n - 1) // n) * n - - 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): - if self.state is not WriterState.EMPTY: - raise ValueError(f'Expected output file to be empty, got {self.state}') - - 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") - self.ti_data += struct.pack(f"{self.pack_prefix}Q", len(encoded_name)) - self.ti_data += encoded_name - n_dims = len(tensor_shape) - self.ti_data += struct.pack(f"{self.pack_prefix}I", n_dims) - for i in range(n_dims): - self.ti_data += struct.pack(f"{self.pack_prefix}Q", tensor_shape[n_dims - 1 - i]) - if raw_dtype is None: - dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16 - else: - dtype = raw_dtype - self.ti_data += struct.pack(f"{self.pack_prefix}I", dtype) - self.ti_data += struct.pack(f"{self.pack_prefix}Q", self.offset_tensor) - 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: Sequence[int] | None = None, raw_dtype: GGMLQuantizationType | None = None): - if self.endianess == GGUFEndian.BIG: - tensor.byteswap(inplace=True) - 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) - self.temp_file = fp - - shape: Sequence[int] = raw_shape if raw_shape is not None else tensor.shape - self.add_tensor_info(name, shape, tensor.dtype, tensor.nbytes, raw_dtype = raw_dtype) - - if self.temp_file is None: - self.tensors.append(tensor) - return - - tensor.tofile(self.temp_file) - self.write_padding(self.temp_file, tensor.nbytes) - - def write_padding(self, fp: IO[bytes], 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)) - - def write_tensor_data(self, tensor: np.ndarray[Any, Any]): - if self.state is not WriterState.TI_DATA: - raise ValueError(f'Expected output file to contain tensor info, got {self.state}') - - if self.endianess==GGUFEndian.BIG: - tensor.byteswap(inplace=True) - self.write_padding(self.fout, self.fout.tell()) - tensor.tofile(self.fout) - self.write_padding(self.fout, tensor.nbytes) - - def write_tensors_to_file(self): - self.write_ti_data_to_file() - - self.write_padding(self.fout, self.fout.tell()) - - if self.temp_file is None: - while True: - try: - tensor = self.tensors.pop(0) - except IndexError: - break - tensor.tofile(self.fout) - self.write_padding(self.fout, tensor.nbytes) - return - - self.temp_file.seek(0) - - shutil.copyfileobj(self.temp_file, self.fout) - self.flush() - self.temp_file.close() - - def flush(self): - self.fout.flush() - - def close(self): - self.fout.close() - - def add_architecture(self): - self.add_string(KEY_GENERAL_ARCHITECTURE, self.arch) - - def add_author(self, author: str): - self.add_string(KEY_GENERAL_AUTHOR, author) - - def add_tensor_data_layout(self, layout: str): - self.add_string(KEY_TENSOR_DATA_LAYOUT.format(arch=self.arch), layout) - - def add_url(self, url: str): - self.add_string(KEY_GENERAL_URL, url) - - def add_description(self, description: str): - self.add_string(KEY_GENERAL_DESCRIPTION, description) - - def add_source_url(self, url: str): - self.add_string(KEY_GENERAL_SOURCE_URL, url) - - def add_source_hf_repo(self, repo: str): - self.add_string(KEY_GENERAL_SOURCE_HF_REPO, repo) - - def add_file_type(self, ftype: int): - self.add_uint32(KEY_GENERAL_FILE_TYPE, ftype) - - def add_name(self, name: str): - self.add_string(KEY_GENERAL_NAME, name) - - def add_quantization_version(self, quantization_version: GGMLQuantizationType): - self.add_uint32( - KEY_GENERAL_QUANTIZATION_VERSION, quantization_version) - - def add_custom_alignment(self, alignment: int): - self.data_alignment = alignment - self.add_uint32(KEY_GENERAL_ALIGNMENT, alignment) - - def add_context_length(self, length: int): - self.add_uint32( - KEY_CONTEXT_LENGTH.format(arch=self.arch), length) - - def add_embedding_length(self, length: int): - self.add_uint32( - KEY_EMBEDDING_LENGTH.format(arch=self.arch), length) - - def add_block_count(self, length: int): - self.add_uint32( - KEY_BLOCK_COUNT.format(arch=self.arch), length) - - def add_feed_forward_length(self, length: int): - self.add_uint32( - KEY_FEED_FORWARD_LENGTH.format(arch=self.arch), length) - - def add_parallel_residual(self, use: bool): - self.add_bool( - KEY_USE_PARALLEL_RESIDUAL.format(arch=self.arch), use) - - def add_head_count(self, count: int): - self.add_uint32( - KEY_ATTENTION_HEAD_COUNT.format(arch=self.arch), count) - - def add_head_count_kv(self, count: int): - self.add_uint32( - KEY_ATTENTION_HEAD_COUNT_KV.format(arch=self.arch), count) - - def add_max_alibi_bias(self, bias: float): - self.add_float32( - KEY_ATTENTION_MAX_ALIBI_BIAS.format(arch=self.arch), bias) - - def add_clamp_kqv(self, value: float): - self.add_float32( - KEY_ATTENTION_CLAMP_KQV.format(arch=self.arch), value) - - def add_layer_norm_eps(self, value: float): - self.add_float32( - KEY_ATTENTION_LAYERNORM_EPS.format(arch=self.arch), value) - - def add_layer_norm_rms_eps(self, value: float): - self.add_float32( - KEY_ATTENTION_LAYERNORM_RMS_EPS.format(arch=self.arch), value) - - def add_rope_dimension_count(self, count: int): - self.add_uint32( - KEY_ROPE_DIMENSION_COUNT.format(arch=self.arch), count) - - def add_rope_freq_base(self, value: float): - self.add_float32(KEY_ROPE_FREQ_BASE.format(arch=self.arch), value) - - def add_rope_scaling_type(self, value: RopeScalingType): - self.add_string(KEY_ROPE_SCALING_TYPE.format(arch=self.arch), value.value) - - def add_rope_scaling_factor(self, value: float): - self.add_float32(KEY_ROPE_SCALING_FACTOR.format(arch=self.arch), value) - - def add_rope_scaling_orig_ctx_len(self, value: int): - self.add_uint32(KEY_ROPE_SCALING_ORIG_CTX_LEN.format(arch=self.arch), value) - - def add_rope_scaling_finetuned(self, value: bool): - self.add_bool(KEY_ROPE_SCALING_FINETUNED.format(arch=self.arch), value) - - def add_tokenizer_model(self, model: str): - self.add_string(KEY_TOKENIZER_MODEL, model) - - 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: Sequence[str] | Sequence[bytes] | Sequence[bytearray]): - self.add_array(KEY_TOKENIZER_MERGES, merges) - - 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]): - self.add_array(KEY_TOKENIZER_SCORES, scores) - - def add_bos_token_id(self, id: int): - self.add_uint32(KEY_TOKENIZER_BOS_ID, id) - - def add_eos_token_id(self, id: int): - self.add_uint32(KEY_TOKENIZER_EOS_ID, id) - - def add_unk_token_id(self, id: int): - self.add_uint32(KEY_TOKENIZER_UNK_ID, id) - - def add_sep_token_id(self, id: int): - self.add_uint32(KEY_TOKENIZER_SEP_ID, id) - - def add_pad_token_id(self, id: int): - self.add_uint32(KEY_TOKENIZER_PAD_ID, id) - - -class SpecialVocab: - merges: list[str] - special_token_ids: dict[str, int] - - def __init__( - self, path: str | os.PathLike[str], load_merges: bool = False, - special_token_types: tuple[str, ...] | None = None, - n_vocab: int | None = None, - ): - self.special_token_ids = {} - self.n_vocab = n_vocab - self.load_merges = load_merges - self.merges = [] - if special_token_types is not None: - self.special_token_types = special_token_types - else: - self.special_token_types = ('bos', 'eos', 'unk', 'sep', 'pad') - self._load(Path(path)) - - def _load(self, path: Path) -> None: - if not self._try_load_from_tokenizer_json(path): - self._try_load_from_config_json(path) - - def _set_special_token(self, typ: str, tid: Any): - if not isinstance(tid, int) or tid < 0: - return - if self.n_vocab is None or tid < self.n_vocab: - self.special_token_ids[typ] = tid - return - print(f'gguf: WARNING: Special token type {typ}, id {tid} out of range, must be under {self.n_vocab} - skipping', - file = sys.stderr) - - - def _try_load_from_tokenizer_json(self, path: Path) -> bool: - tokenizer_file = path / 'tokenizer.json' - if not tokenizer_file.is_file(): - return False - with open(tokenizer_file, encoding = 'utf-8') as f: - tokenizer = json.load(f) - if self.load_merges: - merges = tokenizer.get('model', {}).get('merges') - if isinstance(merges, list) and len(merges) > 0 and isinstance(merges[0], str): - self.merges = merges - tokenizer_config_file = path / 'tokenizer_config.json' - added_tokens = tokenizer.get('added_tokens') - if added_tokens is None or not tokenizer_config_file.is_file(): - return True - with open(tokenizer_config_file, encoding = 'utf-8') as f: - tokenizer_config = json.load(f) - for typ in self.special_token_types: - entry = tokenizer_config.get(f'{typ}_token') - if isinstance(entry, str): - tc_content = entry - elif isinstance(entry, dict): - entry_content = entry.get('content') - if not isinstance(entry_content, str): - continue - tc_content = entry_content - else: - continue - # We only need the first match here. - maybe_token_id = next(( - atok.get('id') for atok in added_tokens - if atok.get('content') == tc_content), None) - self._set_special_token(typ, maybe_token_id) - return True - - def _try_load_from_config_json(self, path: Path) -> bool: - config_file = path / 'config.json' - if not config_file.is_file(): - return False - with open(config_file, encoding = 'utf-8') as f: - config = json.load(f) - for typ in self.special_token_types: - self._set_special_token(typ, config.get(f'{typ}_token_id')) - return True - - def add_to_gguf(self, gw: GGUFWriter, quiet: bool = False) -> None: - if len(self.merges) > 0: - if not quiet: - 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: 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', file = sys.stderr) - continue - if not quiet: - print(f'gguf: Setting special token type {typ} to {tokid}') - handler(tokid) - - def __repr__(self) -> str: - return f'' - - -# Example usage: -if __name__ == "__main__": - # Example usage with a file - gguf_writer = GGUFWriter("example.gguf", "llama") - - gguf_writer.add_architecture() - gguf_writer.add_block_count(12) - gguf_writer.add_uint32("answer", 42) # Write a 32-bit integer - gguf_writer.add_float32("answer_in_float", 42.0) # Write a 32-bit float - gguf_writer.add_custom_alignment(64) - - tensor1 = np.ones((32,), dtype=np.float32) * 100.0 - tensor2 = np.ones((64,), dtype=np.float32) * 101.0 - tensor3 = np.ones((96,), dtype=np.float32) * 102.0 - - gguf_writer.add_tensor("tensor1", tensor1) - gguf_writer.add_tensor("tensor2", tensor2) - gguf_writer.add_tensor("tensor3", tensor3) - - gguf_writer.write_header_to_file() - gguf_writer.write_kv_data_to_file() - gguf_writer.write_tensors_to_file() - - gguf_writer.close() +importlib.reload(gguf) diff --git a/gguf-py/gguf/gguf_reader.py b/gguf-py/gguf/gguf_reader.py new file mode 100644 index 0000000000000..8682765edbac0 --- /dev/null +++ b/gguf-py/gguf/gguf_reader.py @@ -0,0 +1,264 @@ +# +# GGUF file reading/modification support. For API usage information, +# please see the files scripts/ for some fairly simple examples. +# +from __future__ import annotations + +import os +from collections import OrderedDict +from typing import Any, Literal, NamedTuple, TypeVar, Union + +import numpy as np +import numpy.typing as npt + +if __name__ == "__main__": + import sys + from pathlib import Path + + # Allow running file in package as a script. + sys.path.insert(0, str(Path(__file__).parent.parent)) + +from gguf.constants import ( + GGML_QUANT_SIZES, + GGUF_DEFAULT_ALIGNMENT, + GGUF_MAGIC, + GGUF_VERSION, + GGMLQuantizationType, + GGUFValueType, +) + + +READER_SUPPORTED_VERSIONS = [2, GGUF_VERSION] + + +class ReaderField(NamedTuple): + # Offset to start of this field. + offset: int + + # Name of the field (not necessarily from file data). + name: str + + # Data parts. Some types have multiple components, such as strings + # that consist of a length followed by the string data. + parts: list[npt.NDArray[Any]] = [] + + # Indexes into parts that we can call the actual data. For example + # an array of strings will be populated with indexes to the actual + # string data. + data: list[int] = [-1] + + types: list[GGUFValueType] = [] + + +class ReaderTensor(NamedTuple): + name: str + tensor_type: GGMLQuantizationType + shape: npt.NDArray[np.uint32] + n_elements: int + n_bytes: int + data_offset: int + data: npt.NDArray[Any] + field: ReaderField + + +class GGUFReader: + # I - same as host, S - swapped + byte_order: Literal['I' | 'S'] = 'I' + alignment: int = GGUF_DEFAULT_ALIGNMENT + + # Note: Internal helper, API may change. + gguf_scalar_to_np: dict[GGUFValueType, type[np.generic]] = { + GGUFValueType.UINT8: np.uint8, + GGUFValueType.INT8: np.int8, + GGUFValueType.UINT16: np.uint16, + GGUFValueType.INT16: np.int16, + GGUFValueType.UINT32: np.uint32, + GGUFValueType.INT32: np.int32, + GGUFValueType.FLOAT32: np.float32, + GGUFValueType.UINT64: np.uint64, + GGUFValueType.INT64: np.int64, + GGUFValueType.FLOAT64: np.float64, + GGUFValueType.BOOL: np.bool_, + } + + def __init__(self, path: os.PathLike[str] | str, mode: Literal['r' | 'r+' | 'c'] = 'r'): + self.data = np.memmap(path, mode = mode) + offs = 0 + if self._get(offs, np.uint32, override_order = '<')[0] != GGUF_MAGIC: + raise ValueError('GGUF magic invalid') + offs += 4 + temp_version = self._get(offs, np.uint32) + if temp_version[0] & 65535 == 0: + # If we get 0 here that means it's (probably) a GGUF file created for + # the opposite byte order of the machine this script is running on. + self.byte_order = 'S' + temp_version = temp_version.newbyteorder(self.byte_order) + version = temp_version[0] + if version not in READER_SUPPORTED_VERSIONS: + raise ValueError(f'Sorry, file appears to be version {version} which we cannot handle') + self.fields: OrderedDict[str, ReaderField] = OrderedDict() + self.tensors: list[ReaderTensor] = [] + offs += self._push_field(ReaderField(offs, 'GGUF.version', [temp_version], [0], [GGUFValueType.UINT32])) + temp_counts = self._get(offs, np.uint64, 2) + offs += self._push_field(ReaderField(offs, 'GGUF.tensor_count', [temp_counts[:1]], [0], [GGUFValueType.UINT64])) + offs += self._push_field(ReaderField(offs, 'GGUF.kv_count', [temp_counts[1:]], [0], [GGUFValueType.UINT64])) + tensor_count, kv_count = temp_counts + offs = self._build_fields(offs, kv_count) + offs, tensors_fields = self._build_tensors_fields(offs, tensor_count) + new_align = self.fields.get('general.alignment') + if new_align is not None: + if new_align.types != [GGUFValueType.UINT64]: + raise ValueError('Bad type for general.alignment field') + self.alignment = new_align.parts[-1][0] + padding = offs % self.alignment + if padding != 0: + offs += self.alignment - padding + self._build_tensors(offs, tensors_fields) + + _DT = TypeVar('_DT', bound = npt.DTypeLike) + + # Fetch a key/value metadata field by key. + def get_field(self, key: str) -> Union[ReaderField, None]: + return self.fields.get(key, None) + + # Fetch a tensor from the list by index. + def get_tensor(self, idx: int) -> ReaderTensor: + return self.tensors[idx] + + def _get( + self, offset: int, dtype: npt.DTypeLike, count: int = 1, override_order: None | Literal['I' | 'S' | '<'] = None, + ) -> npt.NDArray[Any]: + count = int(count) + itemsize = int(np.empty([], dtype = dtype).itemsize) + end_offs = offset + itemsize * count + return ( + self.data[offset:end_offs] + .view(dtype = dtype)[:count] + .newbyteorder(override_order or self.byte_order) + ) + + def _push_field(self, field: ReaderField, skip_sum: bool = False) -> int: + if field.name in self.fields: + raise KeyError(f'Duplicate {field.name} already in list at offset {field.offset}') + self.fields[field.name] = field + return 0 if skip_sum else sum(int(part.nbytes) for part in field.parts) + + def _get_str(self, offset: int) -> tuple[npt.NDArray[np.uint64], npt.NDArray[np.uint8]]: + slen = self._get(offset, np.uint64) + return slen, self._get(offset + 8, np.uint8, slen[0]) + + def _get_field_parts( + self, orig_offs: int, raw_type: int, + ) -> tuple[int, list[npt.NDArray[Any]], list[int], list[GGUFValueType]]: + offs = orig_offs + types: list[GGUFValueType] = [] + gtype = GGUFValueType(raw_type) + types.append(gtype) + # Handle strings. + if gtype == GGUFValueType.STRING: + sparts: list[npt.NDArray[Any]] = list(self._get_str(offs)) + size = sum(int(part.nbytes) for part in sparts) + return size, sparts, [1], types + # Check if it's a simple scalar type. + nptype = self.gguf_scalar_to_np.get(gtype) + if nptype is not None: + val = self._get(offs, nptype) + return int(val.nbytes), [val], [0], types + # Handle arrays. + if gtype == GGUFValueType.ARRAY: + raw_itype = self._get(offs, np.uint32) + offs += int(raw_itype.nbytes) + alen = self._get(offs, np.uint64) + offs += int(alen.nbytes) + aparts: list[npt.NDArray[Any]] = [raw_itype, alen] + data_idxs: list[int] = [] + for idx in range(alen[0]): + curr_size, curr_parts, curr_idxs, curr_types = self._get_field_parts(offs, raw_itype[0]) + if idx == 0: + types += curr_types + idxs_offs = len(aparts) + aparts += curr_parts + data_idxs += (idx + idxs_offs for idx in curr_idxs) + offs += curr_size + return offs - orig_offs, aparts, data_idxs, types + # We can't deal with this one. + raise ValueError('Unknown/unhandled field type {gtype}') + + def _get_tensor(self, orig_offs: int) -> ReaderField: + offs = orig_offs + name_len, name_data = self._get_str(offs) + offs += int(name_len.nbytes + name_data.nbytes) + n_dims = self._get(offs, np.uint32) + offs += int(n_dims.nbytes) + dims = self._get(offs, np.uint64, n_dims[0]) + offs += int(dims.nbytes) + raw_dtype = self._get(offs, np.uint32) + offs += int(raw_dtype.nbytes) + offset_tensor = self._get(offs, np.uint64) + offs += int(offset_tensor.nbytes) + return ReaderField( + orig_offs, + str(bytes(name_data), encoding = 'utf-8'), + [name_len, name_data, n_dims, dims, raw_dtype, offset_tensor], + [1, 3, 4, 5], + ) + + def _build_fields(self, offs: int, count: int) -> int: + for _ in range(count): + orig_offs = offs + kv_klen, kv_kdata = self._get_str(offs) + offs += int(kv_klen.nbytes + kv_kdata.nbytes) + raw_kv_type = self._get(offs, np.uint32) + offs += int(raw_kv_type.nbytes) + parts: list[npt.NDArray[Any]] = [kv_klen, kv_kdata, raw_kv_type] + idxs_offs = len(parts) + field_size, field_parts, field_idxs, field_types = self._get_field_parts(offs, raw_kv_type[0]) + parts += field_parts + self._push_field(ReaderField( + orig_offs, + str(bytes(kv_kdata), encoding = 'utf-8'), + parts, + [idx + idxs_offs for idx in field_idxs], + field_types, + ), skip_sum = True) + offs += field_size + return offs + + def _build_tensors_fields(self, offs: int, count: int) -> tuple[int, list[ReaderField]]: + tensor_fields = [] + for _ in range(count): + field = self._get_tensor(offs) + offs += sum(int(part.nbytes) for part in field.parts) + tensor_fields.append(field) + return offs, tensor_fields + + def _build_tensors(self, start_offs: int, fields: list[ReaderField]) -> None: + tensors = [] + for field in fields: + _name_len, name_data, _n_dims, dims, raw_dtype, offset_tensor = field.parts + ggml_type = GGMLQuantizationType(raw_dtype[0]) + n_elems = np.prod(dims) + block_size, type_size = GGML_QUANT_SIZES[ggml_type] + n_bytes = n_elems * type_size // block_size + data_offs = int(start_offs + offset_tensor[0]) + item_type: npt.DTypeLike + if ggml_type == GGMLQuantizationType.F32: + item_count = n_elems + item_type = np.float32 + elif ggml_type == GGMLQuantizationType.F16: + item_count = n_elems + item_type = np.float16 + else: + item_count = n_bytes + item_type = np.uint8 + tensors.append(ReaderTensor( + name = str(bytes(name_data), encoding = 'utf-8'), + tensor_type = ggml_type, + shape = dims, + n_elements = n_elems, + n_bytes = n_bytes, + data_offset = data_offs, + data = self._get(data_offs, item_type, item_count), + field = field, + )) + self.tensors = tensors diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py new file mode 100644 index 0000000000000..75fb6976f9ca2 --- /dev/null +++ b/gguf-py/gguf/gguf_writer.py @@ -0,0 +1,409 @@ +from __future__ import annotations + +import os +import shutil +import struct +import tempfile +from enum import Enum, auto +from io import BufferedWriter +from typing import IO, Any, Sequence + +import numpy as np + +from .constants import ( + GGUF_DEFAULT_ALIGNMENT, + GGUF_MAGIC, + GGUF_VERSION, + GGMLQuantizationType, + GGUFEndian, + GGUFValueType, + Keys, + RopeScalingType, + TokenType, +) + + +class WriterState(Enum): + EMPTY = auto() + HEADER = auto() + KV_DATA = auto() + TI_DATA = auto() + + +class GGUFWriter: + fout: BufferedWriter + temp_file: tempfile.SpooledTemporaryFile[bytes] | None + tensors: list[np.ndarray[Any, Any]] + _simple_value_packing = { + GGUFValueType.UINT8: "B", + GGUFValueType.INT8: "b", + GGUFValueType.UINT16: "H", + GGUFValueType.INT16: "h", + GGUFValueType.UINT32: "I", + GGUFValueType.INT32: "i", + GGUFValueType.FLOAT32: "f", + GGUFValueType.UINT64: "Q", + GGUFValueType.INT64: "q", + GGUFValueType.FLOAT64: "d", + GGUFValueType.BOOL: "?", + } + + def __init__( + self, path: os.PathLike[str] | str, arch: str, use_temp_file: bool = True, + endianess: GGUFEndian = GGUFEndian.LITTLE, + ): + self.fout = open(path, "wb") + self.arch = arch + self.endianess = endianess + self.offset_tensor = 0 + self.data_alignment = GGUF_DEFAULT_ALIGNMENT + self.kv_data = b"" + self.kv_data_count = 0 + self.ti_data = b"" + self.ti_data_count = 0 + self.use_temp_file = use_temp_file + self.temp_file = None + self.tensors = [] + print("gguf: This GGUF file is for {0} Endian only".format( + "Big" if self.endianess == GGUFEndian.BIG else "Little", + )) + self.state = WriterState.EMPTY + + self.add_architecture() + + def write_header_to_file(self) -> None: + if self.state is not WriterState.EMPTY: + raise ValueError(f'Expected output file to be empty, got {self.state}') + + self._write_packed(" None: + if self.state is not WriterState.HEADER: + raise ValueError(f'Expected output file to contain the header, got {self.state}') + + self.fout.write(self.kv_data) + self.flush() + self.state = WriterState.KV_DATA + + def write_ti_data_to_file(self) -> None: + if self.state is not WriterState.KV_DATA: + raise ValueError(f'Expected output file to contain KV data, got {self.state}') + + self.fout.write(self.ti_data) + self.flush() + self.state = WriterState.TI_DATA + + def add_key(self, key: str) -> None: + self.add_val(key, GGUFValueType.STRING, add_vtype=False) + + def add_uint8(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.UINT8) + + def add_int8(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.INT8) + + def add_uint16(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.UINT16) + + def add_int16(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.INT16) + + def add_uint32(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.UINT32) + + def add_int32(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.INT32) + + def add_float32(self, key: str, val: float) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.FLOAT32) + + def add_uint64(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.UINT64) + + def add_int64(self, key: str, val: int) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.INT64) + + def add_float64(self, key: str, val: float) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.FLOAT64) + + def add_bool(self, key: str, val: bool) -> None: + self.add_key(key) + self.add_val(val, GGUFValueType.BOOL) + + def add_string(self, key: str, val: str) -> None: + if not val: + return + self.add_key(key) + self.add_val(val, GGUFValueType.STRING) + + def add_array(self, key: str, val: Sequence[Any]) -> None: + if not isinstance(val, Sequence): + raise ValueError("Value must be a sequence for array type") + + self.add_key(key) + self.add_val(val, GGUFValueType.ARRAY) + + def add_val(self, val: Any, vtype: GGUFValueType | None = None, add_vtype: bool = True) -> None: + if vtype is None: + vtype = GGUFValueType.get_type(val) + + if add_vtype: + self.kv_data += self._pack("I", vtype) + self.kv_data_count += 1 + + pack_fmt = self._simple_value_packing.get(vtype) + if pack_fmt is not None: + self.kv_data += self._pack(pack_fmt, val, skip_pack_prefix = vtype == GGUFValueType.BOOL) + elif vtype == GGUFValueType.STRING: + encoded_val = val.encode("utf8") if isinstance(val, str) else val + self.kv_data += self._pack("Q", len(encoded_val)) + self.kv_data += encoded_val + elif vtype == GGUFValueType.ARRAY and isinstance(val, Sequence) and val: + ltype = GGUFValueType.get_type(val[0]) + if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]): + raise ValueError("All items in a GGUF array should be of the same type") + self.kv_data += self._pack("I", ltype) + self.kv_data += self._pack("Q", len(val)) + for item in val: + self.add_val(item, add_vtype=False) + else: + raise ValueError("Invalid GGUF metadata value type or value") + + @staticmethod + def ggml_pad(x: int, n: int) -> int: + return ((x + n - 1) // n) * n + + 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, + ) -> None: + if self.state is not WriterState.EMPTY: + raise ValueError(f'Expected output file to be empty, got {self.state}') + + if raw_dtype is None and tensor_dtype not in (np.float32, np.float16): + raise ValueError("Only F32 and F16 tensors are supported for now") + + encoded_name = name.encode("utf8") + self.ti_data += self._pack("Q", len(encoded_name)) + self.ti_data += encoded_name + n_dims = len(tensor_shape) + self.ti_data += self._pack("I", n_dims) + for i in range(n_dims): + self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i]) + if raw_dtype is None: + dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16 + else: + dtype = raw_dtype + self.ti_data += self._pack("I", dtype) + self.ti_data += self._pack("Q", self.offset_tensor) + 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: Sequence[int] | None = None, + raw_dtype: GGMLQuantizationType | None = None, + ) -> None: + if self.endianess == GGUFEndian.BIG: + tensor.byteswap(inplace=True) + 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) + self.temp_file = fp + + shape: Sequence[int] = raw_shape if raw_shape is not None else tensor.shape + self.add_tensor_info(name, shape, tensor.dtype, tensor.nbytes, raw_dtype = raw_dtype) + + if self.temp_file is None: + self.tensors.append(tensor) + return + + tensor.tofile(self.temp_file) + self.write_padding(self.temp_file, tensor.nbytes) + + def write_padding(self, fp: IO[bytes], n: int, align: int | None = 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)) + + def write_tensor_data(self, tensor: np.ndarray[Any, Any]) -> None: + if self.state is not WriterState.TI_DATA: + raise ValueError(f'Expected output file to contain tensor info, got {self.state}') + + if self.endianess == GGUFEndian.BIG: + tensor.byteswap(inplace=True) + self.write_padding(self.fout, self.fout.tell()) + tensor.tofile(self.fout) + self.write_padding(self.fout, tensor.nbytes) + + def write_tensors_to_file(self) -> None: + self.write_ti_data_to_file() + + self.write_padding(self.fout, self.fout.tell()) + + if self.temp_file is None: + while True: + try: + tensor = self.tensors.pop(0) + except IndexError: + break + tensor.tofile(self.fout) + self.write_padding(self.fout, tensor.nbytes) + return + + self.temp_file.seek(0) + + shutil.copyfileobj(self.temp_file, self.fout) + self.flush() + self.temp_file.close() + + def flush(self) -> None: + self.fout.flush() + + def close(self) -> None: + self.fout.close() + + def add_architecture(self) -> None: + self.add_string(Keys.General.ARCHITECTURE, self.arch) + + def add_author(self, author: str) -> None: + self.add_string(Keys.General.AUTHOR, author) + + def add_tensor_data_layout(self, layout: str) -> None: + self.add_string(Keys.LLM.TENSOR_DATA_LAYOUT.format(arch=self.arch), layout) + + def add_url(self, url: str) -> None: + self.add_string(Keys.General.URL, url) + + def add_description(self, description: str) -> None: + self.add_string(Keys.General.DESCRIPTION, description) + + def add_source_url(self, url: str) -> None: + self.add_string(Keys.General.SOURCE_URL, url) + + def add_source_hf_repo(self, repo: str) -> None: + self.add_string(Keys.General.SOURCE_HF_REPO, repo) + + def add_file_type(self, ftype: int) -> None: + self.add_uint32(Keys.General.FILE_TYPE, ftype) + + def add_name(self, name: str) -> None: + self.add_string(Keys.General.NAME, name) + + def add_quantization_version(self, quantization_version: GGMLQuantizationType) -> None: + self.add_uint32( + Keys.General.QUANTIZATION_VERSION, quantization_version) + + def add_custom_alignment(self, alignment: int) -> None: + self.data_alignment = alignment + self.add_uint32(Keys.General.ALIGNMENT, alignment) + + def add_context_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.CONTEXT_LENGTH.format(arch=self.arch), length) + + def add_embedding_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.EMBEDDING_LENGTH.format(arch=self.arch), length) + + def add_block_count(self, length: int) -> None: + self.add_uint32(Keys.LLM.BLOCK_COUNT.format(arch=self.arch), length) + + def add_feed_forward_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length) + + def add_parallel_residual(self, use: bool) -> None: + self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use) + + def add_head_count(self, count: int) -> None: + self.add_uint32(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count) + + def add_head_count_kv(self, count: int) -> None: + self.add_uint32(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count) + + def add_max_alibi_bias(self, bias: float) -> None: + self.add_float32(Keys.Attention.MAX_ALIBI_BIAS.format(arch=self.arch), bias) + + def add_clamp_kqv(self, value: float) -> None: + self.add_float32(Keys.Attention.CLAMP_KQV.format(arch=self.arch), value) + + def add_layer_norm_eps(self, value: float) -> None: + self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value) + + def add_layer_norm_rms_eps(self, value: float) -> None: + self.add_float32(Keys.Attention.LAYERNORM_RMS_EPS.format(arch=self.arch), value) + + def add_rope_dimension_count(self, count: int) -> None: + self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count) + + def add_rope_freq_base(self, value: float) -> None: + self.add_float32(Keys.Rope.FREQ_BASE.format(arch=self.arch), value) + + def add_rope_scaling_type(self, value: RopeScalingType) -> None: + self.add_string(Keys.Rope.SCALING_TYPE.format(arch=self.arch), value.value) + + def add_rope_scaling_factor(self, value: float) -> None: + self.add_float32(Keys.Rope.SCALING_FACTOR.format(arch=self.arch), value) + + def add_rope_scaling_orig_ctx_len(self, value: int) -> None: + self.add_uint32(Keys.Rope.SCALING_ORIG_CTX_LEN.format(arch=self.arch), value) + + def add_rope_scaling_finetuned(self, value: bool) -> None: + self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value) + + def add_tokenizer_model(self, model: str) -> None: + self.add_string(Keys.Tokenizer.MODEL, model) + + def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None: + self.add_array(Keys.Tokenizer.LIST, tokens) + + def add_token_merges(self, merges: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None: + self.add_array(Keys.Tokenizer.MERGES, merges) + + def add_token_types(self, types: Sequence[TokenType] | Sequence[int]) -> None: + self.add_array(Keys.Tokenizer.TOKEN_TYPE, types) + + def add_token_scores(self, scores: Sequence[float]) -> None: + self.add_array(Keys.Tokenizer.SCORES, scores) + + def add_bos_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.BOS_ID, id) + + def add_eos_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.EOS_ID, id) + + def add_unk_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.UNK_ID, id) + + def add_sep_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.SEP_ID, id) + + def add_pad_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.PAD_ID, id) + + def add_add_bos_token(self, value: bool) -> None: + self.add_bool(Keys.Tokenizer.ADD_BOS, value) + + def add_add_eos_token(self, value: bool) -> None: + self.add_bool(Keys.Tokenizer.ADD_EOS, value) + + def _pack(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> bytes: + pack_prefix = '' + if not skip_pack_prefix: + pack_prefix = '<' if self.endianess == GGUFEndian.LITTLE else '>' + return struct.pack(f'{pack_prefix}{fmt}', value) + + def _write_packed(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> None: + self.fout.write(self._pack(fmt, value, skip_pack_prefix)) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py new file mode 100644 index 0000000000000..22ad8b8fc558d --- /dev/null +++ b/gguf-py/gguf/tensor_mapping.py @@ -0,0 +1,257 @@ +from __future__ import annotations + +from typing import Sequence + +from .constants import MODEL_ARCH, MODEL_TENSOR, MODEL_TENSORS, TENSOR_NAMES + + +class TensorNameMap: + mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { + # Token embeddings + MODEL_TENSOR.TOKEN_EMBD: ( + "gpt_neox.embed_in", # gptneox + "transformer.wte", # gpt2 gpt-j mpt refact + "transformer.word_embeddings", # falcon + "word_embeddings", # bloom + "model.embed_tokens", # llama-hf + "tok_embeddings", # llama-pth + "embeddings.word_embeddings", # bert + "language_model.embedding.word_embeddings", # persimmon + ), + + # Token type embeddings + MODEL_TENSOR.TOKEN_TYPES: ( + "embeddings.token_type_embeddings", # bert + ), + + # Normalization of token embeddings + MODEL_TENSOR.TOKEN_EMBD_NORM: ( + "word_embeddings_layernorm", # bloom + ), + + # Position embeddings + MODEL_TENSOR.POS_EMBD: ( + "transformer.wpe", # gpt2 + "embeddings.position_embeddings", # bert + ), + + # Output + MODEL_TENSOR.OUTPUT: ( + "embed_out", # gptneox + "lm_head", # gpt2 mpt falcon llama-hf baichuan + "output", # llama-pth bloom + "word_embeddings_for_head", # persimmon + ), + + # Output norm + MODEL_TENSOR.OUTPUT_NORM: ( + "gpt_neox.final_layer_norm", # gptneox + "transformer.ln_f", # gpt2 gpt-j falcon + "model.norm", # llama-hf baichuan + "norm", # llama-pth + "embeddings.LayerNorm", # bert + "transformer.norm_f", # mpt + "ln_f", # refact bloom + "language_model.encoder.final_layernorm", # persimmon + ), + + # Rope frequencies + MODEL_TENSOR.ROPE_FREQS: ( + "rope.freqs", # llama-pth + ), + } + + block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { + # Attention norm + MODEL_TENSOR.ATTN_NORM: ( + "gpt_neox.layers.{bid}.input_layernorm", # gptneox + "transformer.h.{bid}.ln_1", # gpt2 gpt-j refact + "transformer.blocks.{bid}.norm_1", # mpt + "transformer.h.{bid}.input_layernorm", # falcon7b + "h.{bid}.input_layernorm", # bloom + "transformer.h.{bid}.ln_mlp", # falcon40b + "model.layers.{bid}.input_layernorm", # llama-hf + "layers.{bid}.attention_norm", # llama-pth + "encoder.layer.{bid}.attention.output.LayerNorm", # bert + "language_model.encoder.layers.{bid}.input_layernorm", # persimmon + "model.layers.{bid}.ln1", # yi + ), + + # Attention norm 2 + MODEL_TENSOR.ATTN_NORM_2: ( + "transformer.h.{bid}.ln_attn", # falcon40b + ), + + # Attention query-key-value + MODEL_TENSOR.ATTN_QKV: ( + "gpt_neox.layers.{bid}.attention.query_key_value", # gptneox + "transformer.h.{bid}.attn.c_attn", # gpt2 + "transformer.blocks.{bid}.attn.Wqkv", # mpt + "transformer.h.{bid}.self_attention.query_key_value", # falcon + "h.{bid}.self_attention.query_key_value", # bloom + "language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon + ), + + # Attention query + MODEL_TENSOR.ATTN_Q: ( + "model.layers.{bid}.self_attn.q_proj", # llama-hf + "layers.{bid}.attention.wq", # llama-pth + "encoder.layer.{bid}.attention.self.query", # bert + "transformer.h.{bid}.attn.q_proj", # gpt-j + ), + + # Attention key + MODEL_TENSOR.ATTN_K: ( + "model.layers.{bid}.self_attn.k_proj", # llama-hf + "layers.{bid}.attention.wk", # llama-pth + "encoder.layer.{bid}.attention.self.key", # bert + "transformer.h.{bid}.attn.k_proj", # gpt-j + ), + + # Attention value + MODEL_TENSOR.ATTN_V: ( + "model.layers.{bid}.self_attn.v_proj", # llama-hf + "layers.{bid}.attention.wv", # llama-pth + "encoder.layer.{bid}.attention.self.value", # bert + "transformer.h.{bid}.attn.v_proj", # gpt-j + ), + + # Attention output + MODEL_TENSOR.ATTN_OUT: ( + "gpt_neox.layers.{bid}.attention.dense", # gptneox + "transformer.h.{bid}.attn.c_proj", # gpt2 refact + "transformer.blocks.{bid}.attn.out_proj", # mpt + "transformer.h.{bid}.self_attention.dense", # falcon + "h.{bid}.self_attention.dense", # bloom + "model.layers.{bid}.self_attn.o_proj", # llama-hf + "layers.{bid}.attention.wo", # llama-pth + "encoder.layer.{bid}.attention.output.dense", # bert + "transformer.h.{bid}.attn.out_proj", # gpt-j + "language_model.encoder.layers.{bid}.self_attention.dense", # persimmon + ), + + # Rotary embeddings + MODEL_TENSOR.ATTN_ROT_EMBD: ( + "model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf + "layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth + ), + + # Feed-forward norm + MODEL_TENSOR.FFN_NORM: ( + "gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox + "transformer.h.{bid}.ln_2", # gpt2 refact + "h.{bid}.post_attention_layernorm", # bloom + "transformer.blocks.{bid}.norm_2", # mpt + "model.layers.{bid}.post_attention_layernorm", # llama-hf + "layers.{bid}.ffn_norm", # llama-pth + "encoder.layer.{bid}.output.LayerNorm", # bert + "language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon + "model.layers.{bid}.ln2", # yi + ), + + # Feed-forward up + MODEL_TENSOR.FFN_UP: ( + "gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox + "transformer.h.{bid}.mlp.c_fc", # gpt2 + "transformer.blocks.{bid}.ffn.up_proj", # mpt + "transformer.h.{bid}.mlp.dense_h_to_4h", # falcon + "h.{bid}.mlp.dense_h_to_4h", # bloom + "model.layers.{bid}.mlp.up_proj", # llama-hf refact + "layers.{bid}.feed_forward.w3", # llama-pth + "encoder.layer.{bid}.intermediate.dense", # bert + "transformer.h.{bid}.mlp.fc_in", # gpt-j + "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon + ), + + # Feed-forward gate + MODEL_TENSOR.FFN_GATE: ( + "model.layers.{bid}.mlp.gate_proj", # llama-hf refact + "layers.{bid}.feed_forward.w1", # llama-pth + ), + + # Feed-forward down + MODEL_TENSOR.FFN_DOWN: ( + "gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox + "transformer.h.{bid}.mlp.c_proj", # gpt2 refact + "transformer.blocks.{bid}.ffn.down_proj", # mpt + "transformer.h.{bid}.mlp.dense_4h_to_h", # falcon + "h.{bid}.mlp.dense_4h_to_h", # bloom + "model.layers.{bid}.mlp.down_proj", # llama-hf + "layers.{bid}.feed_forward.w2", # llama-pth + "encoder.layer.{bid}.output.dense", # bert + "transformer.h.{bid}.mlp.fc_out", # gpt-j + "language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon + ), + + MODEL_TENSOR.ATTN_Q_NORM: ( + "language_model.encoder.layers.{bid}.self_attention.q_layernorm", + ), + + MODEL_TENSOR.ATTN_K_NORM: ( + "language_model.encoder.layers.{bid}.self_attention.k_layernorm", + ), + + MODEL_TENSOR.ROPE_FREQS: ( + "language_model.encoder.layers.{bid}.self_attention.rotary_emb.inv_freq", # persimmon + ), + } + + mapping: dict[str, tuple[MODEL_TENSOR, str]] + + def __init__(self, arch: MODEL_ARCH, n_blocks: int): + self.mapping = {} + for tensor, keys in self.mappings_cfg.items(): + if tensor not in MODEL_TENSORS[arch]: + continue + tensor_name = TENSOR_NAMES[tensor] + self.mapping[tensor_name] = (tensor, tensor_name) + for key in keys: + self.mapping[key] = (tensor, tensor_name) + for bid in range(n_blocks): + for tensor, keys in self.block_mappings_cfg.items(): + if tensor not in MODEL_TENSORS[arch]: + continue + tensor_name = TENSOR_NAMES[tensor].format(bid = bid) + self.mapping[tensor_name] = (tensor, tensor_name) + for key in keys: + key = key.format(bid = bid) + self.mapping[key] = (tensor, tensor_name) + + 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 + for suffix in try_suffixes: + if key.endswith(suffix): + result = self.mapping.get(key[:-len(suffix)]) + if result is not None: + return result[0], result[1] + suffix + return None + + 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] = ()) -> MODEL_TENSOR | None: + result = self.get_type_and_name(key, try_suffixes = try_suffixes) + if result is None: + return None + return result[0] + + def __getitem__(self, key: str) -> str: + try: + return self.mapping[key][1] + except KeyError: + raise KeyError(key) + + def __contains__(self, key: str) -> bool: + return key in self.mapping + + def __repr__(self) -> str: + return repr(self.mapping) + + +def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> TensorNameMap: + return TensorNameMap(arch, n_blocks) diff --git a/gguf-py/gguf/vocab.py b/gguf-py/gguf/vocab.py new file mode 100644 index 0000000000000..71192a928d664 --- /dev/null +++ b/gguf-py/gguf/vocab.py @@ -0,0 +1,164 @@ +from __future__ import annotations + +import json +import os +import sys +from pathlib import Path +from typing import Any, Callable + +from .gguf_writer import GGUFWriter + + +class SpecialVocab: + merges: list[str] + add_special_token: dict[str, bool] + special_token_ids: dict[str, int] + + def __init__( + self, path: str | os.PathLike[str], load_merges: bool = False, + special_token_types: tuple[str, ...] | None = None, + n_vocab: int | None = None, + ): + self.special_token_ids = {} + self.add_special_token = {} + self.n_vocab = n_vocab + self.load_merges = load_merges + self.merges = [] + if special_token_types is not None: + self.special_token_types = special_token_types + else: + self.special_token_types = ('bos', 'eos', 'unk', 'sep', 'pad') + self._load(Path(path)) + + def __repr__(self) -> str: + return ''.format( + len(self.merges), self.special_token_ids or "unset", self.add_special_token or "unset", + ) + + def add_to_gguf(self, gw: GGUFWriter, quiet: bool = False) -> None: + if self.merges: + if not quiet: + print(f'gguf: Adding {len(self.merges)} merge(s).') + gw.add_token_merges(self.merges) + elif self.load_merges: + print( + 'gguf: WARNING: Adding merges requested but no merges found, output may be non-functional.', + file = sys.stderr, + ) + for typ, tokid in self.special_token_ids.items(): + id_handler: Callable[[int], None] | None = getattr(gw, f'add_{typ}_token_id', None) + if id_handler is None: + print( + f'gguf: WARNING: No handler for special token type {typ} with id {tokid} - skipping', + file = sys.stderr, + ) + continue + if not quiet: + print(f'gguf: Setting special token type {typ} to {tokid}') + id_handler(tokid) + for typ, value in self.add_special_token.items(): + add_handler: Callable[[bool], None] | None = getattr(gw, f'add_add_{typ}_token', None) + if add_handler is None: + print( + f'gguf: WARNING: No handler for add_{typ}_token with value {value} - skipping', + file = sys.stderr, + ) + continue + if not quiet: + print(f'gguf: Setting add_{typ}_token to {value}') + add_handler(value) + + def _load(self, path: Path) -> None: + self._try_load_from_tokenizer_json(path) + self._try_load_from_config_json(path) + if self.load_merges and not self.merges: + self._try_load_merges_txt(path) + + def _try_load_merges_txt(self, path: Path) -> bool: + merges_file = path / 'merges.txt' + if not merges_file.is_file(): + return False + with open(merges_file, 'r') as fp: + first_line = next(fp, '').strip() + if not first_line.startswith('#'): + fp.seek(0) + line_num = 0 + else: + line_num = 1 + merges = [] + for line in fp: + line_num += 1 + line = line.strip() + if not line: + continue + parts = line.split(None, 3) + if len(parts) != 2: + print( + f'gguf: WARNING: {merges_file.name}: Line {line_num}: Entry malformed, ignoring', + file = sys.stderr, + ) + continue + merges.append(f'{parts[0]} {parts[1]}') + self.merges = merges + return True + + def _set_special_token(self, typ: str, tid: Any) -> None: + if not isinstance(tid, int) or tid < 0: + return + if self.n_vocab is None or tid < self.n_vocab: + if typ in self.special_token_ids: + return + self.special_token_ids[typ] = tid + return + print( + f'gguf: WARNING: Special token type {typ}, id {tid} out of range, must be under {self.n_vocab} - skipping', + file = sys.stderr, + ) + + def _try_load_from_tokenizer_json(self, path: Path) -> bool: + tokenizer_file = path / 'tokenizer.json' + if not tokenizer_file.is_file(): + return False + with open(tokenizer_file, encoding = 'utf-8') as f: + tokenizer = json.load(f) + if self.load_merges: + merges = tokenizer.get('model', {}).get('merges') + if isinstance(merges, list) and merges and isinstance(merges[0], str): + self.merges = merges + tokenizer_config_file = path / 'tokenizer_config.json' + added_tokens = tokenizer.get('added_tokens') + if added_tokens is None or not tokenizer_config_file.is_file(): + return True + with open(tokenizer_config_file, encoding = 'utf-8') as f: + tokenizer_config = json.load(f) + for typ in self.special_token_types: + add_entry = tokenizer_config.get(f'add_{typ}_token') + if isinstance(add_entry, bool): + self.add_special_token[typ] = add_entry + entry = tokenizer_config.get(f'{typ}_token') + if isinstance(entry, str): + tc_content = entry + elif isinstance(entry, dict): + entry_content = entry.get('content') + if not isinstance(entry_content, str): + continue + tc_content = entry_content + else: + continue + # We only need the first match here. + maybe_token_id = next( + (atok.get('id') for atok in added_tokens if atok.get('content') == tc_content), + None, + ) + self._set_special_token(typ, maybe_token_id) + return True + + def _try_load_from_config_json(self, path: Path) -> bool: + config_file = path / 'config.json' + if not config_file.is_file(): + return False + with open(config_file, encoding = 'utf-8') as f: + config = json.load(f) + for typ in self.special_token_types: + self._set_special_token(typ, config.get(f'{typ}_token_id')) + return True diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index c6cb2c37a0e0a..624e1cda628e1 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -1,11 +1,12 @@ [tool.poetry] name = "gguf" -version = "0.4.6" +version = "0.5.0" description = "Write ML models in GGUF for GGML" authors = ["GGML "] packages = [ {include = "gguf"}, {include = "gguf/py.typed"}, + {include = "scripts"}, ] readme = "README.md" homepage = "https://ggml.ai" @@ -27,3 +28,8 @@ pytest = "^5.2" [build-system] requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" + +[tool.poetry.scripts] +gguf-convert-endian = "scripts:gguf_convert_endian_entrypoint" +gguf-dump = "scripts:gguf_dump_entrypoint" +gguf-set-metadata = "scripts:gguf_set_metadata_entrypoint" diff --git a/gguf-py/scripts/__init__.py b/gguf-py/scripts/__init__.py new file mode 100644 index 0000000000000..77132db7a0e94 --- /dev/null +++ b/gguf-py/scripts/__init__.py @@ -0,0 +1,12 @@ +import os + +from importlib import import_module + + +os.environ["NO_LOCAL_GGUF"] = "TRUE" + +gguf_convert_endian_entrypoint = import_module("scripts.gguf-convert-endian").main +gguf_dump_entrypoint = import_module("scripts.gguf-dump").main +gguf_set_metadata_entrypoint = import_module("scripts.gguf-set-metadata").main + +del import_module, os diff --git a/gguf-py/scripts/gguf-convert-endian.py b/gguf-py/scripts/gguf-convert-endian.py new file mode 100755 index 0000000000000..b79d86e072041 --- /dev/null +++ b/gguf-py/scripts/gguf-convert-endian.py @@ -0,0 +1,113 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse +import os +import sys +from pathlib import Path + +import numpy as np + +# Necessary to load the local gguf package +if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists(): + sys.path.insert(0, str(Path(__file__).parent.parent)) + +import gguf + + +def convert_byteorder(reader: gguf.GGUFReader, args: argparse.Namespace) -> None: + if np.uint32(1) == np.uint32(1).newbyteorder("<"): + # Host is little endian + host_endian = "little" + swapped_endian = "big" + else: + # Sorry PDP or other weird systems that don't use BE or LE. + host_endian = "big" + swapped_endian = "little" + if reader.byte_order == "S": + file_endian = swapped_endian + else: + file_endian = host_endian + if args.order == "native": + order = host_endian + print(f"* Host is {host_endian.upper()} endian, GGUF file seems to be {file_endian.upper()} endian") + if file_endian == order: + print(f"* File is already {order.upper()} endian. Nothing to do.") + sys.exit(0) + print("* Checking tensors for conversion compatibility") + for tensor in reader.tensors: + if tensor.tensor_type not in ( + gguf.GGMLQuantizationType.F32, + gguf.GGMLQuantizationType.F16, + gguf.GGMLQuantizationType.Q8_0, + ): + raise ValueError(f"Cannot handle type {tensor.tensor_type.name} for tensor {repr(tensor.name)}") + print(f"* Preparing to convert from {file_endian.upper()} to {order.upper()}") + if args.dry_run: + return + print("\n*** Warning *** Warning *** Warning **") + print("* This conversion process may damage the file. Ensure you have a backup.") + if order != host_endian: + print("* Requested endian differs from host, you will not be able to load the model on this machine.") + print("* The file will be modified immediately, so if conversion fails or is interrupted") + print("* the file will be corrupted. Enter exactly YES if you are positive you want to proceed:") + response = input("YES, I am sure> ") + if response != "YES": + print("You didn't enter YES. Okay then, see ya!") + sys.exit(0) + print(f"\n* Converting fields ({len(reader.fields)})") + for idx, field in enumerate(reader.fields.values()): + print(f"- {idx:4}: Converting field {repr(field.name)}, part count: {len(field.parts)}") + for part in field.parts: + part.byteswap(inplace=True) + print(f"\n* Converting tensors ({len(reader.tensors)})") + for idx, tensor in enumerate(reader.tensors): + print( + f" - {idx:4}: Converting tensor {repr(tensor.name)}, type={tensor.tensor_type.name}, " + f"elements={tensor.n_elements}... ", + end="", + ) + tensor_type = tensor.tensor_type + for part in tensor.field.parts: + part.byteswap(inplace=True) + if tensor_type != gguf.GGMLQuantizationType.Q8_0: + tensor.data.byteswap(inplace=True) + print() + continue + # A Q8_0 block consists of a f16 delta followed by 32 int8 quants, so 34 bytes + block_size = 34 + n_blocks = len(tensor.data) // block_size + for block_num in range(n_blocks): + block_offs = block_num * block_size + # I know I said f16, but it doesn't matter here - any simple 16 bit type works. + delta = tensor.data[block_offs:block_offs + 2].view(dtype=np.uint16) + delta.byteswap(inplace=True) + if block_num % 100000 == 0: + print(f"[{(n_blocks - block_num) // 1000}K]", end="") + sys.stdout.flush() + print() + print("* Completion") + + +def main() -> None: + parser = argparse.ArgumentParser(description="Convert GGUF file byte order") + parser.add_argument( + "model", type=str, + help="GGUF format model filename", + ) + parser.add_argument( + "order", type=str, choices=['big', 'little', 'native'], + help="Requested byte order", + ) + parser.add_argument( + "--dry-run", action="store_true", + help="Don't actually change anything", + ) + args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"]) + print(f'* Loading: {args.model}') + reader = gguf.GGUFReader(args.model, 'r' if args.dry_run else 'r+') + convert_byteorder(reader, args) + + +if __name__ == "__main__": + main() diff --git a/gguf-py/scripts/gguf-dump.py b/gguf-py/scripts/gguf-dump.py new file mode 100755 index 0000000000000..5141873de7321 --- /dev/null +++ b/gguf-py/scripts/gguf-dump.py @@ -0,0 +1,116 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse +import os +import sys +from pathlib import Path +from typing import Any + +import numpy as np + +# Necessary to load the local gguf package +if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists(): + sys.path.insert(0, str(Path(__file__).parent.parent)) + +from gguf import GGUFReader, GGUFValueType # noqa: E402 + + +def get_file_host_endian(reader: GGUFReader) -> tuple[str, str]: + host_endian = 'LITTLE' if np.uint32(1) == np.uint32(1).newbyteorder("<") else 'BIG' + if reader.byte_order == 'S': + file_endian = 'BIG' if host_endian == 'LITTLE' else 'LITTLE' + else: + file_endian = host_endian + return (host_endian, file_endian) + + +# For more information about what field.parts and field.data represent, +# please see the comments in the modify_gguf.py example. +def dump_metadata(reader: GGUFReader, args: argparse.Namespace) -> None: + host_endian, file_endian = get_file_host_endian(reader) + print(f'* File is {file_endian} endian, script is running on a {host_endian} endian host.') + print(f'\n* Dumping {len(reader.fields)} key/value pair(s)') + for n, field in enumerate(reader.fields.values(), 1): + if not field.types: + pretty_type = 'N/A' + elif field.types[0] == GGUFValueType.ARRAY: + nest_count = len(field.types) - 1 + pretty_type = '[' * nest_count + str(field.types[-1].name) + ']' * nest_count + else: + pretty_type = str(field.types[-1].name) + print(f' {n:5}: {pretty_type:10} | {len(field.data):8} | {field.name}', end = '') + if len(field.types) == 1: + curr_type = field.types[0] + if curr_type == GGUFValueType.STRING: + print(' = {0}'.format(repr(str(bytes(field.parts[-1]), encoding='utf8')[:60])), end = '') + elif field.types[0] in reader.gguf_scalar_to_np: + print(' = {0}'.format(field.parts[-1][0]), end = '') + print() + if args.no_tensors: + return + print(f'\n* Dumping {len(reader.tensors)} tensor(s)') + for n, tensor in enumerate(reader.tensors, 1): + prettydims = ', '.join('{0:5}'.format(d) for d in list(tensor.shape) + [1] * (4 - len(tensor.shape))) + print(f' {n:5}: {tensor.n_elements:10} | {prettydims} | {tensor.tensor_type.name:7} | {tensor.name}') + + +def dump_metadata_json(reader: GGUFReader, args: argparse.Namespace) -> None: + import json + host_endian, file_endian = get_file_host_endian(reader) + metadata: dict[str, Any] = {} + tensors: dict[str, Any] = {} + result = { + "filename": args.model, + "endian": file_endian, + "metadata": metadata, + "tensors": tensors, + } + for idx, field in enumerate(reader.fields.values()): + curr: dict[str, Any] = { + "index": idx, + "type": field.types[0].name if field.types else 'UNKNOWN', + "offset": field.offset, + } + metadata[field.name] = curr + if field.types[:1] == [GGUFValueType.ARRAY]: + curr["array_types"] = [t.name for t in field.types][1:] + if not args.json_array: + continue + itype = field.types[-1] + if itype == GGUFValueType.STRING: + curr["value"] = [str(bytes(field.parts[idx]), encoding="utf-8") for idx in field.data] + else: + curr["value"] = [pv for idx in field.data for pv in field.parts[idx].tolist()] + elif field.types[0] == GGUFValueType.STRING: + curr["value"] = str(bytes(field.parts[-1]), encoding="utf-8") + else: + curr["value"] = field.parts[-1].tolist()[0] + for idx, tensor in enumerate(reader.tensors): + tensors[tensor.name] = { + "index": idx, + "shape": tensor.shape.tolist(), + "type": tensor.tensor_type.name, + "offset": tensor.field.offset, + } + json.dump(result, sys.stdout) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Dump GGUF file metadata") + parser.add_argument("model", type=str, help="GGUF format model filename") + parser.add_argument("--no-tensors", action="store_true", help="Don't dump tensor metadata") + parser.add_argument("--json", action="store_true", help="Produce JSON output") + parser.add_argument("--json-array", action="store_true", help="Include full array values in JSON output (long)") + args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"]) + if not args.json: + print(f'* Loading: {args.model}') + reader = GGUFReader(args.model, 'r') + if args.json: + dump_metadata_json(reader, args) + else: + dump_metadata(reader, args) + + +if __name__ == '__main__': + main() diff --git a/gguf-py/scripts/gguf-set-metadata.py b/gguf-py/scripts/gguf-set-metadata.py new file mode 100755 index 0000000000000..3ebdfa898a779 --- /dev/null +++ b/gguf-py/scripts/gguf-set-metadata.py @@ -0,0 +1,90 @@ +#!/usr/bin/env python3 +import argparse +import os +import sys +from pathlib import Path + +# Necessary to load the local gguf package +if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists(): + sys.path.insert(0, str(Path(__file__).parent.parent)) + +from gguf import GGUFReader # noqa: E402 + + +def minimal_example(filename: str) -> None: + reader = GGUFReader(filename, 'r+') + field = reader.fields['tokenizer.ggml.bos_token_id'] + if field is None: + return + part_index = field.data[0] + field.parts[part_index][0] = 2 # Set tokenizer.ggml.bos_token_id to 2 + # + # So what's this field.data thing? It's helpful because field.parts contains + # _every_ part of the GGUF field. For example, tokenizer.ggml.bos_token_id consists + # of: + # + # Part index 0: Key length (27) + # Part index 1: Key data ("tokenizer.ggml.bos_token_id") + # Part index 2: Field type (4, the id for GGUFValueType.UINT32) + # Part index 3: Field value + # + # Note also that each part is an NDArray slice, so even a part that + # is only a single value like the key length will be a NDArray of + # the key length type (numpy.uint32). + # + # The .data attribute in the Field is a list of relevant part indexes + # and doesn't contain internal GGUF details like the key length part. + # In this case, .data will be [3] - just the part index of the + # field value itself. + + +def set_metadata(reader: GGUFReader, args: argparse.Namespace) -> None: + field = reader.get_field(args.key) + if field is None: + print(f'! Field {repr(args.key)} not found', file = sys.stderr) + sys.exit(1) + # Note that field.types is a list of types. This is because the GGUF + # format supports arrays. For example, an array of UINT32 would + # look like [GGUFValueType.ARRAY, GGUFValueType.UINT32] + handler = reader.gguf_scalar_to_np.get(field.types[0]) if field.types else None + if handler is None: + print( + f'! This tool only supports changing simple values, {repr(args.key)} has unsupported type {field.types}', + file = sys.stderr, + ) + sys.exit(1) + current_value = field.parts[field.data[0]][0] + new_value = handler(args.value) + print(f'* Preparing to change field {repr(args.key)} from {current_value} to {new_value}') + if current_value == new_value: + print(f'- Key {repr(args.key)} already set to requested value {current_value}') + sys.exit(0) + if args.dry_run: + sys.exit(0) + if not args.force: + print('*** Warning *** Warning *** Warning **') + print('* Changing fields in a GGUF file can make it unusable. Proceed at your own risk.') + print('* Enter exactly YES if you are positive you want to proceed:') + response = input('YES, I am sure> ') + if response != 'YES': + print("You didn't enter YES. Okay then, see ya!") + sys.exit(0) + field.parts[field.data[0]][0] = new_value + print('* Field changed. Successful completion.') + + +def main() -> None: + parser = argparse.ArgumentParser(description="Set a simple value in GGUF file metadata") + parser.add_argument("model", type=str, help="GGUF format model filename") + parser.add_argument("key", type=str, help="Metadata key to set") + parser.add_argument("value", type=str, help="Metadata value to set") + parser.add_argument("--dry-run", action="store_true", help="Don't actually change anything") + parser.add_argument("--force", action="store_true", help="Change the field without confirmation") + args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"]) + print(f'* Loading: {args.model}') + reader = GGUFReader(args.model, 'r' if args.dry_run else 'r+') + set_metadata(reader, args) + + +if __name__ == '__main__': + main() diff --git a/gguf-py/tests/test_gguf.py b/gguf-py/tests/test_gguf.py index 512531dd2a8f0..0adeb7d55731a 100644 --- a/gguf-py/tests/test_gguf.py +++ b/gguf-py/tests/test_gguf.py @@ -1,7 +1,7 @@ -import gguf +import gguf # noqa: F401 # TODO: add tests -def test_write_gguf(): +def test_write_gguf() -> None: pass From d96ca7ded77df764db797b68b4a29e34c5b56285 Mon Sep 17 00:00:00 2001 From: Alexey Parfenov Date: Sat, 11 Nov 2023 05:48:21 +0000 Subject: [PATCH 02/39] server : fix crash when prompt exceeds context size (#3996) --- examples/server/server.cpp | 58 +++++++++++++++++++------------------- 1 file changed, 29 insertions(+), 29 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index cbf36ad6752b6..46862a84b99da 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1557,6 +1557,35 @@ struct llama_server_context slot.num_prompt_tokens = prompt_tokens.size(); + if (slot.params.n_keep < 0) + { + slot.params.n_keep = slot.num_prompt_tokens; + } + slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); + + // if input prompt is too big, truncate it + if (slot.num_prompt_tokens >= slot.n_ctx) + { + const int n_left = slot.n_ctx - slot.params.n_keep; + const int n_block_size = n_left / 2; + const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; + + std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep); + new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end()); + + LOG_VERBOSE("input truncated", { + {"n_ctx", slot.n_ctx}, + {"n_keep", slot.params.n_keep}, + {"n_left", n_left}, + {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, + }); + slot.truncated = true; + prompt_tokens = new_tokens; + + slot.num_prompt_tokens = prompt_tokens.size(); + GGML_ASSERT(slot.num_prompt_tokens < slot.n_ctx); + } + if (!slot.params.cache_prompt) { llama_sampling_reset(slot.ctx_sampling); @@ -1566,35 +1595,6 @@ struct llama_server_context } else { - if (slot.params.n_keep < 0) - { - slot.params.n_keep = slot.num_prompt_tokens; - } - slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep); - - // if input prompt is too big, truncate it - if (slot.num_prompt_tokens >= slot.n_ctx) - { - const int n_left = slot.n_ctx - slot.params.n_keep; - const int n_block_size = n_left / 2; - const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size; - - std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep); - new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end()); - - LOG_VERBOSE("input truncated", { - {"n_ctx", slot.n_ctx}, - {"n_keep", slot.params.n_keep}, - {"n_left", n_left}, - {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())}, - }); - slot.truncated = true; - prompt_tokens = new_tokens; - - slot.num_prompt_tokens = prompt_tokens.size(); - GGML_ASSERT(slot.num_prompt_tokens < slot.n_ctx); - } - // push the prompt into the sampling context (do not apply grammar) for (auto &token : prompt_tokens) { From e86fc56f7521ca4b18d1d9939e82abd40c2f1c01 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=2E=20Yusuf=20Sar=C4=B1g=C3=B6z?= Date: Sat, 11 Nov 2023 18:35:31 +0300 Subject: [PATCH 03/39] Fix gguf-convert-endian script (#4037) * Fix gguf-convert-endian script * Bump version and update description --- gguf-py/pyproject.toml | 4 ++-- gguf-py/scripts/gguf-convert-endian.py | 3 +-- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index 624e1cda628e1..e21c3cd94f22a 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -1,7 +1,7 @@ [tool.poetry] name = "gguf" -version = "0.5.0" -description = "Write ML models in GGUF for GGML" +version = "0.5.1" +description = "Read and write ML models in GGUF for GGML" authors = ["GGML "] packages = [ {include = "gguf"}, diff --git a/gguf-py/scripts/gguf-convert-endian.py b/gguf-py/scripts/gguf-convert-endian.py index b79d86e072041..10a16ad063ce6 100755 --- a/gguf-py/scripts/gguf-convert-endian.py +++ b/gguf-py/scripts/gguf-convert-endian.py @@ -28,8 +28,7 @@ def convert_byteorder(reader: gguf.GGUFReader, args: argparse.Namespace) -> None file_endian = swapped_endian else: file_endian = host_endian - if args.order == "native": - order = host_endian + order = host_endian if args.order == "native" else args.order print(f"* Host is {host_endian.upper()} endian, GGUF file seems to be {file_endian.upper()} endian") if file_endian == order: print(f"* File is already {order.upper()} endian. Nothing to do.") From 532dd74e38c29e16ea1cfc4e7eedb4f2fab3f3cd Mon Sep 17 00:00:00 2001 From: Richard Kiss Date: Sat, 11 Nov 2023 22:04:58 -0800 Subject: [PATCH 04/39] Fix some documentation typos/grammar mistakes (#4032) * typos * Update examples/parallel/README.md Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> --------- Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> --- README.md | 2 +- docs/token_generation_performance_tips.md | 2 +- examples/main/README.md | 2 +- examples/parallel/README.md | 2 +- grammars/README.md | 4 ++-- 5 files changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 9c9e36ad07acc..af39e8c0e386e 100644 --- a/README.md +++ b/README.md @@ -424,7 +424,7 @@ Building the program with BLAS support may lead to some performance improvements ``` The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used. - If your GPU is not officialy supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 or 11.0.0 on RDNA3. + If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 or 11.0.0 on RDNA3. The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above): | Option | Legal values | Default | Description | diff --git a/docs/token_generation_performance_tips.md b/docs/token_generation_performance_tips.md index c9acff7d4f18c..d7e863dff5c01 100644 --- a/docs/token_generation_performance_tips.md +++ b/docs/token_generation_performance_tips.md @@ -17,7 +17,7 @@ llama_model_load_internal: [cublas] total VRAM used: 17223 MB If you see these lines, then the GPU is being used. ## Verifying that the CPU is not oversaturated -llama accepts a `-t N` (or `--threads N`) parameter. It's extremely important that this parameter is not too large. If your token generation is extremely slow, try setting this number to 1. If this significantly improves your token generation speed, then your CPU is being oversaturated and you need to explicitly set this parameter to the number of the physicial CPU cores on your machine (even if you utilize a GPU). If in doubt, start with 1 and double the amount until you hit a performance bottleneck, then scale the number down. +llama accepts a `-t N` (or `--threads N`) parameter. It's extremely important that this parameter is not too large. If your token generation is extremely slow, try setting this number to 1. If this significantly improves your token generation speed, then your CPU is being oversaturated and you need to explicitly set this parameter to the number of the physical CPU cores on your machine (even if you utilize a GPU). If in doubt, start with 1 and double the amount until you hit a performance bottleneck, then scale the number down. # Example of runtime flags effect on inference speed benchmark These runs were tested on the following machine: diff --git a/examples/main/README.md b/examples/main/README.md index a3428b48763d0..c7997f66569a5 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -142,7 +142,7 @@ The `--ctx-size` option allows you to set the size of the prompt context used by ### Extended Context Size -Some fine-tuned models have extened the context length by scaling RoPE. For example, if the original pretrained model have a context length (max sequence length) of 4096 (4k) and the fine-tuned model have 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8. +Some fine-tuned models have extended the context length by scaling RoPE. For example, if the original pre-trained model have a context length (max sequence length) of 4096 (4k) and the fine-tuned model have 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8. - `--rope-scale N`: Where N is the linear scaling factor used by the fine-tuned model. diff --git a/examples/parallel/README.md b/examples/parallel/README.md index 4d0fe5cef12fa..df04567337b15 100644 --- a/examples/parallel/README.md +++ b/examples/parallel/README.md @@ -1,3 +1,3 @@ # llama.cpp/example/parallel -Simplified simluation for serving incoming requests in parallel +Simplified simulation of serving incoming requests in parallel diff --git a/grammars/README.md b/grammars/README.md index 7f3b11ca5b592..e1383fa5c6a58 100644 --- a/grammars/README.md +++ b/grammars/README.md @@ -55,7 +55,7 @@ The order of symbols in a sequence matter. For example, in `"1. " move " " move Alternatives, denoted by `|`, give different sequences that are acceptable. For example, in `move ::= pawn | nonpawn | castle`, `move` can be a `pawn` move, a `nonpawn` move, or a `castle`. -Parentheses `()` can be used to group sequences, which allows for embedding alternatives in a larger rule or applying repetition and optptional symbols (below) to a sequence. +Parentheses `()` can be used to group sequences, which allows for embedding alternatives in a larger rule or applying repetition and optional symbols (below) to a sequence. ## Repetition and Optional Symbols @@ -67,7 +67,7 @@ Parentheses `()` can be used to group sequences, which allows for embedding alte Comments can be specified with `#`: ``` -# defines optional whitspace +# defines optional whitespace ws ::= [ \t\n]+ ``` From 21fd874c8d2a14dea2d56724e4357c0824aee6a8 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Sun, 12 Nov 2023 16:39:37 -0700 Subject: [PATCH 05/39] gguf-py: gguf_writer: Use bytearray to build metadata (#4051) * gguf-py: gguf_writer: Use BytesIO to build metadata * Use bytearray instead Bump gguf-py package version --- gguf-py/gguf/gguf_writer.py | 4 ++-- gguf-py/pyproject.toml | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 75fb6976f9ca2..c3b8c588f17cd 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -57,9 +57,9 @@ def __init__( self.endianess = endianess self.offset_tensor = 0 self.data_alignment = GGUF_DEFAULT_ALIGNMENT - self.kv_data = b"" + self.kv_data = bytearray() self.kv_data_count = 0 - self.ti_data = b"" + self.ti_data = bytearray() self.ti_data_count = 0 self.use_temp_file = use_temp_file self.temp_file = None diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index e21c3cd94f22a..af777c3e0f2b6 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "gguf" -version = "0.5.1" +version = "0.5.2" description = "Read and write ML models in GGUF for GGML" authors = ["GGML "] packages = [ From f4ee91abbb17894879eb93a7d2338761ee3dbd18 Mon Sep 17 00:00:00 2001 From: Concedo <39025047+LostRuins@users.noreply.github.com> Date: Mon, 13 Nov 2023 15:45:13 +0800 Subject: [PATCH 06/39] improved estimation --- klite.embd | 38 ++++++++++++++++++++++++++++++-------- koboldcpp.py | 16 +++++++++++----- 2 files changed, 41 insertions(+), 13 deletions(-) diff --git a/klite.embd b/klite.embd index b1674ada87fd1..00dbafa593a47 100644 --- a/klite.embd +++ b/klite.embd @@ -2945,6 +2945,7 @@ Current version: 95 if(error.name!="AbortError") //aborts are silent { msgbox("Error while submitting prompt: " + error); + retry_preserve_last = false; }else{ retry_preserve_last = true; } @@ -3025,16 +3026,23 @@ Current version: 95 { msgbox("Error while submitting prompt: " + error); retry_preserve_last = true; + }else{ + retry_preserve_last = false; } }, })); }) .catch((error) => { - retry_preserve_last = true; console.error('Error:', error); clear_poll_flags(); render_gametext(); - msgbox("Error while submitting prompt: " + error); + if(error.name!="AbortError") //aborts are silent. slightly diff logic + { + msgbox("Error while submitting prompt: " + error); + retry_preserve_last = true; + }else{ + retry_preserve_last = false; + } }); } @@ -7309,6 +7317,10 @@ Current version: 95 document.getElementById('instruct_starttag').value = "<|im_end|>\\n<|im_start|>user\\n"; document.getElementById('instruct_endtag').value = "<|im_end|>\\n<|im_start|>assistant\\n"; break; + case "7": //Input & Output + document.getElementById('instruct_starttag').value = "\\n{{[INPUT]}}\\n"; + document.getElementById('instruct_endtag').value = "\\n{{[OUTPUT]}}\\n"; + break; default: break; } @@ -7832,6 +7844,12 @@ Current version: 95 //flush any streaming text first if(is_using_custom_ep() && pending_response_id!="" && synchro_pending_stream!="") { + //apply a short delay of 1s before button reenables + allow_reenable_submitbtn_timestamp = performance.now() + 500; + setTimeout(()=>{ + update_submit_button(true); + }, 1000); + synchro_polled_response = synchro_pending_stream; poll_in_progress = false; horde_poll_nearly_completed = false; @@ -9257,6 +9275,7 @@ Current version: 95 if(gentxt!="") { gametext_arr.push(gentxt); + retry_preserve_last = false; }else{ retry_preserve_last = true; //do not delete last message if retry is hit } @@ -9517,6 +9536,7 @@ Current version: 95 { handle_incoming_text(gentxt, genworker, genmdl, genkudos); } + retry_preserve_last = false; }else{ retry_preserve_last = true; } @@ -9850,6 +9870,7 @@ Current version: 95 } } + var allow_reenable_submitbtn_timestamp = performance.now(); function update_submit_button(full_update) { if (perfdata == null) { @@ -9870,7 +9891,7 @@ Current version: 95 document.getElementById("btnsend").innerHTML = "No AI
Loaded"; } } - else if (pending_response_id == "") { + else if (pending_response_id == "" && performance.now() >= allow_reenable_submitbtn_timestamp) { if(full_update) { document.getElementById("btnsend").disabled = false; @@ -10614,7 +10635,7 @@ Current version: 95 backLongPressTimer = setTimeout(()=>{ console.log("Clear story"); - if (pending_response_id == "" && gametext_arr.length > 0) { + if (!document.getElementById("btnsend").disabled && pending_response_id == "" && gametext_arr.length > 0) { last_reply_was_empty = false; while(gametext_arr.length > 0) { @@ -10641,7 +10662,7 @@ Current version: 95 clearTimeout(backLongPressTimer); } function btn_back() { - if (pending_response_id == "" && gametext_arr.length > 0) { + if (!document.getElementById("btnsend").disabled && pending_response_id == "" && gametext_arr.length > 0) { last_reply_was_empty = false; retry_preserve_last = false; if(retry_prev_text!="") @@ -10665,7 +10686,7 @@ Current version: 95 redoLongPressTimer = setTimeout(()=>{ console.log("Redo All story"); - if (pending_response_id == "" && redo_arr.length > 0) { + if (!document.getElementById("btnsend").disabled && pending_response_id == "" && redo_arr.length > 0) { last_reply_was_empty = false; retry_preserve_last = false; while(redo_arr.length > 0) @@ -10685,7 +10706,7 @@ Current version: 95 clearTimeout(redoLongPressTimer); } function btn_redo() { - if (pending_response_id == "") { + if (!document.getElementById("btnsend").disabled && pending_response_id == "") { if (redo_arr.length > 0) { last_reply_was_empty = false; retry_prev_text = ""; @@ -10705,7 +10726,7 @@ Current version: 95 } function btn_retry() { - if (pending_response_id == "" && (gametext_arr.length > 1 || + if (!document.getElementById("btnsend").disabled && pending_response_id == "" && (gametext_arr.length > 1 || (gametext_arr.length > 0 && (current_memory != "" || current_anote != "")))) { last_reply_was_empty = false; let boxtextstash = document.getElementById("input_text").value; @@ -11952,6 +11973,7 @@ Current version: 95 + diff --git a/koboldcpp.py b/koboldcpp.py index 5d4e9f4048f5c..21d59e13e7764 100755 --- a/koboldcpp.py +++ b/koboldcpp.py @@ -1177,14 +1177,20 @@ def autoset_gpu_layers(filepath): #shitty algo to determine how many layers to u global gui_layers_untouched fsize = os.path.getsize(filepath) if fsize>10000000: #dont bother with models < 10mb - mem = MaxMemory[0] - sizeperlayer = fsize*0.05714 cs = int(contextsize_text[context_var.get()]) + mem = MaxMemory[0] + layerlimit = 0 + if cs and cs > 4096: - sizeperlayer *= 1.2 + fsize *= 1.2 elif cs and cs > 2048: - sizeperlayer *= 1.1 - layerlimit = int(min(200,mem/sizeperlayer)) + fsize *= 1.1 + + if mem < fsize*1.6: + sizeperlayer = fsize*0.052 + layerlimit = int(min(200,mem/sizeperlayer)) + else: + layerlimit = 200 #assume full offload old_gui_layers_untouched = gui_layers_untouched gui_layers_zeroed = gpulayers_var.get()=="" or gpulayers_var.get()=="0" if (gui_layers_untouched or gui_layers_zeroed) and layerlimit>0: From bb50a792ec2a49944470c82694fa364345e95170 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Mon, 13 Nov 2023 01:58:15 -0700 Subject: [PATCH 07/39] Add ReLU and SQR CUDA ops to (partially) fix Persimmon offloading (#4041) * Add ReLU and SQR CUDA ops to fix Persimmon offloading * Persimmon loader: More helpful error on CUDA/ROCM when offloading too many layers --- ggml-cuda.cu | 72 ++++++++++++++++++++++++++++++++++++++++++++++++++++ llama.cpp | 7 +++++ 2 files changed, 79 insertions(+) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index f87f18802c8f8..8d03ba6641981 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -433,6 +433,8 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_ #define CUDA_MUL_BLOCK_SIZE 256 #define CUDA_GELU_BLOCK_SIZE 256 #define CUDA_SILU_BLOCK_SIZE 256 +#define CUDA_RELU_BLOCK_SIZE 256 +#define CUDA_SQR_BLOCK_SIZE 256 #define CUDA_CPY_BLOCK_SIZE 32 #define CUDA_SCALE_BLOCK_SIZE 256 #define CUDA_CLAMP_BLOCK_SIZE 256 @@ -553,6 +555,24 @@ static __global__ void silu_f32(const float * x, float * dst, const int k) { dst[i] = x[i] / (1.0f + expf(-x[i])); } +static __global__ void relu_f32(const float * x, float * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + + if (i >= k) { + return; + } + dst[i] = fmaxf(x[i], 0); +} + +static __global__ void sqr_f32(const float * x, float * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + + if (i >= k) { + return; + } + dst[i] = x[i] * x[i]; +} + static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { @@ -4759,6 +4779,16 @@ static void silu_f32_cuda(const float * x, float * dst, const int k, cudaStream_ silu_f32<<>>(x, dst, k); } +static void relu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE; + relu_f32<<>>(x, dst, k); +} + +static void sqr_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_SQR_BLOCK_SIZE - 1) / CUDA_SQR_BLOCK_SIZE; + sqr_f32<<>>(x, dst, k); +} + 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); if (ncols < 1024) { @@ -6128,6 +6158,34 @@ inline void ggml_cuda_op_silu( (void) src1_dd; } +inline void ggml_cuda_op_relu( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + relu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_cuda_op_sqr( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + sqr_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + inline void ggml_cuda_op_norm( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { @@ -7160,6 +7218,14 @@ static void ggml_cuda_silu(const ggml_tensor * src0, const ggml_tensor * src1, g ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_silu); } +static void ggml_cuda_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_relu); +} + +static void ggml_cuda_sqr(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_sqr); +} + static void ggml_cuda_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_norm); } @@ -7891,6 +7957,9 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_ case GGML_UNARY_OP_SILU: func = ggml_cuda_silu; break; + case GGML_UNARY_OP_RELU: + func = ggml_cuda_relu; + break; default: return false; } break; @@ -7909,6 +7978,9 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_ case GGML_OP_SCALE: func = ggml_cuda_scale; break; + case GGML_OP_SQR: + func = ggml_cuda_sqr; + break; case GGML_OP_CLAMP: if (!any_on_device) { return false; diff --git a/llama.cpp b/llama.cpp index d682d2864d283..a5f3876cc19e0 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2877,6 +2877,13 @@ static void llm_load_tensors( ggml_backend_type backend_output; if (n_gpu_layers > int(n_layer)) { +#ifdef GGML_USE_CUBLAS + if (n_gpu_layers > int(n_layer + 1)) { + LLAMA_LOG_ERROR("%s: CUDA backend missing Persimmon CUDA ops, can offload at most %ld layers. See: https://github.com/ggerganov/llama.cpp/issues/4038\n", + __func__, n_layer + 1); + throw std::runtime_error("Persimmon CUDA offload failed"); + } +#endif // norm is not performance relevant on its own but keeping it in VRAM reduces data copying // on Windows however this is detrimental unless everything is on the GPU #ifndef _WIN32 From 4760e7cc0b68570d58f55e8dda469805d1759d0d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 13 Nov 2023 14:16:23 +0200 Subject: [PATCH 08/39] sync : ggml (backend v2) (#3912) * sync : ggml (backend v2) (wip) * sync : migrate examples and llama.cpp to dynamic graphs (wip) * sync : update tests + fix max op params to 64 ggml-ci * sync : ggml-cuda ggml-ci * llama : fix save/load state context size ggml-ci * sync : try to fix build on tvOS * sync : pass custom graph sizes in training examples * sync : update graph copies to new ggml API * sync : update sync-ggml.sh with new files * scripts : fix header in sync script * train : fix context size calculations * llama : increase inference graph size up to 4096 nodes * train : allocate grads for backward graphs * train : allocate grads for gb_tmp --- common/train.cpp | 1 + common/train.h | 2 + examples/benchmark/benchmark-matmult.cpp | 21 +- examples/export-lora/export-lora.cpp | 4 +- examples/finetune/finetune.cpp | 23 +- examples/llava/clip.cpp | 2 +- examples/metal/metal.cpp | 10 +- .../train-text-from-scratch.cpp | 23 +- ggml-alloc.c | 586 +++++---- ggml-alloc.h | 84 +- ggml-backend-impl.h | 87 ++ ggml-backend.c | 591 +++++++++- ggml-backend.h | 147 ++- ggml-cuda.cu | 16 +- ggml-impl.h | 14 +- ggml-metal.m | 25 +- ggml.c | 1047 ++++++++++------- ggml.h | 89 +- llama.cpp | 40 +- scripts/sync-ggml.sh | 12 +- tests/test-grad0.cpp | 7 +- tests/test-opt.cpp | 11 +- 22 files changed, 1986 insertions(+), 856 deletions(-) create mode 100644 ggml-backend-impl.h diff --git a/common/train.cpp b/common/train.cpp index bc15b7a03c0cd..964b156b5abe4 100644 --- a/common/train.cpp +++ b/common/train.cpp @@ -32,6 +32,7 @@ struct train_state * init_train_state() { state->opt = new struct ggml_opt_context; state->opt->ctx = NULL; state->opt->params = ggml_opt_default_params(GGML_OPT_ADAM); + state->opt->params.graph_size = LLAMA_TRAIN_MAX_NODES; state->opt->loss_after = 0.0f; return state; diff --git a/common/train.h b/common/train.h index d86c93cc4f147..263d940c04298 100644 --- a/common/train.h +++ b/common/train.h @@ -9,6 +9,8 @@ #include "ggml.h" #include "llama.h" +#define LLAMA_TRAIN_MAX_NODES 16384 + typedef std::string mt19937_state; struct train_state { diff --git a/examples/benchmark/benchmark-matmult.cpp b/examples/benchmark/benchmark-matmult.cpp index 76e3f57ccce8e..284733b1035c9 100644 --- a/examples/benchmark/benchmark-matmult.cpp +++ b/examples/benchmark/benchmark-matmult.cpp @@ -171,7 +171,8 @@ int main(int argc, char ** argv) { struct ggml_tensor * m11xm2 = ggml_mul_mat(ctx, m11, m2); // printf("Creating compute graph\n"); - struct ggml_cgraph gf = ggml_build_forward(m11xm2); + struct ggml_cgraph * gf = ggml_new_graph(ctx); + ggml_build_forward_expand(gf, m11xm2); printf("n_threads=%i\n", benchmark_params.n_threads); @@ -180,9 +181,9 @@ int main(int argc, char ** argv) { std::vector work_buffer; - ggml_graph_compute_helper(work_buffer, &gf, benchmark_params.n_threads); + ggml_graph_compute_helper(work_buffer, gf, benchmark_params.n_threads); - TENSOR_DUMP(gf.nodes[0]); + TENSOR_DUMP(gf->nodes[0]); printf("\n------ Test 2 - Matrix Mult via %s code\n", ggml_type_name(qtype)); @@ -200,7 +201,8 @@ int main(int argc, char ** argv) { struct ggml_tensor * q31 = ggml_mul_mat(ctx, q11, m2); // printf("Creating compute graph\n"); - struct ggml_cgraph gf31 = ggml_build_forward(q31); + struct ggml_cgraph * gf31 = ggml_new_graph(ctx); + ggml_build_forward_expand(gf31, q31); // Set up a second graph computation to make sure we override the CPU cache lines // printf("Creating new tensor q12 & Running quantize\n"); @@ -211,7 +213,8 @@ int main(int argc, char ** argv) { struct ggml_tensor * q32 = ggml_mul_mat(ctx, q12, m2); //printf("Creating compute graph\n"); - struct ggml_cgraph gf32 = ggml_build_forward(q32); + struct ggml_cgraph * gf32 = ggml_new_graph(ctx); + ggml_build_forward_expand(gf32, q32); printf("n_threads=%i\n", benchmark_params.n_threads); const int dimx = sizex; @@ -223,7 +226,7 @@ int main(int argc, char ** argv) { // Let's use the F32 result from above as a reference for the quantized multiplication - float sum_of_F32_reference = tensor_sum_elements(gf.nodes[0]); + float sum_of_F32_reference = tensor_sum_elements(gf->nodes[0]); printf("Iteration;NThreads; SizeX; SizeY; SizeZ; Required_FLOPS; Elapsed_u_Seconds; gigaFLOPS\n"); printf("=====================================================================================\n"); @@ -233,7 +236,7 @@ int main(int argc, char ** argv) { long long int start = ggml_time_us(); //printf("Running ggml_graph_compute\n"); - ggml_graph_compute_helper(work_buffer, &gf31, benchmark_params.n_threads); + ggml_graph_compute_helper(work_buffer, gf31, benchmark_params.n_threads); long long int stop = ggml_time_us(); long long int usec = stop-start; @@ -251,7 +254,7 @@ int main(int argc, char ** argv) { // Check that the matrix multiplication result is in the right ballpark // We cannot use the exact value from the F32 multiplication because the quantizuation will be slightly different - float sum_of_Q4_result = tensor_sum_elements(gf31.nodes[0]); + float sum_of_Q4_result = tensor_sum_elements(gf31->nodes[0]); float delta = std::abs(sum_of_Q4_result - sum_of_F32_reference); float allowed_delta = (sum_of_F32_reference) / 1000 / 1000; // Let's accept an epsilon of 10^-6 @@ -266,7 +269,7 @@ int main(int argc, char ** argv) { } // Running a different graph computation to make sure we override the CPU cache lines - ggml_graph_compute_helper(work_buffer, &gf32, benchmark_params.n_threads); + ggml_graph_compute_helper(work_buffer, gf32, benchmark_params.n_threads); } printf("\n"); printf("Average%78.2f\n",gflops_sum/((double)benchmark_params.n_iterations)); diff --git a/examples/export-lora/export-lora.cpp b/examples/export-lora/export-lora.cpp index d803cfd5cb2d5..c8754ce70f37d 100644 --- a/examples/export-lora/export-lora.cpp +++ b/examples/export-lora/export-lora.cpp @@ -240,7 +240,7 @@ static struct lora_data * load_lora(struct lora_info * info) { } struct ggml_init_params params_ggml; - params_ggml.mem_size = ggml_tensor_overhead() * GGML_MAX_NODES; + params_ggml.mem_size = ggml_tensor_overhead() * GGML_DEFAULT_GRAPH_SIZE; params_ggml.mem_buffer = NULL; params_ggml.no_alloc = true; result->ctx = ggml_init(params_ggml); @@ -334,7 +334,7 @@ static bool apply_lora(struct ggml_tensor * tensor, struct lora_data * lora, int float scaling = lora->info.scale * (float)lora->lora_alpha / (float)lora->lora_r; struct ggml_init_params params; - params.mem_size = GGML_OBJECT_SIZE + GGML_GRAPH_SIZE + ggml_tensor_overhead()*4 + GGML_MEM_ALIGN*5; + params.mem_size = GGML_OBJECT_SIZE + ggml_graph_overhead() + ggml_tensor_overhead()*4 + GGML_MEM_ALIGN*5; params.mem_buffer = NULL; params.no_alloc = true; struct ggml_context * ctx = NULL; diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index fa7dbe496b2c5..5a6cf22ce1b95 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -772,7 +772,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( if (enable_checkpointing) { ggml_build_backward_gradient_checkpointing(ctx, gf, gb, gb_tmp, checkpoints.data(), (int) checkpoints.size()); } else { - *gb = *gf; + ggml_graph_cpy(gf, gb); ggml_build_backward_expand(ctx, gf, gb, true); } @@ -1615,6 +1615,7 @@ int main(int argc, char ** argv) { opt->params = ggml_opt_default_params(GGML_OPT_ADAM); opt->params.print_forward_graph = false; opt->params.print_backward_graph = false; + opt->params.graph_size = LLAMA_TRAIN_MAX_NODES; opt->params.n_threads = params.common.n_threads; opt->params.past = params.common.opt_past; opt->params.delta = params.common.opt_delta; @@ -1741,11 +1742,9 @@ int main(int argc, char ** argv) { ggml_allocr_free(alloc); // context for compute tensors without their data - size_t estimated_compute_size_wo_data = ( - ggml_tensor_overhead()*GGML_MAX_NODES*2 - + (GGML_OBJECT_SIZE+GGML_GRAPH_SIZE)*( - params.common.use_checkpointing ? 3 : 2 - ) + const size_t estimated_compute_size_wo_data = ( + 2*LLAMA_TRAIN_MAX_NODES*ggml_tensor_overhead() + + (params.common.use_checkpointing ? 3 : 2)*(GGML_OBJECT_SIZE+ggml_graph_overhead_custom(LLAMA_TRAIN_MAX_NODES, true)) ); struct ggml_init_params ctx_compute_params = { estimated_compute_size_wo_data, // mem_size @@ -1768,11 +1767,11 @@ int main(int argc, char ** argv) { for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) { ctx_compute = ggml_init(ctx_compute_params); alloc = ggml_allocr_new_measure(tensor_alignment); - gf = ggml_new_graph(ctx_compute); + gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gf->order = (enum ggml_cgraph_eval_order) order; - gb = ggml_new_graph(ctx_compute); + gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gb_tmp = params.common.use_checkpointing - ? ggml_new_graph(ctx_compute) + ? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true) : NULL; loss = llama_build_lora_finetune_graphs( &model, &lora, alloc, ctx_compute, @@ -1801,11 +1800,11 @@ int main(int argc, char ** argv) { mem_compute_data.resize(max_compute_size); ctx_compute = ggml_init(ctx_compute_params); alloc = ggml_allocr_new(mem_compute_data.data(), mem_compute_data.size(), tensor_alignment); - gf = ggml_new_graph(ctx_compute); + gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gf->order = best_order; - gb = ggml_new_graph(ctx_compute); + gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gb_tmp = params.common.use_checkpointing - ? ggml_new_graph(ctx_compute) + ? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true) : NULL; loss = llama_build_lora_finetune_graphs( &model, &lora, alloc, ctx_compute, diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 3c909c7d3c6ab..c26ee4957090c 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -664,7 +664,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { // measure mem requirement and allocate { static const size_t tensor_alignment = 32; - new_clip->buf_compute.resize(ggml_tensor_overhead()*GGML_MAX_NODES + ggml_graph_overhead()); + new_clip->buf_compute.resize(ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead()); new_clip->alloc = ggml_allocr_new_measure(tensor_alignment); clip_image_f32_batch batch; batch.size = 1; diff --git a/examples/metal/metal.cpp b/examples/metal/metal.cpp index c05a4fa933d31..16c1146f94e33 100644 --- a/examples/metal/metal.cpp +++ b/examples/metal/metal.cpp @@ -34,7 +34,7 @@ int main(int argc, char ** argv) { struct ggml_context * ctx_data = NULL; struct ggml_context * ctx_eval = NULL; - struct ggml_cgraph gf = ggml_graph_import(fname_cgraph, &ctx_data, &ctx_eval); + struct ggml_cgraph * gf = ggml_graph_import(fname_cgraph, &ctx_data, &ctx_eval); // this allocates all Metal resources and memory buffers auto * ctx_metal = ggml_metal_init(1); @@ -46,13 +46,13 @@ int main(int argc, char ** argv) { // main { - struct ggml_tensor * input = ggml_graph_get_tensor(&gf, "embd"); + struct ggml_tensor * input = ggml_graph_get_tensor(gf, "embd"); *(int32_t *) input->data = 1; // BOS ggml_metal_set_tensor(ctx_metal, input); // warmup - ggml_metal_graph_compute(ctx_metal, &gf); + ggml_metal_graph_compute(ctx_metal, gf); const int n_iter = 16; @@ -60,7 +60,7 @@ int main(int argc, char ** argv) { // the actual inference happens here for (int i = 0; i < n_iter; ++i) { - ggml_metal_graph_compute(ctx_metal, &gf); + ggml_metal_graph_compute(ctx_metal, gf); } const int64_t t1 = ggml_time_us(); @@ -70,7 +70,7 @@ int main(int argc, char ** argv) { // debug output { - struct ggml_tensor * logits = gf.nodes[gf.n_nodes - 1]; + struct ggml_tensor * logits = gf->nodes[gf->n_nodes - 1]; ggml_metal_get_tensor(ctx_metal, logits); float * ptr = (float *) ggml_get_data(logits); diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index 2a257e63215e3..f049a3923669b 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -436,7 +436,7 @@ static struct ggml_tensor * llama_build_train_graphs( if (enable_checkpointing) { ggml_build_backward_gradient_checkpointing(ctx, gf, gb, gb_tmp, checkpoints.data(), (int) checkpoints.size()); } else { - *gb = *gf; + ggml_graph_cpy(gf, gb); ggml_build_backward_expand(ctx, gf, gb, true); } @@ -1006,6 +1006,7 @@ int main(int argc, char ** argv) { opt->params = ggml_opt_default_params(GGML_OPT_ADAM); opt->params.print_forward_graph = false; opt->params.print_backward_graph = false; + opt->params.graph_size = LLAMA_TRAIN_MAX_NODES; opt->params.n_threads = params.common.n_threads; opt->params.past = params.common.opt_past; opt->params.delta = params.common.opt_delta; @@ -1108,11 +1109,9 @@ int main(int argc, char ** argv) { ggml_allocr_free(alloc); // context for compute tensors without their data - size_t estimated_compute_size_wo_data = ( - ggml_tensor_overhead()*GGML_MAX_NODES*2 - + (GGML_OBJECT_SIZE+GGML_GRAPH_SIZE)*( - params.common.use_checkpointing ? 3 : 2 - ) + const size_t estimated_compute_size_wo_data = ( + 2*LLAMA_TRAIN_MAX_NODES*ggml_tensor_overhead() + + (params.common.use_checkpointing ? 3 : 2)*(GGML_OBJECT_SIZE+ggml_graph_overhead_custom(LLAMA_TRAIN_MAX_NODES, true)) ); struct ggml_init_params ctx_compute_params = { estimated_compute_size_wo_data, // mem_size @@ -1135,11 +1134,11 @@ int main(int argc, char ** argv) { for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) { ctx_compute = ggml_init(ctx_compute_params); alloc = ggml_allocr_new_measure(tensor_alignment); - gf = ggml_new_graph(ctx_compute); + gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gf->order = (enum ggml_cgraph_eval_order) order; - gb = ggml_new_graph(ctx_compute); + gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gb_tmp = params.common.use_checkpointing - ? ggml_new_graph(ctx_compute) + ? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true) : NULL; loss = llama_build_train_graphs( &model, alloc, ctx_compute, @@ -1168,11 +1167,11 @@ int main(int argc, char ** argv) { mem_compute_data.resize(max_compute_size); ctx_compute = ggml_init(ctx_compute_params); alloc = ggml_allocr_new(mem_compute_data.data(), mem_compute_data.size(), tensor_alignment); - gf = ggml_new_graph(ctx_compute); + gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gf->order = best_order; - gb = ggml_new_graph(ctx_compute); + gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true); gb_tmp = params.common.use_checkpointing - ? ggml_new_graph(ctx_compute) + ? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true) : NULL; loss = llama_build_train_graphs( &model, alloc, ctx_compute, diff --git a/ggml-alloc.c b/ggml-alloc.c index b553eb7c13271..cdfe4caf69613 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -1,51 +1,21 @@ #include "ggml-alloc.h" -#include "ggml-backend.h" +#include "ggml-backend-impl.h" #include "ggml.h" +#include "ggml-impl.h" #include +#include #include #include #include #include - -#define UNUSED(x) (void)(x) #define MAX(a, b) ((a) > (b) ? (a) : (b)) -#define GGML_MAX_CONCUR (2*GGML_MAX_NODES) +#define MAX_FREE_BLOCKS 256 //#define GGML_ALLOCATOR_DEBUG -//#define AT_PRINTF printf -#define AT_PRINTF(...) ((void)0) - -struct hash_node { - struct ggml_tensor * t; - int n_children; - int n_views; -}; - -static size_t hash(void * p) { - return (size_t)p % GGML_GRAPH_HASHTABLE_SIZE; -} - -static struct hash_node * hash_get(struct hash_node hash_table[], struct ggml_tensor * t) { - size_t h = hash(t); - - // linear probing - size_t i = h; - while (hash_table[i].t != NULL) { - if (hash_table[i].t == t) { - return &hash_table[i]; - } - i = (i + 1) % GGML_GRAPH_HASHTABLE_SIZE; - if (i == h) { - // hash table is full - GGML_ASSERT(false); - } - } - - hash_table[i].t = t; - return &hash_table[i]; -} +//#define AT_PRINTF(...) fprintf(stderr, __VA_ARGS__) +#define AT_PRINTF(...) // TODO: GGML_PAD ? static size_t aligned_offset(const void * buffer, size_t offset, size_t alignment) { @@ -59,20 +29,18 @@ struct free_block { size_t size; }; -#define MAX_FREE_BLOCKS 256 - -struct ggml_allocr { +struct ggml_tallocr { struct ggml_backend_buffer * buffer; bool buffer_owned; - void * data; + void * base; size_t alignment; + int n_free_blocks; struct free_block free_blocks[MAX_FREE_BLOCKS]; - struct hash_node hash_table[GGML_GRAPH_HASHTABLE_SIZE]; + size_t max_size; + bool measure; - int parse_seq[GGML_MAX_CONCUR]; - int parse_seq_len; #ifdef GGML_ALLOCATOR_DEBUG struct ggml_tensor * allocated_tensors[1024]; @@ -80,7 +48,7 @@ struct ggml_allocr { }; #ifdef GGML_ALLOCATOR_DEBUG -static void add_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { +static void add_allocated_tensor(ggml_tallocr_t alloc, struct ggml_tensor * tensor) { for (int i = 0; i < 1024; i++) { if (alloc->allocated_tensors[i] == NULL) { alloc->allocated_tensors[i] = tensor; @@ -89,7 +57,7 @@ static void add_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tensor } GGML_ASSERT(!"out of allocated_tensors"); } -static void remove_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { +static void remove_allocated_tensor(ggml_tallocr_t alloc, struct ggml_tensor * tensor) { for (int i = 0; i < 1024; i++) { if (alloc->allocated_tensors[i] == tensor || (alloc->allocated_tensors[i] != NULL && alloc->allocated_tensors[i]->data == tensor->data)) { @@ -103,7 +71,7 @@ static void remove_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tens #endif // check if a tensor is allocated by this buffer -static bool ggml_allocr_is_own(struct ggml_allocr * alloc, const struct ggml_tensor * tensor) { +static bool ggml_tallocr_is_own(ggml_tallocr_t alloc, const struct ggml_tensor * tensor) { return tensor->buffer == alloc->buffer; } @@ -111,7 +79,7 @@ static bool ggml_is_view(struct ggml_tensor * t) { return t->view_src != NULL; } -void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { +void ggml_tallocr_alloc(ggml_tallocr_t alloc, struct ggml_tensor * tensor) { GGML_ASSERT(!ggml_is_view(tensor)); // views generally get data pointer from one of their sources GGML_ASSERT(tensor->data == NULL); // avoid allocating tensor which already has memory allocated @@ -162,9 +130,10 @@ void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) } tensor->data = addr; - AT_PRINTF("%s: allocated data at %p\n", __func__, tensor->data); tensor->buffer = alloc->buffer; - ggml_backend_buffer_init_tensor(alloc->buffer, tensor); + if (!alloc->measure) { + ggml_backend_buffer_init_tensor(alloc->buffer, tensor); + } #ifdef GGML_ALLOCATOR_DEBUG add_allocated_tensor(alloc, tensor); @@ -180,16 +149,16 @@ void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) } #endif - alloc->max_size = MAX(alloc->max_size, (char*)addr - (char*)alloc->data + size); + alloc->max_size = MAX(alloc->max_size, (char*)addr - (char*)alloc->base + size); } // this is a very naive implementation, but for our case the number of free blocks should be very small -static void ggml_allocr_free_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { - if (ggml_allocr_is_own(alloc, tensor) == false) { +static void ggml_tallocr_free_tensor(ggml_tallocr_t alloc, struct ggml_tensor * tensor) { + if (ggml_tallocr_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 - AT_PRINTF("ignoring %s (their buffer: %p, our buffer: %p)\n", tensor->name, (void *)tensor->buffer, (void *)alloc->buffer); + // AT_PRINTF("ignoring %s (their buffer: %p, our buffer: %p)\n", tensor->name, (void *)tensor->buffer, (void *)alloc->buffer); return; } @@ -199,7 +168,9 @@ static void ggml_allocr_free_tensor(struct ggml_allocr * alloc, struct ggml_tens size = aligned_offset(NULL, size, alloc->alignment); AT_PRINTF("%s: freeing %s at %p (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, ptr, size, alloc->n_free_blocks); - ggml_backend_buffer_free_tensor(alloc->buffer, tensor); + if (!alloc->measure) { + ggml_backend_buffer_free_tensor(alloc->buffer, tensor); + } #ifdef GGML_ALLOCATOR_DEBUG remove_allocated_tensor(alloc, tensor); @@ -253,91 +224,180 @@ static void ggml_allocr_free_tensor(struct ggml_allocr * alloc, struct ggml_tens alloc->n_free_blocks++; } -void ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, const int * list, int n) { - for (int i = 0; i < n; i++) { - alloc->parse_seq[i] = list[i]; - } - alloc->parse_seq_len = n; -} - -void ggml_allocr_reset(struct ggml_allocr * alloc) { +void ggml_tallocr_reset(ggml_tallocr_t alloc) { alloc->n_free_blocks = 1; - size_t align_offset = aligned_offset(alloc->data, 0, alloc->alignment); - alloc->free_blocks[0].addr = (char *)alloc->data + align_offset; - alloc->free_blocks[0].size = ggml_backend_buffer_get_size(alloc->buffer) - align_offset; + size_t align_offset = aligned_offset(alloc->base, 0, alloc->alignment); + alloc->free_blocks[0].addr = (char *)alloc->base + align_offset; + + if (alloc->measure) { + alloc->free_blocks[0].size = SIZE_MAX/2; // restrict maximum size of a measure allocator to half size_t max to avoid overflows + } else { + alloc->free_blocks[0].size = ggml_backend_buffer_get_size(alloc->buffer) - align_offset; + } } -struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment) { +ggml_tallocr_t ggml_tallocr_new(void * data, size_t size, size_t alignment) { struct ggml_backend_buffer * buffer = ggml_backend_cpu_buffer_from_ptr(NULL, data, size); - struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr)); + ggml_tallocr_t alloc = (ggml_tallocr_t)malloc(sizeof(struct ggml_tallocr)); - *alloc = (struct ggml_allocr){ + *alloc = (struct ggml_tallocr) { /*.buffer = */ buffer, /*.buffer_owned = */ true, /*.base = */ ggml_backend_buffer_get_base(buffer), /*.alignment = */ alignment, /*.n_free_blocks = */ 0, /*.free_blocks = */ {{0}}, - /*.hash_table = */ {{0}}, /*.max_size = */ 0, /*.measure = */ false, - /*.parse_seq = */ {0}, - /*.parse_seq_len = */ 0, #ifdef GGML_ALLOCATOR_DEBUG /*.allocated_tensors = */ {0}, #endif }; - ggml_allocr_reset(alloc); + ggml_tallocr_reset(alloc); + + return alloc; +} + +ggml_tallocr_t ggml_tallocr_new_measure(size_t alignment) { + ggml_tallocr_t alloc = ggml_tallocr_new((void *)0x1000, SIZE_MAX/2, alignment); + alloc->measure = true; return alloc; } -struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) { - struct ggml_allocr * alloc = ggml_allocr_new((void *)0x1000, (size_t)-0x1001, alignment); +ggml_tallocr_t ggml_tallocr_new_measure_from_backend(struct ggml_backend * backend) { + // create a backend buffer to get the correct tensor allocation sizes + ggml_backend_buffer_t buffer = ggml_backend_alloc_buffer(backend, 1); + + // TODO: move alloc initialization to a common ggml_tallocr_new_impl function + ggml_tallocr_t alloc = ggml_tallocr_new_from_buffer(buffer); + alloc->buffer_owned = true; alloc->measure = true; + ggml_tallocr_reset(alloc); + return alloc; +} +ggml_tallocr_t ggml_tallocr_new_from_backend(struct ggml_backend * backend, size_t size) { + ggml_backend_buffer_t buffer = ggml_backend_alloc_buffer(backend, size); + ggml_tallocr_t alloc = ggml_tallocr_new_from_buffer(buffer); + alloc->buffer_owned = true; return alloc; } -struct ggml_allocr * ggml_allocr_new_from_buffer(struct ggml_backend_buffer * buffer) { - struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr)); +ggml_tallocr_t ggml_tallocr_new_from_buffer(struct ggml_backend_buffer * buffer) { + ggml_tallocr_t alloc = (ggml_tallocr_t)malloc(sizeof(struct ggml_tallocr)); - *alloc = (struct ggml_allocr){ + *alloc = (struct ggml_tallocr) { /*.buffer = */ buffer, /*.buffer_owned = */ false, /*.base = */ ggml_backend_buffer_get_base(buffer), /*.alignment = */ ggml_backend_buffer_get_alignment(buffer), /*.n_free_blocks = */ 0, /*.free_blocks = */ {{0}}, - /*.hash_table = */ {{0}}, /*.max_size = */ 0, /*.measure = */ false, - /*.parse_seq = */ {0}, - /*.parse_seq_len = */ 0, #ifdef GGML_ALLOCATOR_DEBUG /*.allocated_tensors = */ {0}, #endif }; - ggml_allocr_reset(alloc); + ggml_tallocr_reset(alloc); return alloc; } -void ggml_allocr_free(struct ggml_allocr * alloc) { +struct ggml_backend_buffer * ggml_tallocr_get_buffer(ggml_tallocr_t alloc) { + return alloc->buffer; +} + +void ggml_tallocr_free(ggml_tallocr_t alloc) { + if (alloc == NULL) { + return; + } + if (alloc->buffer_owned) { ggml_backend_buffer_free(alloc->buffer); } free(alloc); } -bool ggml_allocr_is_measure(struct ggml_allocr * alloc) { +bool ggml_tallocr_is_measure(ggml_tallocr_t alloc) { return alloc->measure; } -//////////// compute graph allocator +size_t ggml_tallocr_max_size(ggml_tallocr_t alloc) { + return alloc->max_size; +} + +// graph allocator + +struct hash_node { + int n_children; + int n_views; +}; + +struct ggml_gallocr { + ggml_tallocr_t talloc; + struct ggml_hash_set hash_set; + struct hash_node * hash_values; + size_t hash_values_size; + ggml_tallocr_t * hash_allocs; + int * parse_seq; + int parse_seq_len; +}; + +ggml_gallocr_t ggml_gallocr_new(void) { + ggml_gallocr_t galloc = (ggml_gallocr_t)malloc(sizeof(struct ggml_gallocr)); + + *galloc = (struct ggml_gallocr) { + /*.talloc = */ NULL, + /*.hash_set = */ {0}, + /*.hash_values = */ NULL, + /*.hash_values_size = */ 0, + /*.hash_allocs = */ NULL, + /*.parse_seq = */ NULL, + /*.parse_seq_len = */ 0, + }; + + return galloc; +} + +void ggml_gallocr_free(ggml_gallocr_t galloc) { + if (galloc == NULL) { + return; + } + + if (galloc->hash_set.keys != NULL) { + free(galloc->hash_set.keys); + } + if (galloc->hash_values != NULL) { + free(galloc->hash_values); + } + if (galloc->hash_allocs != NULL) { + free(galloc->hash_allocs); + } + if (galloc->parse_seq != NULL) { + free(galloc->parse_seq); + } + free(galloc); +} + +void ggml_gallocr_set_parse_seq(ggml_gallocr_t galloc, const int * list, int n) { + free(galloc->parse_seq); + galloc->parse_seq = malloc(sizeof(int) * n); + + for (int i = 0; i < n; i++) { + galloc->parse_seq[i] = list[i]; + } + galloc->parse_seq_len = n; +} + +static struct hash_node * hash_get(ggml_gallocr_t galloc, struct ggml_tensor * t) { + size_t i = ggml_hash_find_or_insert(galloc->hash_set, t); + return &galloc->hash_values[i]; +} static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) { if (a->type != b->type) { @@ -378,27 +438,40 @@ static bool ggml_op_can_inplace(enum ggml_op op) { } } -static void init_view(struct ggml_allocr * alloc, struct ggml_tensor * view, bool update_backend) { - assert(view->view_src != NULL && view->view_src->data != NULL); +static ggml_tallocr_t node_tallocr(ggml_gallocr_t galloc, struct ggml_tensor * node) { + if (galloc->talloc != NULL) { + return galloc->talloc; + } + + return galloc->hash_allocs[ggml_hash_find_or_insert(galloc->hash_set, node)]; +} + +static void init_view(ggml_gallocr_t galloc, struct ggml_tensor * view, bool update_backend) { + ggml_tallocr_t alloc = node_tallocr(galloc, view); + //printf("init_view: %s from src %s\n", view->name, view->view_src->name); + GGML_ASSERT(view->view_src != NULL && view->view_src->data != NULL); if (update_backend) { view->backend = view->view_src->backend; } - view->buffer = view->view_src->buffer; view->data = (char *)view->view_src->data + view->view_offs; // FIXME: the view should be initialized by the owning buffer, but currently this breaks the CUDA backend // due to the ggml_tensor_extra_gpu ring buffer overwriting the KV cache extras - assert(ggml_allocr_is_measure(alloc) || !view->buffer || view->buffer->backend == alloc->buffer->backend); - ggml_backend_buffer_init_tensor(alloc->buffer, view); + assert(ggml_tallocr_is_measure(alloc) || !view->buffer || view->buffer->backend == alloc->buffer->backend); + + if (!alloc->measure) { + ggml_backend_buffer_init_tensor(alloc->buffer, view); + } } -static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) { - struct hash_node * ht = alloc->hash_table; +static void allocate_node(ggml_gallocr_t galloc, struct ggml_tensor * node) { + ggml_tallocr_t alloc = node_tallocr(galloc, node); + if (node->data == NULL) { if (ggml_is_view(node)) { - init_view(alloc, node, true); + init_view(galloc, node, true); } else { // see if we can reuse a parent's buffer (inplace) if (ggml_op_can_inplace(node->op)) { @@ -409,16 +482,16 @@ 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 (ggml_allocr_is_own(alloc, parent) == false) { + if (ggml_tallocr_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; } - struct hash_node * p_hn = hash_get(ht, parent); + struct hash_node * p_hn = hash_get(galloc, parent); if (parent->data != NULL && p_hn->n_children == 1 && p_hn->n_views == 0 && ggml_are_same_layout(node, parent)) { if (ggml_is_view(parent)) { struct ggml_tensor * view_src = parent->view_src; - struct hash_node * view_src_hn = hash_get(ht, view_src); + struct hash_node * view_src_hn = hash_get(galloc, view_src); if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) { // TODO: the offset of the view parent must be kept to ensure that the op doesn't overwrite // the parent's data that it will need later (same layout requirement). the problem is that then @@ -428,170 +501,267 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) AT_PRINTF("reusing view parent %s (%s) for %s\n", parent->name, view_src->name, node->name); node->view_src = view_src; view_src_hn->n_views += 1; - init_view(alloc, node, false); + init_view(galloc, node, false); return; } } else { AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name); node->view_src = parent; p_hn->n_views += 1; - init_view(alloc, node, false); + init_view(galloc, node, false); return; } } } } - ggml_allocr_alloc(alloc, node); + ggml_tallocr_alloc(alloc, node); } } } -size_t ggml_allocr_alloc_graph_n( - struct ggml_allocr * alloc, - struct ggml_cgraph ** graphs, int n_graphs, - struct ggml_tensor *** inputs, struct ggml_tensor *** outputs) { +static void free_node(ggml_gallocr_t galloc, struct ggml_tensor * node) { + ggml_tallocr_t alloc = node_tallocr(galloc, node); - // reset hash table - struct hash_node * ht = alloc->hash_table; - memset(ht, 0, sizeof(struct hash_node) * GGML_GRAPH_HASHTABLE_SIZE); + ggml_tallocr_free_tensor(alloc, node); +} + +static void ggml_tallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * gf) { + const int * parse_seq = galloc->parse_seq; + int parse_seq_len = galloc->parse_seq_len; // count number of children and views - for (int g = 0; g < n_graphs; g++) { - struct ggml_cgraph * gf = graphs[g]; - for (int i = 0; i < gf->n_nodes; i++) { + for (int i = 0; i < gf->n_nodes; i++) { + struct ggml_tensor * node = gf->nodes[i]; + + if (ggml_is_view(node)) { + struct ggml_tensor * view_src = node->view_src; + hash_get(galloc, view_src)->n_views += 1; + if (node->buffer == NULL && node->data != NULL) { + // view of a pre-allocated tensor, didn't call init_view() yet + init_view(galloc, node, true); + } + } + + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * parent = node->src[j]; + if (parent == NULL) { + break; + } + hash_get(galloc, parent)->n_children += 1; + if (ggml_is_view(parent) && parent->buffer == NULL && parent->data != NULL) { + init_view(galloc, parent, true); + } + } + } + + // allocate tensors + // if we have parse_seq then we allocate nodes following the list, and we only free nodes at barriers + int last_barrier_pos = 0; + int n_nodes = parse_seq_len ? parse_seq_len : gf->n_nodes; + + for (int ind = 0; ind < n_nodes; ind++) { + // allocate a node if there is no parse_seq or this is not a barrier + if (parse_seq_len == 0 || parse_seq[ind] != -1) { + int i = parse_seq_len ? parse_seq[ind] : ind; struct ggml_tensor * node = gf->nodes[i]; - if (ggml_is_view(node)) { - struct ggml_tensor * view_src = node->view_src; - hash_get(ht, view_src)->n_views += 1; - if (node->buffer == NULL && node->data != NULL) { - // view of a pre-allocated tensor, didn't call init_view() yet - init_view(alloc, node, true); + // allocate parents (leafs) + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * parent = node->src[j]; + if (parent == NULL) { + break; } + allocate_node(galloc, parent); } + // allocate node + allocate_node(galloc, node); + + AT_PRINTF("exec: %s (%s) <= ", ggml_op_name(node->op), node->name); for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * parent = node->src[j]; if (parent == NULL) { break; } - hash_get(ht, parent)->n_children += 1; - if (ggml_is_view(parent) && parent->buffer == NULL && parent->data != NULL) { - init_view(alloc, parent, true); + AT_PRINTF("%s", parent->name); + if (j < GGML_MAX_SRC - 1 && node->src[j + 1] != NULL) { + AT_PRINTF(", "); } } + AT_PRINTF("\n"); } - } - - // allocate tensors - for (int g = 0; g < n_graphs; g++) { - struct ggml_cgraph * gf = graphs[g]; - AT_PRINTF("####### graph %d/%d\n", g, n_graphs); - // graph inputs are allocated first to ensure that they are not overwritten by each other - if (inputs != NULL && inputs[g] != NULL) { - for (int i = 0; inputs[g][i] != NULL; i++) { - struct ggml_tensor * input = inputs[g][i]; - AT_PRINTF("input: %s\n", input->name); - allocate_node(alloc, input); - } - } - // if we have parse_seq then we allocate nodes following the list, and we only free nodes at barriers - int last_barrier_pos = 0; - int n_nodes = alloc->parse_seq_len ? alloc->parse_seq_len : gf->n_nodes; - for (int ind = 0; ind < n_nodes; ind++) { - // allocate a node if there is no parse_seq or this is not a barrier - if ((alloc->parse_seq_len==0) || alloc->parse_seq[ind] != -1) { - int i = alloc->parse_seq_len ? alloc->parse_seq[ind] : ind; - struct ggml_tensor * node = gf->nodes[i]; + // update parents + // update immediately if there is no parse_seq + // update only at barriers if there is parse_seq + if ((parse_seq_len == 0) || parse_seq[ind] == -1) { + int update_start = parse_seq_len ? last_barrier_pos : ind; + int update_end = parse_seq_len ? ind : ind + 1; + for (int i = update_start; i < update_end; i++) { + int node_i = parse_seq_len ? parse_seq[i] : i; + struct ggml_tensor * node = gf->nodes[node_i]; - // allocate parents (leafs) for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * parent = node->src[j]; if (parent == NULL) { break; } - allocate_node(alloc, parent); - } + struct hash_node * p_hn = hash_get(galloc, parent); + p_hn->n_children -= 1; - // allocate node - allocate_node(alloc, node); + //AT_PRINTF("parent %s: %d children, %d views\n", parent->name, parent->n_children, parent->n_views); - AT_PRINTF("exec: %s (%s) <= ", ggml_op_name(node->op), node->name); - for (int j = 0; j < GGML_MAX_SRC; j++) { - struct ggml_tensor * parent = node->src[j]; - if (parent == NULL) { - break; - } - AT_PRINTF("%s", parent->name); - if (j < GGML_MAX_SRC - 1 && node->src[j + 1] != NULL) { - AT_PRINTF(", "); - } - } - 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) { - 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++) { - int node_i = alloc->parse_seq_len ? alloc->parse_seq[i] : i; - struct ggml_tensor * node = gf->nodes[node_i]; - - for (int j = 0; j < GGML_MAX_SRC; j++) { - struct ggml_tensor * parent = node->src[j]; - if (parent == NULL) { - break; - } - struct hash_node * p_hn = hash_get(ht, parent); - p_hn->n_children -= 1; - - //AT_PRINTF("parent %s: %d children, %d views\n", parent->name, parent->n_children, parent->n_views); - - if (p_hn->n_children == 0 && p_hn->n_views == 0) { - if (ggml_is_view(parent)) { - struct ggml_tensor * view_src = parent->view_src; - struct hash_node * view_src_hn = hash_get(ht, view_src); - 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_allocr_free_tensor(alloc, view_src); - } - } - else { - if (parent->data != node->data) { - ggml_allocr_free_tensor(alloc, parent); - } + if (p_hn->n_children == 0 && p_hn->n_views == 0) { + if (ggml_is_view(parent)) { + struct ggml_tensor * view_src = parent->view_src; + struct hash_node * view_src_hn = hash_get(galloc, view_src); + 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) { + free_node(galloc, view_src); } } + else { + free_node(galloc, parent); + } } } - AT_PRINTF("\n"); - if (alloc->parse_seq_len) { - last_barrier_pos = ind + 1; - } } - } - // free graph outputs here that wouldn't be freed otherwise because they have no children - if (outputs != NULL && outputs[g] != NULL) { - for (int i = 0; outputs[g][i] != NULL; i++) { - struct ggml_tensor * output = outputs[g][i]; - AT_PRINTF("output: %s\n", output->name); - ggml_allocr_free_tensor(alloc, output); + AT_PRINTF("\n"); + if (parse_seq_len) { + last_barrier_pos = ind + 1; } } } +} - return alloc->max_size; +size_t ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, ggml_tallocr_t talloc, struct ggml_cgraph * graph) { + size_t hash_size = graph->visited_hash_table.size; + + // check if the hash table is initialized and large enough + if (galloc->hash_set.size < hash_size) { + if (galloc->hash_set.keys != NULL) { + free(galloc->hash_set.keys); + } + if (galloc->hash_values != NULL) { + free(galloc->hash_values); + } + galloc->hash_set.keys = malloc(sizeof(struct ggml_tensor *) * hash_size); + galloc->hash_set.size = hash_size; + galloc->hash_values = malloc(sizeof(struct hash_node) * hash_size); + } + + // reset hash table + memset(galloc->hash_set.keys, 0, sizeof(struct ggml_tensor *) * hash_size); + memset(galloc->hash_values, 0, sizeof(struct hash_node) * hash_size); + + galloc->talloc = talloc; + ggml_tallocr_alloc_graph_impl(galloc, graph); + galloc->talloc = NULL; + + size_t max_size = ggml_tallocr_max_size(talloc); + + return max_size; } -size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph) { - return ggml_allocr_alloc_graph_n(alloc, &graph, 1, NULL, NULL); +void ggml_gallocr_alloc_graph_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, struct ggml_hash_set hash_set, ggml_tallocr_t * hash_node_talloc) { + const size_t hash_size = hash_set.size; + + GGML_ASSERT(hash_size >= (size_t)(graph->n_nodes + graph->n_leafs)); + + galloc->talloc = NULL; + + // alloc hash_values if needed + if (galloc->hash_values == NULL || galloc->hash_values_size < hash_size) { + free(galloc->hash_values); + galloc->hash_values = malloc(sizeof(struct hash_node) * hash_size); + galloc->hash_values_size = hash_size; + } + + // free hash_set.keys if needed + if (galloc->hash_set.keys != NULL) { + free(galloc->hash_set.keys); + } + galloc->hash_set = hash_set; + + // reset hash values + memset(galloc->hash_values, 0, sizeof(struct hash_node) * hash_size); + + galloc->hash_allocs = hash_node_talloc; + + ggml_tallocr_alloc_graph_impl(galloc, graph); + + // remove unowned resources + galloc->hash_set.keys = NULL; + galloc->hash_allocs = NULL; } -size_t ggml_allocr_max_size(struct ggml_allocr * alloc) { - return alloc->max_size; +// legacy API wrapper + +struct ggml_allocr { + ggml_tallocr_t talloc; + ggml_gallocr_t galloc; +}; + +static ggml_allocr_t ggml_allocr_new_impl(ggml_tallocr_t talloc) { + ggml_allocr_t alloc = (ggml_allocr_t)malloc(sizeof(struct ggml_allocr)); + *alloc = (struct ggml_allocr) { + /*.talloc = */ talloc, + /*.galloc = */ ggml_gallocr_new(), + }; + return alloc; +} + +ggml_allocr_t ggml_allocr_new(void * data, size_t size, size_t alignment) { + return ggml_allocr_new_impl(ggml_tallocr_new(data, size, alignment)); +} + +ggml_allocr_t ggml_allocr_new_measure(size_t alignment) { + return ggml_allocr_new_impl(ggml_tallocr_new_measure(alignment)); +} + +ggml_allocr_t ggml_allocr_new_from_buffer(struct ggml_backend_buffer * buffer) { + return ggml_allocr_new_impl(ggml_tallocr_new_from_buffer(buffer)); +} + +ggml_allocr_t ggml_allocr_new_from_backend(struct ggml_backend * backend, size_t size) { + return ggml_allocr_new_impl(ggml_tallocr_new_from_backend(backend, size)); +} + +ggml_allocr_t ggml_allocr_new_measure_from_backend(struct ggml_backend * backend) { + return ggml_allocr_new_impl(ggml_tallocr_new_measure_from_backend(backend)); +} + +struct ggml_backend_buffer * ggml_allocr_get_buffer(ggml_allocr_t alloc) { + return ggml_tallocr_get_buffer(alloc->talloc); +} + +void ggml_allocr_set_parse_seq(ggml_allocr_t alloc, const int * list, int n) { + ggml_gallocr_set_parse_seq(alloc->galloc, list, n); +} + +void ggml_allocr_free(ggml_allocr_t alloc) { + ggml_gallocr_free(alloc->galloc); + ggml_tallocr_free(alloc->talloc); + free(alloc); +} + +bool ggml_allocr_is_measure(ggml_allocr_t alloc) { + return ggml_tallocr_is_measure(alloc->talloc); +} + +void ggml_allocr_reset(ggml_allocr_t alloc) { + ggml_tallocr_reset(alloc->talloc); +} + +void ggml_allocr_alloc(ggml_allocr_t alloc, struct ggml_tensor * tensor) { + ggml_tallocr_alloc(alloc->talloc, tensor); +} + +size_t ggml_allocr_max_size(ggml_allocr_t alloc) { + return ggml_tallocr_max_size(alloc->talloc); +} + +size_t ggml_allocr_alloc_graph(ggml_allocr_t alloc, struct ggml_cgraph * graph) { + return ggml_gallocr_alloc_graph(alloc->galloc, alloc->talloc, graph); } diff --git a/ggml-alloc.h b/ggml-alloc.h index e38758878b91a..dde2a06bf8030 100644 --- a/ggml-alloc.h +++ b/ggml-alloc.h @@ -6,27 +6,79 @@ extern "C" { #endif +struct ggml_backend; struct ggml_backend_buffer; -GGML_API struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment); -GGML_API struct ggml_allocr * ggml_allocr_new_measure(size_t alignment); -GGML_API struct ggml_allocr * ggml_allocr_new_from_buffer(struct ggml_backend_buffer * buffer); +// +// Legacy API +// + +typedef struct ggml_allocr * ggml_allocr_t; + +// initialize allocator for use with CPU backend only +GGML_API ggml_allocr_t ggml_allocr_new(void * data, size_t size, size_t alignment); +GGML_API ggml_allocr_t ggml_allocr_new_measure(size_t alignment); + +// initialize allocator for use with ggml-backend +GGML_API ggml_allocr_t ggml_allocr_new_from_buffer(struct ggml_backend_buffer * buffer); +GGML_API ggml_allocr_t ggml_allocr_new_from_backend(struct ggml_backend * backend, size_t size); // allocates an owned buffer +GGML_API ggml_allocr_t ggml_allocr_new_measure_from_backend(struct ggml_backend * backend); + +GGML_API struct ggml_backend_buffer * ggml_allocr_get_buffer(ggml_allocr_t alloc); // tell the allocator to parse nodes following the order described in the list // you should call this if your graph are optimized to execute out-of-order -GGML_API void ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, const int * list, int n); - -GGML_API void ggml_allocr_free (struct ggml_allocr * alloc); -GGML_API bool ggml_allocr_is_measure (struct ggml_allocr * alloc); -GGML_API void ggml_allocr_reset (struct ggml_allocr * alloc); -GGML_API void ggml_allocr_alloc (struct ggml_allocr * alloc, struct ggml_tensor * tensor); -GGML_API size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph); -GGML_API size_t ggml_allocr_max_size (struct ggml_allocr * alloc); - -GGML_API size_t ggml_allocr_alloc_graph_n( - struct ggml_allocr * alloc, - struct ggml_cgraph ** graphs, int n_graphs, - struct ggml_tensor *** inputs, struct ggml_tensor *** outputs); +GGML_API void ggml_allocr_set_parse_seq(ggml_allocr_t alloc, const int * list, int n); + +GGML_API void ggml_allocr_free (ggml_allocr_t alloc); +GGML_API bool ggml_allocr_is_measure (ggml_allocr_t alloc); +GGML_API void ggml_allocr_reset (ggml_allocr_t alloc); +GGML_API void ggml_allocr_alloc (ggml_allocr_t alloc, struct ggml_tensor * tensor); +GGML_API size_t ggml_allocr_max_size (ggml_allocr_t alloc); + +GGML_API size_t ggml_allocr_alloc_graph(ggml_allocr_t alloc, struct ggml_cgraph * graph); + +// +// ggml-backend v2 API +// + +// Seperate tensor and graph allocator objects +// This is necessary for multi-backend allocation because the graph allocator needs to use multiple tensor allocators +// The original API is kept as a wrapper around the new API + +// Tensor allocator +typedef struct ggml_tallocr * ggml_tallocr_t; + +GGML_API ggml_tallocr_t ggml_tallocr_new(void * data, size_t size, size_t alignment); +GGML_API ggml_tallocr_t ggml_tallocr_new_measure(size_t alignment); +GGML_API ggml_tallocr_t ggml_tallocr_new_from_buffer(struct ggml_backend_buffer * buffer); +GGML_API ggml_tallocr_t ggml_tallocr_new_from_backend(struct ggml_backend * backend, size_t size); // allocates an owned buffer +GGML_API ggml_tallocr_t ggml_tallocr_new_measure_from_backend(struct ggml_backend * backend); + +GGML_API struct ggml_backend_buffer * ggml_tallocr_get_buffer(ggml_tallocr_t talloc); + +GGML_API void ggml_tallocr_free (ggml_tallocr_t talloc); +GGML_API bool ggml_tallocr_is_measure (ggml_tallocr_t talloc); +GGML_API void ggml_tallocr_reset (ggml_tallocr_t talloc); +GGML_API void ggml_tallocr_alloc (ggml_tallocr_t talloc, struct ggml_tensor * tensor); +GGML_API size_t ggml_tallocr_max_size (ggml_tallocr_t talloc); + + +// Graph allocator +typedef struct ggml_gallocr * ggml_gallocr_t; + +GGML_API ggml_gallocr_t ggml_gallocr_new(void); +GGML_API void ggml_gallocr_free(ggml_gallocr_t galloc); + +GGML_API void ggml_gallocr_set_parse_seq(ggml_gallocr_t galloc, const int * list, int n); +GGML_API size_t ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, ggml_tallocr_t talloc, struct ggml_cgraph * graph); + +// Allocate tensors from the allocators given by the hash table +GGML_API void ggml_gallocr_alloc_graph_n( + ggml_gallocr_t galloc, + struct ggml_cgraph * graph, + struct ggml_hash_set hash_set, + ggml_tallocr_t * hash_node_talloc); #ifdef __cplusplus } diff --git a/ggml-backend-impl.h b/ggml-backend-impl.h new file mode 100644 index 0000000000000..211e3d4247387 --- /dev/null +++ b/ggml-backend-impl.h @@ -0,0 +1,87 @@ +#pragma once + +// ggml-backend internal header + +#include "ggml-backend.h" + +#ifdef __cplusplus +extern "C" { +#endif + + // + // Backend buffer + // + + typedef void * ggml_backend_buffer_context_t; + + struct ggml_backend_buffer_i { + void (*free_buffer) (ggml_backend_buffer_t buffer); + void * (*get_base) (ggml_backend_buffer_t buffer); // get base pointer + size_t (*get_alloc_size)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-allocation callback + void (*init_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // post-allocation callback + void (*free_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-free callback + }; + + struct ggml_backend_buffer { + struct ggml_backend_buffer_i iface; + + ggml_backend_t backend; + ggml_backend_buffer_context_t context; + + size_t size; + }; + + GGML_API ggml_backend_buffer_t ggml_backend_buffer_init( + struct ggml_backend * backend, + struct ggml_backend_buffer_i iface, + ggml_backend_buffer_context_t context, + size_t size); + + // + // Backend + // + + typedef void * ggml_backend_context_t; + + struct ggml_backend_i { + const char * (*get_name)(ggml_backend_t backend); + + void (*free)(ggml_backend_t backend); + + // buffer allocation + ggml_backend_buffer_t (*alloc_buffer)(ggml_backend_t backend, size_t size); + + // get buffer alignment + size_t (*get_alignment)(ggml_backend_t backend); + + // tensor data access + // these functions can be asynchronous, helper functions are provided for synchronous access that automatically call synchronize + void (*set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); + void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); + void (*synchronize) (ggml_backend_t backend); + + // (optional) copy tensor between different backends, allow for single-copy tranfers + void (*cpy_tensor_from)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); + void (*cpy_tensor_to) (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); + + // compute graph with a plan + ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, struct ggml_cgraph * cgraph); + void (*graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan); + void (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan); + + // compute graph without a plan + void (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph); + + // check if the backend supports an operation + bool (*supports_op)(ggml_backend_t backend, const struct ggml_tensor * op); + }; + + struct ggml_backend { + struct ggml_backend_i iface; + + ggml_backend_context_t context; + }; + +#ifdef __cplusplus +} +#endif diff --git a/ggml-backend.c b/ggml-backend.c index ca8d83dafe47c..f6e5fceed0f4d 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -1,7 +1,9 @@ -#include "ggml-backend.h" +#include "ggml-backend-impl.h" #include "ggml-alloc.h" +#include "ggml-impl.h" #include +#include #include #include #include @@ -33,6 +35,10 @@ ggml_backend_buffer_t ggml_backend_buffer_init( } void ggml_backend_buffer_free(ggml_backend_buffer_t buffer) { + if (buffer == NULL) { + return; + } + if (buffer->iface.free_buffer != NULL) { buffer->iface.free_buffer(buffer); } @@ -43,15 +49,20 @@ size_t ggml_backend_buffer_get_alignment(ggml_backend_buffer_t buffer) { return ggml_backend_get_alignment(buffer->backend); } -void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) { - return buffer->iface.get_base(buffer); -} - size_t ggml_backend_buffer_get_size(ggml_backend_buffer_t buffer) { return buffer->size; } +void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) { + void * base = buffer->iface.get_base(buffer); + + GGML_ASSERT(base != NULL && "backend buffer base cannot be NULL"); + + return base; +} + size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { + // get_alloc_size is optional, defaults to ggml_nbytes if (buffer->iface.get_alloc_size) { return buffer->iface.get_alloc_size(buffer, tensor); } @@ -59,12 +70,14 @@ size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct g } void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { + // init_tensor is optional if (buffer->iface.init_tensor) { buffer->iface.init_tensor(buffer, tensor); } } void ggml_backend_buffer_free_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { + // free_tensor is optional if (buffer->iface.free_tensor) { buffer->iface.free_tensor(buffer, tensor); } @@ -73,14 +86,21 @@ void ggml_backend_buffer_free_tensor(ggml_backend_buffer_t buffer, struct ggml_t // backend ggml_backend_t ggml_get_backend(const struct ggml_tensor * tensor) { - return tensor->buffer->backend; + return tensor->buffer ? tensor->buffer->backend : NULL; } const char * ggml_backend_name(ggml_backend_t backend) { + if (backend == NULL) { + return "NULL"; + } return backend->iface.get_name(backend); } void ggml_backend_free(ggml_backend_t backend) { + if (backend == NULL) { + return; + } + backend->iface.free(backend); } @@ -101,13 +121,23 @@ void ggml_backend_tensor_get_async(const struct ggml_tensor * tensor, void * dat } void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - ggml_get_backend(tensor)->iface.set_tensor_async(ggml_get_backend(tensor), tensor, data, offset, size); - ggml_get_backend(tensor)->iface.synchronize(ggml_get_backend(tensor)); + ggml_backend_t backend = ggml_get_backend(tensor); + + GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); + GGML_ASSERT(backend != NULL && "tensor backend not set"); + + backend->iface.set_tensor_async(backend, tensor, data, offset, size); + backend->iface.synchronize(backend); } void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { - ggml_get_backend(tensor)->iface.get_tensor_async(ggml_get_backend(tensor), tensor, data, offset, size); - ggml_get_backend(tensor)->iface.synchronize(ggml_get_backend(tensor)); + ggml_backend_t backend = ggml_get_backend(tensor); + + GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); + GGML_ASSERT(backend != NULL && "tensor backend not set"); + + backend->iface.get_tensor_async(backend, tensor, data, offset, size); + backend->iface.synchronize(backend); } void ggml_backend_synchronize(ggml_backend_t backend) { @@ -156,7 +186,7 @@ void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst //printf("dst: %s ne: [%d %d %d %d] nb: [%d %d %d %d]\n", dst->name, (int)dst->ne[0], (int)dst->ne[1], (int)dst->ne[2], (int)dst->ne[3], (int)dst->nb[0], (int)dst->nb[1], (int)dst->nb[2], (int)dst->nb[3]); GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts"); - // printf("cpy tensor %s from %s to %s (%lu bytes)\n", src->name, ggml_backend_name(src->backend), ggml_backend_name(dst->backend), ggml_nbytes(src)); + // fprintf(stderr, "cpy tensor %s from %s to %s (%lu bytes)\n", src->name, ggml_backend_name(src->backend), ggml_backend_name(dst->backend), ggml_nbytes(src)); if (src == dst) { return; @@ -234,6 +264,8 @@ static ggml_backend_buffer_t ggml_backend_cpu_alloc_buffer(ggml_backend_t backen size += TENSOR_ALIGNMENT; // malloc may return an address that is not aligned void * data = malloc(size); // TODO: maybe use GGML_ALIGNED_MALLOC? + GGML_ASSERT(data != NULL && "failed to allocate buffer"); + return ggml_backend_buffer_init(backend, cpu_backend_buffer_i, data, size); } @@ -271,8 +303,7 @@ static void ggml_backend_cpu_cpy_tensor_from(ggml_backend_t backend, struct ggml } static void ggml_backend_cpu_cpy_tensor_to(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) { - // for a backend such as CUDA that can queue async calls, it is ok to do this asynchronously, but it may not be the case for other backends - ggml_backend_tensor_set_async(dst, src->data, 0, ggml_nbytes(src)); + ggml_backend_tensor_set(dst, src->data, 0, ggml_nbytes(src)); UNUSED(backend); } @@ -383,3 +414,537 @@ void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) { ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(ggml_backend_t backend_cpu, void * ptr, size_t size) { return ggml_backend_buffer_init(backend_cpu, cpu_backend_buffer_i_from_ptr, ptr, size); } + +// scheduler + +#define GGML_MAX_BACKENDS 4 +#define GGML_MAX_SPLITS 256 +#define GGML_MAX_SPLIT_INPUTS 16 + +struct ggml_backend_sched_split { + ggml_tallocr_t tallocr; + int i_start; + int i_end; + struct ggml_tensor * inputs[GGML_MAX_SPLIT_INPUTS]; + int n_inputs; + struct ggml_cgraph * graph; +}; + +struct ggml_backend_sched { + int n_backends; + ggml_backend_t backends[GGML_MAX_BACKENDS]; + ggml_tallocr_t tallocs[GGML_MAX_BACKENDS]; + + ggml_gallocr_t galloc; + + struct ggml_hash_set hash_set; + ggml_tallocr_t * node_talloc; // [hash_set.size] + struct ggml_tensor * (* node_copies)[GGML_MAX_BACKENDS]; // [hash_set.size][GGML_MAX_BACKENDS] + + struct ggml_cgraph * graph; + struct ggml_backend_sched_split splits[GGML_MAX_SPLITS]; + int n_splits; + + struct ggml_context * ctx; + + // align context_buffer to GGML_MEM_ALIGN + #ifdef _MSC_VER + __declspec(align(GGML_MEM_ALIGN)) + #else + __attribute__((aligned(GGML_MEM_ALIGN))) + #endif + char context_buffer[GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS*sizeof(struct ggml_tensor) + GGML_MAX_SPLITS*sizeof(struct ggml_cgraph)]; +}; + +#define hash_id(node) ggml_hash_find_or_insert(sched->hash_set, node) +#define node_allocr(node) sched->node_talloc[hash_id(node)] + +static bool ggml_is_view_op(enum ggml_op op) { + return op == GGML_OP_VIEW || op == GGML_OP_RESHAPE || op == GGML_OP_PERMUTE || op == GGML_OP_TRANSPOSE; +} + +// returns the priority of the backend, lower is better +static int sched_backend_prio(ggml_backend_sched_t sched, ggml_backend_t backend) { + for (int i = 0; i < sched->n_backends; i++) { + if (sched->backends[i] == backend) { + return i; + } + } + return INT_MAX; +} + +static int sched_allocr_prio(ggml_backend_sched_t sched, ggml_tallocr_t allocr) { + for (int i = 0; i < sched->n_backends; i++) { + if (sched->tallocs[i] == allocr) { + return i; + } + } + return INT_MAX; +} + +// returns the backend that should be used for the node based on the current locations +char causes[GGML_DEFAULT_GRAPH_SIZE*4 + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS][128]; // debug, remove +static ggml_backend_t sched_backend_from_cur(ggml_backend_sched_t sched, struct ggml_tensor * node) { + // if the dst tensor is already allocated in a buffer, we must assume that it is critical to keep it there + // ie. kv cache updates + // note that this doesn't allow fallback to CPU. need to add output tensors to the splits to copy the data back to the original backend. + // dst + ggml_backend_t cur_backend = ggml_get_backend(node); + if (cur_backend != NULL) { + sprintf(causes[hash_id(node)], "1.dst"); + return cur_backend; + } + + // view_src + if (node->view_src != NULL && ggml_get_backend(node->view_src) != NULL) { + sprintf(causes[hash_id(node)], "1.vsrc"); + return ggml_get_backend(node->view_src); + } + + // src + int cur_prio = INT_MAX; + size_t cur_size = 0; + + for (int i = 0; i < GGML_MAX_SRC; i++) { + const struct ggml_tensor * src = node->src[i]; + if (src == NULL) { + break; + } + ggml_backend_t src_backend = ggml_get_backend(src); + if (src_backend != NULL) { + int src_prio = sched_backend_prio(sched, src_backend); + size_t src_size = ggml_nbytes(src); + if (src_prio < cur_prio && src_size >= cur_size) { + cur_prio = src_prio; + cur_size = src_size; + cur_backend = src_backend; + sprintf(causes[hash_id(node)], "1.src%d", i); + } + } + } + return cur_backend; +} + +static char * fmt_size(size_t size) { + static char buffer[128]; + if (size >= 1024*1024) { + sprintf(buffer, "%zuM", size/1024/1024); + } else { + sprintf(buffer, "%zuK", size/1024); + } + return buffer; +} + +static void sched_print_assignments(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { + int cur_split = 0; + for (int i = 0; i < graph->n_nodes; i++) { + if (cur_split < sched->n_splits && i == sched->splits[cur_split].i_start) { + ggml_backend_t split_backend = ggml_tallocr_get_buffer(sched->splits[cur_split].tallocr)->backend; + fprintf(stderr, "\n## SPLIT #%d: %s # %d inputs: ", cur_split, ggml_backend_name(split_backend), sched->splits[cur_split].n_inputs); + for (int j = 0; j < sched->splits[cur_split].n_inputs; j++) { + fprintf(stderr, "[%s (%5.5s)] ", sched->splits[cur_split].inputs[j]->name, fmt_size(ggml_nbytes(sched->splits[cur_split].inputs[j]))); + } + fprintf(stderr, "\n"); + cur_split++; + } + struct ggml_tensor * node = graph->nodes[i]; + if (ggml_is_view_op(node->op)) { + continue; + } + ggml_tallocr_t node_allocr = node_allocr(node); + ggml_backend_t node_backend = node_allocr ? ggml_tallocr_get_buffer(node_allocr)->backend : NULL; + fprintf(stderr, "node #%3d (%10.10s): %20.20s (%4.4s) [%4.4s %8.8s]:", i, ggml_op_name(node->op), node->name, fmt_size(ggml_nbytes(node)), node_allocr ? ggml_backend_name(node_backend) : "NULL", causes[hash_id(node)]); + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + break; + } + ggml_tallocr_t src_allocr = node_allocr(src); + ggml_backend_t src_backend = src_allocr ? ggml_tallocr_get_buffer(src_allocr)->backend : NULL; + fprintf(stderr, " %20.20s (%4.4s) [%4.4s %8.8s]", src->name, fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", causes[hash_id(src)]); + } + fprintf(stderr, "\n"); + } +} + +// creates a copy of the tensor with the same memory layout +static struct ggml_tensor * ggml_dup_tensor_layout(struct ggml_context * ctx, const struct ggml_tensor * tensor) { + struct ggml_tensor * dup = ggml_dup_tensor(ctx, tensor); + for (int i = 0; i < GGML_MAX_DIMS; i++) { + dup->nb[i] = tensor->nb[i]; + } + return dup; +} + +// assigns backends to ops and splits the graph into subgraphs that can be computed on the same backend +// TODO: merge passes +static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { + // reset state + size_t hash_size = sched->hash_set.size; + memset(sched->hash_set.keys, 0, sizeof(sched->hash_set.keys[0]) * hash_size); + memset(sched->node_talloc, 0, sizeof(sched->node_talloc[0]) * hash_size); + memset(sched->node_copies, 0, sizeof(sched->node_copies[0]) * hash_size); + sched->n_splits = 0; + + struct ggml_init_params params = { + /*.mem_size = */ sizeof(sched->context_buffer), + /*.mem_buffer = */ sched->context_buffer, + /*.no_alloc = */ true + }; + + if (sched->ctx != NULL) { + ggml_free(sched->ctx); + } + + sched->ctx = ggml_init(params); + + // pass 1: assign backends to ops with allocated inputs + for (int i = 0; i < graph->n_leafs; i++) { + struct ggml_tensor * leaf = graph->leafs[i]; + if (node_allocr(leaf) != NULL) { + // do not overwrite user assignments + continue; + } + ggml_backend_t leaf_backend = ggml_get_backend(leaf); + if (leaf_backend == NULL && leaf->view_src != NULL) { + leaf_backend = ggml_get_backend(leaf->view_src); + } + if (leaf_backend != NULL) { + node_allocr(leaf) = ggml_backend_sched_get_tallocr(sched, leaf_backend); + } + } + + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + if (node_allocr(node) != NULL) { + // do not overwrite user assignments + continue; + } + ggml_backend_t node_backend = sched_backend_from_cur(sched, node); + if (node_backend != NULL) { + node_allocr(node) = ggml_backend_sched_get_tallocr(sched, node_backend); + } + } + //printf("PASS 1 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); + + // pass 2: assign backends to ops from current assignments + // TODO: + // - reuse sched_backend_from_cur + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + ggml_tallocr_t node_allocr = node_allocr(node); + if (node_allocr == NULL) { + int cur_prio = INT_MAX; + size_t cur_size = 0; + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + break; + } + ggml_tallocr_t src_allocr = node_allocr(src); + if (src_allocr != NULL) { + int src_prio = sched_allocr_prio(sched, src_allocr); + size_t src_size = ggml_nbytes(src); + if (src_prio < cur_prio && src_size >= cur_size) { + cur_prio = src_prio; + cur_size = src_size; + node_allocr = src_allocr; + sprintf(causes[hash_id(node)], "2.src%d", j); + } + } + } + if (node_allocr != NULL) { + node_allocr(node) = node_allocr; + } + } + } + //printf("PASS 2 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); + + // pass 3: assign backends to remaining src from dst (should only be leafs) + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + ggml_tallocr_t node_allocr = node_allocr(node); + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + break; + } + ggml_tallocr_t src_allocr = node_allocr(src); + if (src_allocr == NULL) { + node_allocr(src) = node_allocr; + } + } + } + //printf("PASS 3 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); + + // pass 4: split graph, find tensors that need to be copied + // TODO: + // - when switching from a less preferred backend to a more preferred backend, check if it is possible to move the switch to an earlier point for the same cost + // find first backend + int cur_split = 0; + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + if (node->view_src == NULL) { + sched->splits[0].tallocr = node_allocr(node); + break; + } + } + sched->splits[0].i_start = 0; + sched->splits[0].n_inputs = 0; + memset(sched->splits[0].inputs, 0, sizeof(sched->splits[0].inputs)); //HACK + ggml_tallocr_t cur_allocr = sched->splits[0].tallocr; + size_t cur_backend_id = sched_allocr_prio(sched, cur_allocr); + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + + if (ggml_is_view_op(node->op)) { + continue; + } + + ggml_tallocr_t node_allocr = node_allocr(node); + + if (node_allocr != cur_allocr) { + sched->splits[cur_split].i_end = i; + cur_split++; + GGML_ASSERT(cur_split < GGML_MAX_SPLITS); + sched->splits[cur_split].tallocr = node_allocr; + sched->splits[cur_split].i_start = i; + sched->splits[cur_split].n_inputs = 0; + memset(sched->splits[cur_split].inputs, 0, sizeof(sched->splits[cur_split].inputs)); //HACK + cur_allocr = node_allocr; + cur_backend_id = sched_allocr_prio(sched, cur_allocr); + } + + // find inputs that are not on the same backend + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + break; + } + ggml_tallocr_t src_allocr = node_allocr(src); + if (src_allocr != node_allocr) { + int n_inputs = sched->splits[cur_split].n_inputs++; + GGML_ASSERT(n_inputs < GGML_MAX_SPLIT_INPUTS); + sched->splits[cur_split].inputs[n_inputs] = (struct ggml_tensor *)src; + + // create copies + size_t id = hash_id(src); + if (sched->node_copies[id][cur_backend_id] == NULL) { + struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src); + sched->node_copies[id][cur_backend_id] = tensor_copy; + node_allocr(tensor_copy) = cur_allocr; + ggml_backend_t backend = ggml_tallocr_get_buffer(cur_allocr)->backend; + ggml_format_name(tensor_copy, "%s#%s", ggml_backend_name(backend), src->name); + } + node->src[j] = sched->node_copies[id][cur_backend_id]; + } + } + } + sched->splits[cur_split].i_end = graph->n_nodes; + sched->n_splits = cur_split + 1; + + //fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); fflush(stdout); + +#if 1 + // sanity check: all sources should have the same backend as the node + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + ggml_tallocr_t node_allocr = node_allocr(node); + if (node_allocr == NULL) { + fprintf(stderr, "!!!!!!! %s has no backend\n", node->name); + } + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + break; + } + ggml_tallocr_t src_allocr = node_allocr(src); + if (src_allocr != node_allocr /* && src_backend != NULL */) { // ignore nulls for now + fprintf(stderr, "!!!! %s has backend %s, src %d (%s) has backend %s\n", + node->name, node_allocr ? ggml_backend_name(ggml_tallocr_get_buffer(node_allocr)->backend) : "NULL", + j, src->name, src_allocr ? ggml_backend_name(ggml_tallocr_get_buffer(src_allocr)->backend) : "NULL"); + } + } + } +#endif + + // create copies of the graph for each split + // FIXME: avoid this copy, pass split inputs to ggml_gallocr_alloc_graph_n in some other way + struct ggml_cgraph * graph_copy = ggml_new_graph_custom(sched->ctx, graph->n_nodes + sched->n_splits*GGML_MAX_SPLIT_INPUTS, false); + for (int i = 0; i < sched->n_splits; i++) { + struct ggml_backend_sched_split * split = &sched->splits[i]; + split->graph = ggml_graph_view(sched->ctx, graph, split->i_start, split->i_end); + + // add inputs to the graph copy so that they are allocated by ggml-alloc at the start of the split + for (int j = 0; j < split->n_inputs; j++) { + struct ggml_tensor * input = split->inputs[j]; + struct ggml_tensor * input_cpy = sched->node_copies[hash_id(input)][sched_allocr_prio(sched, split->tallocr)]; + input_cpy->src[0] = input; + graph_copy->nodes[graph_copy->n_nodes++] = input_cpy; + } + + for (int j = split->i_start; j < split->i_end; j++) { + graph_copy->nodes[graph_copy->n_nodes++] = graph->nodes[j]; + } + } + sched->graph = graph_copy; +} + +static void sched_alloc_splits(ggml_backend_sched_t sched) { + ggml_gallocr_alloc_graph_n( + sched->galloc, + sched->graph, + sched->hash_set, + sched->node_talloc); +} + +static void sched_compute_splits(ggml_backend_sched_t sched) { + uint64_t copy_us[GGML_MAX_BACKENDS] = {0}; + uint64_t compute_us[GGML_MAX_BACKENDS] = {0}; + + struct ggml_backend_sched_split * splits = sched->splits; + + for (int i = 0; i < sched->n_splits; i++) { + struct ggml_backend_sched_split * split = &splits[i]; + ggml_backend_t split_backend = ggml_tallocr_get_buffer(split->tallocr)->backend; + int split_backend_id = sched_backend_prio(sched, split_backend); + + // copy the input tensors to the split backend + uint64_t copy_start_us = ggml_time_us(); + for (int j = 0; j < split->n_inputs; j++) { + struct ggml_tensor * input_cpy = sched->node_copies[hash_id(split->inputs[j])][sched_backend_prio(sched, split_backend)]; + if (split->inputs[j]->buffer == NULL) { + if (split->inputs[j]->view_src == NULL) { + fprintf(stderr, "input %s has no buffer and no view_src\n", split->inputs[j]->name); + exit(1); + } + struct ggml_tensor * view = split->inputs[j]; + view->backend = view->view_src->backend; + view->buffer = view->view_src->buffer; + view->data = (char *)view->view_src->data + view->view_offs; + ggml_backend_buffer_init_tensor(ggml_backend_sched_get_buffer(sched, view->buffer->backend), view); + } + if (input_cpy->buffer == NULL) { + fprintf(stderr, "input_cpy %s has no buffer\n", input_cpy->name); + exit(1); + } + GGML_ASSERT(split->inputs[j]->buffer->backend != input_cpy->buffer->backend); + GGML_ASSERT(input_cpy->buffer->backend == split_backend); + ggml_backend_tensor_copy(split->inputs[j], input_cpy); + } + // ggml_backend_synchronize(split_backend); + int64_t copy_end_us = ggml_time_us(); + copy_us[split_backend_id] += copy_end_us - copy_start_us; + +#if 0 + char split_filename[GGML_MAX_NAME]; + snprintf(split_filename, GGML_MAX_NAME, "split_%i_%s.dot", i, ggml_backend_name(split_backend)); + ggml_graph_dump_dot(split->graph, NULL, split_filename); +#endif + + uint64_t compute_start_us = ggml_time_us(); + ggml_backend_graph_compute(split_backend, split->graph); + // ggml_backend_synchronize(split_backend); + uint64_t compute_end_us = ggml_time_us(); + compute_us[split_backend_id] += compute_end_us - compute_start_us; + } + +#if 0 + // per-backend timings + fprintf(stderr, "sched_compute_splits times (%d splits):\n", sched->n_splits); + for (int i = 0; i < sched->n_backends; i++) { + if (copy_us[i] > 0 || compute_us[i] > 0) { + fprintf(stderr, "\t%5.5s: %lu us copy, %lu us compute\n", ggml_backend_name(sched->backends[i]), copy_us[i], compute_us[i]); + } + } +#endif +} + +static void sched_reset(ggml_backend_sched_t sched) { + for (int i = 0; i < sched->n_backends; i++) { + ggml_tallocr_reset(sched->tallocs[i]); + } +} + +ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, int n_backends) { + GGML_ASSERT(n_backends <= GGML_MAX_BACKENDS); + + struct ggml_backend_sched * sched = malloc(sizeof(struct ggml_backend_sched)); + memset(sched, 0, sizeof(struct ggml_backend_sched)); + + fprintf(stderr, "ggml_backend_sched size: %lu KB\n", sizeof(struct ggml_backend_sched)/1024); + + sched->n_backends = n_backends; + for (int i = 0; i < n_backends; i++) { + sched->backends[i] = backends[i]; + } + + sched->galloc = ggml_gallocr_new(); + + // init measure allocs for each backend + for (int i = 0; i < n_backends; i++) { + sched->tallocs[i] = ggml_tallocr_new_measure_from_backend(backends[i]); + } + + return sched; +} + +void ggml_backend_sched_free(ggml_backend_sched_t sched) { + if (sched == NULL) { + return; + } + for (int i = 0; i < sched->n_backends; i++) { + ggml_tallocr_free(sched->tallocs[i]); + } + ggml_gallocr_free(sched->galloc); + free(sched->hash_set.keys); + free(sched->node_talloc); + free(sched->node_copies); + free(sched); +} + +void ggml_backend_sched_init_measure(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) { + // initialize hash tables + size_t hash_size = measure_graph->visited_hash_table.size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS; + sched->hash_set.size = hash_size; + sched->hash_set.keys = malloc(sizeof(sched->hash_set.keys[0]) * hash_size); + sched->node_talloc = malloc(sizeof(sched->node_talloc[0]) * hash_size); + sched->node_copies = malloc(sizeof(sched->node_copies[0]) * hash_size); + + sched_split_graph(sched, measure_graph); + sched_alloc_splits(sched); + + // allocate buffers and reset allocators + for (int i = 0; i < sched->n_backends; i++) { + size_t size = ggml_tallocr_max_size(sched->tallocs[i]); + ggml_tallocr_free(sched->tallocs[i]); + sched->tallocs[i] = ggml_tallocr_new_from_backend(sched->backends[i], size); + } + + sched_reset(sched); +} + +void ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { + GGML_ASSERT(sched->hash_set.size >= graph->visited_hash_table.size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS); + + sched_split_graph(sched, graph); + sched_alloc_splits(sched); + sched_compute_splits(sched); + sched_reset(sched); +} + +ggml_tallocr_t ggml_backend_sched_get_tallocr(ggml_backend_sched_t sched, ggml_backend_t backend) { + int backend_index = sched_backend_prio(sched, backend); + return sched->tallocs[backend_index]; +} + +ggml_backend_buffer_t ggml_backend_sched_get_buffer(ggml_backend_sched_t sched, ggml_backend_t backend) { + int backend_index = sched_backend_prio(sched, backend); + return ggml_tallocr_get_buffer(sched->tallocs[backend_index]); +} + +void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) { + int backend_index = sched_backend_prio(sched, backend); + GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends); + node_allocr(node) = sched->tallocs[backend_index]; +} diff --git a/ggml-backend.h b/ggml-backend.h index da134b0dbed51..966687320ac96 100644 --- a/ggml-backend.h +++ b/ggml-backend.h @@ -1,51 +1,20 @@ #pragma once #include "ggml.h" +#include "ggml-alloc.h" #ifdef __cplusplus extern "C" { #endif - struct ggml_backend; - struct ggml_backend_buffer; - - // type-erased backend-specific types / wrappers - typedef void * ggml_backend_context_t; - typedef void * ggml_backend_graph_plan_t; - typedef void * ggml_backend_buffer_context_t; - - // avoid accessing internals of these types - typedef struct ggml_backend * ggml_backend_t; - typedef struct ggml_backend_buffer * ggml_backend_buffer_t; // - // backend buffer + // Backend buffer // - struct ggml_backend_buffer_i { - void (*free_buffer) (ggml_backend_buffer_t buffer); - void * (*get_base) (ggml_backend_buffer_t buffer); // get base pointer - size_t (*get_alloc_size)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-allocation callback - void (*init_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // post-allocation callback - void (*free_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-free callback - }; - - // TODO: hide behind API - struct ggml_backend_buffer { - struct ggml_backend_buffer_i iface; - - ggml_backend_t backend; - ggml_backend_buffer_context_t context; - - size_t size; - }; + struct ggml_backend_buffer; + typedef struct ggml_backend_buffer * ggml_backend_buffer_t; // backend buffer functions - GGML_API ggml_backend_buffer_t ggml_backend_buffer_init( - struct ggml_backend * backend, - struct ggml_backend_buffer_i iface, - ggml_backend_buffer_context_t context, - size_t size); - GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer); GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer); GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer); @@ -55,50 +24,13 @@ extern "C" { GGML_API void ggml_backend_buffer_free_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // - // backend + // Backend // - struct ggml_backend_i { - const char * (*get_name)(ggml_backend_t backend); - - void (*free)(ggml_backend_t backend); - - // buffer allocation - ggml_backend_buffer_t (*alloc_buffer)(ggml_backend_t backend, size_t size); - - // get buffer alignment - size_t (*get_alignment)(ggml_backend_t backend); - - // tensor data access - // these functions can be asynchronous, helper functions are provided for synchronous access that automatically call synchronize - void (*set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); - void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); - void (*synchronize) (ggml_backend_t backend); - - // (optional) copy tensor between different backends, allow for single-copy tranfers - void (*cpy_tensor_from)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); - void (*cpy_tensor_to) (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); - - // compute graph with a plan - ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, struct ggml_cgraph * cgraph); - void (*graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan); - void (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan); - - // compute graph without a plan - void (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph); - - // check if the backend supports an operation - bool (*supports_op)(ggml_backend_t backend, const struct ggml_tensor * op); - }; - - // TODO: hide behind API - struct ggml_backend { - struct ggml_backend_i iface; - - ggml_backend_context_t context; - }; + struct ggml_backend; + typedef struct ggml_backend * ggml_backend_t; + typedef void * ggml_backend_graph_plan_t; - // backend helper functions GGML_API ggml_backend_t ggml_get_backend(const struct ggml_tensor * tensor); GGML_API const char * ggml_backend_name(ggml_backend_t backend); @@ -133,11 +65,72 @@ extern "C" { GGML_API ggml_backend_t ggml_backend_cpu_init(void); GGML_API bool ggml_backend_is_cpu(ggml_backend_t backend); - GGML_API void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads); + // Create a backend buffer from an existing pointer GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(ggml_backend_t backend_cpu, void * ptr, size_t size); + + // + // Backend scheduler + // + + // The backend scheduler allows for multiple backends to be used together + // Handles compute buffer allocation, assignment of tensors to backends, and copying of tensors between backends + // The backends are selected based on: + // - the backend that supports the operation + // - the location of the pre-allocated tensors (e.g. the weights) + /* + Example usage: + + sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, num_backends); + // sched is initialized with measure allocators and cannot be used until allocated with a measure graph + + // initialize buffers from a measure graph + measure_graph = build_graph(sched); // use the allocr to allocate inputs as needed + + // in build_graph: + build_graph(...) { + // allocating tensors in a specific backend (optional, recommended: pre-allocate inputs in a different buffer) + alloc_cpu = ggml_backend_sched_get_allocr(sched, backend_cpu); + ggml_allocr_alloc(alloc_cpu, tensor); + + // manually assigning nodes to a backend (optional, shouldn't be needed in most cases) + struct ggml_tensor * node = ggml_mul_mat(ctx, ...); + ggml_backend_sched_set_node_backend(sched, node, backend_gpu); + } + + // allocate backend buffers from measure graph + ggml_backend_sched_init_measure(sched, measure_graph); + + // the scheduler is now ready to compute graphs + + // compute + graph = build_graph(sched); + ggml_backend_sched_graph_compute(sched, graph); + */ + + struct ggml_backend_sched; + typedef struct ggml_backend_sched * ggml_backend_sched_t; + + // Initialize a backend scheduler + GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, int n_backends); + + GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched); + + // Initialize backend buffers from a measure graph + GGML_API void ggml_backend_sched_init_measure(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); + + GGML_API ggml_tallocr_t ggml_backend_sched_get_tallocr(ggml_backend_sched_t sched, ggml_backend_t backend); + GGML_API ggml_backend_buffer_t ggml_backend_sched_get_buffer (ggml_backend_sched_t sched, ggml_backend_t backend); + + GGML_API void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend); + + // Allocate a graph on the backend scheduler + GGML_API void ggml_backend_sched_graph_compute( + ggml_backend_sched_t sched, + struct ggml_cgraph * graph); + #ifdef __cplusplus } #endif diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 8d03ba6641981..1634024466542 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -81,6 +81,7 @@ #include "ggml-cuda.h" #include "ggml.h" +#include "ggml-backend-impl.h" #define MIN_CC_DP4A 610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products #define CC_VOLTA 700 @@ -7751,11 +7752,11 @@ static size_t g_temp_tensor_extra_index = 0; static ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() { if (g_temp_tensor_extras == nullptr) { - g_temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_MAX_NODES]; + g_temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_DEFAULT_GRAPH_SIZE]; } size_t alloc_index = g_temp_tensor_extra_index; - g_temp_tensor_extra_index = (g_temp_tensor_extra_index + 1) % GGML_MAX_NODES; + g_temp_tensor_extra_index = (g_temp_tensor_extra_index + 1) % GGML_DEFAULT_GRAPH_SIZE; ggml_tensor_extra_gpu * extra = &g_temp_tensor_extras[alloc_index]; memset(extra, 0, sizeof(*extra)); @@ -8070,11 +8071,11 @@ struct ggml_backend_buffer_context_cuda { ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() { if (temp_tensor_extras == nullptr) { - temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_MAX_NODES]; + temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_DEFAULT_GRAPH_SIZE]; } size_t alloc_index = temp_tensor_extra_index; - temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_MAX_NODES; + temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_DEFAULT_GRAPH_SIZE; ggml_tensor_extra_gpu * extra = &temp_tensor_extras[alloc_index]; memset(extra, 0, sizeof(*extra)); @@ -8160,7 +8161,12 @@ static ggml_backend_buffer_t ggml_backend_cuda_alloc_buffer(ggml_backend_t backe ggml_cuda_set_device(g_main_device); ggml_backend_buffer_context_cuda * ctx = new ggml_backend_buffer_context_cuda; + + size = std::max(size, (size_t)1); // cudaMalloc returns null for size 0 + + ggml_cuda_set_device(g_main_device); CUDA_CHECK(cudaMalloc(&ctx->device, size)); + return ggml_backend_buffer_init(backend, cuda_backend_buffer_interface, ctx, size); } @@ -8227,6 +8233,8 @@ static void ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph for (int i = 0; i < cgraph->n_nodes; i++) { ggml_tensor * node = cgraph->nodes[i]; + if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE) + continue; assert(node->backend == GGML_BACKEND_GPU); for (int j = 0; j < GGML_MAX_SRC; j++) { if (node->src[j] != nullptr) { diff --git a/ggml-impl.h b/ggml-impl.h index 5ec18a50c8da5..d88f261449f05 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -230,7 +230,19 @@ inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) { #endif - // TODO: backend v2 PR +#define GGML_HASHTABLE_FULL ((size_t)-1) +#define GGML_HASHTABLE_ALREADY_EXISTS ((size_t)-2) + +bool ggml_hash_contains (const struct ggml_hash_set hash_set, struct ggml_tensor * key); + +// returns GGML_HASHTABLE_FULL if table is full, otherwise the current index of the key or where it should be inserted +size_t ggml_hash_find (const struct ggml_hash_set hash_set, struct ggml_tensor * key); + +// returns GGML_HAHSHTABLE_ALREADY_EXISTS if key already exists, index otherwise, asserts if table is full +size_t ggml_hash_insert ( struct ggml_hash_set hash_set, struct ggml_tensor * key); + +// return index, asserts if table is full +size_t ggml_hash_find_or_insert( struct ggml_hash_set hash_set, struct ggml_tensor * key); #ifdef __cplusplus } diff --git a/ggml-metal.m b/ggml-metal.m index 78ae4485da8e2..c2cda0bf546d3 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1,5 +1,6 @@ #import "ggml-metal.h" +#import "ggml-backend-impl.h" #import "ggml.h" #import @@ -23,7 +24,7 @@ #define UNUSED(x) (void)(x) -#define GGML_MAX_CONCUR (2*GGML_MAX_NODES) +#define GGML_MAX_CONCUR (2*GGML_DEFAULT_GRAPH_SIZE) struct ggml_metal_buffer { const char * name; @@ -744,6 +745,20 @@ void ggml_metal_graph_compute( struct ggml_tensor * src1 = gf->nodes[i]->src[1]; struct ggml_tensor * dst = gf->nodes[i]; + switch (dst->op) { + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_TRANSPOSE: + case GGML_OP_PERMUTE: + { + // noop -> next node + } continue; + default: + { + } break; + } + const int64_t ne00 = src0 ? src0->ne[0] : 0; const int64_t ne01 = src0 ? src0->ne[1] : 0; const int64_t ne02 = src0 ? src0->ne[2] : 0; @@ -797,14 +812,6 @@ void ggml_metal_graph_compute( //} switch (dst->op) { - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_TRANSPOSE: - case GGML_OP_PERMUTE: - { - // noop - } break; case GGML_OP_CONCAT: { const int64_t nb = ne00; diff --git a/ggml.c b/ggml.c index 009d5b3985e55..da78e6de9586b 100644 --- a/ggml.c +++ b/ggml.c @@ -100,6 +100,49 @@ typedef void * thread_ret_t; #include #endif +#if defined(__APPLE__) +#include +#endif + +#if (defined(__linux__) || defined(__APPLE__) || defined(__FreeBSD__) || defined(__NetBSD__) || defined(__OpenBSD__)) && \ + (!defined(TARGET_OS_TV) && !defined(TARGET_OS_WATCH)) + +#include + +void ggml_print_backtrace(void) { + /* + #include + #include + + void * trace[100]; + + int nptrs = backtrace(trace, sizeof(trace)/sizeof(trace[0])); + + backtrace_symbols_fd(trace, nptrs, STDERR_FILENO); + */ + + // backtrack_symbols does not show line numbers, use gdb instead + char attach[32]; + snprintf(attach, sizeof(attach), "attach %d", getpid()); + int pid = fork(); + if (pid == 0) { + execlp("gdb", "gdb", "--batch", + "-ex", "set style enabled on", + "-ex", attach, + "-ex", "bt -frame-info source-and-location", + "-ex", "detach", + "-ex", "quit", + NULL); + } else { + waitpid(pid, NULL, 0); + } +} +#else +void ggml_print_backtrace(void) { + // platform not supported +} +#endif + /*#define GGML_PERF*/ #define GGML_DEBUG 0 #define GGML_GELU_FP16 @@ -1352,6 +1395,7 @@ inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = tanhf(x[i]); } inline static void ggml_vec_elu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expf(x[i])-1; } inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; } +inline static void ggml_vec_leaky_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.1f*x[i]; } static const float GELU_COEF_A = 0.044715f; static const float GELU_QUICK_COEF = -1.702f; @@ -3769,6 +3813,14 @@ struct ggml_tensor * ggml_relu_inplace( return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_RELU); } +// ggml_leaky + +struct ggml_tensor * ggml_leaky( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_LEAKY); +} + // ggml_gelu struct ggml_tensor * ggml_gelu( @@ -5411,7 +5463,7 @@ struct ggml_tensor * ggml_conv_transpose_2d_p0( // ggml_pool_* -static int64_t ggml_calc_pool_output_size(int64_t ins, int ks, int s, int p) { +static int64_t ggml_calc_pool_output_size(int64_t ins, int ks, int s, float p) { return (ins + 2 * p - ks) / s + 1; } @@ -5458,8 +5510,8 @@ struct ggml_tensor * ggml_pool_2d( int k1, int s0, int s1, - int p0, - int p1) { + float p0, + float p1) { bool is_node = false; @@ -8921,6 +8973,48 @@ static void ggml_compute_forward_silu( } } +// ggml_compute_forward_leaky + +static void ggml_compute_forward_leaky_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + assert(params->ith == 0); + assert(ggml_are_same_shape(src0, dst)); + + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + assert(dst->nb[0] == sizeof(float)); + assert(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + ggml_vec_leaky_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_leaky( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_leaky_f32(params, src0, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + // ggml_compute_forward_silu_back static void ggml_compute_forward_silu_back_f32( @@ -12454,14 +12548,11 @@ static void ggml_compute_forward_pool_1d( ggml_compute_forward_pool_1d_sk_p0(params, op, src0, k0, dst); } -// ggml_compute_forward_pool_2d_sk_p0 +// ggml_compute_forward_pool_2d -static void ggml_compute_forward_pool_2d_sk_p0( +static void ggml_compute_forward_pool_2d( const struct ggml_compute_params * params, - const enum ggml_op_pool op, const struct ggml_tensor * src, - const int k0, - const int k1, struct ggml_tensor * dst) { assert(src->type == GGML_TYPE_F32); assert(params->ith == 0); @@ -12470,6 +12561,14 @@ static void ggml_compute_forward_pool_2d_sk_p0( return; } + const int32_t * opts = (const int32_t *)dst->op_params; + enum ggml_op_pool op = opts[0]; + const int k0 = opts[1]; + const int k1 = opts[2]; + const int s0 = opts[3]; + const int s1 = opts[4]; + const int p0 = opts[5]; + const int p1 = opts[6]; const char * cdata = (const char*)src->data; const char * const data_end = cdata + ggml_nbytes(src); @@ -12480,6 +12579,8 @@ static void ggml_compute_forward_pool_2d_sk_p0( float * dplane = (float *)dst->data; const int ka = k0 * k1; + const int offset0 = -p0; + const int offset1 = -p1; while (cdata < data_end) { for (int oy = 0; oy < py; ++oy) { @@ -12492,13 +12593,15 @@ static void ggml_compute_forward_pool_2d_sk_p0( case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break; } - const int ix = ox * k0; - const int iy = oy * k1; + const int ix = offset0 + ox * s0; + const int iy = offset1 + oy * s1; for (int ky = 0; ky < k1; ++ky) { + if (iy + ky < 0 || iy + ky >= src->ne[1]) continue; const float * const srow = (const float *)(cdata + src->nb[1] * (iy + ky)); for (int kx = 0; kx < k0; ++kx) { int j = ix + kx; + if (j < 0 || j >= src->ne[0]) continue; switch (op) { case GGML_OP_POOL_AVG: *out += srow[j]; break; case GGML_OP_POOL_MAX: if (srow[j] > *out) *out = srow[j]; break; @@ -12519,29 +12622,6 @@ static void ggml_compute_forward_pool_2d_sk_p0( } } -// ggml_compute_forward_pool_2d - -static void ggml_compute_forward_pool_2d( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - struct ggml_tensor * dst) { - - const int32_t * opts = (const int32_t *)dst->op_params; - enum ggml_op_pool op = opts[0]; - const int k0 = opts[1]; - const int k1 = opts[2]; - const int s0 = opts[3]; - const int s1 = opts[4]; - const int p0 = opts[5]; - const int p1 = opts[6]; - GGML_ASSERT(p0 == 0); - GGML_ASSERT(p1 == 0); // padding not supported - GGML_ASSERT(k0 == s0); - GGML_ASSERT(k1 == s1); // only s = k supported - - ggml_compute_forward_pool_2d_sk_p0(params, op, src0, k0, k1, dst); -} - // ggml_compute_forward_upscale static void ggml_compute_forward_upscale_f32( @@ -13743,6 +13823,10 @@ static void ggml_compute_forward_unary( { ggml_compute_forward_silu(params, src0, dst); } break; + case GGML_UNARY_OP_LEAKY: + { + ggml_compute_forward_leaky(params, src0, dst); + } break; default: { GGML_ASSERT(false); @@ -14651,62 +14735,109 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm //////////////////////////////////////////////////////////////////////////////// -static_assert(GGML_GRAPH_HASHTABLE_SIZE > GGML_MAX_NODES * 2, "GGML_GRAPH_HT_SIZE is too small"); +static size_t ggml_hash_size(size_t min_sz) { + // next primes after powers of two + static const size_t primes[] = { + 2, 3, 5, 11, 17, 37, 67, 131, 257, 521, 1031, + 2053, 4099, 8209, 16411, 32771, 65537, 131101, + 262147, 524309, 1048583, 2097169, 4194319, 8388617, + 16777259, 33554467, 67108879, 134217757, 268435459, + 536870923, 1073741827, 2147483659 + }; + static const size_t n_primes = sizeof(primes)/sizeof(primes[0]); + + // find the smallest prime that is larger or equal to min_sz + size_t l = 0; + size_t r = n_primes; + while (l < r) { + size_t m = (l + r)/2; + if (primes[m] < min_sz) { + l = m + 1; + } else { + r = m; + } + } + size_t sz = l < n_primes ? primes[l] : min_sz | 1; + return sz; +} -static size_t hash(void * p) { - return (size_t)p % GGML_GRAPH_HASHTABLE_SIZE; +static size_t ggml_hash(const void * p) { + return (size_t)p; } -static size_t hash_find(void * hash_table[], void * p) { - size_t h = hash(p); +size_t ggml_hash_find(const struct ggml_hash_set hash_set, struct ggml_tensor * key) { + size_t h = ggml_hash(key) % hash_set.size; // linear probing size_t i = h; - while (hash_table[i] != NULL && hash_table[i] != p) { - i = (i + 1) % GGML_GRAPH_HASHTABLE_SIZE; + while (hash_set.keys[i] != NULL && hash_set.keys[i] != key) { + i = (i + 1) % hash_set.size; if (i == h) { // visited all hash table entries -> not found - return GGML_GRAPH_HASHTABLE_SIZE; + return GGML_HASHTABLE_FULL; } } return i; } -static bool hash_insert(void * hash_table[], void * p) { - size_t i = hash_find(hash_table, p); +bool ggml_hash_contains(struct ggml_hash_set hash_set, struct ggml_tensor * key) { + size_t i = ggml_hash_find(hash_set, key); + return i != GGML_HASHTABLE_FULL && hash_set.keys[i] == key; +} - GGML_ASSERT(i < GGML_GRAPH_HASHTABLE_SIZE); // assert that not full +size_t ggml_hash_insert(struct ggml_hash_set hash_set, struct ggml_tensor * key) { + size_t i = ggml_hash_find(hash_set, key); - if (hash_table[i] == p) { - return true; + GGML_ASSERT(i != GGML_HASHTABLE_FULL); + + if (hash_set.keys[i] == key) { + return GGML_HASHTABLE_ALREADY_EXISTS; } // insert - GGML_ASSERT(hash_table[i] == NULL); - hash_table[i] = p; - return false; + GGML_ASSERT(hash_set.keys[i] == NULL); + hash_set.keys[i] = key; + return i; +} + +size_t ggml_hash_find_or_insert(struct ggml_hash_set hash_set, struct ggml_tensor * key) { + size_t i = ggml_hash_find(hash_set, key); + + GGML_ASSERT(i != GGML_HASHTABLE_FULL); + + hash_set.keys[i] = key; + return i; +} + +static struct ggml_hash_set ggml_hash_set_new(size_t size) { + size = ggml_hash_size(size); + struct ggml_hash_set result; + result.size = size; + result.keys = malloc(sizeof(struct ggml_tensor *) * size); + memset(result.keys, 0, sizeof(struct ggml_tensor *) * size); + return result; } -static bool hash_contains(void * hash_table[], void * p) { - size_t i = hash_find(hash_table, p); - return (i < GGML_GRAPH_HASHTABLE_SIZE) && (hash_table[i] == p); +static void ggml_hash_set_free(struct ggml_hash_set hash_set) { + free(hash_set.keys); } struct hash_map { - void * keys[GGML_GRAPH_HASHTABLE_SIZE]; - void * vals[GGML_GRAPH_HASHTABLE_SIZE]; + struct ggml_hash_set set; + struct ggml_tensor ** vals; }; -static struct hash_map * new_hash_map(void) { +static struct hash_map * ggml_new_hash_map(size_t size) { struct hash_map * result = malloc(sizeof(struct hash_map)); - for (int i=0; ikeys[i] = NULL; - result->vals[i] = NULL; - } + result->set = ggml_hash_set_new(size); + result->vals = malloc(sizeof(struct ggml_tensor *) * result->set.size); + memset(result->vals, 0, sizeof(struct ggml_tensor *) * result->set.size); return result; } -static void free_hash_map(struct hash_map * map) { +static void ggml_hash_map_free(struct hash_map * map) { + ggml_hash_set_free(map->set); + free(map->vals); free(map); } @@ -14726,7 +14857,7 @@ static struct ggml_tensor * ggml_recompute_graph_node( return node; } - if (!hash_contains(graph->visited_hash_table, node)) { + if (!ggml_hash_contains(graph->visited_hash_table, node)) { return node; } @@ -14741,17 +14872,17 @@ static struct ggml_tensor * ggml_recompute_graph_node( return node; } - size_t i = hash_find(replacements->keys, node); - GGML_ASSERT(i < GGML_GRAPH_HASHTABLE_SIZE); // assert that not full - if (replacements->keys[i] == node) { - return (struct ggml_tensor *) replacements->vals[i]; + size_t i = ggml_hash_find(replacements->set, node); + GGML_ASSERT(i != GGML_HASHTABLE_FULL); // assert that not full + if (replacements->set.keys[i] == node) { + return replacements->vals[i]; } struct ggml_tensor * clone = ggml_new_tensor(ctx, node->type, node->n_dims, node->ne); // insert clone into replacements - GGML_ASSERT(replacements->keys[i] == NULL); // assert that we don't overwrite - replacements->keys[i] = node; + GGML_ASSERT(replacements->set.keys[i] == NULL); // assert that we don't overwrite + replacements->set.keys[i] = node; replacements->vals[i] = clone; clone->op = node->op; @@ -14788,26 +14919,26 @@ void ggml_build_backward_gradient_checkpointing( struct ggml_cgraph * gb_tmp, struct ggml_tensor * * checkpoints, int n_checkpoints) { - *gb_tmp = *gf; + ggml_graph_cpy(gf, gb_tmp); ggml_build_backward_expand(ctx, gf, gb_tmp, true); if (n_checkpoints <= 0) { - *gb = *gb_tmp; + ggml_graph_cpy(gb_tmp, gb); return; } - struct hash_map * replacements = new_hash_map(); + struct hash_map * replacements = ggml_new_hash_map(gf->n_nodes + gf->n_leafs + n_checkpoints); // insert checkpoints in replacements for (int i = 0; i < n_checkpoints; ++i) { - size_t k = hash_find(replacements->keys, checkpoints[i]); - GGML_ASSERT(k < GGML_GRAPH_HASHTABLE_SIZE); // assert that not full - GGML_ASSERT(replacements->keys[k] == NULL); // assert that we don't overwrite - replacements->keys[k] = checkpoints[i]; - replacements->vals[k] = checkpoints[i]; + size_t k = ggml_hash_find(replacements->set, checkpoints[i]); + GGML_ASSERT(k != GGML_HASHTABLE_FULL); // assert that not full + GGML_ASSERT(replacements->set.keys[k] == NULL); // assert that we don't overwrite + replacements->set.keys[k] = checkpoints[i]; + replacements->vals[k] = checkpoints[i]; } - *gb = *gf; + ggml_graph_cpy(gf, gb); // rewrite gb_tmp->nodes[gf->n_nodes:gb_tmp->n_nodes], // replacing references to gb_tmp->nodes[0:gf->n_nodes] ( == gf->nodes[0:gf->n_nodes]), // by recomputing them from checkpoints @@ -14824,21 +14955,21 @@ void ggml_build_backward_gradient_checkpointing( ggml_build_forward_expand(gb, node); } - free_hash_map(replacements); + ggml_hash_map_free(replacements); } // functions to change gradients considering the case that input a might be initial gradient with zero value -static struct ggml_tensor * ggml_add_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, void * zero_table[]) { - if (hash_contains(zero_table, a)) { +static struct ggml_tensor * ggml_add_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_hash_set zero_table) { + if (ggml_hash_contains(zero_table, a)) { return b; } else { return ggml_add_impl(ctx, a, b, false); } } -static struct ggml_tensor * ggml_acc_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset, void * zero_table[]) { - if (hash_contains(zero_table, a)) { +static struct ggml_tensor * ggml_acc_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset, struct ggml_hash_set zero_table) { + if (ggml_hash_contains(zero_table, a)) { struct ggml_tensor * a_zero = ggml_scale(ctx, a, ggml_new_f32(ctx, 0)); return ggml_acc_impl(ctx, a_zero, b, nb1, nb2, nb3, offset, false); } else { @@ -14846,23 +14977,23 @@ static struct ggml_tensor * ggml_acc_or_set(struct ggml_context * ctx, struct gg } } -static struct ggml_tensor * ggml_add1_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, void * zero_table[]) { - if (hash_contains(zero_table, a)) { +static struct ggml_tensor * ggml_add1_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_hash_set zero_table) { + if (ggml_hash_contains(zero_table, a)) { return ggml_repeat(ctx, b, a); } else { return ggml_add1_impl(ctx, a, b, false); } } -static struct ggml_tensor * ggml_sub_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, void * zero_table[]) { - if (hash_contains(zero_table, a)) { +static struct ggml_tensor * ggml_sub_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_hash_set zero_table) { + if (ggml_hash_contains(zero_table, a)) { return ggml_neg(ctx, b); } else { return ggml_sub_impl(ctx, a, b, false); } } -static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor * tensor, void * zero_table[]) { +static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor * tensor, struct ggml_hash_set zero_table) { struct ggml_tensor * src0 = tensor->src[0]; struct ggml_tensor * src1 = tensor->src[1]; @@ -15695,7 +15826,7 @@ static void ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor * } // check if already visited - if (hash_insert(cgraph->visited_hash_table, node)) { + if (ggml_hash_insert(cgraph->visited_hash_table, node) == GGML_HASHTABLE_ALREADY_EXISTS) { return; } @@ -15711,7 +15842,7 @@ static void ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor * if (node->op == GGML_OP_NONE && node->grad == NULL) { // reached a leaf node, not part of the gradient graph (e.g. a constant) - GGML_ASSERT(cgraph->n_leafs < GGML_MAX_NODES); + GGML_ASSERT(cgraph->n_leafs < cgraph->size); if (strlen(node->name) == 0) { ggml_format_name(node, "leaf_%d", cgraph->n_leafs); @@ -15720,22 +15851,24 @@ static void ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor * cgraph->leafs[cgraph->n_leafs] = node; cgraph->n_leafs++; } else { - GGML_ASSERT(cgraph->n_nodes < GGML_MAX_NODES); + GGML_ASSERT(cgraph->n_nodes < cgraph->size); if (strlen(node->name) == 0) { ggml_format_name(node, "node_%d", cgraph->n_nodes); } cgraph->nodes[cgraph->n_nodes] = node; - cgraph->grads[cgraph->n_nodes] = node->grad; + if (cgraph->grads) { + cgraph->grads[cgraph->n_nodes] = node->grad; + } cgraph->n_nodes++; } } static void ggml_build_forward_impl(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor, bool expand) { if (!expand) { - cgraph->n_nodes = 0; - cgraph->n_leafs = 0; + // TODO: this branch isn't accessible anymore, maybe move this to ggml_build_forward_expand + ggml_graph_clear(cgraph); } const int n0 = cgraph->n_nodes; @@ -15756,25 +15889,6 @@ void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * ggml_build_forward_impl(cgraph, tensor, true); } -struct ggml_cgraph ggml_build_forward(struct ggml_tensor * tensor) { - struct ggml_cgraph result = { - /*.n_nodes =*/ 0, - /*.n_leafs =*/ 0, - /*.nodes =*/ { NULL }, - /*.grads =*/ { NULL }, - /*.leafs =*/ { NULL }, - /*.hash_table =*/ { NULL }, - /*.order =*/ GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT, - /*.perf_runs =*/ 0, - /*.perf_cycles =*/ 0, - /*.perf_time_us =*/ 0, - }; - - ggml_build_forward_impl(&result, tensor, false); - - return result; -} - void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep) { GGML_ASSERT(gf->n_nodes > 0); @@ -15791,11 +15905,10 @@ void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * } // remember original gradients which start with zero values - void ** zero_table = malloc(sizeof(void *) * GGML_GRAPH_HASHTABLE_SIZE); - memset(zero_table, 0, sizeof(void*) * GGML_GRAPH_HASHTABLE_SIZE); + struct ggml_hash_set zero_table = ggml_hash_set_new(gf->size); for (int i = 0; i < gf->n_nodes; i++) { if (gf->grads[i]) { - hash_insert(zero_table, gf->grads[i]); + ggml_hash_insert(zero_table, gf->grads[i]); } } @@ -15818,26 +15931,54 @@ void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * } } - free(zero_table); + ggml_hash_set_free(zero_table); } -struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep) { - struct ggml_cgraph result = *gf; - ggml_build_backward_expand(ctx, gf, &result, keep); - return result; +static size_t ggml_graph_nbytes(size_t size, bool grads) { + size_t nbytes = sizeof(struct ggml_cgraph); + nbytes += size * sizeof(struct ggml_tensor *) * 2; // leafs + nodes + if (grads) { + nbytes += size * sizeof(struct ggml_tensor *); // grads + } + nbytes += ggml_hash_size(size * 2) * sizeof(struct ggml_tensor *); // hash set + return nbytes; } -struct ggml_cgraph * ggml_new_graph(struct ggml_context * ctx) { - struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_GRAPH, GGML_GRAPH_SIZE); +size_t ggml_graph_overhead_custom(size_t size, bool grads) { + return GGML_OBJECT_SIZE + GGML_PAD(ggml_graph_nbytes(size, grads), GGML_MEM_ALIGN); +} + +size_t ggml_graph_overhead(void) { + return ggml_graph_overhead_custom(GGML_DEFAULT_GRAPH_SIZE, false); +} + +struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t size, bool grads) { + const size_t obj_size = ggml_graph_nbytes(size, grads); + struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_GRAPH, obj_size); struct ggml_cgraph * cgraph = (struct ggml_cgraph *) ((char *) ctx->mem_buffer + obj->offs); + struct ggml_tensor ** data_start = (struct ggml_tensor **) (cgraph + 1); + + size_t hash_size = ggml_hash_size(size * 2); + struct ggml_tensor ** nodes_ptr = data_start; + struct ggml_tensor ** leafs_ptr = nodes_ptr + size; + struct ggml_tensor ** hash_keys_ptr = leafs_ptr + size; + struct ggml_tensor ** grads_ptr = grads ? hash_keys_ptr + hash_size : NULL; + + // check that we allocated the correct amount of memory + assert(obj_size == (size_t) ( + (grads ? (char *)(grads_ptr + size) : (char *)(hash_keys_ptr + hash_size)) - (char *)cgraph)); + + memset(hash_keys_ptr, 0, hash_size * sizeof(struct ggml_tensor *)); + *cgraph = (struct ggml_cgraph) { + /*.size =*/ size, /*.n_nodes =*/ 0, /*.n_leafs =*/ 0, - /*.nodes =*/ { NULL }, - /*.grads =*/ { NULL }, - /*.leafs =*/ { NULL }, - /*.hash_table =*/ { NULL }, + /*.nodes =*/ nodes_ptr, + /*.grads =*/ grads_ptr, + /*.leafs =*/ leafs_ptr, + /*.hash_table =*/ { hash_size, hash_keys_ptr }, /*.order =*/ GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT, /*.perf_runs =*/ 0, /*.perf_cycles =*/ 0, @@ -15847,14 +15988,85 @@ struct ggml_cgraph * ggml_new_graph(struct ggml_context * ctx) { return cgraph; } -struct ggml_cgraph * ggml_build_forward_ctx(struct ggml_context * ctx, struct ggml_tensor * tensor) { - struct ggml_cgraph * cgraph = ggml_new_graph(ctx); - ggml_build_forward_impl(cgraph, tensor, false); +struct ggml_cgraph * ggml_new_graph(struct ggml_context * ctx) { + return ggml_new_graph_custom(ctx, GGML_DEFAULT_GRAPH_SIZE, false); +} + +struct ggml_cgraph * ggml_graph_view(struct ggml_context * ctx, struct ggml_cgraph * cgraph0, int i0, int i1) { + const size_t obj_size = sizeof(struct ggml_cgraph); + struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_GRAPH, obj_size); + struct ggml_cgraph * cgraph = (struct ggml_cgraph *) ((char *) ctx->mem_buffer + obj->offs); + + *cgraph = (struct ggml_cgraph) { + /*.size =*/ 0, + /*.n_nodes =*/ i1 - i0, + /*.n_leafs =*/ 0, + /*.nodes =*/ cgraph0->nodes + i0, + /*.grads =*/ cgraph0->grads ? cgraph0->grads + i0 : NULL, + /*.leafs =*/ NULL, + /*.hash_table =*/ { 0, NULL }, + /*.order =*/ cgraph0->order, + /*.perf_runs =*/ 0, + /*.perf_cycles =*/ 0, + /*.perf_time_us =*/ 0, + }; + return cgraph; } -size_t ggml_graph_overhead(void) { - return GGML_OBJECT_SIZE + GGML_PAD(GGML_GRAPH_SIZE, GGML_MEM_ALIGN); +void ggml_graph_cpy(struct ggml_cgraph * src, struct ggml_cgraph * dst) { + GGML_ASSERT(dst->size >= src->n_leafs); + GGML_ASSERT(dst->size >= src->n_nodes); + GGML_ASSERT(dst->visited_hash_table.size >= src->visited_hash_table.size); + + dst->n_leafs = src->n_leafs; + dst->n_nodes = src->n_nodes; + dst->order = src->order; + + for (int i = 0; i < src->n_leafs; ++i) { + dst->leafs[i] = src->leafs[i]; + } + + for (int i = 0; i < src->n_nodes; ++i) { + dst->nodes[i] = src->nodes[i]; + } + + if (src->grads) { + GGML_ASSERT(dst->grads != NULL); + for (int i = 0; i < src->n_nodes; ++i) { + dst->grads[i] = src->grads[i]; + } + } + + for (size_t i = 0; i < src->visited_hash_table.size; ++i) { + if (src->visited_hash_table.keys[i]) { + ggml_hash_insert(dst->visited_hash_table, src->visited_hash_table.keys[i]); + } + } +} + +struct ggml_cgraph * ggml_graph_dup(struct ggml_context * ctx, struct ggml_cgraph * cgraph) { + struct ggml_cgraph * result = ggml_new_graph_custom(ctx, cgraph->size, cgraph->grads != NULL); + ggml_graph_cpy(cgraph, result); + return result; +} + +void ggml_graph_reset(struct ggml_cgraph * cgraph) { + GGML_ASSERT(cgraph->grads != NULL); + + for (int i = 0; i < cgraph->n_nodes; i++) { + struct ggml_tensor * grad = cgraph->grads[i]; + + if (grad) { + ggml_set_zero(grad); + } + } +} + +void ggml_graph_clear(struct ggml_cgraph * cgraph) { + cgraph->n_leafs = 0; + cgraph->n_nodes = 0; + memset(cgraph->visited_hash_table.keys, 0, cgraph->visited_hash_table.size * sizeof(struct ggml_tensor *)); } // @@ -16007,13 +16219,252 @@ static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const node->perf_time_us += time_us_cur; } +static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { + int n_tasks = 0; + + switch (node->op) { + case GGML_OP_CPY: + case GGML_OP_DUP: + case GGML_OP_ADD: + case GGML_OP_ADD1: + case GGML_OP_ACC: + { + n_tasks = n_threads; + } break; + case GGML_OP_SUB: + case GGML_OP_DIV: + case GGML_OP_SQR: + case GGML_OP_SQRT: + case GGML_OP_LOG: + case GGML_OP_SUM: + case GGML_OP_SUM_ROWS: + case GGML_OP_MEAN: + case GGML_OP_ARGMAX: + case GGML_OP_REPEAT: + case GGML_OP_REPEAT_BACK: + { + n_tasks = 1; + } break; + case GGML_OP_UNARY: + switch (ggml_get_unary_op(node)) { + case GGML_UNARY_OP_ABS: + case GGML_UNARY_OP_SGN: + case GGML_UNARY_OP_NEG: + case GGML_UNARY_OP_STEP: + case GGML_UNARY_OP_TANH: + case GGML_UNARY_OP_ELU: + case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_LEAKY: + { + n_tasks = 1; + } break; + + case GGML_UNARY_OP_GELU: + case GGML_UNARY_OP_GELU_QUICK: + case GGML_UNARY_OP_SILU: + { + n_tasks = n_threads; + } break; + } + break; + case GGML_OP_SILU_BACK: + case GGML_OP_MUL: + case GGML_OP_NORM: + case GGML_OP_RMS_NORM: + case GGML_OP_RMS_NORM_BACK: + case GGML_OP_GROUP_NORM: + case GGML_OP_CONCAT: + { + n_tasks = n_threads; + } break; + case GGML_OP_MUL_MAT: + { + n_tasks = n_threads; + + // TODO: use different scheduling for different matrix sizes + //const int nr0 = ggml_nrows(node->src[0]); + //const int nr1 = ggml_nrows(node->src[1]); + + //n_tasks = MIN(n_threads, MAX(1, nr0/128)); + //printf("nr0 = %8d, nr1 = %8d, nr0*nr1 = %8d, n_tasks%d\n", nr0, nr1, nr0*nr1, n_tasks); + +#if defined(GGML_USE_CUBLAS) + if (ggml_cuda_can_mul_mat(node->src[0], node->src[1], node)) { + n_tasks = 1; // TODO: this actually is doing nothing + // the threads are still spinning + } +#elif defined(GGML_USE_CLBLAST) + if (ggml_cl_can_mul_mat(node->src[0], node->src[1], node)) { + n_tasks = 1; // TODO: this actually is doing nothing + // the threads are still spinning + } +#endif +#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) + if (ggml_compute_forward_mul_mat_use_blas(node->src[0], node->src[1], node)) { + n_tasks = 1; // TODO: this actually is doing nothing + // the threads are still spinning + } +#endif + } break; + case GGML_OP_OUT_PROD: + { + n_tasks = n_threads; + } break; + case GGML_OP_SCALE: + case GGML_OP_SET: + case GGML_OP_CONT: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + case GGML_OP_GET_ROWS: + case GGML_OP_GET_ROWS_BACK: + case GGML_OP_DIAG: + { + n_tasks = 1; + } break; + case GGML_OP_DIAG_MASK_ZERO: + case GGML_OP_DIAG_MASK_INF: + case GGML_OP_SOFT_MAX: + case GGML_OP_SOFT_MAX_BACK: + case GGML_OP_ROPE: + case GGML_OP_ROPE_BACK: + case GGML_OP_ADD_REL_POS: + { + n_tasks = n_threads; + } break; + case GGML_OP_ALIBI: + { + n_tasks = 1; //TODO + } break; + case GGML_OP_CLAMP: + { + n_tasks = 1; //TODO + } break; + case GGML_OP_CONV_1D: + { + n_tasks = n_threads; + } break; + case GGML_OP_CONV_1D_STAGE_0: + { + n_tasks = n_threads; + } break; + case GGML_OP_CONV_1D_STAGE_1: + { + n_tasks = n_threads; + } break; + case GGML_OP_CONV_TRANSPOSE_1D: + { + n_tasks = n_threads; + } break; + case GGML_OP_CONV_2D: + { + n_tasks = n_threads; + } break; + case GGML_OP_CONV_2D_STAGE_0: + { + n_tasks = n_threads; + } break; + case GGML_OP_CONV_2D_STAGE_1: + { + n_tasks = n_threads; + } break; + case GGML_OP_CONV_TRANSPOSE_2D: + { + n_tasks = n_threads; + } break; + case GGML_OP_POOL_1D: + case GGML_OP_POOL_2D: + { + n_tasks = 1; + } break; + case GGML_OP_UPSCALE: + { + n_tasks = n_threads; + } break; + case GGML_OP_FLASH_ATTN: + { + n_tasks = n_threads; + } break; + case GGML_OP_FLASH_FF: + { + n_tasks = n_threads; + } break; + case GGML_OP_FLASH_ATTN_BACK: + { + n_tasks = n_threads; + } break; + case GGML_OP_WIN_PART: + case GGML_OP_WIN_UNPART: + case GGML_OP_GET_REL_POS: + case GGML_OP_MAP_UNARY: + case GGML_OP_MAP_BINARY: + case GGML_OP_MAP_CUSTOM1_F32: + case GGML_OP_MAP_CUSTOM2_F32: + case GGML_OP_MAP_CUSTOM3_F32: + { + n_tasks = 1; + } break; + case GGML_OP_MAP_CUSTOM1: + { + struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) node->op_params; + if (p->n_tasks == GGML_N_TASKS_MAX) { + n_tasks = n_threads; + } else { + n_tasks = MIN(p->n_tasks, n_threads); + } + } break; + case GGML_OP_MAP_CUSTOM2: + { + struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) node->op_params; + if (p->n_tasks == GGML_N_TASKS_MAX) { + n_tasks = n_threads; + } else { + n_tasks = MIN(p->n_tasks, n_threads); + } + } break; + case GGML_OP_MAP_CUSTOM3: + { + struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) node->op_params; + if (p->n_tasks == GGML_N_TASKS_MAX) { + n_tasks = n_threads; + } else { + n_tasks = MIN(p->n_tasks, n_threads); + } + } break; + case GGML_OP_CROSS_ENTROPY_LOSS: + { + n_tasks = n_threads; + } break; + case GGML_OP_CROSS_ENTROPY_LOSS_BACK: + { + n_tasks = n_threads; + } break; + case GGML_OP_NONE: + { + n_tasks = 1; + } break; + case GGML_OP_COUNT: + { + GGML_ASSERT(false); + } break; + default: + { + GGML_ASSERT(false); + } break; + } + + assert(n_tasks > 0); + + return n_tasks; +} + static thread_ret_t ggml_graph_compute_thread(void * data) { struct ggml_compute_state * state = (struct ggml_compute_state *) data; const struct ggml_cgraph * cgraph = state->shared->cgraph; const struct ggml_cplan * cplan = state->shared->cplan; - const int * n_tasks_arr = cplan->n_tasks; const int n_threads = state->shared->n_threads; set_numa_thread_affinity(state->ith, n_threads); @@ -16038,9 +16489,9 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { if (node_n != -1) { /* FINALIZE */ - struct ggml_tensor * node = state->shared->cgraph->nodes[node_n]; + struct ggml_tensor * node = cgraph->nodes[node_n]; if (GGML_OP_HAS_FINALIZE[node->op]) { - params.nth = n_tasks_arr[node_n]; + params.nth = ggml_get_n_tasks(node, n_threads); ggml_compute_forward(¶ms, node); } ggml_graph_compute_perf_stats_node(node, state->shared); @@ -16051,7 +16502,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { GGML_PRINT_DEBUG_5("%s: %d/%d\n", __func__, node_n, cgraph->n_nodes); struct ggml_tensor * node = cgraph->nodes[node_n]; - const int n_tasks = n_tasks_arr[node_n]; + const int n_tasks = ggml_get_n_tasks(node, n_threads); state->shared->perf_node_start_cycles = ggml_perf_cycles(); state->shared->perf_node_start_time_us = ggml_perf_time_us(); @@ -16109,7 +16560,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { /* COMPUTE */ struct ggml_tensor * node = cgraph->nodes[node_n]; - const int n_tasks = n_tasks_arr[node_n]; + const int n_tasks = ggml_get_n_tasks(node, n_threads); struct ggml_compute_params params = { /*.type =*/ GGML_TASK_COMPUTE, @@ -16143,121 +16594,46 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { struct ggml_tensor * node = cgraph->nodes[i]; + size_t cur = 0; + switch (node->op) { case GGML_OP_CPY: case GGML_OP_DUP: { n_tasks = n_threads; - size_t cur = 0; if (ggml_is_quantized(node->type)) { cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks; } - - work_size = MAX(work_size, cur); } break; case GGML_OP_ADD: case GGML_OP_ADD1: { n_tasks = n_threads; - size_t cur = 0; - if (ggml_is_quantized(node->src[0]->type)) { cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks; } - - work_size = MAX(work_size, cur); } break; case GGML_OP_ACC: { n_tasks = n_threads; - size_t cur = 0; - if (ggml_is_quantized(node->src[0]->type)) { cur = ggml_type_size(GGML_TYPE_F32) * node->src[1]->ne[0] * n_tasks; } - - work_size = MAX(work_size, cur); - } break; - case GGML_OP_SUB: - case GGML_OP_DIV: - case GGML_OP_SQR: - case GGML_OP_SQRT: - case GGML_OP_LOG: - case GGML_OP_SUM: - case GGML_OP_SUM_ROWS: - case GGML_OP_MEAN: - case GGML_OP_ARGMAX: - case GGML_OP_REPEAT: - case GGML_OP_REPEAT_BACK: - { - n_tasks = 1; - } break; - - case GGML_OP_UNARY: - { - switch (ggml_get_unary_op(node)) { - case GGML_UNARY_OP_ABS: - case GGML_UNARY_OP_SGN: - case GGML_UNARY_OP_NEG: - case GGML_UNARY_OP_STEP: - case GGML_UNARY_OP_TANH: - case GGML_UNARY_OP_ELU: - case GGML_UNARY_OP_RELU: - { - n_tasks = 1; - } break; - - case GGML_UNARY_OP_GELU: - case GGML_UNARY_OP_GELU_QUICK: - case GGML_UNARY_OP_SILU: - { - n_tasks = n_threads; - } break; - } } break; - case GGML_OP_SILU_BACK: - case GGML_OP_MUL: - case GGML_OP_NORM: - case GGML_OP_RMS_NORM: - case GGML_OP_RMS_NORM_BACK: - case GGML_OP_GROUP_NORM: - { - n_tasks = n_threads; - } break; - case GGML_OP_CONCAT: case GGML_OP_MUL_MAT: { - n_tasks = n_threads; - - // TODO: use different scheduling for different matrix sizes - //const int nr0 = ggml_nrows(node->src[0]); - //const int nr1 = ggml_nrows(node->src[1]); - - //n_tasks = MIN(n_threads, MAX(1, nr0/128)); - //printf("nr0 = %8d, nr1 = %8d, nr0*nr1 = %8d, n_tasks%d\n", nr0, nr1, nr0*nr1, n_tasks); - - size_t cur = 0; const enum ggml_type vec_dot_type = type_traits[node->src[0]->type].vec_dot_type; -#if defined(GGML_USE_CUBLAS) - if (ggml_cuda_can_mul_mat(node->src[0], node->src[1], node)) { - n_tasks = 1; // TODO: this actually is doing nothing - // the threads are still spinning - } else -#elif defined(GGML_USE_CLBLAST) +#if defined(GGML_USE_CLBLAST) if (ggml_cl_can_mul_mat(node->src[0], node->src[1], node)) { - n_tasks = 1; // TODO: this actually is doing nothing - // the threads are still spinning cur = ggml_cl_mul_mat_get_wsize(node->src[0], node->src[1], node); } else #endif #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) if (ggml_compute_forward_mul_mat_use_blas(node->src[0], node->src[1], node)) { - n_tasks = 1; // TODO: this actually is doing nothing - // the threads are still spinning if (node->src[0]->type != GGML_TYPE_F32) { // here we need memory just for single 2D matrix from src0 cur = ggml_type_size(GGML_TYPE_F32)*(node->src[0]->ne[0]*node->src[0]->ne[1]); @@ -16266,62 +16642,18 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { #endif if (node->src[1]->type != vec_dot_type) { cur = ggml_type_size(vec_dot_type)*ggml_nelements(node->src[1])/ggml_blck_size(vec_dot_type); - } else { - cur = 0; } - - work_size = MAX(work_size, cur); } break; case GGML_OP_OUT_PROD: { n_tasks = n_threads; - size_t cur = 0; - if (ggml_is_quantized(node->src[0]->type)) { cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks; } - - work_size = MAX(work_size, cur); - } break; - case GGML_OP_SCALE: - { - n_tasks = 1; - } break; - case GGML_OP_SET: - case GGML_OP_CONT: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - case GGML_OP_GET_ROWS: - case GGML_OP_GET_ROWS_BACK: - case GGML_OP_DIAG: - { - n_tasks = 1; - } break; - case GGML_OP_DIAG_MASK_ZERO: - case GGML_OP_DIAG_MASK_INF: - case GGML_OP_SOFT_MAX: - case GGML_OP_SOFT_MAX_BACK: - case GGML_OP_ROPE: - case GGML_OP_ROPE_BACK: - case GGML_OP_ADD_REL_POS: - { - n_tasks = n_threads; - } break; - case GGML_OP_ALIBI: - { - n_tasks = 1; //TODO - } break; - case GGML_OP_CLAMP: - { - n_tasks = 1; //TODO } break; case GGML_OP_CONV_1D: { - n_tasks = n_threads; - GGML_ASSERT(node->src[0]->ne[3] == 1); GGML_ASSERT(node->src[1]->ne[2] == 1); GGML_ASSERT(node->src[1]->ne[3] == 1); @@ -16342,8 +16674,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { UNUSED(ne10); UNUSED(ne11); - size_t cur = 0; - if (node->src[0]->type == GGML_TYPE_F16 && node->src[1]->type == GGML_TYPE_F32) { cur = sizeof(ggml_fp16_t)*(ne0*ne1*ew0); @@ -16353,21 +16683,9 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { } else { GGML_ASSERT(false); } - - work_size = MAX(work_size, cur); - } break; - case GGML_OP_CONV_1D_STAGE_0: - { - n_tasks = n_threads; - } break; - case GGML_OP_CONV_1D_STAGE_1: - { - n_tasks = n_threads; } break; case GGML_OP_CONV_TRANSPOSE_1D: { - n_tasks = n_threads; - GGML_ASSERT(node->src[0]->ne[3] == 1); GGML_ASSERT(node->src[1]->ne[2] == 1); GGML_ASSERT(node->src[1]->ne[3] == 1); @@ -16379,7 +16697,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { const int64_t ne10 = node->src[1]->ne[0]; // L const int64_t ne11 = node->src[1]->ne[1]; // Cin - size_t cur = 0; if (node->src[0]->type == GGML_TYPE_F16 && node->src[1]->type == GGML_TYPE_F32) { cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02; @@ -16391,13 +16708,9 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { } else { GGML_ASSERT(false); } - - work_size = MAX(work_size, cur); } break; case GGML_OP_CONV_2D: { - n_tasks = n_threads; - const int64_t ne00 = node->src[0]->ne[0]; // W const int64_t ne01 = node->src[0]->ne[1]; // H const int64_t ne02 = node->src[0]->ne[2]; // C @@ -16417,8 +16730,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { UNUSED(ne03); UNUSED(ne2); - size_t cur = 0; - if (node->src[0]->type == GGML_TYPE_F16 && node->src[1]->type == GGML_TYPE_F32) { // im2col: [N*OH*OW, IC*KH*KW] @@ -16429,21 +16740,9 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { } else { GGML_ASSERT(false); } - - work_size = MAX(work_size, cur); - } break; - case GGML_OP_CONV_2D_STAGE_0: - { - n_tasks = n_threads; - } break; - case GGML_OP_CONV_2D_STAGE_1: - { - n_tasks = n_threads; } break; case GGML_OP_CONV_TRANSPOSE_2D: { - n_tasks = n_threads; - const int64_t ne00 = node->src[0]->ne[0]; // W const int64_t ne01 = node->src[0]->ne[1]; // H const int64_t ne02 = node->src[0]->ne[2]; // Channels Out @@ -16453,141 +16752,66 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { const int64_t ne11 = node->src[1]->ne[1]; // H const int64_t ne12 = node->src[1]->ne[2]; // Channels In - size_t cur = 0; cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02*ne03; cur += sizeof(ggml_fp16_t)*ne10*ne11*ne12; - - work_size = MAX(work_size, cur); - } break; - case GGML_OP_POOL_1D: - case GGML_OP_POOL_2D: - { - n_tasks = 1; - } break; - case GGML_OP_UPSCALE: - { - n_tasks = n_threads; } break; case GGML_OP_FLASH_ATTN: { n_tasks = n_threads; - size_t cur = 0; - const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL); if (node->src[1]->type == GGML_TYPE_F32) { cur = sizeof(float)*ne11*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2 - } - - if (node->src[1]->type == GGML_TYPE_F16) { + } else if (node->src[1]->type == GGML_TYPE_F16) { cur = sizeof(float)*ne11*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2 } - - work_size = MAX(work_size, cur); } break; case GGML_OP_FLASH_FF: { n_tasks = n_threads; - size_t cur = 0; - if (node->src[1]->type == GGML_TYPE_F32) { cur = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2 - } - - if (node->src[1]->type == GGML_TYPE_F16) { + } else if (node->src[1]->type == GGML_TYPE_F16) { cur = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2 } - - work_size = MAX(work_size, cur); } break; case GGML_OP_FLASH_ATTN_BACK: { n_tasks = n_threads; - size_t cur = 0; - const int64_t D = node->src[0]->ne[0]; const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL); const int64_t mxDn = MAX(D, ne11) * 2; // *2 because of S and SM in ggml_compute_forward_flash_attn_back if (node->src[1]->type == GGML_TYPE_F32) { cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 - } - - if (node->src[1]->type == GGML_TYPE_F16) { + } else if (node->src[1]->type == GGML_TYPE_F16) { cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 } - - work_size = MAX(work_size, cur); - } break; - case GGML_OP_WIN_PART: - case GGML_OP_WIN_UNPART: - case GGML_OP_GET_REL_POS: - case GGML_OP_MAP_UNARY: - case GGML_OP_MAP_BINARY: - case GGML_OP_MAP_CUSTOM1_F32: - case GGML_OP_MAP_CUSTOM2_F32: - case GGML_OP_MAP_CUSTOM3_F32: - { - n_tasks = 1; - } break; - case GGML_OP_MAP_CUSTOM1: - { - struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) node->op_params; - if (p->n_tasks == GGML_N_TASKS_MAX) { - n_tasks = n_threads; - } else { - n_tasks = MIN(p->n_tasks, n_threads); - } - } break; - case GGML_OP_MAP_CUSTOM2: - { - struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) node->op_params; - if (p->n_tasks == GGML_N_TASKS_MAX) { - n_tasks = n_threads; - } else { - n_tasks = MIN(p->n_tasks, n_threads); - } - } break; - case GGML_OP_MAP_CUSTOM3: - { - struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) node->op_params; - if (p->n_tasks == GGML_N_TASKS_MAX) { - n_tasks = n_threads; - } else { - n_tasks = MIN(p->n_tasks, n_threads); - } } break; + case GGML_OP_CROSS_ENTROPY_LOSS: { n_tasks = n_threads; - size_t cur = ggml_type_size(node->type)*(n_tasks + node->src[0]->ne[0]*n_tasks); - - work_size = MAX(work_size, cur); - } break; - case GGML_OP_CROSS_ENTROPY_LOSS_BACK: - { - n_tasks = n_threads; - } break; - case GGML_OP_NONE: - { - n_tasks = 1; + cur = ggml_type_size(node->type)*(n_tasks + node->src[0]->ne[0]*n_tasks); } break; case GGML_OP_COUNT: { GGML_ASSERT(false); } break; + default: + break; } - cplan.n_tasks[i] = n_tasks; + work_size = MAX(work_size, cur); } if (work_size > 0) { @@ -16609,12 +16833,6 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { if (cplan->work_size > 0) { GGML_ASSERT(cplan->work_data); } - - for (int i = 0; i < cgraph->n_nodes; ++i) { - if (cgraph->nodes[i]->op != GGML_OP_NONE) { - GGML_ASSERT(cplan->n_tasks[i] > 0); - } - } } const int n_threads = cplan->n_threads; @@ -16687,16 +16905,6 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { return compute_status; } -void ggml_graph_reset(struct ggml_cgraph * cgraph) { - for (int i = 0; i < cgraph->n_nodes; i++) { - struct ggml_tensor * grad = cgraph->grads[i]; - - if (grad) { - ggml_set_zero(grad); - } - } -} - void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) { struct ggml_cplan cplan = ggml_graph_plan(cgraph, n_threads); @@ -16823,12 +17031,12 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) { const uint32_t magic = GGML_FILE_MAGIC; const uint32_t version = GGML_FILE_VERSION; const uint32_t n_leafs = cgraph->n_leafs; - const uint32_t nodes = cgraph->n_nodes; + const uint32_t n_nodes = cgraph->n_nodes; fwrite(&magic, sizeof(uint32_t), 1, fout); fwrite(&version, sizeof(uint32_t), 1, fout); fwrite(&n_leafs, sizeof(uint32_t), 1, fout); - fwrite(&nodes, sizeof(uint32_t), 1, fout); + fwrite(&n_nodes, sizeof(uint32_t), 1, fout); fwrite(&size_eval, sizeof(uint64_t), 1, fout); } @@ -16916,7 +17124,7 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) { if (idx == -1) { for (int k = 0; k < cgraph->n_nodes; ++k) { if (args[j] == cgraph->nodes[k]) { - idx = GGML_MAX_NODES + k; + idx = cgraph->n_leafs + k; break; } } @@ -16943,11 +17151,11 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) { } } -struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval) { +struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval) { assert(*ctx_data == NULL); assert(*ctx_eval == NULL); - struct ggml_cgraph result = { 0 }; + struct ggml_cgraph * result = NULL; struct ggml_tensor * data = NULL; @@ -17019,13 +17227,11 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** const uint32_t n_leafs = *(const uint32_t *) ptr; ptr += sizeof(n_leafs); const uint32_t n_nodes = *(const uint32_t *) ptr; ptr += sizeof(n_nodes); const uint64_t size_eval = *(const uint64_t *) ptr; ptr += sizeof(size_eval); - - result.n_leafs = n_leafs; - result.n_nodes = n_nodes; + const int graph_size = MAX(n_leafs, n_nodes); // create the data context { - const size_t overhead = (n_leafs + n_nodes)*ggml_tensor_overhead(); + const size_t overhead = (n_leafs + n_nodes)*ggml_tensor_overhead() + ggml_graph_overhead_custom(graph_size, false); struct ggml_init_params params = { .mem_size = size_eval + overhead, @@ -17041,6 +17247,12 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** } } + result = ggml_new_graph_custom(*ctx_eval, graph_size, false); + + result->n_leafs = n_leafs; + result->n_nodes = n_nodes; + + // leafs { uint32_t type; @@ -17079,7 +17291,7 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** tensor->nb[j] = nb[j]; } - result.leafs[i] = tensor; + result->leafs[i] = tensor; ptr += ggml_nbytes(tensor); @@ -17131,10 +17343,10 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** continue; } - if (arg_idx < GGML_MAX_NODES) { - args[j] = result.leafs[arg_idx]; + if (arg_idx < result->n_leafs) { + args[j] = result->leafs[arg_idx]; } else { - args[j] = result.nodes[arg_idx - GGML_MAX_NODES]; + args[j] = result->nodes[arg_idx - result->n_leafs]; } } @@ -17186,7 +17398,7 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** tensor->src[j] = args[j]; } - result.nodes[i] = tensor; + result->nodes[i] = tensor; fprintf(stderr, "%s: loaded node %d: '%16s', %3d dims, %9zu bytes\n", __func__, i, tensor->name, n_dims, ggml_nbytes(tensor)); } @@ -18091,10 +18303,11 @@ struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) { case GGML_OPT_ADAM: { result = (struct ggml_opt_params) { - .type = GGML_OPT_ADAM, - .n_threads = 1, - .past = 0, - .delta = 1e-5f, + .type = GGML_OPT_ADAM, + .graph_size = GGML_DEFAULT_GRAPH_SIZE, + .n_threads = 1, // FIXME: GGML_DEFAULT_N_THREADS ? + .past = 0, + .delta = 1e-5f, .max_no_improvement = 100, @@ -18121,10 +18334,11 @@ struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) { case GGML_OPT_LBFGS: { result = (struct ggml_opt_params) { - .type = GGML_OPT_LBFGS, - .n_threads = 1, - .past = 0, - .delta = 1e-5f, + .type = GGML_OPT_LBFGS, + .graph_size = GGML_DEFAULT_GRAPH_SIZE, + .n_threads = 1, + .past = 0, + .delta = 1e-5f, .max_no_improvement = 0, @@ -18266,14 +18480,11 @@ enum ggml_opt_result ggml_opt_resume( struct ggml_tensor * f) { // build forward + backward compute graphs - struct ggml_tensor * gfbuf = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / ggml_type_size(GGML_TYPE_I32)+ (sizeof(struct ggml_cgraph) % ggml_type_size(GGML_TYPE_I32) ? 1 : 0)); - struct ggml_tensor * gbbuf = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / ggml_type_size(GGML_TYPE_I32)+ (sizeof(struct ggml_cgraph) % ggml_type_size(GGML_TYPE_I32) ? 1 : 0)); - - struct ggml_cgraph * gf = (struct ggml_cgraph *) gfbuf->data; - struct ggml_cgraph * gb = (struct ggml_cgraph *) gbbuf->data; + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx, opt->params.graph_size, true); + ggml_build_forward_expand(gf, f); - *gf = ggml_build_forward (f); - *gb = ggml_build_backward(ctx, gf, true); + struct ggml_cgraph * gb = ggml_graph_dup(ctx, gf); + ggml_build_backward_expand(ctx, gf, gb, true); return ggml_opt_resume_g(ctx, opt, f, gf, gb, NULL, NULL); } diff --git a/ggml.h b/ggml.h index 26654fc8ecdc8..0118c99dbafdd 100644 --- a/ggml.h +++ b/ggml.h @@ -58,7 +58,8 @@ // { // ... // -// struct ggml_cgraph gf = ggml_build_forward(f); +// struct ggml_cgraph * gf = ggml_new_graph(ctx); +// ggml_build_forward_expand(gf, f); // // // set the input variable and parameter values // ggml_set_f32(x, 2.0f); @@ -213,15 +214,14 @@ #define GGML_QNT_VERSION 2 // bump this on quantization format changes #define GGML_QNT_VERSION_FACTOR 1000 // do not change this -#define GGML_MAX_DIMS 4 -#define GGML_MAX_NODES 16384 -#define GGML_MAX_PARAMS 1024 -#define GGML_MAX_CONTEXTS 64 -#define GGML_MAX_SRC 6 -#define GGML_MAX_NAME 64 -#define GGML_MAX_OP_PARAMS 64 -#define GGML_DEFAULT_N_THREADS 4 - +#define GGML_MAX_DIMS 4 +#define GGML_MAX_PARAMS 1024 +#define GGML_MAX_CONTEXTS 64 +#define GGML_MAX_SRC 6 +#define GGML_MAX_NAME 64 +#define GGML_MAX_OP_PARAMS 64 +#define GGML_DEFAULT_N_THREADS 4 +#define GGML_DEFAULT_GRAPH_SIZE 2048 #if UINTPTR_MAX == 0xFFFFFFFF #define GGML_MEM_ALIGN 4 #else @@ -245,7 +245,10 @@ do { \ if (!(x)) { \ fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \ - abort(); \ + fflush(stderr); \ + fflush(stdout); \ + ggml_print_backtrace(); \ + exit(1); \ } \ } while (0) @@ -451,6 +454,7 @@ extern "C" { GGML_UNARY_OP_GELU, GGML_UNARY_OP_GELU_QUICK, GGML_UNARY_OP_SILU, + GGML_UNARY_OP_LEAKY }; enum ggml_object_type { @@ -531,37 +535,33 @@ extern "C" { int n_threads; - // the `n_tasks` of nodes, 1:1 mapping to cgraph nodes - int n_tasks[GGML_MAX_NODES]; - // abort ggml_graph_compute when true bool (*abort_callback)(void * data); void * abort_callback_data; }; - // next prime after GGML_MAX_NODES - // #define GGML_GRAPH_HASHTABLE_SIZE 4099 - // next prime after GGML_MAX_NODES * 2 (nodes + leafs) - // #define GGML_GRAPH_HASHTABLE_SIZE 8273 - // #define GGML_GRAPH_HASHTABLE_SIZE 16411 - #define GGML_GRAPH_HASHTABLE_SIZE 32771 - enum ggml_cgraph_eval_order { GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT = 0, GGML_CGRAPH_EVAL_ORDER_RIGHT_TO_LEFT, GGML_CGRAPH_EVAL_ORDER_COUNT }; + struct ggml_hash_set { + size_t size; + struct ggml_tensor ** keys; + }; + // computation graph struct ggml_cgraph { + int size; int n_nodes; int n_leafs; - struct ggml_tensor * nodes[GGML_MAX_NODES]; - struct ggml_tensor * grads[GGML_MAX_NODES]; - struct ggml_tensor * leafs[GGML_MAX_NODES]; + struct ggml_tensor ** nodes; + struct ggml_tensor ** grads; + struct ggml_tensor ** leafs; - void * visited_hash_table[GGML_GRAPH_HASHTABLE_SIZE]; + struct ggml_hash_set visited_hash_table; enum ggml_cgraph_eval_order order; @@ -571,8 +571,6 @@ extern "C" { int64_t perf_time_us; }; - static const size_t GGML_GRAPH_SIZE = sizeof(struct ggml_cgraph); - // scratch buffer struct ggml_scratch { size_t offs; @@ -617,6 +615,8 @@ extern "C" { GGML_API int64_t ggml_cycles(void); GGML_API int64_t ggml_cycles_per_ms(void); + GGML_API void ggml_print_backtrace(void); + GGML_API void ggml_numa_init(void); // call once for better performance on NUMA systems GGML_API bool ggml_is_numa(void); // true if init detected that system has >1 NUMA node @@ -709,7 +709,7 @@ extern "C" { // Context tensor enumeration and lookup GGML_API struct ggml_tensor * ggml_get_first_tensor(struct ggml_context * ctx); GGML_API struct ggml_tensor * ggml_get_next_tensor (struct ggml_context * ctx, struct ggml_tensor * tensor); - GGML_API struct ggml_tensor * ggml_get_tensor (struct ggml_context * ctx, const char * name); + GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name); GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor); GGML_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value); @@ -943,6 +943,10 @@ extern "C" { struct ggml_context * ctx, struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_leaky( + struct ggml_context * ctx, + struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_relu_inplace( struct ggml_context * ctx, struct ggml_tensor * a); @@ -1482,6 +1486,8 @@ extern "C" { int s0, // stride int p0); // padding + // the result will have 2*p0 padding for the first dimension + // and 2*p1 padding for the second dimension GGML_API struct ggml_tensor * ggml_pool_2d( struct ggml_context * ctx, struct ggml_tensor * a, @@ -1490,8 +1496,8 @@ extern "C" { int k1, int s0, int s1, - int p0, - int p1); + float p0, + float p1); // nearest interpolate // used in stable-diffusion @@ -1732,19 +1738,22 @@ extern "C" { GGML_API void ggml_build_forward_expand (struct ggml_cgraph * cgraph, struct ggml_tensor * tensor); GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep); - GGML_API struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor); - GGML_API struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep); - // graph allocation in a context - GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx); - GGML_API struct ggml_cgraph * ggml_build_forward_ctx(struct ggml_context * ctx, struct ggml_tensor * tensor); + GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx); // size = GGML_DEFAULT_GRAPH_SIZE, grads = false + GGML_API struct ggml_cgraph * ggml_new_graph_custom (struct ggml_context * ctx, size_t size, bool grads); + GGML_API struct ggml_cgraph * ggml_graph_dup (struct ggml_context * ctx, struct ggml_cgraph * cgraph); + GGML_API struct ggml_cgraph * ggml_graph_view (struct ggml_context * ctx, struct ggml_cgraph * cgraph, int i0, int i1); + GGML_API void ggml_graph_cpy (struct ggml_cgraph * src, struct ggml_cgraph * dst); + GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph); // zero grads + GGML_API void ggml_graph_clear (struct ggml_cgraph * cgraph); + GGML_API size_t ggml_graph_overhead(void); + GGML_API size_t ggml_graph_overhead_custom(size_t size, bool grads); // ggml_graph_plan() has to be called before ggml_graph_compute() // when plan.work_size > 0, caller must allocate memory for plan.work_data GGML_API struct ggml_cplan ggml_graph_plan (struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/); - GGML_API int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan); - GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph); + GGML_API int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan); // same as ggml_graph_compute() but the work data is allocated as a part of the context // note: the drawback of this API is that you must have ensured that the context has enough memory for the work data @@ -1752,8 +1761,8 @@ extern "C" { GGML_API struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name); - GGML_API void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname); - GGML_API struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval); + GGML_API void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname); + GGML_API struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval); // print info and performance information for the graph GGML_API void ggml_graph_print(const struct ggml_cgraph * cgraph); @@ -1816,6 +1825,8 @@ extern "C" { struct ggml_opt_params { enum ggml_opt_type type; + size_t graph_size; + int n_threads; // delta-based convergence test diff --git a/llama.cpp b/llama.cpp index a5f3876cc19e0..76ee4ea2300e8 100644 --- a/llama.cpp +++ b/llama.cpp @@ -91,6 +91,8 @@ #define LLAMA_ATTRIBUTE_FORMAT(...) #endif +#define LLAMA_MAX_NODES 4096 + // // logging // @@ -3618,7 +3620,7 @@ struct llm_build_context { } struct ggml_cgraph * build_llama() { - struct ggml_cgraph * gf = ggml_new_graph(ctx0); + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); GGML_ASSERT(n_embd_head == hparams.n_rot); @@ -3730,7 +3732,7 @@ struct llm_build_context { } struct ggml_cgraph * build_baichuan() { - struct ggml_cgraph * gf = ggml_new_graph(ctx0); + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -3850,7 +3852,7 @@ struct llm_build_context { } struct ggml_cgraph * build_falcon() { - struct ggml_cgraph * gf = ggml_new_graph(ctx0); + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -3972,7 +3974,7 @@ struct llm_build_context { } struct ggml_cgraph * build_starcoder() { - struct ggml_cgraph * gf = ggml_new_graph(ctx0); + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); struct ggml_tensor * cur; struct ggml_tensor * pos; @@ -4071,7 +4073,7 @@ struct llm_build_context { } struct ggml_cgraph * build_persimmon() { - struct ggml_cgraph * gf = ggml_new_graph(ctx0); + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); const int64_t n_rot = n_embd_head / 2; @@ -4281,7 +4283,7 @@ struct llm_build_context { } struct ggml_cgraph * build_refact() { - struct ggml_cgraph * gf = ggml_new_graph(ctx0); + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -4372,7 +4374,7 @@ struct llm_build_context { } struct ggml_cgraph * build_bloom() { - struct ggml_cgraph * gf = ggml_new_graph(ctx0); + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -4466,7 +4468,7 @@ struct llm_build_context { } struct ggml_cgraph * build_mpt() { - struct ggml_cgraph * gf = ggml_new_graph(ctx0); + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); struct ggml_tensor * cur; struct ggml_tensor * inpL; @@ -8208,7 +8210,7 @@ struct llama_context * llama_new_context_with_model( { static const size_t tensor_alignment = 32; // the compute buffer is used to store the tensor and graph structs, while the allocator buffer is used for the tensor data - ctx->buf_compute.resize(ggml_tensor_overhead()*GGML_MAX_NODES + ggml_graph_overhead()); + ctx->buf_compute.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead()); // create measure allocator ctx->alloc = ggml_allocr_new_measure(tensor_alignment); @@ -8597,8 +8599,8 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat if (kv_buf_size) { const size_t elt_size = ggml_element_size(kv_self.k); - ggml_context * cpy_ctx = ggml_init({ 4096, NULL, /* no_alloc */ true }); - ggml_cgraph gf{}; + ggml_context * cpy_ctx = ggml_init({ 6*ggml_tensor_overhead() + ggml_graph_overhead(), NULL, /* no_alloc */ true }); + ggml_cgraph * gf = ggml_new_graph(cpy_ctx); ggml_tensor * kout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_head, n_layer); std::vector kout3d_data(ggml_nbytes(kout3d), 0); @@ -8616,9 +8618,9 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat kv_head, n_embd, n_layer, elt_size*n_ctx, elt_size*n_ctx*n_embd, 0); - ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, k3d, kout3d)); - ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, v3d, vout3d)); - ggml_graph_compute_helper(ctx->work_buffer, &gf, /*n_threads*/ 1); + ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, k3d, kout3d)); + ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, v3d, vout3d)); + ggml_graph_compute_helper(ctx->work_buffer, gf, /*n_threads*/ 1); ggml_free(cpy_ctx); @@ -8725,8 +8727,8 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { const size_t elt_size = ggml_element_size(kv_self.k); - ggml_context * cpy_ctx = ggml_init({ 4096, NULL, /* no_alloc */ true }); - ggml_cgraph gf{}; + ggml_context * cpy_ctx = ggml_init({ 6*ggml_tensor_overhead() + ggml_graph_overhead(), NULL, /* no_alloc */ true }); + ggml_cgraph * gf = ggml_new_graph(cpy_ctx); ggml_tensor * kin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_head, n_layer); kin3d->data = (void *) inp; @@ -8744,9 +8746,9 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { kv_head, n_embd, n_layer, elt_size*n_ctx, elt_size*n_ctx*n_embd, 0); - ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, kin3d, k3d)); - ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, vin3d, v3d)); - ggml_graph_compute_helper(ctx->work_buffer, &gf, /*n_threads*/ 1); + ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, kin3d, k3d)); + ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, vin3d, v3d)); + ggml_graph_compute_helper(ctx->work_buffer, gf, /*n_threads*/ 1); ggml_free(cpy_ctx); } diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh index 4311268bd2d17..4024531b10f70 100755 --- a/scripts/sync-ggml.sh +++ b/scripts/sync-ggml.sh @@ -2,14 +2,20 @@ cp -rpv ../ggml/src/ggml.c ./ggml.c cp -rpv ../ggml/src/ggml-alloc.c ./ggml-alloc.c +cp -rpv ../ggml/src/ggml-backend-impl.h ./ggml-backend-impl.h cp -rpv ../ggml/src/ggml-backend.c ./ggml-backend.c -cp -rpv ../ggml/src/ggml-cuda.h ./ggml-cuda.h cp -rpv ../ggml/src/ggml-cuda.cu ./ggml-cuda.cu -cp -rpv ../ggml/src/ggml-opencl.h ./ggml-opencl.h -cp -rpv ../ggml/src/ggml-opencl.cpp ./ggml-opencl.cpp +cp -rpv ../ggml/src/ggml-cuda.h ./ggml-cuda.h +cp -rpv ../ggml/src/ggml-impl.h ./ggml-impl.h cp -rpv ../ggml/src/ggml-metal.h ./ggml-metal.h cp -rpv ../ggml/src/ggml-metal.m ./ggml-metal.m cp -rpv ../ggml/src/ggml-metal.metal ./ggml-metal.metal +cp -rpv ../ggml/src/ggml-mpi.h ./ggml-mpi.h +cp -rpv ../ggml/src/ggml-mpi.c ./ggml-mpi.c +cp -rpv ../ggml/src/ggml-opencl.cpp ./ggml-opencl.cpp +cp -rpv ../ggml/src/ggml-opencl.h ./ggml-opencl.h +cp -rpv ../ggml/src/ggml-quants.c ./ggml-quants.c +cp -rpv ../ggml/src/ggml-quants.h ./ggml-quants.h cp -rpv ../ggml/include/ggml/ggml.h ./ggml.h cp -rpv ../ggml/include/ggml/ggml-alloc.h ./ggml-alloc.h cp -rpv ../ggml/include/ggml/ggml-backend.h ./ggml-backend.h diff --git a/tests/test-grad0.cpp b/tests/test-grad0.cpp index 0a559b27ab370..7fe9154ddbb16 100644 --- a/tests/test-grad0.cpp +++ b/tests/test-grad0.cpp @@ -231,9 +231,10 @@ static bool check_gradient( printf("GGML_N_THREADS = %d\n", n_threads); } - struct ggml_cgraph * gf = ggml_build_forward_ctx(ctx0, f); - struct ggml_cgraph * gb = ggml_new_graph(ctx0); - *gb = *gf; + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, GGML_DEFAULT_GRAPH_SIZE, true); + struct ggml_cgraph * gb = ggml_new_graph_custom(ctx0, GGML_DEFAULT_GRAPH_SIZE, true); + ggml_build_forward_expand(gf, f); + ggml_graph_cpy(gf, gb); ggml_build_backward_expand(ctx0, gf, gb, false); ggml_graph_compute_with_ctx(ctx0, gf, n_threads); diff --git a/tests/test-opt.cpp b/tests/test-opt.cpp index bb8af59620b14..2c9997fca7705 100644 --- a/tests/test-opt.cpp +++ b/tests/test-opt.cpp @@ -109,10 +109,11 @@ int main(void) { struct ggml_tensor * d = ggml_sub(ctx, c, ab); struct ggml_tensor * e = ggml_sum(ctx, ggml_sqr(ctx, d)); - struct ggml_cgraph ge = ggml_build_forward(e); - ggml_graph_reset(&ge); + struct ggml_cgraph * ge = ggml_new_graph_custom(ctx, GGML_DEFAULT_GRAPH_SIZE, true); + ggml_build_forward_expand(ge, e); + ggml_graph_reset(ge); - ggml_graph_compute_with_ctx(ctx, &ge, /*n_threads*/ 1); + ggml_graph_compute_with_ctx(ctx, ge, /*n_threads*/ 1); const float fe = ggml_get_f32_1d(e, 0); printf("%s: e = %.4f\n", __func__, fe); @@ -121,9 +122,9 @@ int main(void) { ggml_opt(ctx, opt_params, e); - ggml_graph_reset(&ge); + ggml_graph_reset(ge); - ggml_graph_compute_with_ctx(ctx, &ge, /*n_threads*/ 1); + ggml_graph_compute_with_ctx(ctx, ge, /*n_threads*/ 1); const float fe_opt = ggml_get_f32_1d(e, 0); printf("%s: original e = %.4f\n", __func__, fe); From c049b37d7baf558944501705b91ac89b26ee3e41 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 13 Nov 2023 14:18:08 +0200 Subject: [PATCH 09/39] readme : update hot topics --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index af39e8c0e386e..c7d23277845bc 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ ### Hot topics -- ⚠️ **Upcoming change that might break functionality. Help with testing is needed:** https://github.com/ggerganov/llama.cpp/pull/3912 +- *No hot topics atm. Open to suggestions about what is hot today* ---- From 3d68f364f15778dc326f5024f2e5af1ad6dfddef Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 13 Nov 2023 16:55:52 +0200 Subject: [PATCH 10/39] ggml : sync (im2col, GPU conv, 32-bit arm compat) (#4060) ggml-ci --- ggml-cuda.cu | 106 +++- ggml-impl.h | 6 - ggml-metal.h | 2 +- ggml-metal.m | 106 +++- ggml-metal.metal | 108 +++- ggml-quants.c | 241 ++++++--- ggml.c | 1287 ++++++++-------------------------------------- ggml.h | 19 +- 8 files changed, 693 insertions(+), 1182 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 1634024466542..7be63925f4eda 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -4489,6 +4489,13 @@ static __device__ void cpy_1_f32_f16(const char * cxi, char * cdsti) { *dsti = __float2half(*xi); } +static __device__ void cpy_1_f16_f16(const char * cxi, char * cdsti) { + const half * xi = (const half *) cxi; + half * dsti = (half *) cdsti; + + *dsti = *xi; +} + template static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int nb00, const int nb01, const int nb02, @@ -4742,6 +4749,25 @@ static __global__ void clamp_f32(const float * x, float * dst, const float min, dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]); } +static __global__ void im2col_f32_f16( + const float * x, half * dst, + int ofs0, int ofs1, int IW, int IH, int CHW, + int s0, int s1, int p0, int p1, int d0, int d1) { + const int iiw = blockIdx.z * s0 + threadIdx.z * d0 - p0; + const int iih = blockIdx.y * s1 + threadIdx.y * d1 - p1; + + const int offset_dst = + (threadIdx.x * gridDim.y * gridDim.z + blockIdx.y * gridDim.z + blockIdx.z) * CHW + + (blockIdx.x * (blockDim.y * blockDim.z) + threadIdx.y * blockDim.z + threadIdx.z); + + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { + dst[offset_dst] = __float2half(0.0f); + } else { + const int offset_src = threadIdx.x * ofs0 + blockIdx.x * ofs1; + dst[offset_dst] = __float2half(x[offset_src + iih * IW + iiw]); + } +} + template static void get_rows_cuda(const void * x, const int32_t * y, float * dst, const int nrows, const int ncols, cudaStream_t stream) { const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1); @@ -5642,6 +5668,16 @@ static void ggml_cpy_f32_f16_cuda( (cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12); } +static void ggml_cpy_f16_f16_cuda( + const char * cx, char * cdst, const int ne, + const int ne00, const int ne01, const int nb00, const int nb01, const int nb02, + const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) { + + const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE; + cpy_f32_f16<<>> + (cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12); +} + static void scale_f32_cuda(const float * x, float * dst, const float scale, const int k, cudaStream_t stream) { const int num_blocks = (k + CUDA_SCALE_BLOCK_SIZE - 1) / CUDA_SCALE_BLOCK_SIZE; scale_f32<<>>(x, dst, scale, k); @@ -5725,6 +5761,15 @@ static void soft_max_f32_cuda(const float * x, float * dst, const int ncols_x, c soft_max_f32<<>>(x, dst, ncols_x); } +static void im2col_f32_f16_cuda(const float * x, half * dst, + int OH, int IW, int IH, int OW, int IC, + int KH, int KW, int N, int ofs0, int ofs1, + int s0, int s1, int p0, int p1, int d0, int d1, cudaStream_t stream) { + dim3 block_nums(IC, OH, OW); + dim3 block_dims(N, KH, KW); + im2col_f32_f16<<>>(x, dst, ofs0, ofs1, IW, IH, (IC * KH * KW), s0, s1, p0, p1, d0, d1); +} + // buffer pool for cuda #define MAX_CUDA_BUFFERS 256 @@ -6522,8 +6567,7 @@ inline void ggml_cuda_op_mul_mat_cublas( src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &src1_as); to_fp16_cuda(src1_ddf_i, src1_as_f16, ne, stream); } - const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddq_i : src1_as_f16; - + const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddf_i : src1_as_f16; size_t dst_as = 0; half * dst_f16 = (half *) ggml_cuda_pool_malloc(row_diff*src1_ncols * sizeof(half), &dst_as); @@ -6698,6 +6742,45 @@ inline void ggml_cuda_op_alibi( (void) src1_dd; } +inline void ggml_cuda_op_im2col( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F16); + + const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t*)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t*)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t*)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t*)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t*)(dst->op_params))[5]; + + const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1; + + const int64_t N = src1->ne[is_2D ? 3 : 2]; + const int64_t IC = src1->ne[is_2D ? 2 : 1]; + const int64_t IH = is_2D ? src1->ne[1] : 1; + const int64_t IW = src1->ne[0]; + + const int64_t KH = is_2D ? src0->ne[1] : 1; + const int64_t KW = src0->ne[0]; + + const int64_t OH = is_2D ? dst->ne[2] : 1; + const int64_t OW = dst->ne[1]; + + const size_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4; // nb is byte offset, src is type float32 + const size_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32 + + im2col_f32_f16_cuda(src1_dd, (half*) dst_dd, + OH, IW, IH, OW, IC, KH, KW, N, + ofs0, ofs1, s0, s1, p0, p1, d0, d1, main_stream); + + (void) src0; + (void) src0_dd; +} + inline void ggml_cuda_op_diag_mask_inf( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) { @@ -7610,6 +7693,9 @@ static void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, gg } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) { ggml_cpy_f32_f16_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) { + ggml_cpy_f16_f16_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, + ne10, ne11, nb10, nb11, nb12, main_stream); } else { fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__, ggml_type_name(src0->type), ggml_type_name(src1->type)); @@ -7641,6 +7727,10 @@ static void ggml_cuda_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_alibi); } +void ggml_cuda_im2col(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_im2col); +} + static void ggml_cuda_nop(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { (void) src0; (void) src1; @@ -7934,6 +8024,15 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_ return false; } + if (tensor->op == GGML_OP_MUL_MAT) { + if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) { +#ifndef NDEBUG + fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = %d, src1->ne[3] = %d - fallback to CPU\n", __func__, tensor->name, tensor->src[0]->ne[3], tensor->src[1]->ne[3]); +#endif + return false; + } + } + switch (tensor->op) { case GGML_OP_REPEAT: func = ggml_cuda_repeat; @@ -8012,6 +8111,9 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_ case GGML_OP_ALIBI: func = ggml_cuda_alibi; break; + case GGML_OP_IM2COL: + func = ggml_cuda_im2col; + break; default: return false; } diff --git a/ggml-impl.h b/ggml-impl.h index d88f261449f05..06c07339e9269 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -39,12 +39,6 @@ extern "C" { #endif #endif -#undef MIN -#undef MAX - -#define MIN(a, b) ((a) < (b) ? (a) : (b)) -#define MAX(a, b) ((a) > (b) ? (a) : (b)) - // 16-bit float // on Arm, we use __fp16 // on x86, we use uint16_t diff --git a/ggml-metal.h b/ggml-metal.h index 096b844e32c6f..be2731f8ba476 100644 --- a/ggml-metal.h +++ b/ggml-metal.h @@ -26,7 +26,7 @@ #include // max memory buffers that can be mapped to the device -#define GGML_METAL_MAX_BUFFERS 16 +#define GGML_METAL_MAX_BUFFERS 64 #define GGML_METAL_MAX_COMMAND_BUFFERS 32 struct ggml_tensor; diff --git a/ggml-metal.m b/ggml-metal.m index c2cda0bf546d3..3d22b0b27e444 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -86,6 +86,7 @@ GGML_METAL_DECL_KERNEL(rms_norm); GGML_METAL_DECL_KERNEL(norm); GGML_METAL_DECL_KERNEL(mul_mv_f32_f32); + GGML_METAL_DECL_KERNEL(mul_mv_f16_f16); GGML_METAL_DECL_KERNEL(mul_mv_f16_f32); GGML_METAL_DECL_KERNEL(mul_mv_f16_f32_1row); GGML_METAL_DECL_KERNEL(mul_mv_f16_f32_l4); @@ -114,6 +115,7 @@ GGML_METAL_DECL_KERNEL(rope_f32); GGML_METAL_DECL_KERNEL(rope_f16); GGML_METAL_DECL_KERNEL(alibi_f32); + GGML_METAL_DECL_KERNEL(im2col_f16); GGML_METAL_DECL_KERNEL(cpy_f32_f16); GGML_METAL_DECL_KERNEL(cpy_f32_f32); GGML_METAL_DECL_KERNEL(cpy_f16_f16); @@ -126,7 +128,7 @@ // MSL code // TODO: move the contents here when ready // for now it is easier to work in a separate file -static NSString * const msl_library_source = @"see metal.metal"; +//static NSString * const msl_library_source = @"see metal.metal"; // Here to assist with NSBundle Path Hack @interface GGMLMetalClass : NSObject @@ -142,7 +144,8 @@ void ggml_metal_log_set_callback(ggml_log_callback log_callback, void * user_dat ggml_metal_log_user_data = user_data; } -static void ggml_metal_log(enum ggml_log_level level, const char* format, ...){ +GGML_ATTRIBUTE_FORMAT(2, 3) +static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ if (ggml_metal_log_callback != NULL) { va_list args; va_start(args, format); @@ -210,7 +213,13 @@ static void ggml_metal_log(enum ggml_log_level level, const char* format, ...){ } else { GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__); - NSString * sourcePath = [bundle pathForResource:@"ggml-metal" ofType:@"metal"]; + NSString * sourcePath; + NSString * ggmlMetalPathResources = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"]; + if (ggmlMetalPathResources) { + sourcePath = [ggmlMetalPathResources stringByAppendingPathComponent:@"ggml-metal.metal"]; + } else { + sourcePath = [bundle pathForResource:@"ggml-metal" ofType:@"metal"]; + } if (sourcePath == nil) { GGML_METAL_LOG_WARN("%s: error: could not use bundle path to find ggml-metal.metal, falling back to trying cwd\n", __func__); sourcePath = @"ggml-metal.metal"; @@ -281,6 +290,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char* format, ...){ GGML_METAL_ADD_KERNEL(rms_norm); GGML_METAL_ADD_KERNEL(norm); GGML_METAL_ADD_KERNEL(mul_mv_f32_f32); + GGML_METAL_ADD_KERNEL(mul_mv_f16_f16); GGML_METAL_ADD_KERNEL(mul_mv_f16_f32); GGML_METAL_ADD_KERNEL(mul_mv_f16_f32_1row); GGML_METAL_ADD_KERNEL(mul_mv_f16_f32_l4); @@ -311,6 +321,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char* format, ...){ GGML_METAL_ADD_KERNEL(rope_f32); GGML_METAL_ADD_KERNEL(rope_f16); GGML_METAL_ADD_KERNEL(alibi_f32); + GGML_METAL_ADD_KERNEL(im2col_f16); GGML_METAL_ADD_KERNEL(cpy_f32_f16); GGML_METAL_ADD_KERNEL(cpy_f32_f32); GGML_METAL_ADD_KERNEL(cpy_f16_f16); @@ -329,7 +340,7 @@ static void ggml_metal_log(enum ggml_log_level level, const char* format, ...){ // https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf for (int i = MTLGPUFamilyApple1 + 20; i >= MTLGPUFamilyApple1; --i) { if ([ctx->device supportsFamily:i]) { - GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyApple%d (%d)\n", __func__, i - MTLGPUFamilyApple1 + 1, i); + GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyApple%d (%d)\n", __func__, i - (int) MTLGPUFamilyApple1 + 1, i); break; } } @@ -380,6 +391,7 @@ 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_mv_f32_f32); + GGML_METAL_DEL_KERNEL(mul_mv_f16_f16); GGML_METAL_DEL_KERNEL(mul_mv_f16_f32); GGML_METAL_DEL_KERNEL(mul_mv_f16_f32_1row); GGML_METAL_DEL_KERNEL(mul_mv_f16_f32_l4); @@ -410,6 +422,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_DEL_KERNEL(rope_f32); GGML_METAL_DEL_KERNEL(rope_f16); GGML_METAL_DEL_KERNEL(alibi_f32); + GGML_METAL_DEL_KERNEL(im2col_f16); GGML_METAL_DEL_KERNEL(cpy_f32_f16); GGML_METAL_DEL_KERNEL(cpy_f32_f32); GGML_METAL_DEL_KERNEL(cpy_f16_f16); @@ -467,6 +480,10 @@ int ggml_metal_if_optimized(struct ggml_metal_context * ctx) { const int64_t tsize = ggml_nbytes(t); + if (t->buffer && t->buffer->backend && t->buffer->backend->context) { + ctx = t->buffer->backend->context; + } + // find the view that contains the tensor fully for (int i = 0; i < ctx->n_buffers; ++i) { const int64_t ioffs = (int64_t) t->data - (int64_t) ctx->buffers[i].data; @@ -567,7 +584,7 @@ bool ggml_metal_add_buffer( ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); if (ctx->device.currentAllocatedSize > ctx->device.recommendedMaxWorkingSetSize) { - GGML_METAL_LOG_WARN(", warning: current allocated size is greater than the recommended max working set size\n", __func__); + GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__); } else { GGML_METAL_LOG_INFO("\n"); } @@ -1024,7 +1041,7 @@ void ggml_metal_graph_compute( [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setThreadgroupMemoryLength:MAX(16, nth/32*sizeof(float)) atIndex:0]; + [encoder setThreadgroupMemoryLength:GGML_PAD(nth/32*sizeof(float), 16) atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; @@ -1133,6 +1150,7 @@ void ggml_metal_graph_compute( switch (src0t) { case GGML_TYPE_F32: { + GGML_ASSERT(src1t == GGML_TYPE_F32); [encoder setComputePipelineState:ctx->pipeline_mul_mv_f32_f32]; nrows = 4; } break; @@ -1140,13 +1158,18 @@ void ggml_metal_graph_compute( { nth0 = 32; nth1 = 1; - if (ne11 * ne12 < 4) { - [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32_1row]; - } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) { - [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32_l4]; - nrows = ne11; + if (src1t == GGML_TYPE_F32) { + if (ne11 * ne12 < 4) { + [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32_1row]; + } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) { + [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32_l4]; + nrows = ne11; + } else { + [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32]; + nrows = 4; + } } else { - [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32]; + [encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f16]; nrows = 4; } } break; @@ -1336,7 +1359,7 @@ void ggml_metal_graph_compute( [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; [encoder setBytes:&eps length:sizeof( float) atIndex:4]; - [encoder setThreadgroupMemoryLength:nth/32*sizeof(float) atIndex:0]; + [encoder setThreadgroupMemoryLength:GGML_PAD(nth/32*sizeof(float), 16) atIndex:0]; const int64_t nrows = ggml_nrows(src0); @@ -1355,7 +1378,7 @@ void ggml_metal_graph_compute( [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; [encoder setBytes:&eps length:sizeof( float) atIndex:4]; - [encoder setThreadgroupMemoryLength:MAX(16, nth*sizeof(float)) atIndex:0]; + [encoder setThreadgroupMemoryLength:GGML_PAD(nth*sizeof(float), 16) atIndex:0]; const int64_t nrows = ggml_nrows(src0); @@ -1410,8 +1433,7 @@ void ggml_metal_graph_compute( const int n_past = ((int32_t *) dst->op_params)[0]; const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; - // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal - const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; + const int n_orig_ctx = ((int32_t *) dst->op_params)[3]; float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); @@ -1459,6 +1481,58 @@ void ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; + case GGML_OP_IM2COL: + { + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F16); + + const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; + const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; + + const int32_t N = src1->ne[is_2D ? 3 : 2]; + const int32_t IC = src1->ne[is_2D ? 2 : 1]; + const int32_t IH = is_2D ? src1->ne[1] : 1; + const int32_t IW = src1->ne[0]; + + const int32_t KH = is_2D ? src0->ne[1] : 1; + const int32_t KW = src0->ne[0]; + + const int32_t OH = is_2D ? dst->ne[2] : 1; + const int32_t OW = dst->ne[1]; + + const int32_t CHW = IC * KH * KW; + + const int32_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4; + const int32_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4; + + switch (src0->type) { + case GGML_TYPE_F32: GGML_ASSERT(false && "not implemented"); break; + case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_im2col_f16]; break; + default: GGML_ASSERT(false); + }; + + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ofs0 length:sizeof( int32_t) atIndex:2]; + [encoder setBytes:&ofs1 length:sizeof( int32_t) atIndex:3]; + [encoder setBytes:&IW length:sizeof( int32_t) atIndex:4]; + [encoder setBytes:&IH length:sizeof( int32_t) atIndex:5]; + [encoder setBytes:&CHW length:sizeof( int32_t) atIndex:6]; + [encoder setBytes:&s0 length:sizeof( int32_t) atIndex:7]; + [encoder setBytes:&s1 length:sizeof( int32_t) atIndex:8]; + [encoder setBytes:&p0 length:sizeof( int32_t) atIndex:9]; + [encoder setBytes:&p1 length:sizeof( int32_t) atIndex:10]; + [encoder setBytes:&d0 length:sizeof( int32_t) atIndex:11]; + [encoder setBytes:&d1 length:sizeof( int32_t) atIndex:12]; + + [encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)]; + } break; case GGML_OP_DUP: case GGML_OP_CPY: case GGML_OP_CONT: diff --git a/ggml-metal.metal b/ggml-metal.metal index 7c35f23a7612f..5d1357cd72d45 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -792,7 +792,7 @@ kernel void kernel_mul_mv_f32_f32( constant int64_t & ne0, constant int64_t & ne1, uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]]) { + uint tiisg[[thread_index_in_simdgroup]]) { const int64_t r0 = tgpig.x; const int64_t rb = tgpig.y*N_F32_F32; @@ -844,6 +844,79 @@ kernel void kernel_mul_mv_f32_f32( } } +#define N_F16_F16 4 + +kernel void kernel_mul_mv_f16_f16( + 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 rb = tgpig.y*N_F16_F16; + const int64_t im = tgpig.z; + + device const half * x = (device const half *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02); + + if (ne00 < 128) { + for (int row = 0; row < N_F16_F16; ++row) { + int r1 = rb + row; + if (r1 >= ne11) { + break; + } + + device const half * y = (device const half *) (src1 + r1*nb11 + im*nb12); + + float sumf = 0; + for (int i = tiisg; i < ne00; i += 32) { + sumf += (half) x[i] * (half) 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_F16; ++row) { + int r1 = rb + row; + if (r1 >= ne11) { + break; + } + + device const half * y = (device const half *) (src1 + r1*nb11 + im*nb12); + device const half4 * y4 = (device const half4 *) y; + + float sumf = 0; + for (int i = tiisg; i < ne00/4; i += 32) { + for (int k = 0; k < 4; ++k) sumf += (half) 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 += (half) x[i] * y[i]; + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } + } + } +} + kernel void kernel_mul_mv_f16_f32_1row( device const char * src0, device const char * src1, @@ -1229,6 +1302,39 @@ kernel void kernel_rope( template [[host_name("kernel_rope_f32")]] kernel rope_t kernel_rope; template [[host_name("kernel_rope_f16")]] kernel rope_t kernel_rope; +kernel void kernel_im2col_f16( + device const float * x, + device half * dst, + constant int32_t & ofs0, + constant int32_t & ofs1, + constant int32_t & IW, + constant int32_t & IH, + constant int32_t & CHW, + constant int32_t & s0, + constant int32_t & s1, + constant int32_t & p0, + constant int32_t & p1, + constant int32_t & d0, + constant int32_t & d1, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tgpg[[threadgroups_per_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + const int32_t iiw = tgpig[2] * s0 + tpitg[2] * d0 - p0; + const int32_t iih = tgpig[1] * s1 + tpitg[1] * d1 - p1; + + const int32_t offset_dst = + (tpitg[0] * tgpg[1] * tgpg[2] + tgpig[1] * tgpg[2] + tgpig[2]) * CHW + + (tgpig[0] * (ntg[1] * ntg[2]) + tpitg[1] * ntg[2] + tpitg[2]); + + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { + dst[offset_dst] = 0.0f; + } else { + const int32_t offset_src = tpitg[0] * ofs0 + tgpig[0] * ofs1; + dst[offset_dst] = x[offset_src + iih * IW + iiw]; + } +} + kernel void kernel_cpy_f16_f16( device const half * src0, device half * dst, diff --git a/ggml-quants.c b/ggml-quants.c index 740be6dc5c798..a48eda7320c46 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -14,26 +14,6 @@ // #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__ @@ -47,13 +27,15 @@ inline static int32_t vaddvq_s32(int32x4_t v) { #if defined(_MSC_VER) || defined(__MINGW32__) #include #else -#if !defined(__riscv) && !defined(__s390__) +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__) +#if !defined(__riscv) #include #endif #endif #endif #endif #endif +#endif #ifdef __riscv_v_intrinsic #include @@ -61,6 +43,7 @@ inline static int32_t vaddvq_s32(int32x4_t v) { #undef MIN #undef MAX + #define MIN(a, b) ((a) < (b) ? (a) : (b)) #define MAX(a, b) ((a) > (b) ? (a) : (b)) @@ -283,9 +266,31 @@ static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 #endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) #if defined(__ARM_NEON) - #if !defined(__aarch64__) +// 64-bit compatibility + +// vaddvq_s16 +// vpaddq_s16 +// vaddvq_s32 +// vaddvq_f32 +// vmaxvq_f32 +// vcvtnq_s32_f32 + +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); } @@ -311,6 +316,96 @@ inline static int32x4_t vcvtnq_s32_f32(float32x4_t v) { return res; } +// vld1q_s16_x2 +// vld1q_u8_x2 +// vld1q_u8_x4 +// vld1q_s8_x2 +// vld1q_s8_x4 +// TODO: double-check these work correctly + +typedef struct ggml_int16x8x2_t { + int16x8_t val[2]; +} ggml_int16x8x2_t; + +inline static ggml_int16x8x2_t ggml_vld1q_s16_x2(const int16_t * ptr) { + ggml_int16x8x2_t res; + + res.val[0] = vld1q_s16(ptr + 0); + res.val[1] = vld1q_s16(ptr + 8); + + return res; +} + +typedef struct ggml_uint8x16x2_t { + uint8x16_t val[2]; +} ggml_uint8x16x2_t; + +inline static ggml_uint8x16x2_t ggml_vld1q_u8_x2(const uint8_t * ptr) { + ggml_uint8x16x2_t res; + + res.val[0] = vld1q_u8(ptr + 0); + res.val[1] = vld1q_u8(ptr + 16); + + return res; +} + +typedef struct ggml_uint8x16x4_t { + uint8x16_t val[4]; +} ggml_uint8x16x4_t; + +inline static ggml_uint8x16x4_t ggml_vld1q_u8_x4(const uint8_t * ptr) { + ggml_uint8x16x4_t res; + + res.val[0] = vld1q_u8(ptr + 0); + res.val[1] = vld1q_u8(ptr + 16); + res.val[2] = vld1q_u8(ptr + 32); + res.val[3] = vld1q_u8(ptr + 48); + + return res; +} + +typedef struct ggml_int8x16x2_t { + int8x16_t val[2]; +} ggml_int8x16x2_t; + +inline static ggml_int8x16x2_t ggml_vld1q_s8_x2(const int8_t * ptr) { + ggml_int8x16x2_t res; + + res.val[0] = vld1q_s8(ptr + 0); + res.val[1] = vld1q_s8(ptr + 16); + + return res; +} + +typedef struct ggml_int8x16x4_t { + int8x16_t val[4]; +} ggml_int8x16x4_t; + +inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) { + ggml_int8x16x4_t res; + + res.val[0] = vld1q_s8(ptr + 0); + res.val[1] = vld1q_s8(ptr + 16); + res.val[2] = vld1q_s8(ptr + 32); + res.val[3] = vld1q_s8(ptr + 48); + + return res; +} + +#else + +#define ggml_int16x8x2_t int16x8x2_t +#define ggml_uint8x16x2_t uint8x16x2_t +#define ggml_uint8x16x4_t uint8x16x4_t +#define ggml_int8x16x2_t int8x16x2_t +#define ggml_int8x16x4_t int8x16x4_t + +#define ggml_vld1q_s16_x2 vld1q_s16_x2 +#define ggml_vld1q_u8_x2 vld1q_u8_x2 +#define ggml_vld1q_u8_x4 vld1q_u8_x4 +#define ggml_vld1q_s8_x2 vld1q_s8_x2 +#define ggml_vld1q_s8_x4 vld1q_s8_x4 + #endif #endif @@ -3557,7 +3652,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri const int32x4_t vzero = vdupq_n_s32(0); #endif - int8x16x2_t q2bytes; + ggml_int8x16x2_t q2bytes; uint8_t aux[16]; float sum = 0; @@ -3576,8 +3671,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri vst1q_u8(aux, scales); const uint8x16_t mins = vshrq_n_u8(mins_and_scales, 4); - const int16x8x2_t q8sums = vld1q_s16_x2(y[i].bsums); - const int16x8x2_t mins16 = {vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mins))), vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mins)))}; + const ggml_int16x8x2_t q8sums = ggml_vld1q_s16_x2(y[i].bsums); + const ggml_int16x8x2_t mins16 = {vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mins))), vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mins)))}; const int32x4_t s0 = vaddq_s32(vmull_s16(vget_low_s16 (mins16.val[0]), vget_low_s16 (q8sums.val[0])), vmull_s16(vget_high_s16(mins16.val[0]), vget_high_s16(q8sums.val[0]))); const int32x4_t s1 = vaddq_s32(vmull_s16(vget_low_s16 (mins16.val[1]), vget_low_s16 (q8sums.val[1])), @@ -3605,7 +3700,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri #endif #define SHIFT_MULTIPLY_ACCUM_WITH_SCALE(shift, index)\ - q8bytes = vld1q_s8_x2(q8); q8 += 32;\ + q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32;\ q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.val[0], (shift)), m3));\ q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.val[1], (shift)), m3));\ MULTIPLY_ACCUM_WITH_SCALE((index)); @@ -3613,9 +3708,9 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri for (int j = 0; j < QK_K/128; ++j) { - const uint8x16x2_t q2bits = vld1q_u8_x2(q2); q2 += 32; + const ggml_uint8x16x2_t q2bits = ggml_vld1q_u8_x2(q2); q2 += 32; - int8x16x2_t q8bytes = vld1q_s8_x2(q8); q8 += 32; + ggml_int8x16x2_t q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(q2bits.val[0], m3)); q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(q2bits.val[1], m3)); MULTIPLY_ACCUM_WITH_SCALE(0); @@ -3949,7 +4044,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri const int32x4_t vzero = vdupq_n_s32(0); #endif - int8x16x4_t q2bytes; + ggml_int8x16x4_t q2bytes; uint32_t aux32[2]; const uint8_t * scales = (const uint8_t *)aux32; @@ -3974,7 +4069,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri const uint8x16_t q2bits = vld1q_u8(q2); - const int8x16x4_t q8bytes = vld1q_s8_x4(q8); + const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(q2bits, m3)); q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 2), m3)); @@ -4238,7 +4333,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri const uint8x16_t m3 = vshlq_n_u8(m0, 3); const int8_t m32 = 32; - int8x16x4_t q3bytes; + ggml_int8x16x4_t q3bytes; float sum = 0; @@ -4250,9 +4345,9 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri const uint8_t * restrict qh = x[i].hmask; const int8_t * restrict q8 = y[i].qs; - uint8x16x2_t qhbits = vld1q_u8_x2(qh); + ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh); - uint8x16x4_t q3h; + ggml_uint8x16x4_t q3h; int32_t isum = 0; @@ -4268,9 +4363,9 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri for (int j = 0; j < QK_K/128; ++j) { - const uint8x16x2_t q3bits = vld1q_u8_x2(q3); q3 += 32; - const int8x16x4_t q8bytes_1 = vld1q_s8_x4(q8); q8 += 64; - const int8x16x4_t q8bytes_2 = vld1q_s8_x4(q8); q8 += 64; + const ggml_uint8x16x2_t q3bits = ggml_vld1q_u8_x2(q3); q3 += 32; + const ggml_int8x16x4_t q8bytes_1 = ggml_vld1q_s8_x4(q8); q8 += 64; + const ggml_int8x16x4_t q8bytes_2 = ggml_vld1q_s8_x4(q8); q8 += 64; q3h.val[0] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[0]), 2); q3h.val[1] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[1]), 2); @@ -4772,7 +4867,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri const uint8x16_t m3b = vdupq_n_u8(0x3); const uint8x16_t mh = vdupq_n_u8(4); - int8x16x4_t q3bytes; + ggml_int8x16x4_t q3bytes; uint16_t aux16[2]; int8_t * scales = (int8_t *)aux16; @@ -4781,11 +4876,11 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri for (int i = 0; i < nb; ++i) { - uint8x16x4_t q3h; + ggml_uint8x16x4_t q3h; const uint8x8_t hbits = vld1_u8(x[i].hmask); const uint8x16_t q3bits = vld1q_u8(x[i].qs); - const int8x16x4_t q8bytes = vld1q_s8_x4(y[i].qs); + const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(y[i].qs); const uint16_t a = *(const uint16_t *)x[i].scales; aux16[0] = a & 0x0f0f; @@ -5134,8 +5229,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri const int32x4_t mzero = vdupq_n_s32(0); #endif - int8x16x2_t q4bytes; - int8x16x2_t q8bytes; + ggml_int8x16x2_t q4bytes; + ggml_int8x16x2_t q8bytes; float sumf = 0; @@ -5170,17 +5265,17 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri for (int j = 0; j < QK_K/64; ++j) { - const uint8x16x2_t q4bits = vld1q_u8_x2(q4); q4 += 32; + const ggml_uint8x16x2_t q4bits = ggml_vld1q_u8_x2(q4); q4 += 32; #ifdef __ARM_FEATURE_DOTPROD - q8bytes = vld1q_s8_x2(q8); q8 += 32; + q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); const int32x4_t p1 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); sumi1 += vaddvq_s32(p1) * scales[2*j+0]; - q8bytes = vld1q_s8_x2(q8); q8 += 32; + q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4)); q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4)); @@ -5188,7 +5283,7 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri sumi2 += vaddvq_s32(p2) * scales[2*j+1]; #else - q8bytes = vld1q_s8_x2(q8); q8 += 32; + q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[0])), @@ -5197,7 +5292,7 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri vmull_s8(vget_high_s8(q4bytes.val[1]), vget_high_s8(q8bytes.val[1]))); sumi1 += vaddvq_s16(vaddq_s16(p0, p1)) * scales[2*j+0]; - q8bytes = vld1q_s8_x2(q8); q8 += 32; + q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4)); q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4)); const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[0])), @@ -5512,8 +5607,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri float sumf = 0; - int8x16x2_t q4bytes; - int8x16x4_t q8bytes; + ggml_int8x16x2_t q4bytes; + ggml_int8x16x4_t q8bytes; float sum_mins = 0.f; @@ -5534,10 +5629,10 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri const float d = y[i].d * (float)x[i].d[0]; - const uint8x16x2_t q4bits = vld1q_u8_x2(q4); + const ggml_uint8x16x2_t q4bits = ggml_vld1q_u8_x2(q4); #ifdef __ARM_FEATURE_DOTPROD - q8bytes = vld1q_s8_x4(q8); + q8bytes = ggml_vld1q_s8_x4(q8); q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); @@ -5551,7 +5646,7 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri const int32_t sumi2 = vaddvq_s32(p2) * scales[1]; #else - q8bytes = vld1q_s8_x4(q8); + q8bytes = ggml_vld1q_s8_x4(q8); q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[0])), @@ -5785,7 +5880,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri const int32x4_t mzero = vdupq_n_s32(0); #endif - int8x16x4_t q5bytes; + ggml_int8x16x4_t q5bytes; float sumf = 0; @@ -5815,16 +5910,16 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri const uint8_t * restrict qh = x[i].qh; const int8_t * restrict q8 = y[i].qs; - uint8x16x2_t qhbits = vld1q_u8_x2(qh); + ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh); - uint8x16x4_t q5h; + ggml_uint8x16x4_t q5h; int32_t sumi = 0; for (int j = 0; j < QK_K/64; ++j) { - const uint8x16x2_t q5bits = vld1q_u8_x2(q5); q5 += 32; - const int8x16x4_t q8bytes = vld1q_s8_x4(q8); q8 += 64; + const ggml_uint8x16x2_t q5bits = ggml_vld1q_u8_x2(q5); q5 += 32; + const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64; q5h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits.val[0]), 4); q5h.val[1] = vshlq_n_u8(vandq_u8(mone, qhbits.val[1]), 4); @@ -6218,8 +6313,8 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri const int32x4_t mzero = vdupq_n_s32(0); #endif - int8x16x4_t q5bytes; - uint8x16x4_t q5h; + ggml_int8x16x4_t q5bytes; + ggml_uint8x16x4_t q5h; float sumf = 0; @@ -6234,8 +6329,8 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri const uint8x8_t qhbits = vld1_u8(qh); - const uint8x16x2_t q5bits = vld1q_u8_x2(q5); - const int8x16x4_t q8bytes = vld1q_s8_x4(q8); + const ggml_uint8x16x2_t q5bits = ggml_vld1q_u8_x2(q5); + const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); const uint8x16_t htmp = vcombine_u8(qhbits, vshr_n_u8(qhbits, 1)); q5h.val[0] = vbicq_u8(mh, vshlq_n_u8(htmp, 4)); @@ -6511,8 +6606,8 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri const uint8x16_t mone = vdupq_n_u8(3); - int8x16x4_t q6bytes; - uint8x16x4_t q6h; + ggml_int8x16x4_t q6bytes; + ggml_uint8x16x4_t q6h; for (int i = 0; i < nb; ++i) { @@ -6524,9 +6619,9 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri const int8_t * restrict scale = x[i].scales; - const int16x8x2_t q8sums = vld1q_s16_x2(y[i].bsums); + const ggml_int16x8x2_t q8sums = ggml_vld1q_s16_x2(y[i].bsums); const int8x16_t scales = vld1q_s8(scale); - const int16x8x2_t q6scales = {vmovl_s8(vget_low_s8(scales)), vmovl_s8(vget_high_s8(scales))}; + const ggml_int16x8x2_t q6scales = {vmovl_s8(vget_low_s8(scales)), vmovl_s8(vget_high_s8(scales))}; const int32x4_t prod = vaddq_s32(vaddq_s32(vmull_s16(vget_low_s16 (q8sums.val[0]), vget_low_s16 (q6scales.val[0])), vmull_s16(vget_high_s16(q8sums.val[0]), vget_high_s16(q6scales.val[0]))), @@ -6538,9 +6633,9 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri for (int j = 0; j < QK_K/128; ++j) { - uint8x16x2_t qhbits = vld1q_u8_x2(qh); qh += 32; - uint8x16x4_t q6bits = vld1q_u8_x4(q6); q6 += 64; - int8x16x4_t q8bytes = vld1q_s8_x4(q8); q8 += 64; + ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh); qh += 32; + ggml_uint8x16x4_t q6bits = ggml_vld1q_u8_x4(q6); q6 += 64; + ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64; q6h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits.val[0]), 4); q6h.val[1] = vshlq_n_u8(vandq_u8(mone, qhbits.val[1]), 4); @@ -6583,7 +6678,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri scale += 2; #endif - q8bytes = vld1q_s8_x4(q8); q8 += 64; + q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64; shifted = vshrq_n_u8(qhbits.val[0], 4); q6h.val[0] = vshlq_n_u8(vandq_u8(mone, shifted), 4); @@ -6987,8 +7082,8 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri const uint8x16_t mone = vdupq_n_u8(3); - int8x16x4_t q6bytes; - uint8x16x4_t q6h; + ggml_int8x16x4_t q6bytes; + ggml_uint8x16x4_t q6h; for (int i = 0; i < nb; ++i) { @@ -7002,9 +7097,9 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri int32_t isum = 0; - uint8x16_t qhbits = vld1q_u8(qh); - uint8x16x2_t q6bits = vld1q_u8_x2(q6); - int8x16x4_t q8bytes = vld1q_s8_x4(q8); + uint8x16_t qhbits = vld1q_u8(qh); + ggml_uint8x16x2_t q6bits = ggml_vld1q_u8_x2(q6); + ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); q6h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits), 4); uint8x16_t shifted = vshrq_n_u8(qhbits, 2); diff --git a/ggml.c b/ggml.c index da78e6de9586b..3202a517b7868 100644 --- a/ggml.c +++ b/ggml.c @@ -271,6 +271,12 @@ inline static void * ggml_aligned_malloc(size_t size) { // floating point type used to accumulate sums typedef double ggml_float; +#undef MIN +#undef MAX + +#define MIN(a, b) ((a) < (b) ? (a) : (b)) +#define MAX(a, b) ((a) > (b) ? (a) : (b)) + // // global data // @@ -604,6 +610,18 @@ ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) { // simd mappings // +#if defined(__ARM_NEON) +#if !defined(__aarch64__) + +// 64-bit compatibility + +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); +} + +#endif +#endif + // we define a common set of C macros which map to specific intrinsics based on the current architecture // we then implement the fundamental computation operations below using only these macros // adding support for new architectures requires to define the corresponding SIMD macros @@ -1616,13 +1634,8 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "ROPE_BACK", "ALIBI", "CLAMP", - "CONV_1D", - "CONV_1D_STAGE_0", - "CONV_1D_STAGE_1", "CONV_TRANSPOSE_1D", - "CONV_2D", - "CONV_2D_STAGE_0", - "CONV_2D_STAGE_1", + "IM2COL", "CONV_TRANSPOSE_2D", "POOL_1D", "POOL_2D", @@ -1653,7 +1666,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "CROSS_ENTROPY_LOSS_BACK", }; -static_assert(GGML_OP_COUNT == 73, "GGML_OP_COUNT != 73"); +static_assert(GGML_OP_COUNT == 68, "GGML_OP_COUNT != 68"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -1703,13 +1716,8 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "rope_back(x)", "alibi(x)", "clamp(x)", - "conv_1d(x)", - "conv_1d_stage_0(x)", - "conv_1d_stage_1(x)", "conv_transpose_1d(x)", - "conv_2d(x)", - "conv_2d_stage_0(x)", - "conv_2d_stage_1(x)", + "im2col(x)", "conv_transpose_2d(x)", "pool_1d(x)", "pool_2d(x)", @@ -1740,7 +1748,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "cross_entropy_loss_back(x,y)", }; -static_assert(GGML_OP_COUNT == 73, "GGML_OP_COUNT != 73"); +static_assert(GGML_OP_COUNT == 68, "GGML_OP_COUNT != 68"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); @@ -1768,13 +1776,7 @@ static void ggml_setup_op_has_task_pass(void) { p[GGML_OP_GET_ROWS_BACK ] = true; p[GGML_OP_DIAG_MASK_INF ] = true; p[GGML_OP_DIAG_MASK_ZERO ] = true; - p[GGML_OP_CONV_1D ] = true; - p[GGML_OP_CONV_1D_STAGE_0 ] = true; - p[GGML_OP_CONV_1D_STAGE_1 ] = true; p[GGML_OP_CONV_TRANSPOSE_1D ] = true; - p[GGML_OP_CONV_2D ] = true; - p[GGML_OP_CONV_2D_STAGE_0 ] = true; - p[GGML_OP_CONV_2D_STAGE_1 ] = true; p[GGML_OP_CONV_TRANSPOSE_2D ] = true; p[GGML_OP_FLASH_ATTN_BACK ] = true; p[GGML_OP_CROSS_ENTROPY_LOSS ] = true; @@ -5128,82 +5130,6 @@ static int64_t ggml_calc_conv_output_size(int64_t ins, int64_t ks, int s, int p, return (ins + 2 * p - d * (ks - 1) - 1) / s + 1; } -// im2col: [N, IC, IL] => [N, OL, IC*K] -// a: [OC，IC, K] -// b: [N, IC, IL] -// result: [N, OL, IC*K] -static struct ggml_tensor * ggml_conv_1d_stage_0( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - int s0, - int p0, - int d0) { - GGML_ASSERT(a->ne[1] == b->ne[1]); - bool is_node = false; - - if (a->grad || b->grad) { - GGML_ASSERT(false); // TODO: implement backward - is_node = true; - } - - const int64_t OL = ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0); - - const int64_t ne[4] = { - a->ne[1] * a->ne[0], - OL, - b->ne[2], - 1, - }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F16, 4, ne); - - int32_t params[] = { s0, p0, d0 }; - ggml_set_op_params(result, params, sizeof(params)); - - result->op = GGML_OP_CONV_1D_STAGE_0; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; - result->src[0] = a; - result->src[1] = b; - - return result; -} - -// ggml_conv_1d_stage_1 - -// gemm: [N, OC, OL] = [OC, IC * K] x [N*OL, IC * K] -// a: [OC, IC, K] -// b: [N, OL, IC * K] -// result: [N, OC, OL] -static struct ggml_tensor * ggml_conv_1d_stage_1( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b) { - - bool is_node = false; - - if (a->grad || b->grad) { - GGML_ASSERT(false); // TODO: implement backward - is_node = true; - } - - const int64_t ne[4] = { - b->ne[1], - a->ne[2], - b->ne[2], - 1, - }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); - - result->op = GGML_OP_CONV_1D_STAGE_1; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; - result->src[0] = a; - result->src[1] = b; - - return result; -} - -// ggml_conv_1d - GGML_API struct ggml_tensor * ggml_conv_1d( struct ggml_context * ctx, struct ggml_tensor * a, @@ -5211,43 +5137,17 @@ GGML_API struct ggml_tensor * ggml_conv_1d( int s0, int p0, int d0) { - struct ggml_tensor * result = ggml_conv_1d_stage_0(ctx, a, b, s0, p0, d0); - result = ggml_conv_1d_stage_1(ctx, a, result); - return result; -} - -// GGML_API struct ggml_tensor * ggml_conv_1d( -// struct ggml_context * ctx, -// struct ggml_tensor * a, -// struct ggml_tensor * b, -// int s0, -// int p0, -// int d0) { -// GGML_ASSERT(ggml_is_matrix(b)); -// GGML_ASSERT(a->ne[1] == b->ne[1]); -// bool is_node = false; - -// if (a->grad || b->grad) { -// GGML_ASSERT(false); // TODO: implement backward -// is_node = true; -// } + struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, 0, p0, 0, d0, 0, false); // [N, OL, IC * K] -// const int64_t ne[4] = { -// ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0), -// a->ne[2], 1, 1, -// }; -// struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); + struct ggml_tensor * result = + ggml_mul_mat(ctx, + ggml_reshape_2d(ctx, im2col, im2col->ne[0], (im2col->ne[2] * im2col->ne[1])), // [N, OL, IC * K] => [N*OL, IC * K] + ggml_reshape_2d(ctx, a, (a->ne[0] * a->ne[1]), a->ne[2])); // [OC，IC, K] => [OC, IC * K] -// int32_t params[] = { s0, p0, d0 }; -// ggml_set_op_params(result, params, sizeof(params)); + result = ggml_reshape_3d(ctx, result, im2col->ne[1], a->ne[2], im2col->ne[2]); // [N, OC, OL] -// result->op = GGML_OP_CONV_1D; -// result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; -// result->src[0] = a; -// result->src[1] = b; - -// return result; -// } + return result; +} // ggml_conv_1d_ph @@ -5310,7 +5210,7 @@ GGML_API struct ggml_tensor * ggml_conv_transpose_1d( // a: [OC，IC, KH, KW] // b: [N, IC, IH, IW] // result: [N, OH, OW, IC*KH*KW] -static struct ggml_tensor * ggml_conv_2d_stage_0( +struct ggml_tensor * ggml_im2col( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, @@ -5319,9 +5219,14 @@ static struct ggml_tensor * ggml_conv_2d_stage_0( int p0, int p1, int d0, - int d1) { + int d1, + bool is_2D) { - GGML_ASSERT(a->ne[2] == b->ne[2]); + if(is_2D) { + GGML_ASSERT(a->ne[2] == b->ne[2]); + } else { + GGML_ASSERT(a->ne[1] == b->ne[1]); + } bool is_node = false; if (a->grad || b->grad) { @@ -5329,81 +5234,51 @@ static struct ggml_tensor * ggml_conv_2d_stage_0( is_node = true; } - const int64_t OH = ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1); - const int64_t OW = ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0); + const int64_t OH = is_2D ? ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1) : 0; + const int64_t OW = ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0); const int64_t ne[4] = { - a->ne[2] * a->ne[1] * a->ne[0], + is_2D ? (a->ne[2] * a->ne[1] * a->ne[0]) : a->ne[1] * a->ne[0], OW, - OH, - b->ne[3], + is_2D ? OH : b->ne[2], + is_2D ? b->ne[3] : 1, }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F16, 4, ne); - int32_t params[] = { s0, s1, p0, p1, d0, d1 }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F16, 4, ne); + int32_t params[] = { s0, s1, p0, p1, d0, d1, (is_2D ? 1 : 0) }; ggml_set_op_params(result, params, sizeof(params)); - result->op = GGML_OP_CONV_2D_STAGE_0; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; - result->src[0] = a; - result->src[1] = b; - - return result; - -} - -// gemm: [N, OC, OH, OW] = [OC, IC * KH * KW] x [N*OH*OW, IC * KH * KW] -// a: [OC, IC, KH, KW] -// b: [N, OH, OW, IC * KH * KW] -// result: [N, OC, OH, OW] -static struct ggml_tensor * ggml_conv_2d_stage_1( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b) { - - bool is_node = false; - - if (a->grad || b->grad) { - GGML_ASSERT(false); // TODO: implement backward - is_node = true; - } - - const int64_t ne[4] = { - b->ne[1], - b->ne[2], - a->ne[3], - b->ne[3], - }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); - - result->op = GGML_OP_CONV_2D_STAGE_1; + result->op = GGML_OP_IM2COL; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; return result; - } // a: [OC，IC, KH, KW] // b: [N, IC, IH, IW] // result: [N, OC, OH, OW] struct ggml_tensor * ggml_conv_2d( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - int s0, - int s1, - int p0, - int p1, - int d0, - int d1) { + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int s0, + int s1, + int p0, + int p1, + int d0, + int d1) { + struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, s1, p0, p1, d0, d1, true); // [N, OH, OW, IC * KH * KW] - struct ggml_tensor * result = ggml_conv_2d_stage_0(ctx, a, b, s0, s1, p0, p1, d0, d1); // [N, OH, OW, IC * KH * KW] - result = ggml_conv_2d_stage_1(ctx, a, result); + struct ggml_tensor * result = + ggml_mul_mat(ctx, + ggml_reshape_2d(ctx, im2col, im2col->ne[0], im2col->ne[3] * im2col->ne[2] * im2col->ne[1]), // [N, OH, OW, IC * KH * KW] => [N*OH*OW, IC * KH * KW] + ggml_reshape_2d(ctx, a, (a->ne[0] * a->ne[1] * a->ne[2]), a->ne[3])); // [OC，IC, KH, KW] => [OC, IC * KH * KW] - return result; + result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], a->ne[3], im2col->ne[3]); // [N, OC, OH, OW] + return result; } // ggml_conv_2d_sk_p0 @@ -9498,6 +9373,8 @@ static bool ggml_compute_forward_mul_mat_use_blas( // TODO: find the optimal values for these if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && + src0->type == GGML_TYPE_F32 && + src1->type == GGML_TYPE_F32 && (ne0 >= 32 && ne1 >= 32 && ne10 >= 32)) { /*printf("BLAS: %d %d %d %d %d\n", ne0, ne1, ne10, ne00, ne01);*/ @@ -9536,7 +9413,7 @@ static void ggml_compute_forward_mul_mat( // we don't support permuted src0 or src1 GGML_ASSERT(nb00 == ggml_type_size(type)); - GGML_ASSERT(nb10 == sizeof(float)); + GGML_ASSERT(nb10 == ggml_type_size(src1->type)); // dst cannot be transposed or permuted GGML_ASSERT(nb0 == sizeof(float)); @@ -11434,9 +11311,9 @@ static void ggml_compute_forward_rope_back( } } -// ggml_compute_forward_conv_1d +// ggml_compute_forward_conv_transpose_1d -static void ggml_compute_forward_conv_1d_f16_f32( +static void ggml_compute_forward_conv_transpose_1d_f16_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, @@ -11453,14 +11330,7 @@ static void ggml_compute_forward_conv_1d_f16_f32( const int ith = params->ith; const int nth = params->nth; - const int nk = ne00; - - // size of the convolution row - the kernel size unrolled across all input channels - const int ew0 = nk*ne01; - - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - const int32_t p0 = ((const int32_t*)(dst->op_params))[1]; - const int32_t d0 = ((const int32_t*)(dst->op_params))[2]; + const int nk = ne00*ne01*ne02; GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); @@ -11468,23 +11338,37 @@ static void ggml_compute_forward_conv_1d_f16_f32( if (params->type == GGML_TASK_INIT) { memset(params->wdata, 0, params->wsize); - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; + // permute kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout) + { + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - for (int64_t i11 = 0; i11 < ne11; i11++) { - const float * const src = (float *)((char *) src1->data + i11*nb11); - ggml_fp16_t * dst_data = wdata; + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i02*nb02 + i01*nb01); + ggml_fp16_t * dst_data = wdata + i01*ne00*ne02; + for (int64_t i00 = 0; i00 < ne00; i00++) { + dst_data[i00*ne02 + i02] = src[i00]; + } + } + } + } - for (int64_t i0 = 0; i0 < ne0; i0++) { - for (int64_t ik = 0; ik < nk; ik++) { - const int idx0 = i0*s0 + ik*d0 - p0; + // permute source data (src1) from (L x Cin) to (Cin x L) + { + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk; + ggml_fp16_t * dst_data = wdata; - if(!(idx0 < 0 || idx0 >= ne10)) { - dst_data[i0*ew0 + i11*nk + ik] = GGML_FP32_TO_FP16(src[idx0]); - } + for (int64_t i11 = 0; i11 < ne11; i11++) { + const float * const src = (float *)((char *) src1->data + i11*nb11); + for (int64_t i10 = 0; i10 < ne10; i10++) { + dst_data[i10*ne11 + i11] = GGML_FP32_TO_FP16(src[i10]); } } } + // need to zero dst since we are accumulating into it + memset(dst->data, 0, ggml_nbytes(dst)); + return; } @@ -11492,8 +11376,10 @@ static void ggml_compute_forward_conv_1d_f16_f32( return; } + const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; + // total rows in dst - const int nr = ne2; + const int nr = ne1; // rows per thread const int dr = (nr + nth - 1)/nth; @@ -11502,22 +11388,26 @@ static void ggml_compute_forward_conv_1d_f16_f32( const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - - for (int i2 = 0; i2 < ne2; i2++) { - for (int i1 = ir0; i1 < ir1; i1++) { - float * dst_data = (float *)((char *) dst->data + i2*nb2 + i1*nb1); + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; + ggml_fp16_t * const wdata_src = wdata + nk; - for (int i0 = 0; i0 < ne0; i0++) { - ggml_vec_dot_f16(ew0, dst_data + i0, - (ggml_fp16_t *) ((char *) src0->data + i1*nb02), - (ggml_fp16_t *) wdata + i2*nb2 + i0*ew0); + for (int i1 = ir0; i1 < ir1; i1++) { + float * dst_data = (float *)((char *) dst->data + i1*nb1); + ggml_fp16_t * wdata_kernel = wdata + i1*ne02*ne00; + for (int i10 = 0; i10 < ne10; i10++) { + const int i1n = i10*ne11; + for (int i00 = 0; i00 < ne00; i00++) { + float v = 0; + ggml_vec_dot_f16(ne02, &v, + (ggml_fp16_t *) wdata_src + i1n, + (ggml_fp16_t *) wdata_kernel + i00*ne02); + dst_data[i10*s0 + i00] += v; } } } } -static void ggml_compute_forward_conv_1d_f32( +static void ggml_compute_forward_conv_transpose_1d_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, @@ -11534,13 +11424,7 @@ static void ggml_compute_forward_conv_1d_f32( const int ith = params->ith; const int nth = params->nth; - const int nk = ne00; - - const int ew0 = nk*ne01; - - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - const int32_t p0 = ((const int32_t*)(dst->op_params))[1]; - const int32_t d0 = ((const int32_t*)(dst->op_params))[2]; + const int nk = ne00*ne01*ne02; GGML_ASSERT(nb00 == sizeof(float)); GGML_ASSERT(nb10 == sizeof(float)); @@ -11548,23 +11432,37 @@ static void ggml_compute_forward_conv_1d_f32( if (params->type == GGML_TASK_INIT) { memset(params->wdata, 0, params->wsize); - float * const wdata = (float *) params->wdata + 0; + // prepare kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout) + { + float * const wdata = (float *) params->wdata + 0; - for (int64_t i11 = 0; i11 < ne11; i11++) { - const float * const src = (float *)((char *) src1->data + i11*nb11); - float * dst_data = wdata; + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + const float * const src = (float *)((char *) src0->data + i02*nb02 + i01*nb01); + float * dst_data = wdata + i01*ne00*ne02; + for (int64_t i00 = 0; i00 < ne00; i00++) { + dst_data[i00*ne02 + i02] = src[i00]; + } + } + } + } - for (int64_t i0 = 0; i0 < ne0; i0++) { - for (int64_t ik = 0; ik < nk; ik++) { - const int idx0 = i0*s0 + ik*d0 - p0; + // prepare source data (src1) + { + float * const wdata = (float *) params->wdata + nk; + float * dst_data = wdata; - if(!(idx0 < 0 || idx0 >= ne10)) { - dst_data[i0*ew0 + i11*nk + ik] = src[idx0]; - } + for (int64_t i11 = 0; i11 < ne11; i11++) { + const float * const src = (float *)((char *) src1->data + i11*nb11); + for (int64_t i10 = 0; i10 < ne10; i10++) { + dst_data[i10*ne11 + i11] = src[i10]; } } } + // need to zero dst since we are accumulating into it + memset(dst->data, 0, ggml_nbytes(dst)); + return; } @@ -11572,8 +11470,10 @@ static void ggml_compute_forward_conv_1d_f32( return; } + const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; + // total rows in dst - const int nr = ne02; + const int nr = ne1; // rows per thread const int dr = (nr + nth - 1)/nth; @@ -11582,94 +11482,50 @@ static void ggml_compute_forward_conv_1d_f32( const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); - float * const wdata = (float *) params->wdata + 0; - - for (int i2 = 0; i2 < ne2; i2++) { - for (int i1 = ir0; i1 < ir1; i1++) { - float * dst_data = (float *)((char *) dst->data + i2*nb2 + i1*nb1); + float * const wdata = (float *) params->wdata + 0; + float * const wdata_src = wdata + nk; - for (int i0 = 0; i0 < ne0; i0++) { - ggml_vec_dot_f32(ew0, dst_data + i0, - (float *) ((char *) src0->data + i1*nb02), - (float *) wdata + i2*nb2 + i0*ew0); + for (int i1 = ir0; i1 < ir1; i1++) { + float * dst_data = (float *)((char *) dst->data + i1*nb1); + float * wdata_kernel = wdata + i1*ne02*ne00; + for (int i10 = 0; i10 < ne10; i10++) { + const int i1n = i10*ne11; + for (int i00 = 0; i00 < ne00; i00++) { + float v = 0; + ggml_vec_dot_f32(ne02, &v, + wdata_src + i1n, + wdata_kernel + i00*ne02); + dst_data[i10*s0 + i00] += v; } } } } -// TODO: reuse ggml_mul_mat or implement ggml_im2col and remove stage_0 and stage_1 -static void gemm_f16_out_f32(int64_t m, int64_t n, int64_t k, - ggml_fp16_t * A, - ggml_fp16_t * B, - float * C, - const int ith, const int nth) { - // does not seem to make a difference - int64_t m0, m1, n0, n1; - // patches per thread - if (m > n) { - n0 = 0; - n1 = n; - - // total patches in dst - const int np = m; - - // patches per thread - const int dp = (np + nth - 1)/nth; - - // patch range for this thread - m0 = dp*ith; - m1 = MIN(m0 + dp, np); - } else { - m0 = 0; - m1 = m; - - // total patches in dst - const int np = n; - - // patches per thread - const int dp = (np + nth - 1)/nth; - - // patch range for this thread - n0 = dp*ith; - n1 = MIN(n0 + dp, np); - } - - // block-tiling attempt - int64_t blck_n = 16; - int64_t blck_m = 16; - - // int64_t CACHE_SIZE = 2 * 1024 * 1024; // 2MB - // int64_t blck_size = CACHE_SIZE / (sizeof(float) + 2 * sizeof(ggml_fp16_t) * K); - // if (blck_size > 0) { - // blck_0 = 4; - // blck_1 = blck_size / blck_0; - // if (blck_1 < 0) { - // blck_1 = 1; - // } - // // blck_0 = (int64_t)sqrt(blck_size); - // // blck_1 = blck_0; - // } - // // printf("%zd %zd %zd %zd\n", blck_size, K, blck_0, blck_1); - - for (int j = n0; j < n1; j+=blck_n) { - for (int i = m0; i < m1; i+=blck_m) { - // printf("i j k => %d %d %d\n", i, j, K); - for (int ii = i; ii < i + blck_m && ii < m1; ii++) { - for (int jj = j; jj < j + blck_n && jj < n1; jj++) { - ggml_vec_dot_f16(k, - C + ii*n + jj, - A + ii * k, - B + jj * k); - } - } - } +static void ggml_compute_forward_conv_transpose_1d( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + const struct ggml_tensor * src1, + struct ggml_tensor * dst) { + switch (src0->type) { + case GGML_TYPE_F16: + { + ggml_compute_forward_conv_transpose_1d_f16_f32(params, src0, src1, dst); + } break; + case GGML_TYPE_F32: + { + ggml_compute_forward_conv_transpose_1d_f32(params, src0, src1, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; } } -// src0: kernel [OC, IC, K] -// src1: signal [N, IC, IL] -// dst: result [N, OL, IC*K] -static void ggml_compute_forward_conv_1d_stage_0_f32( +// src0: kernel [OC, IC, KH, KW] +// src1: image [N, IC, IH, IW] +// dst: result [N, OH, OW, IC*KH*KW] +static void ggml_compute_forward_im2col_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, @@ -11683,26 +11539,35 @@ static void ggml_compute_forward_conv_1d_stage_0_f32( GGML_TENSOR_BINARY_OP_LOCALS; - const int64_t N = ne12; - const int64_t IC = ne11; - const int64_t IL = ne10; - - const int64_t K = ne00; - - const int64_t OL = ne1; + const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; + const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; const int ith = params->ith; const int nth = params->nth; - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - const int32_t p0 = ((const int32_t*)(dst->op_params))[1]; - const int32_t d0 = ((const int32_t*)(dst->op_params))[2]; + const int64_t N = is_2D ? ne13 : ne12; + const int64_t IC = is_2D ? ne12 : ne11; + const int64_t IH = is_2D ? ne11 : 1; + const int64_t IW = ne10; + + const int64_t KH = is_2D ? ne01 : 1; + const int64_t KW = ne00; + + const int64_t OH = is_2D ? ne2 : 1; + const int64_t OW = ne1; + + int ofs0 = is_2D ? nb13 : nb12; + int ofs1 = is_2D ? nb12 : nb11; GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); if (params->type == GGML_TASK_INIT) { - memset(dst->data, 0, ggml_nbytes(dst)); return; } @@ -11710,424 +11575,27 @@ static void ggml_compute_forward_conv_1d_stage_0_f32( return; } - // im2col: [N, IC, IL] => [N, OL, IC*K] + // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] { ggml_fp16_t * const wdata = (ggml_fp16_t *) dst->data; for (int64_t in = 0; in < N; in++) { - for (int64_t iol = 0; iol < OL; iol++) { - for (int64_t iic = ith; iic < IC; iic+=nth) { - - // micro kernel - ggml_fp16_t * dst_data = wdata + (in*OL + iol)*(IC*K); // [IC, K] - const float * const src_data = (float *)((char *) src1->data + in*nb12 + iic*nb11); // [IL] - - for (int64_t ik = 0; ik < K; ik++) { - const int64_t iil = iol*s0 + ik*d0 - p0; - - if (!(iil < 0 || iil >= IL)) { - dst_data[iic*K + ik] = GGML_FP32_TO_FP16(src_data[iil]); - } - } - } - } - } - } -} - -// gemm: [N, OC, OL] = [OC, IC * K] x [N*OL, IC * K] -// src0: [OC, IC, K] -// src1: [N, OL, IC * K] -// result: [N, OC, OL] -static void ggml_compute_forward_conv_1d_stage_1_f16( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F16); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - if (params->type == GGML_TASK_INIT) { - return; - } - - if (params->type == GGML_TASK_FINALIZE) { - return; - } - - GGML_TENSOR_BINARY_OP_LOCALS; - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb0 == sizeof(float)); - - const int N = ne12; - const int OL = ne11; - - const int OC = ne02; - const int IC = ne01; - const int K = ne00; - - const int ith = params->ith; - const int nth = params->nth; - - int64_t m = OC; - int64_t n = OL; - int64_t k = IC * K; - - // [N, OC, OL] = [OC, IC * K] x [N*OL, IC * K] - for (int i = 0; i < N; i++) { - ggml_fp16_t * A = (ggml_fp16_t *)src0->data; // [m, k] - ggml_fp16_t * B = (ggml_fp16_t *)src1->data + i * m * k; // [n, k] - float * C = (float *)dst->data + i * m * n; // [m, n] - - gemm_f16_out_f32(m, n, k, A, B, C, ith, nth); - } -} - -static void ggml_compute_forward_conv_1d( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - switch(src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_conv_1d_f16_f32(params, src0, src1, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_conv_1d_f32(params, src0, src1, dst); - } break; - default: - { - GGML_ASSERT(false); - } break; - } -} - -static void ggml_compute_forward_conv_1d_stage_0( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - switch(src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_conv_1d_stage_0_f32(params, src0, src1, dst); - } break; - default: - { - GGML_ASSERT(false); - } break; - } -} - -static void ggml_compute_forward_conv_1d_stage_1( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - switch(src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_conv_1d_stage_1_f16(params, src0, src1, dst); - } break; - default: - { - GGML_ASSERT(false); - } break; - } -} - -// ggml_compute_forward_conv_transpose_1d - -static void ggml_compute_forward_conv_transpose_1d_f16_f32( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const int nk = ne00*ne01*ne02; - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(float)); - - if (params->type == GGML_TASK_INIT) { - memset(params->wdata, 0, params->wsize); - - // permute kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout) - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i02*nb02 + i01*nb01); - ggml_fp16_t * dst_data = wdata + i01*ne00*ne02; - for (int64_t i00 = 0; i00 < ne00; i00++) { - dst_data[i00*ne02 + i02] = src[i00]; - } - } - } - } - - // permute source data (src1) from (L x Cin) to (Cin x L) - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk; - ggml_fp16_t * dst_data = wdata; - - for (int64_t i11 = 0; i11 < ne11; i11++) { - const float * const src = (float *)((char *) src1->data + i11*nb11); - for (int64_t i10 = 0; i10 < ne10; i10++) { - dst_data[i10*ne11 + i11] = GGML_FP32_TO_FP16(src[i10]); - } - } - } - - // need to zero dst since we are accumulating into it - memset(dst->data, 0, ggml_nbytes(dst)); - - return; - } - - if (params->type == GGML_TASK_FINALIZE) { - return; - } - - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - - // total rows in dst - const int nr = ne1; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - ggml_fp16_t * const wdata_src = wdata + nk; - - for (int i1 = ir0; i1 < ir1; i1++) { - float * dst_data = (float *)((char *) dst->data + i1*nb1); - ggml_fp16_t * wdata_kernel = wdata + i1*ne02*ne00; - for (int i10 = 0; i10 < ne10; i10++) { - const int i1n = i10*ne11; - for (int i00 = 0; i00 < ne00; i00++) { - float v = 0; - ggml_vec_dot_f16(ne02, &v, - (ggml_fp16_t *) wdata_src + i1n, - (ggml_fp16_t *) wdata_kernel + i00*ne02); - dst_data[i10*s0 + i00] += v; - } - } - } -} - -static void ggml_compute_forward_conv_transpose_1d_f32( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const int nk = ne00*ne01*ne02; - - GGML_ASSERT(nb00 == sizeof(float)); - GGML_ASSERT(nb10 == sizeof(float)); - - if (params->type == GGML_TASK_INIT) { - memset(params->wdata, 0, params->wsize); - - // prepare kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout) - { - float * const wdata = (float *) params->wdata + 0; - - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - const float * const src = (float *)((char *) src0->data + i02*nb02 + i01*nb01); - float * dst_data = wdata + i01*ne00*ne02; - for (int64_t i00 = 0; i00 < ne00; i00++) { - dst_data[i00*ne02 + i02] = src[i00]; - } - } - } - } - - // prepare source data (src1) - { - float * const wdata = (float *) params->wdata + nk; - float * dst_data = wdata; - - for (int64_t i11 = 0; i11 < ne11; i11++) { - const float * const src = (float *)((char *) src1->data + i11*nb11); - for (int64_t i10 = 0; i10 < ne10; i10++) { - dst_data[i10*ne11 + i11] = src[i10]; - } - } - } - - // need to zero dst since we are accumulating into it - memset(dst->data, 0, ggml_nbytes(dst)); - - return; - } - - if (params->type == GGML_TASK_FINALIZE) { - return; - } - - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - - // total rows in dst - const int nr = ne1; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - float * const wdata = (float *) params->wdata + 0; - float * const wdata_src = wdata + nk; - - for (int i1 = ir0; i1 < ir1; i1++) { - float * dst_data = (float *)((char *) dst->data + i1*nb1); - float * wdata_kernel = wdata + i1*ne02*ne00; - for (int i10 = 0; i10 < ne10; i10++) { - const int i1n = i10*ne11; - for (int i00 = 0; i00 < ne00; i00++) { - float v = 0; - ggml_vec_dot_f32(ne02, &v, - wdata_src + i1n, - wdata_kernel + i00*ne02); - dst_data[i10*s0 + i00] += v; - } - } - } -} - -static void ggml_compute_forward_conv_transpose_1d( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_conv_transpose_1d_f16_f32(params, src0, src1, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_conv_transpose_1d_f32(params, src0, src1, dst); - } break; - default: - { - GGML_ASSERT(false); - } break; - } -} - -// ggml_compute_forward_conv_2d - -// src0: kernel [OC, IC, KH, KW] -// src1: image [N, IC, IH, IW] -// dst: result [N, OH, OW, IC*KH*KW] -static void ggml_compute_forward_conv_2d_stage_0_f32( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F16); - - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - GGML_TENSOR_BINARY_OP_LOCALS; - - const int64_t N = ne13; - const int64_t IC = ne12; - const int64_t IH = ne11; - const int64_t IW = ne10; - - // const int64_t OC = ne03; - // const int64_t IC = ne02; - const int64_t KH = ne01; - const int64_t KW = ne00; - - const int64_t OH = ne2; - const int64_t OW = ne1; - - const int ith = params->ith; - const int nth = params->nth; - - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - const int32_t s1 = ((const int32_t*)(dst->op_params))[1]; - const int32_t p0 = ((const int32_t*)(dst->op_params))[2]; - const int32_t p1 = ((const int32_t*)(dst->op_params))[3]; - const int32_t d0 = ((const int32_t*)(dst->op_params))[4]; - const int32_t d1 = ((const int32_t*)(dst->op_params))[5]; - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(float)); - - if (params->type == GGML_TASK_INIT) { - memset(dst->data, 0, ggml_nbytes(dst)); - return; - } - - if (params->type == GGML_TASK_FINALIZE) { - return; - } - - // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) dst->data; - - for (int64_t in = 0; in < N; in++) { - for (int64_t ioh = 0; ioh < OH; ioh++) { - for (int64_t iow = 0; iow < OW; iow++) { - for (int64_t iic = ith; iic < IC; iic+=nth) { + for (int64_t ioh = 0; ioh < OH; ioh++) { // 1 + for (int64_t iow = 0; iow < OW; iow++) { + for (int64_t iic = ith; iic < IC; iic += nth) { // micro kernel ggml_fp16_t * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] - const float * const src_data = (float *)((char *) src1->data + in*nb13 + iic*nb12); // [IH, IW] + const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW] - for (int64_t ikh = 0; ikh < KH; ikh++) { + for (int64_t ikh = 0; ikh < KH; ikh++) { // 1 for (int64_t ikw = 0; ikw < KW; ikw++) { const int64_t iiw = iow*s0 + ikw*d0 - p0; const int64_t iih = ioh*s1 + ikh*d1 - p1; - if (!(iih < 0 || iih >= IH || iiw < 0 || iiw >= IW)) { + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { + dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0; + } else { dst_data[iic*(KH*KW) + ikh*KW + ikw] = GGML_FP32_TO_FP16(src_data[iih*IW + iiw]); } } @@ -12139,223 +11607,7 @@ static void ggml_compute_forward_conv_2d_stage_0_f32( } } -// gemm: [N, OC, OH, OW] = [OC, IC * KH * KW] x [N*OH*OW, IC * KH * KW] -// src0: [OC, IC, KH, KW] -// src1: [N, OH, OW, IC * KH * KW] -// result: [N, OC, OH, OW] -static void ggml_compute_forward_conv_2d_stage_1_f16( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F16); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - if (params->type == GGML_TASK_INIT) { - return; - } - - if (params->type == GGML_TASK_FINALIZE) { - return; - } - - GGML_TENSOR_BINARY_OP_LOCALS; - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb0 == sizeof(float)); - - const int N = ne13; - const int OH = ne12; - const int OW = ne11; - - const int OC = ne03; - const int IC = ne02; - const int KH = ne01; - const int KW = ne00; - - const int ith = params->ith; - const int nth = params->nth; - - int64_t m = OC; - int64_t n = OH * OW; - int64_t k = IC * KH * KW; - - // [N, OC, OH, OW] = [OC, IC * KH * KW] x [N*OH*OW, IC * KH * KW] - for (int i = 0; i < N; i++) { - ggml_fp16_t * A = (ggml_fp16_t *)src0->data; // [m, k] - ggml_fp16_t * B = (ggml_fp16_t *)src1->data + i * m * k; // [n, k] - float * C = (float *)dst->data + i * m * n; // [m, n] - - gemm_f16_out_f32(m, n, k, A, B, C, ith, nth); - } -} - -static void ggml_compute_forward_conv_2d_f16_f32( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - GGML_TENSOR_BINARY_OP_LOCALS - - // src1: image [N, IC, IH, IW] - // src0: kernel [OC, IC, KH, KW] - // dst: result [N, OC, OH, OW] - // ne12: IC - // ne0: OW - // ne1: OH - // nk0: KW - // nk1: KH - // ne13: N - - const int N = ne13; - const int IC = ne12; - const int IH = ne11; - const int IW = ne10; - - const int OC = ne03; - // const int IC = ne02; - const int KH = ne01; - const int KW = ne00; - - const int OH = ne1; - const int OW = ne0; - - const int ith = params->ith; - const int nth = params->nth; - - // const int nk0 = ne00; - // const int nk1 = ne01; - - // size of the convolution row - the kernel size unrolled across all channels - // const int ew0 = nk0*nk1*ne02; - // ew0: IC*KH*KW - - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - const int32_t s1 = ((const int32_t*)(dst->op_params))[1]; - const int32_t p0 = ((const int32_t*)(dst->op_params))[2]; - const int32_t p1 = ((const int32_t*)(dst->op_params))[3]; - const int32_t d0 = ((const int32_t*)(dst->op_params))[4]; - const int32_t d1 = ((const int32_t*)(dst->op_params))[5]; - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(float)); - - if (params->type == GGML_TASK_INIT) { - memset(params->wdata, 0, params->wsize); - - // prepare source data (src1) - // im2col: [N, IC, IH, IW] => [N*OH*OW, IC*KH*KW] - - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - - for (int in = 0; in < N; in++) { - for (int iic = 0; iic < IC; iic++) { - for (int ioh = 0; ioh < OH; ioh++) { - for (int iow = 0; iow < OW; iow++) { - - // micro kernel - ggml_fp16_t * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] - const float * const src_data = (float *)((char *) src1->data + in*nb13 + iic*nb12); // [IH, IW] - - for (int ikh = 0; ikh < KH; ikh++) { - for (int ikw = 0; ikw < KW; ikw++) { - const int iiw = iow*s0 + ikw*d0 - p0; - const int iih = ioh*s1 + ikh*d1 - p1; - - if (!(iih < 0 || iih >= IH || iiw < 0 || iiw >= IW)) { - dst_data[iic*(KH*KW) + ikh*KW + ikw] = GGML_FP32_TO_FP16(src_data[iih*IW + iiw]); - } - } - } - } - } - } - } - } - - return; - } - - if (params->type == GGML_TASK_FINALIZE) { - return; - } - - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - // wdata: [N*OH*OW, IC*KH*KW] - // dst: result [N, OC, OH, OW] - // src0: kernel [OC, IC, KH, KW] - - int64_t m = OC; - int64_t n = OH * OW; - int64_t k = IC * KH * KW; - - // [N, OC, OH, OW] = [OC, IC * KH * KW] x [N*OH*OW, IC * KH * KW] - for (int i = 0; i < N; i++) { - ggml_fp16_t * A = (ggml_fp16_t *)src0->data; // [m, k] - ggml_fp16_t * B = (ggml_fp16_t *)wdata + i * m * k; // [n, k] - float * C = (float *)dst->data + i * m * n; // [m * k] - - gemm_f16_out_f32(m, n, k, A, B, C, ith, nth); - } -} - -static void ggml_compute_forward_conv_2d( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_conv_2d_f16_f32(params, src0, src1, dst); - } break; - case GGML_TYPE_F32: - { - //ggml_compute_forward_conv_2d_f32(params, src0, src1, dst); - GGML_ASSERT(false); - } break; - default: - { - GGML_ASSERT(false); - } break; - } -} - -static void ggml_compute_forward_conv_2d_stage_0( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_conv_2d_stage_0_f32(params, src0, src1, dst); - } break; - case GGML_TYPE_F32: - { - GGML_ASSERT(false); - } break; - default: - { - GGML_ASSERT(false); - } break; - } -} - -static void ggml_compute_forward_conv_2d_stage_1( +static void ggml_compute_forward_im2col( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, @@ -12363,7 +11615,7 @@ static void ggml_compute_forward_conv_2d_stage_1( switch (src0->type) { case GGML_TYPE_F16: { - ggml_compute_forward_conv_2d_stage_1_f16(params, src0, src1, dst); + ggml_compute_forward_im2col_f16(params, src0, src1, dst); } break; case GGML_TYPE_F32: { @@ -14580,33 +13832,13 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_clamp(params, tensor->src[0], tensor); } break; - case GGML_OP_CONV_1D: - { - ggml_compute_forward_conv_1d(params, tensor->src[0], tensor->src[1], tensor); - } break; - case GGML_OP_CONV_1D_STAGE_0: - { - ggml_compute_forward_conv_1d_stage_0(params, tensor->src[0], tensor->src[1], tensor); - } break; - case GGML_OP_CONV_1D_STAGE_1: - { - ggml_compute_forward_conv_1d_stage_1(params, tensor->src[0], tensor->src[1], tensor); - } break; case GGML_OP_CONV_TRANSPOSE_1D: { ggml_compute_forward_conv_transpose_1d(params, tensor->src[0], tensor->src[1], tensor); } break; - case GGML_OP_CONV_2D: - { - ggml_compute_forward_conv_2d(params, tensor->src[0], tensor->src[1], tensor); - } break; - case GGML_OP_CONV_2D_STAGE_0: - { - ggml_compute_forward_conv_2d_stage_0(params, tensor->src[0], tensor->src[1], tensor); - } break; - case GGML_OP_CONV_2D_STAGE_1: + case GGML_OP_IM2COL: { - ggml_compute_forward_conv_2d_stage_1(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_im2col(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_CONV_TRANSPOSE_2D: { @@ -15588,31 +14820,11 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { GGML_ASSERT(false); // TODO: not implemented } break; - case GGML_OP_CONV_1D: - { - GGML_ASSERT(false); // TODO: not implemented - } break; - case GGML_OP_CONV_1D_STAGE_0: - { - GGML_ASSERT(false); // TODO: not implemented - } break; - case GGML_OP_CONV_1D_STAGE_1: - { - GGML_ASSERT(false); // TODO: not implemented - } break; case GGML_OP_CONV_TRANSPOSE_1D: { GGML_ASSERT(false); // TODO: not implemented } break; - case GGML_OP_CONV_2D: - { - GGML_ASSERT(false); // TODO: not implemented - } break; - case GGML_OP_CONV_2D_STAGE_0: - { - GGML_ASSERT(false); // TODO: not implemented - } break; - case GGML_OP_CONV_2D_STAGE_1: + case GGML_OP_IM2COL: { GGML_ASSERT(false); // TODO: not implemented } break; @@ -16341,31 +15553,11 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { { n_tasks = 1; //TODO } break; - case GGML_OP_CONV_1D: - { - n_tasks = n_threads; - } break; - case GGML_OP_CONV_1D_STAGE_0: - { - n_tasks = n_threads; - } break; - case GGML_OP_CONV_1D_STAGE_1: - { - n_tasks = n_threads; - } break; case GGML_OP_CONV_TRANSPOSE_1D: { n_tasks = n_threads; } break; - case GGML_OP_CONV_2D: - { - n_tasks = n_threads; - } break; - case GGML_OP_CONV_2D_STAGE_0: - { - n_tasks = n_threads; - } break; - case GGML_OP_CONV_2D_STAGE_1: + case GGML_OP_IM2COL: { n_tasks = n_threads; } break; @@ -16450,6 +15642,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { } break; default: { + printf("%s: op %s not implemented\n", __func__, ggml_op_name(node->op)); GGML_ASSERT(false); } break; } @@ -16652,38 +15845,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks; } } break; - case GGML_OP_CONV_1D: - { - GGML_ASSERT(node->src[0]->ne[3] == 1); - GGML_ASSERT(node->src[1]->ne[2] == 1); - GGML_ASSERT(node->src[1]->ne[3] == 1); - - const int64_t ne00 = node->src[0]->ne[0]; - const int64_t ne01 = node->src[0]->ne[1]; - const int64_t ne02 = node->src[0]->ne[2]; - - const int64_t ne10 = node->src[1]->ne[0]; - const int64_t ne11 = node->src[1]->ne[1]; - - const int64_t ne0 = node->ne[0]; - const int64_t ne1 = node->ne[1]; - const int64_t nk = ne00; - const int64_t ew0 = nk * ne01; - - UNUSED(ne02); - UNUSED(ne10); - UNUSED(ne11); - - if (node->src[0]->type == GGML_TYPE_F16 && - node->src[1]->type == GGML_TYPE_F32) { - cur = sizeof(ggml_fp16_t)*(ne0*ne1*ew0); - } else if (node->src[0]->type == GGML_TYPE_F32 && - node->src[1]->type == GGML_TYPE_F32) { - cur = sizeof(float)*(ne0*ne1*ew0); - } else { - GGML_ASSERT(false); - } - } break; case GGML_OP_CONV_TRANSPOSE_1D: { GGML_ASSERT(node->src[0]->ne[3] == 1); @@ -16709,37 +15870,9 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { GGML_ASSERT(false); } } break; - case GGML_OP_CONV_2D: + case GGML_OP_IM2COL: { - const int64_t ne00 = node->src[0]->ne[0]; // W - const int64_t ne01 = node->src[0]->ne[1]; // H - const int64_t ne02 = node->src[0]->ne[2]; // C - const int64_t ne03 = node->src[0]->ne[3]; // N - - const int64_t ne10 = node->src[1]->ne[0]; // W - const int64_t ne11 = node->src[1]->ne[1]; // H - const int64_t ne12 = node->src[1]->ne[2]; // C - - const int64_t ne0 = node->ne[0]; - const int64_t ne1 = node->ne[1]; - const int64_t ne2 = node->ne[2]; - const int64_t ne3 = node->ne[3]; - const int64_t nk = ne00*ne01; - const int64_t ew0 = nk * ne02; - - UNUSED(ne03); - UNUSED(ne2); - - if (node->src[0]->type == GGML_TYPE_F16 && - node->src[1]->type == GGML_TYPE_F32) { - // im2col: [N*OH*OW, IC*KH*KW] - cur = sizeof(ggml_fp16_t)*(ne3*ne0*ne1*ew0); - } else if (node->src[0]->type == GGML_TYPE_F32 && - node->src[1]->type == GGML_TYPE_F32) { - cur = sizeof(float)* (ne10*ne11*ne12); - } else { - GGML_ASSERT(false); - } + n_tasks = n_threads; } break; case GGML_OP_CONV_TRANSPOSE_2D: { diff --git a/ggml.h b/ggml.h index 0118c99dbafdd..8e6b646066b7a 100644 --- a/ggml.h +++ b/ggml.h @@ -403,13 +403,8 @@ extern "C" { GGML_OP_ROPE_BACK, GGML_OP_ALIBI, GGML_OP_CLAMP, - GGML_OP_CONV_1D, - GGML_OP_CONV_1D_STAGE_0, // internal - GGML_OP_CONV_1D_STAGE_1, // internal GGML_OP_CONV_TRANSPOSE_1D, - GGML_OP_CONV_2D, - GGML_OP_CONV_2D_STAGE_0, // internal - GGML_OP_CONV_2D_STAGE_1, // internal + GGML_OP_IM2COL, GGML_OP_CONV_TRANSPOSE_2D, GGML_OP_POOL_1D, GGML_OP_POOL_2D, @@ -1403,6 +1398,18 @@ extern "C" { float min, float max); + GGML_API struct ggml_tensor * ggml_im2col( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int s0, + int s1, + int p0, + int p1, + int d0, + int d1, + bool is_2D); + GGML_API struct ggml_tensor * ggml_conv_1d( struct ggml_context * ctx, struct ggml_tensor * a, From bd90eca237b498dd106d315dcb9ad3e6fae3906f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=2E=20Yusuf=20Sar=C4=B1g=C3=B6z?= Date: Mon, 13 Nov 2023 18:20:52 +0300 Subject: [PATCH 11/39] llava : fix regression for square images in #3613 (#4056) --- examples/llava/clip.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index c26ee4957090c..fc0656c231a0c 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -761,7 +761,7 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip temp->ny = img->ny; temp->size = img->size; temp->data = new uint8_t[temp->size](); - *temp->data = *img->data; // copy + memcpy(&temp->data[0], &img->data[0], temp->size); // copy } const int nx = temp->nx; From b46d12f86d56bef3dc8b596dfb3d22f3b08102be Mon Sep 17 00:00:00 2001 From: afrideva <95653597+afrideva@users.noreply.github.com> Date: Mon, 13 Nov 2023 17:03:40 -0800 Subject: [PATCH 12/39] convert.py: also look for plain model.safetensors (#4043) * add safetensors to convert.py help message * Check for single-file safetensors model * Update convert.py "model" option help message * revert convert.py help message change --- convert.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/convert.py b/convert.py index a4b87e08849bc..3d6216f1d4e7a 100755 --- a/convert.py +++ b/convert.py @@ -1036,7 +1036,8 @@ def load_some_model(path: Path) -> ModelPlus: # Be extra-friendly and accept either a file or a directory: if path.is_dir(): # Check if it's a set of safetensors files first - files = list(path.glob("model-00001-of-*.safetensors")) + globs = ["model-00001-of-*.safetensors", "model.safetensors"] + files = [file for glob in globs for file in path.glob(glob)] if not files: # Try the PyTorch patterns too, with lower priority globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt", "pytorch_model.bin"] @@ -1123,7 +1124,7 @@ def main(args_in: list[str] | None = None) -> None: parser.add_argument("--outtype", choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)") parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file") 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 (*.pth, *.pt, *.bin)") + parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin, *.safetensors)") parser.add_argument("--vocabtype", choices=["spm", "bpe"], help="vocab format (default: spm)", default="spm") parser.add_argument("--ctx", type=int, help="model training context (default: based on input)") parser.add_argument("--concurrency", type=int, help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default = DEFAULT_CONCURRENCY) From 36eed0c42c5b0bf74af81fb9243d262014f9382f Mon Sep 17 00:00:00 2001 From: Galunid Date: Tue, 14 Nov 2023 11:17:12 +0100 Subject: [PATCH 13/39] stablelm : StableLM support (#3586) * Add support for stablelm-3b-4e1t * Supports GPU offloading of (n-1) layers --- README.md | 1 + convert-hf-to-gguf.py | 30 ++- gguf-py/gguf/constants.py | 17 ++ llama.cpp | 284 +++++++++++++++++++++++- models/ggml-vocab-stablelm-3b-4e1t.gguf | Bin 0 -> 1768581 bytes tests/CMakeLists.txt | 2 + 6 files changed, 322 insertions(+), 12 deletions(-) create mode 100644 models/ggml-vocab-stablelm-3b-4e1t.gguf diff --git a/README.md b/README.md index c7d23277845bc..4de06476569f9 100644 --- a/README.md +++ b/README.md @@ -93,6 +93,7 @@ as the main playground for developing new features for the [ggml](https://github - [X] [Persimmon 8B](https://github.com/ggerganov/llama.cpp/pull/3410) - [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417) - [X] [Bloom](https://github.com/ggerganov/llama.cpp/pull/3553) +- [X] [StableLM-3b-4e1t](https://github.com/ggerganov/llama.cpp/pull/3586) **Bindings:** diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index f7fe29fd4262a..e7db7591260af 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -150,8 +150,6 @@ def load_hparams(dir_model): @staticmethod def from_model_architecture(model_architecture): - if model_architecture == "StableLMEpochForCausalLM": - return StableLMModel if model_architecture == "GPTNeoXForCausalLM": return GPTNeoXModel if model_architecture == "BloomForCausalLM": @@ -168,6 +166,8 @@ def from_model_architecture(model_architecture): return RefactModel if model_architecture == "PersimmonForCausalLM": return PersimmonModel + if model_architecture in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"): + return StableLMModel return Model def _is_model_safetensors(self) -> bool: @@ -201,6 +201,8 @@ def _get_model_architecture(self) -> gguf.MODEL_ARCH: return gguf.MODEL_ARCH.REFACT if arch == "PersimmonForCausalLM": return gguf.MODEL_ARCH.PERSIMMON + if arch in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"): + return gguf.MODEL_ARCH.STABLELM raise NotImplementedError(f'Architecture "{arch}" not supported!') @@ -294,15 +296,6 @@ def _set_vocab_sentencepiece(self): special_vocab.add_to_gguf(self.gguf_writer) -class StableLMModel(Model): - def set_gguf_parameters(self): - super().set_gguf_parameters() - self.gguf_writer.add_rope_dimension_count( - int(self.hparams["rope_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])), - ) - self.gguf_writer.add_layer_norm_eps(1e-5) - - class GPTNeoXModel(Model): def set_gguf_parameters(self): block_count = self.hparams["num_hidden_layers"] @@ -824,6 +817,21 @@ def write_tensors(self): self.gguf_writer.add_tensor(new_name, data) +class StableLMModel(Model): + def set_gguf_parameters(self): + hparams = self.hparams + block_count = hparams["num_hidden_layers"] + + self.gguf_writer.add_name(dir_model.name) + self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) + self.gguf_writer.add_embedding_length(hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) + self.gguf_writer.add_rope_dimension_count(int(hparams["rope_pct"]*(hparams["hidden_size"] // hparams["num_attention_heads"]))) + self.gguf_writer.add_head_count(hparams["num_attention_heads"]) + self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True) + self.gguf_writer.add_layer_norm_eps(1e-5) + ###### CONVERSION LOGIC ###### def parse_args() -> argparse.Namespace: diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index bf1ccf66922d0..7f63361bd32bc 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -90,6 +90,7 @@ class MODEL_ARCH(IntEnum): REFACT = auto() BERT = auto() BLOOM = auto() + STABLELM = auto() class MODEL_TENSOR(IntEnum): @@ -129,6 +130,7 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH.REFACT: "refact", MODEL_ARCH.BERT: "bert", MODEL_ARCH.BLOOM: "bloom", + MODEL_ARCH.STABLELM: "stablelm", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -299,6 +301,21 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.STABLELM: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], MODEL_ARCH.GPT2: [ # TODO ], diff --git a/llama.cpp b/llama.cpp index 76ee4ea2300e8..01522fdb4e74f 100644 --- a/llama.cpp +++ b/llama.cpp @@ -192,6 +192,7 @@ enum llm_arch { LLM_ARCH_PERSIMMON, LLM_ARCH_REFACT, LLM_ARCH_BLOOM, + LLM_ARCH_STABLELM, LLM_ARCH_UNKNOWN, }; @@ -207,6 +208,7 @@ static std::map LLM_ARCH_NAMES = { { LLM_ARCH_PERSIMMON, "persimmon" }, { LLM_ARCH_REFACT, "refact" }, { LLM_ARCH_BLOOM, "bloom" }, + { LLM_ARCH_STABLELM, "stablelm" }, }; enum llm_kv { @@ -495,6 +497,25 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, }, }, + { + LLM_ARCH_STABLELM, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ROPE_FREQS, "rope_freqs" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, + { LLM_ARCH_UNKNOWN, { @@ -2216,6 +2237,16 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_STABLELM: + { + GGUF_GET_KEY(ctx, hparams.f_norm_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_EPS)); + + switch (hparams.n_layer) { + case 32: model.type = e_model::MODEL_3B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; + default: (void)0; } @@ -3087,6 +3118,81 @@ static void llm_load_tensors( } } } break; + case LLM_ARCH_STABLELM: + { + model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU); + + // output + { + ggml_backend_type backend_norm; + ggml_backend_type backend_output; + + if (n_gpu_layers > int(n_layer)) { + // norm is not performance relevant on its own but keeping it in VRAM reduces data copying + // on Windows however this is detrimental unless everything is on the GPU +#ifndef _WIN32 + backend_norm = llama_backend_offload; +#else + backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload; +#endif // _WIN32 + + backend_output = llama_backend_offload_split; + } else { + backend_norm = GGML_BACKEND_CPU; + backend_output = GGML_BACKEND_CPU; + } + + model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm); + model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm); + model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output); + + if (backend_norm == GGML_BACKEND_GPU) { + vram_weights += ggml_nbytes(model.output_norm); + } + if (backend_output == GGML_BACKEND_GPU_SPLIT) { + vram_weights += ggml_nbytes(model.output); + } + } + + const uint32_t n_ff = hparams.n_ff; + + const int i_gpu_start = n_layer - n_gpu_layers; + + model.layers.resize(n_layer); + + for (uint32_t i = 0; i < n_layer; ++i) { + /* + llama_model_loader: - tensor 4: blk.0.attn_output.weight f16 [ 2560, 2560, 1, 1 ] + */ + const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT + const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend); + layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend); + + layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split); + layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split); + layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split); + layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); + + layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); + layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend); + + layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split); + layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); + layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); + + if (backend == GGML_BACKEND_GPU) { + vram_weights += + ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) + + ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) + + ggml_nbytes(layer.ffn_gate) + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up); + } + } + } break; + default: throw std::runtime_error("unknown architecture"); } @@ -4565,6 +4671,177 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_stablelm() { + struct ggml_cgraph * gf = ggml_new_graph(ctx0); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + cb(inpL, "inp_embd", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + cb(inp_pos, "inp_pos", -1); + + // KQ_scale + struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); + cb(KQ_scale, "KQ_scale", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + cb(KQ_mask, "KQ_mask", -1); + + // shift the entire K-cache if needed + if (do_rope_shift) { + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, hparams.n_rot, freq_base, freq_scale, cb); + } + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, + model.layers[il].attn_norm_b, + LLM_NORM, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(tmpq, "tmpq", il); + + struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(tmpk, "tmpk", il); + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + + // RoPE the first n_rot of q/k, pass the other half, and concat. + struct ggml_tensor * qrot = ggml_cont(ctx0, ggml_view_3d( + ctx0, tmpq, hparams.n_rot, n_head, n_tokens, + ggml_element_size(tmpq) * n_embd_head, + ggml_element_size(tmpq) * n_embd_head * n_head, + 0 + )); + cb(qrot, "qrot", il); + + struct ggml_tensor * krot = ggml_cont(ctx0, ggml_view_3d( + ctx0, tmpk, hparams.n_rot, n_head, n_tokens, + ggml_element_size(tmpk) * n_embd_head, + ggml_element_size(tmpk) * n_embd_head * n_head_kv, + 0 + )); + cb(krot, "krot", il); + + // get the second half of tmpq, e.g tmpq[n_rot:, :, :] + struct ggml_tensor * qpass = ggml_view_3d( + ctx0, tmpq, (n_embd_head - hparams.n_rot), n_head, n_tokens, + ggml_element_size(tmpq) * n_embd_head, + ggml_element_size(tmpq) * n_embd_head * n_head, + ggml_element_size(tmpq) * hparams.n_rot + ); + cb(qpass, "qpass", il); + + struct ggml_tensor * kpass = ggml_view_3d( + ctx0, tmpk, (n_embd_head - hparams.n_rot), n_head_kv, n_tokens, + ggml_element_size(tmpk) * (n_embd_head), + ggml_element_size(tmpk) * (n_embd_head) * n_head_kv, + ggml_element_size(tmpk) * hparams.n_rot + ); + cb(kpass, "kpass", il); + + struct ggml_tensor * qrotated = ggml_rope_custom( + ctx0, qrot, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(qrotated, "qrotated", il); + + struct ggml_tensor * krotated = ggml_rope_custom( + ctx0, krot, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(krotated, "krotated", il); + + // ggml currently only supports concatenation on dim=2 + // so we need to permute qrot, qpass, concat, then permute back. + qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3)); + cb(qrotated, "qrotated", il); + + krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3)); + cb(krotated, "krotated", il); + + qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3)); + cb(qpass, "qpass", il); + + kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3)); + cb(kpass, "kpass", il); + + struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass); + cb(Qcur, "Qcur", il); + + struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass); + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 2, 1, 0, 3)); + cb(Q, "Q", il); + + Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3)); + cb(Kcur, "Kcur", il); + + llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); + + cur = llm_build_kqv(ctx0, hparams, kv_self, + model.layers[il].wo, NULL, + Q, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il); + cb(cur, "kqv_out", il); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, + model.layers[il].ffn_norm_b, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + } + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, + model.output_norm_b, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; // @@ -5034,6 +5311,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_mpt(); } break; + case LLM_ARCH_STABLELM: + { + result = llm.build_stablelm(); + } break; default: GGML_ASSERT(false); } @@ -5209,7 +5490,8 @@ static int llama_decode_internal( model.arch == LLM_ARCH_FALCON || model.arch == LLM_ARCH_REFACT || model.arch == LLM_ARCH_MPT || - model.arch == LLM_ARCH_STARCODER; + model.arch == LLM_ARCH_STARCODER || + model.arch == LLM_ARCH_STABLELM; const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3; if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) { diff --git a/models/ggml-vocab-stablelm-3b-4e1t.gguf b/models/ggml-vocab-stablelm-3b-4e1t.gguf new file mode 100644 index 0000000000000000000000000000000000000000..ebb0cdb7d6a4ac313f45758010a6fda6ac530443 GIT binary patch literal 1768581 zcmd?S`IlVRapx(|zV`IYnYE{u+BB*FU1ae|XgVPmlh)TaW8$H9T8Q`+Lp2?#~y~`rqL<``7+v^Qt$jhok>s z_3}x__ixzGy!&50`&d2d z)q_DZ-pvpGdms6T&o}z(4cq>shy8Xjo?B->$-n>j+0J(Adhl?ko$goDLGJIr{>VrE 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BIG llama_build_and_test_executable(test-grammar-parser.cpp) llama_build_and_test_executable(test-llama-grammar.cpp) llama_build_and_test_executable(test-grad0.cpp) # SLOW From 6bb4908a17150b49373b5f977685b2e180a04f6f Mon Sep 17 00:00:00 2001 From: Michael Potter Date: Tue, 14 Nov 2023 09:34:41 -0800 Subject: [PATCH 14/39] Fix MacOS Sonoma model quantization (#4052) Co-authored-by: Jared Van Bortel Co-authored-by: Georgi Gerganov --- CMakeLists.txt | 9 +++++++++ Makefile | 5 +++++ ggml-quants.c | 5 +++++ 3 files changed, 19 insertions(+) diff --git a/CMakeLists.txt b/CMakeLists.txt index 7b4eb18403c0b..db1f42f1eda6a 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -458,6 +458,15 @@ if (LLAMA_LTO) endif() endif() +# this version of Apple ld64 is buggy +execute_process( + COMMAND ${CMAKE_C_COMPILER} ${CMAKE_EXE_LINKER_FLAGS} -Wl,-v + ERROR_VARIABLE output +) +if (output MATCHES "dyld-1015\.7") + add_compile_definitions(HAVE_BUGGY_APPLE_LINKER) +endif() + # Architecture specific # TODO: probably these flags need to be tweaked on some architectures # feel free to update the Makefile for your architecture and send a pull request or issue diff --git a/Makefile b/Makefile index d6be254a0f362..36d08811e32b6 100644 --- a/Makefile +++ b/Makefile @@ -239,6 +239,11 @@ else endif endif +# this version of Apple ld64 is buggy +ifneq '' '$(findstring dyld-1015.7,$(shell $(CC) $(LDFLAGS) -Wl,-v 2>&1))' + MK_CPPFLAGS += -DHAVE_BUGGY_APPLE_LINKER +endif + # OS specific # TODO: support Windows ifneq '' '$(filter $(UNAME_S),Linux Darwin FreeBSD NetBSD OpenBSD Haiku)' diff --git a/ggml-quants.c b/ggml-quants.c index a48eda7320c46..cf2860b8cbd59 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -1368,7 +1368,12 @@ static float make_qkx2_quants(int n, int nmax, const float * restrict x, const f float max = x[0]; float sum_w = weights[0]; float sum_x = sum_w * x[0]; +#ifdef HAVE_BUGGY_APPLE_LINKER + // use 'volatile' to prevent unroll and work around a bug in Apple ld64 1015.7 + for (volatile int i = 1; i < n; ++i) { +#else for (int i = 1; i < n; ++i) { +#endif if (x[i] < min) min = x[i]; if (x[i] > max) max = x[i]; float w = weights[i]; From 8b919b5b57761e14c58a0c247e49ef6975312b8a Mon Sep 17 00:00:00 2001 From: Concedo <39025047+LostRuins@users.noreply.github.com> Date: Wed, 15 Nov 2023 16:21:52 +0800 Subject: [PATCH 15/39] allow customized rope to use model set values --- gpttype_adapter.cpp | 33 ++++++++++++++++++++++++++------- koboldcpp.py | 4 ++-- model_adapter.cpp | 8 -------- model_adapter.h | 1 - 4 files changed, 28 insertions(+), 18 deletions(-) diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp index 510536f64e8fa..5af7ab90094dc 100644 --- a/gpttype_adapter.cpp +++ b/gpttype_adapter.cpp @@ -697,10 +697,12 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in //determine rope scaling params float rope_freq_scale = 1.0f; float rope_freq_base = 10000.0f; + bool overwriteRope = false; if(inputs.rope_freq_scale>0.0f) { rope_freq_scale = inputs.rope_freq_scale; rope_freq_base = inputs.rope_freq_base; + overwriteRope = true; printf("Using Custom RoPE scaling (scale:%.3f, base:%.1f).\n",rope_freq_scale,rope_freq_base); } else @@ -722,13 +724,9 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in rope_freq_base = (effectivenctx <= 2048 ? 10000.0f : (effectivenctx <= 3072 ? 26000.0f : (effectivenctx <= 4096 ? 32000.0f : (effectivenctx <= 6144 ? 54000.0f : (effectivenctx <= 8192 ? 82684.0f : (effectivenctx <= 12288 ? 140000.0f : (effectivenctx <= 16384 ? 200000.0f : (effectivenctx <= 24576 ? 320000.0f : 440000.0f)))))))); - if(file_format_meta.freq_base_train > rope_freq_base) - { - rope_freq_base = file_format_meta.freq_base_train; - } } - printf("Using automatic RoPE scaling (scale:%.3f, base:%.1f)\n",rope_freq_scale,rope_freq_base); + printf("Using automatic RoPE scaling. If the model has customized RoPE settings, they will be used directly instead!\n"); } gptj_ctx_v3.hparams.rope_freq_scale = neox_ctx_v3.hparams.rope_freq_scale = rope_freq_scale; gptj_ctx_v3.hparams.rope_freq_base = neox_ctx_v3.hparams.rope_freq_base = rope_freq_base; @@ -903,8 +901,7 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in } #endif model_params.main_gpu = cu_parseinfo_maindevice; - llama_ctx_params.rope_freq_base = rope_freq_base; - llama_ctx_params.rope_freq_scale = rope_freq_scale; + llama_ctx_params.n_batch = blasbatchsize; llama_ctx_params.n_threads = n_threads; llama_ctx_params.n_threads_batch = n_blasthreads; @@ -932,6 +929,28 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in } llama_model * llamamodel = llama_load_model_from_file(modelname.c_str(), model_params); + if(overwriteRope) + { + llama_ctx_params.rope_freq_base = rope_freq_base; + llama_ctx_params.rope_freq_scale = rope_freq_scale; + } + else + { + //if the model modifes rope in any way, use the model values. Otherwise, use our automatic ones + if(llamamodel->hparams.rope_freq_base_train!=10000.0f || + llamamodel->hparams.rope_freq_scale_train!=1.0f || + llamamodel->hparams.rope_scaling_type_train==2) + { + printf("Automatic RoPE Scaling: Using model internal values.\n"); + } + else + { + llama_ctx_params.rope_freq_base = rope_freq_base; + llama_ctx_params.rope_freq_scale = rope_freq_scale; + printf("Automatic RoPE Scaling: Using (scale:%.3f, base:%.1f).\n", rope_freq_scale, rope_freq_base); + } + } + llama_ctx_v4 = llama_new_context_with_model(llamamodel, llama_ctx_params); if (llama_ctx_v4 == NULL) diff --git a/koboldcpp.py b/koboldcpp.py index 21d59e13e7764..7a04cfadd2a88 100755 --- a/koboldcpp.py +++ b/koboldcpp.py @@ -388,7 +388,7 @@ def bring_terminal_to_foreground(): modelbusy = threading.Lock() requestsinqueue = 0 defaultport = 5001 -KcppVersion = "1.49" +KcppVersion = "1.50" showdebug = True showsamplerwarning = True showmaxctxwarning = True @@ -1452,7 +1452,7 @@ def togglehorde(a,b,c): labels[idx].grid_forget() if usehorde_var.get()==1 and (horde_name_var.get()=="koboldcpp" or horde_name_var.get()=="") and model_var.get()!="": basefile = os.path.basename(model_var.get()) - horde_name_var.set(os.path.splitext(basefile)[0]) + horde_name_var.set(sanitize_string(os.path.splitext(basefile)[0])) makecheckbox(network_tab, "Configure for Horde", usehorde_var, 6, command=togglehorde) togglehorde(1,1,1) diff --git a/model_adapter.cpp b/model_adapter.cpp index bebd4b28e892f..ec57d4c9c2814 100644 --- a/model_adapter.cpp +++ b/model_adapter.cpp @@ -290,14 +290,6 @@ void print_tok_vec(std::vector &embd) } int filever = gguf_get_version(ctx); fileformatmeta->fileversion = filever; - - //try to adapt if the rope_freq_base_train exceeds the auto one - fkey = modelarch+".rope.freq_base"; - keyidx = gguf_find_key(ctx, fkey.c_str()); - if (keyidx != -1) { - float fbt = gguf_get_val_f32(ctx, keyidx); - fileformatmeta->freq_base_train = (fbt > 1.0f ? fbt : 0.0f); - } } gguf_free(ctx); } diff --git a/model_adapter.h b/model_adapter.h index bb859dfa1cfbe..65536c6d4a673 100644 --- a/model_adapter.h +++ b/model_adapter.h @@ -55,7 +55,6 @@ struct FileFormatExtraMeta { int n_ctx_train = 2048; int fileversion = 0; - float freq_base_train = 0; }; enum ModelLoadResult From 914e375602c37bd2e59984cab4548881664c3106 Mon Sep 17 00:00:00 2001 From: Concedo <39025047+LostRuins@users.noreply.github.com> Date: Wed, 15 Nov 2023 18:37:50 +0800 Subject: [PATCH 16/39] support custom dalle urls --- klite.embd | 32 ++++++++++++++++++++++++++------ 1 file changed, 26 insertions(+), 6 deletions(-) diff --git a/klite.embd b/klite.embd index 00dbafa593a47..57e6845326dc1 100644 --- a/klite.embd +++ b/klite.embd @@ -6,7 +6,7 @@ It requires no dependencies, installation or setup. Just copy this single static HTML file anywhere and open it in a browser, or from a webserver. Please go to https://github.com/LostRuins/lite.koboldai.net for updates on Kobold Lite. Kobold Lite is under the AGPL v3.0 License unless otherwise exempted. Please do not remove this line. -Current version: 95 +Current version: 96 -Concedo --> @@ -3187,7 +3187,7 @@ Current version: 95 const oai_submit_endpoint = "/completions"; const oai_submit_endpoint_turbo = "/chat/completions"; - const oai_image_endpoint_full = "https://api.openai.com/v1/images/generations"; + const default_oai_image_endpoint = "/images/generations"; const scale_submit_endpoint = "https://dashboard.scale.com/spellbook/api/v2/deploy/" @@ -3282,6 +3282,7 @@ Current version: 95 saved_oai_key: "", //do not ever share this in save files! saved_oai_addr: default_oai_base, //do not ever share this in save files! saved_dalle_key: "", + saved_dalle_url: (default_oai_base + "/v1" + default_oai_image_endpoint), saved_openrouter_key: "", saved_claude_key: "", //do not ever share this in save files! saved_claude_addr: default_claude_base, //do not ever share this in save files! @@ -3970,6 +3971,18 @@ Current version: 95 } },false); } + function set_dalle_url() + { + inputBox("Enter DALL-E API URL.\n\nNote: DALL-E is known to rephrase and rewrite submitted image prompts before generating, for censorship purposes. There is nothing Kobold Lite can do about that. ","DALL-E API URL",localsettings.saved_dalle_url,"Input DALL-E API URL", ()=>{ + let userinput = getInputBoxValue(); + userinput = userinput.trim(); + if (userinput != null && userinput!="") { + localsettings.saved_dalle_url = userinput.trim(); + }else{ + localsettings.saved_dalle_url = (default_oai_base + "/v1" + default_oai_image_endpoint); + } + },false); + } function generate_dalle_image(req_payload, onImagesDone) { @@ -3988,7 +4001,7 @@ Current version: 95 //remove all null fields dalle_payload = Object.fromEntries(Object.entries(dalle_payload).filter(([_, v]) => v != null)); - let gen_endpoint = oai_image_endpoint_full; + let gen_endpoint = localsettings.saved_dalle_url; console.log(dalle_payload); fetch(gen_endpoint, { @@ -4426,6 +4439,7 @@ Current version: 95 new_save_storyobj.savedsettings.home_cluster = text_hordes[0].baseurl; new_save_storyobj.savedsettings.saved_oai_key = ""; new_save_storyobj.savedsettings.saved_dalle_key = ""; + new_save_storyobj.savedsettings.saved_dalle_url = ""; new_save_storyobj.savedsettings.saved_oai_addr = ""; new_save_storyobj.savedsettings.saved_claude_key = ""; new_save_storyobj.savedsettings.saved_claude_addr = ""; @@ -4707,6 +4721,7 @@ Current version: 95 let tmp_oai1 = localsettings.saved_oai_key; let tmp_oai2 = localsettings.saved_oai_addr; let tmp_oai3 = localsettings.saved_dalle_key; + let tmp_oai4 = localsettings.saved_dalle_url; let tmp_or1 = localsettings.saved_openrouter_key; let tmp_claude1 = localsettings.saved_claude_key; let tmp_claude2 = localsettings.saved_claude_addr; @@ -4735,6 +4750,7 @@ Current version: 95 localsettings.saved_oai_key = tmp_oai1; localsettings.saved_oai_addr = tmp_oai2; localsettings.saved_dalle_key = tmp_oai3; + localsettings.saved_dalle_url = tmp_oai4; localsettings.saved_openrouter_key = tmp_or1; localsettings.saved_claude_key = tmp_claude1; localsettings.saved_claude_addr = tmp_claude2; @@ -6212,6 +6228,7 @@ Current version: 95 localsettings.saved_oai_key = custom_oai_key; localsettings.saved_oai_addr = custom_oai_endpoint; localsettings.saved_dalle_key = custom_oai_key; + localsettings.saved_dalle_url = custom_oai_endpoint + default_oai_image_endpoint; }else{ localsettings.saved_openrouter_key = custom_oai_key; } @@ -9007,9 +9024,9 @@ Current version: 95 } else if(localsettings.generate_images_mode==3) //dalle { - if(localsettings.saved_dalle_key=="") + if(localsettings.saved_dalle_key=="" || localsettings.saved_dalle_url=="") { - msgbox("Error: A valid DALL-E Key is required to generate images with DALL-E.\nThis is usually the same as your OpenAI API key, but can be customized in settings.","Invalid DALL-E Key"); + msgbox("Error: A valid DALL-E URL and Key is required to generate images with DALL-E.\nThis is usually the same as your OpenAI API key, but can be customized in settings.","Invalid DALL-E Key"); } else { @@ -12120,7 +12137,10 @@ Current version: 95

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From 1cf2850d52bb027aa215e039ed9a0c61beeef8d3 Mon Sep 17 00:00:00 2001 From: slaren Date: Wed, 15 Nov 2023 13:58:13 +0100 Subject: [PATCH 17/39] ggml-cuda : increase max graph size (#4084) --- ggml-cuda.cu | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 7be63925f4eda..c0c9edd56dbc2 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -88,6 +88,8 @@ #define CC_OFFSET_AMD 1000000 #define CC_RDNA2 (CC_OFFSET_AMD + 1030) +#define GGML_CUDA_MAX_NODES 8192 + // define this if you want to always fallback to MMQ kernels and not use cuBLAS for matrix multiplication // on modern hardware, using cuBLAS is recommended as it utilizes F16 tensor cores which are very performant // for large computational tasks. the drawback is that this requires some extra amount of VRAM: @@ -7727,7 +7729,7 @@ static void ggml_cuda_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_alibi); } -void ggml_cuda_im2col(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_cuda_im2col(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_im2col); } @@ -7842,11 +7844,11 @@ static size_t g_temp_tensor_extra_index = 0; static ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() { if (g_temp_tensor_extras == nullptr) { - g_temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_DEFAULT_GRAPH_SIZE]; + g_temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_CUDA_MAX_NODES]; } size_t alloc_index = g_temp_tensor_extra_index; - g_temp_tensor_extra_index = (g_temp_tensor_extra_index + 1) % GGML_DEFAULT_GRAPH_SIZE; + g_temp_tensor_extra_index = (g_temp_tensor_extra_index + 1) % GGML_CUDA_MAX_NODES; ggml_tensor_extra_gpu * extra = &g_temp_tensor_extras[alloc_index]; memset(extra, 0, sizeof(*extra)); @@ -8173,11 +8175,11 @@ struct ggml_backend_buffer_context_cuda { ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() { if (temp_tensor_extras == nullptr) { - temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_DEFAULT_GRAPH_SIZE]; + temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_CUDA_MAX_NODES]; } size_t alloc_index = temp_tensor_extra_index; - temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_DEFAULT_GRAPH_SIZE; + temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_CUDA_MAX_NODES; ggml_tensor_extra_gpu * extra = &temp_tensor_extras[alloc_index]; memset(extra, 0, sizeof(*extra)); From a6fc554e268634494f33b0de76f9dde650dd292f Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Wed, 15 Nov 2023 11:34:47 -0500 Subject: [PATCH 18/39] llama : restore prefix space in llama tokenizer (#4081) --- llama.cpp | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 01522fdb4e74f..92c4536cb948e 100644 --- a/llama.cpp +++ b/llama.cpp @@ -6283,7 +6283,10 @@ static std::vector llama_tokenize_internal(const llama_vocab & // by modifying llm_tokenizer_x to operate with string offsets like pre-tokenizer // and passing 'add space prefix' as bool argument // - auto raw_text = (special ? "" : " ") + fragment.raw_text.substr(fragment.offset, fragment.length); + auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length); + if (&fragment == &fragment_buffer.front()) { + raw_text = " " + raw_text; // prefix with space if the first token is not special + } #ifdef PRETOKENIZERDEBUG fprintf(stderr,"TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str()); From a3f708afce4324e03b36a7915a0a2da80057fb1d Mon Sep 17 00:00:00 2001 From: Concedo <39025047+LostRuins@users.noreply.github.com> Date: Thu, 16 Nov 2023 00:58:08 +0800 Subject: [PATCH 19/39] added more fields to the openai compatible completions APIs --- koboldcpp.py | 93 ++++++++++++++++++++++++++-------------------------- 1 file changed, 46 insertions(+), 47 deletions(-) diff --git a/koboldcpp.py b/koboldcpp.py index 7a04cfadd2a88..af3e22edc5b35 100755 --- a/koboldcpp.py +++ b/koboldcpp.py @@ -425,56 +425,16 @@ def log_message(self, format, *args): async def generate_text(self, genparams, api_format, stream_flag): global friendlymodelname - def run_blocking(): + def run_blocking(): #api format 1=basic,2=kai,3=oai,4=oai-chat if api_format==1: genparams["prompt"] = genparams.get('text', "") genparams["top_k"] = int(genparams.get('top_k', 120)) - genparams["max_length"] = genparams.get('max', 80) - elif api_format==3: - frqp = genparams.get('frequency_penalty', 0.1) - scaled_rep_pen = genparams.get('presence_penalty', frqp) + 1 - genparams["max_length"] = genparams.get('max_tokens', 80) - genparams["rep_pen"] = scaled_rep_pen - # openai allows either a string or a list as a stop sequence - if isinstance(genparams.get('stop',[]), list): - genparams["stop_sequence"] = genparams.get('stop', []) - else: - genparams["stop_sequence"] = [genparams.get('stop')] - elif api_format==4: - # translate openai chat completion messages format into one big string. - messages_array = genparams.get('messages', []) - adapter_obj = genparams.get('adapter', {}) - messages_string = "" - system_message_start = adapter_obj.get("system_start", "\n### Instruction:\n") - system_message_end = adapter_obj.get("system_end", "") - user_message_start = adapter_obj.get("user_start", "\n### Instruction:\n") - user_message_end = adapter_obj.get("user_end", "") - assistant_message_start = adapter_obj.get("assistant_start", "\n### Response:\n") - assistant_message_end = adapter_obj.get("assistant_end", "") - - for message in messages_array: - if message['role'] == "system": - messages_string += system_message_start - elif message['role'] == "user": - messages_string += user_message_start - elif message['role'] == "assistant": - messages_string += assistant_message_start - - messages_string += message['content'] - - if message['role'] == "system": - messages_string += system_message_end - elif message['role'] == "user": - messages_string += user_message_end - elif message['role'] == "assistant": - messages_string += assistant_message_end - - messages_string += assistant_message_start - - genparams["prompt"] = messages_string + genparams["max_length"] = genparams.get('max', 100) + + elif api_format==3 or api_format==4: frqp = genparams.get('frequency_penalty', 0.1) scaled_rep_pen = genparams.get('presence_penalty', frqp) + 1 - genparams["max_length"] = genparams.get('max_tokens', 80) + genparams["max_length"] = genparams.get('max_tokens', 100) genparams["rep_pen"] = scaled_rep_pen # openai allows either a string or a list as a stop sequence if isinstance(genparams.get('stop',[]), list): @@ -482,11 +442,47 @@ def run_blocking(): else: genparams["stop_sequence"] = [genparams.get('stop')] + genparams["sampler_seed"] = genparams.get('seed', -1) + genparams["use_default_badwordsids"] = genparams.get('ignore_eos', False) + genparams["mirostat"] = genparams.get('mirostat_mode', 0) + + if api_format==4: + # translate openai chat completion messages format into one big string. + messages_array = genparams.get('messages', []) + adapter_obj = genparams.get('adapter', {}) + messages_string = "" + system_message_start = adapter_obj.get("system_start", "\n### Instruction:\n") + system_message_end = adapter_obj.get("system_end", "") + user_message_start = adapter_obj.get("user_start", "\n### Instruction:\n") + user_message_end = adapter_obj.get("user_end", "") + assistant_message_start = adapter_obj.get("assistant_start", "\n### Response:\n") + assistant_message_end = adapter_obj.get("assistant_end", "") + + for message in messages_array: + if message['role'] == "system": + messages_string += system_message_start + elif message['role'] == "user": + messages_string += user_message_start + elif message['role'] == "assistant": + messages_string += assistant_message_start + + messages_string += message['content'] + + if message['role'] == "system": + messages_string += system_message_end + elif message['role'] == "user": + messages_string += user_message_end + elif message['role'] == "assistant": + messages_string += assistant_message_end + + messages_string += assistant_message_start + genparams["prompt"] = messages_string + return generate( prompt=genparams.get('prompt', ""), memory=genparams.get('memory', ""), max_context_length=genparams.get('max_context_length', maxctx), - max_length=genparams.get('max_length', 80), + max_length=genparams.get('max_length', 100), temperature=genparams.get('temperature', 0.7), top_k=genparams.get('top_k', 100), top_a=genparams.get('top_a', 0.0), @@ -578,6 +574,9 @@ async def handle_sse_stream(self, api_format): if api_format == 4: # if oai chat, set format to expected openai streaming response event_str = json.dumps({"id":"koboldcpp","object":"chat.completion.chunk","created":1,"model":friendlymodelname,"choices":[{"index":0,"finish_reason":"length","delta":{'role':'assistant','content':tokenStr}}]}) await self.send_oai_sse_event(event_str) + elif api_format == 3: # non chat completions + event_str = json.dumps({"id":"koboldcpp","object":"text_completion","created":1,"model":friendlymodelname,"choices":[{"index":0,"finish_reason":"length","text":tokenStr}]}) + await self.send_oai_sse_event(event_str) else: event_str = json.dumps({"token": tokenStr}) await self.send_kai_sse_event(event_str) @@ -817,7 +816,7 @@ def do_POST(self): bring_terminal_to_foreground() # Check if streaming chat completions, if so, set stream mode to true - if api_format == 4 and "stream" in genparams and genparams["stream"]: + if (api_format == 4 or api_format == 3) and "stream" in genparams and genparams["stream"]: sse_stream_flag = True gen = asyncio.run(self.handle_request(genparams, api_format, sse_stream_flag)) From 8da46278e1a57107591653275f8e03a281de94f0 Mon Sep 17 00:00:00 2001 From: texmex76 <40733439+texmex76@users.noreply.github.com> Date: Thu, 16 Nov 2023 16:01:48 +0100 Subject: [PATCH 20/39] gguf : fix potential infinite loops while parsing (#4100) Co-authored-by: Bernhard Gstrein --- ggml.c | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/ggml.c b/ggml.c index 3202a517b7868..ada1067da56d4 100644 --- a/ggml.c +++ b/ggml.c @@ -18073,7 +18073,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p { ctx->kv = malloc(ctx->header.n_kv * sizeof(struct gguf_kv)); - for (uint32_t i = 0; i < ctx->header.n_kv; ++i) { + for (uint64_t i = 0; i < ctx->header.n_kv; ++i) { struct gguf_kv * kv = &ctx->kv[i]; //fprintf(stderr, "%s: reading kv %d\n", __func__, i); @@ -18120,7 +18120,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p case GGUF_TYPE_STRING: { kv->value.arr.data = malloc(kv->value.arr.n * sizeof(struct gguf_str)); - for (uint32_t j = 0; j < kv->value.arr.n; ++j) { + for (uint64_t j = 0; j < kv->value.arr.n; ++j) { ok = ok && gguf_fread_str(file, &((struct gguf_str *) kv->value.arr.data)[j], &offset); } } break; @@ -18148,7 +18148,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p { ctx->infos = malloc(ctx->header.n_tensors * sizeof(struct gguf_tensor_info)); - for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) { + for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) { struct gguf_tensor_info * info = &ctx->infos[i]; for (int j = 0; j < GGML_MAX_DIMS; ++j) { @@ -18195,7 +18195,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p // compute the total size of the data section, taking into account the alignment { ctx->size = 0; - for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) { + for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) { struct gguf_tensor_info * info = &ctx->infos[i]; const int64_t ne = @@ -18264,7 +18264,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p ggml_set_no_alloc(ctx_data, true); // create the tensors - for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) { + for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) { const int64_t ne[GGML_MAX_DIMS] = { ctx->infos[i].ne[0], ctx->infos[i].ne[1], From 91f6499393d2d999331fbfdba47a7f8b9f913f0d Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Thu, 16 Nov 2023 19:14:37 -0700 Subject: [PATCH 21/39] Respect tokenizer.ggml.add_bos_token value when tokenizing (#4040) * gguf-py: gguf-dump: Respect --no-tensor flag in JSON mode. * Respect add_bos_token GGUF metadata value * gguf-py: Try to fix SpecialVocab giving up too easily for the Nth time --- common/common.cpp | 6 ++++++ common/common.h | 4 ++++ examples/infill/infill.cpp | 2 +- examples/llava/llava-cli.cpp | 3 ++- examples/main/main.cpp | 2 +- examples/perplexity/perplexity.cpp | 8 +++----- examples/server/server.cpp | 9 ++++++--- gguf-py/gguf/vocab.py | 25 +++++++++++++---------- gguf-py/pyproject.toml | 2 +- gguf-py/scripts/gguf-dump.py | 15 +++++++------- llama.cpp | 32 ++++++++++++++++++++++++++++++ llama.h | 6 ++++++ 12 files changed, 85 insertions(+), 29 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 6a711420004b4..e119317d6097e 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1072,6 +1072,12 @@ std::string llama_detokenize_bpe(llama_context * ctx, const std::vector & tokens); +// Uses the value from the model metadata if possible, otherwise +// defaults to true when model type is SPM, otherwise false. +bool llama_should_add_bos_token(const llama_model * model); + // // YAML utils // diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index 62f5ce3c16a32..11f7410edd6f8 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -230,7 +230,7 @@ int main(int argc, char ** argv) { LOG_TEE("\n"); LOG_TEE("%s\n", get_system_info(params).c_str()); } - const bool add_bos = llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM; + const bool add_bos = llama_should_add_bos_token(model); LOG("add_bos: %d\n", add_bos); bool suff_rm_leading_spc = params.escape; diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index 633afd1dad1bf..31f8cd8e0ef7b 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -208,9 +208,10 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ int n_past = 0; const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict; + const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx_llava->ctx_llama)); // llava chat format is "\nUSER:\n\nASSISTANT:" - eval_string(ctx_llava->ctx_llama, "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:", params->n_batch, &n_past, true); + eval_string(ctx_llava->ctx_llama, "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:", params->n_batch, &n_past, add_bos); llava_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past); eval_string(ctx_llava->ctx_llama, (prompt + "\nASSISTANT:").c_str(), params->n_batch, &n_past, false); diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 8d985c82ac21a..99d219d6571d0 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -229,7 +229,7 @@ int main(int argc, char ** argv) { } } - const bool add_bos = llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM; + const bool add_bos = llama_should_add_bos_token(model); LOG("add_bos: %d\n", add_bos); std::vector embd_inp; diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index de60c5227f7c1..9a77beca6df32 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -149,8 +149,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & // Output: `perplexity: 13.5106 [114/114]` // BOS tokens will be added for each chunk before eval - const bool is_spm = llama_vocab_type(llama_get_model(ctx)) == LLAMA_VOCAB_TYPE_SPM; - const bool add_bos = is_spm; + const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); fprintf(stderr, "%s: tokenizing the input ..\n", __func__); @@ -288,8 +287,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par // Output: `perplexity: 13.5106 [114/114]` // BOS tokens will be added for each chunk before eval - const bool is_spm = llama_vocab_type(llama_get_model(ctx)) == LLAMA_VOCAB_TYPE_SPM; - const bool add_bos = is_spm; + const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); const int n_ctx = llama_n_ctx(ctx); auto tim1 = std::chrono::high_resolution_clock::now(); @@ -481,7 +479,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { fprintf(stderr, "================================= is_spm = %d\n", is_spm); // This is needed as usual for LLaMA models - const bool add_bos = is_spm; + const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); // Number of tasks to use when computing the score if ( params.hellaswag_tasks < hs_task_count ) { diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 46862a84b99da..bb87b532b2c18 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -501,6 +501,7 @@ struct llama_server_context bool multimodal = false; bool clean_kv_cache = true; bool all_slots_are_idle = false; + bool add_bos_token = true; int32_t id_gen; int32_t n_ctx; // total context for all clients / slots @@ -573,6 +574,8 @@ struct llama_server_context n_ctx = llama_n_ctx(ctx); + add_bos_token = llama_should_add_bos_token(model); + return true; } @@ -864,7 +867,7 @@ struct llama_server_context } void update_system_prompt() { - system_tokens = ::llama_tokenize(ctx, system_prompt, true); + system_tokens = ::llama_tokenize(ctx, system_prompt, add_bos_token); llama_batch_clear(batch); @@ -1552,7 +1555,7 @@ struct llama_server_context } else { - prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt + prompt_tokens = tokenize(slot.prompt, system_prompt.empty() && add_bos_token); // add BOS if there isn't system prompt } slot.num_prompt_tokens = prompt_tokens.size(); @@ -1629,7 +1632,7 @@ struct llama_server_context const bool has_images = process_images(slot); // process the prefix of first image - std::vector prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, true) : prompt_tokens; + std::vector prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, add_bos_token) : prompt_tokens; for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past) { llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot.n_past, { slot.id }, false); diff --git a/gguf-py/gguf/vocab.py b/gguf-py/gguf/vocab.py index 71192a928d664..b9f50a0afed7a 100644 --- a/gguf-py/gguf/vocab.py +++ b/gguf-py/gguf/vocab.py @@ -117,17 +117,18 @@ def _set_special_token(self, typ: str, tid: Any) -> None: def _try_load_from_tokenizer_json(self, path: Path) -> bool: tokenizer_file = path / 'tokenizer.json' - if not tokenizer_file.is_file(): - return False - with open(tokenizer_file, encoding = 'utf-8') as f: - tokenizer = json.load(f) - if self.load_merges: - merges = tokenizer.get('model', {}).get('merges') - if isinstance(merges, list) and merges and isinstance(merges[0], str): - self.merges = merges + if tokenizer_file.is_file(): + with open(tokenizer_file, encoding = 'utf-8') as f: + tokenizer = json.load(f) + if self.load_merges: + merges = tokenizer.get('model', {}).get('merges') + if isinstance(merges, list) and merges and isinstance(merges[0], str): + self.merges = merges + added_tokens = tokenizer.get('added_tokens', {}) + else: + added_tokens = {} tokenizer_config_file = path / 'tokenizer_config.json' - added_tokens = tokenizer.get('added_tokens') - if added_tokens is None or not tokenizer_config_file.is_file(): + if not tokenizer_config_file.is_file(): return True with open(tokenizer_config_file, encoding = 'utf-8') as f: tokenizer_config = json.load(f) @@ -135,6 +136,10 @@ def _try_load_from_tokenizer_json(self, path: Path) -> bool: add_entry = tokenizer_config.get(f'add_{typ}_token') if isinstance(add_entry, bool): self.add_special_token[typ] = add_entry + if not added_tokens: + # We will need this to get the content for the token, so if it's empty + # may as well just give up. + continue entry = tokenizer_config.get(f'{typ}_token') if isinstance(entry, str): tc_content = entry diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index af777c3e0f2b6..6e3f9e85549d0 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "gguf" -version = "0.5.2" +version = "0.5.3" description = "Read and write ML models in GGUF for GGML" authors = ["GGML "] packages = [ diff --git a/gguf-py/scripts/gguf-dump.py b/gguf-py/scripts/gguf-dump.py index 5141873de7321..dbf8915089275 100755 --- a/gguf-py/scripts/gguf-dump.py +++ b/gguf-py/scripts/gguf-dump.py @@ -86,13 +86,14 @@ def dump_metadata_json(reader: GGUFReader, args: argparse.Namespace) -> None: curr["value"] = str(bytes(field.parts[-1]), encoding="utf-8") else: curr["value"] = field.parts[-1].tolist()[0] - for idx, tensor in enumerate(reader.tensors): - tensors[tensor.name] = { - "index": idx, - "shape": tensor.shape.tolist(), - "type": tensor.tensor_type.name, - "offset": tensor.field.offset, - } + if not args.no_tensors: + for idx, tensor in enumerate(reader.tensors): + tensors[tensor.name] = { + "index": idx, + "shape": tensor.shape.tolist(), + "type": tensor.tensor_type.name, + "offset": tensor.field.offset, + } json.dump(result, sys.stdout) diff --git a/llama.cpp b/llama.cpp index 92c4536cb948e..3f6b7fe761b64 100644 --- a/llama.cpp +++ b/llama.cpp @@ -255,6 +255,8 @@ enum llm_kv { LLM_KV_TOKENIZER_UNK_ID, LLM_KV_TOKENIZER_SEP_ID, LLM_KV_TOKENIZER_PAD_ID, + LLM_KV_TOKENIZER_ADD_BOS, + LLM_KV_TOKENIZER_ADD_EOS, LLM_KV_TOKENIZER_HF_JSON, LLM_KV_TOKENIZER_RWKV, }; @@ -303,6 +305,8 @@ static std::map LLM_KV_NAMES = { { LLM_KV_TOKENIZER_UNK_ID, "tokenizer.ggml.unknown_token_id" }, { LLM_KV_TOKENIZER_SEP_ID, "tokenizer.ggml.seperator_token_id" }, { LLM_KV_TOKENIZER_PAD_ID, "tokenizer.ggml.padding_token_id" }, + { LLM_KV_TOKENIZER_ADD_BOS, "tokenizer.ggml.add_bos_token" }, + { LLM_KV_TOKENIZER_ADD_EOS, "tokenizer.ggml.add_eos_token" }, { LLM_KV_TOKENIZER_HF_JSON, "tokenizer.huggingface.json" }, { LLM_KV_TOKENIZER_RWKV, "tokenizer.rwkv.world" }, }; @@ -1276,6 +1280,9 @@ struct llama_vocab { id special_sep_id = -1; id special_pad_id = -1; + int special_add_bos = -1; // -1 unknown, 1 add, 0 don't add. + int special_add_eos = -1; // -1 unknown, 1 add, 0 don't add. + id linefeed_id = 13; id special_prefix_id = 32007; id special_middle_id = 32009; @@ -2388,6 +2395,23 @@ static void llm_load_vocab( __func__, key.c_str(), id, old_id); id = old_id; } + + } + + // Handle add_bos_token and add_eos_token + std::string key = kv(LLM_KV_TOKENIZER_ADD_BOS); + int kid = gguf_find_key(ctx, key.c_str()); + enum gguf_type ktype = kid < 0 ? GGUF_TYPE_COUNT : gguf_get_kv_type(ctx, kid); + vocab.special_add_bos = ktype == GGUF_TYPE_BOOL ? gguf_get_val_bool(ctx, kid) : -1; + if (ktype != GGUF_TYPE_BOOL && ktype != GGUF_TYPE_COUNT) { + LLAMA_LOG_WARN("%s: bad field type %d for '%s' - ignoring\n", __func__, ktype, key.c_str()); + } + key = kv(LLM_KV_TOKENIZER_ADD_EOS); + kid = gguf_find_key(ctx, key.c_str()); + ktype = kid < 0 ? GGUF_TYPE_COUNT : gguf_get_kv_type(ctx, kid); + vocab.special_add_eos = ktype == GGUF_TYPE_BOOL ? gguf_get_val_bool(ctx, kid) : -1; + if (ktype != GGUF_TYPE_BOOL && ktype != GGUF_TYPE_COUNT) { + LLAMA_LOG_WARN("%s: bad field type %d for '%s' - ignoring\n", __func__, ktype, key.c_str()); } } @@ -9288,6 +9312,14 @@ llama_token llama_token_nl(const struct llama_model * model) { return model->vocab.linefeed_id; } +int llama_add_bos_token(const struct llama_model * model) { + return model->vocab.special_add_bos; +} + +int llama_add_eos_token(const struct llama_model * model) { + return model->vocab.special_add_eos; +} + llama_token llama_token_prefix(const struct llama_model * model) { return model->vocab.special_prefix_id; } diff --git a/llama.h b/llama.h index e8dc04bb54b81..0a5d6c60ff15b 100644 --- a/llama.h +++ b/llama.h @@ -517,6 +517,12 @@ extern "C" { LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line + // Returns -1 if unknown, 1 for true or 0 for false. + LLAMA_API int llama_add_bos_token(const struct llama_model * model); + + // Returns -1 if unknown, 1 for true or 0 for false. + LLAMA_API int llama_add_eos_token(const struct llama_model * model); + // codellama infill tokens LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle From 4f447a48339977073a1af4f33ae873465ff64994 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 17 Nov 2023 10:00:15 +0200 Subject: [PATCH 22/39] llama : fix data units (#4101) * llama : fix data units ggml-ci * Revert "llama : fix data units" This reverts commit f5feac831fe225ed7f3db938d115732a49dccfc4. * llama : disambiguate data units ggml-ci --- ggml-cuda.cu | 4 ++-- ggml-metal.m | 14 +++++++------- llama.cpp | 32 ++++++++++++++++---------------- 3 files changed, 25 insertions(+), 25 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index c0c9edd56dbc2..9aa61fe4da7b0 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -5840,7 +5840,7 @@ static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) { return ptr; } #ifdef DEBUG_CUDA_MALLOC - fprintf(stderr, "%s: %d buffers, max_size = %u MB, tot_size = %u MB, requested %u MB\n", __func__, nnz, + fprintf(stderr, "%s: %d buffers, max_size = %u MiB, tot_size = %u MiB, requested %u MiB\n", __func__, nnz, (uint32_t)(max_size/1024/1024), (uint32_t)(tot_size/1024/1024), (uint32_t)(size/1024/1024)); #endif void * ptr; @@ -5978,7 +5978,7 @@ void * ggml_cuda_host_malloc(size_t size) { // The allocation error can be bypassed. A null ptr will assigned out of this function. // This can fixed the OOM error in WSL. cudaGetLastError(); - fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory: %s\n", + fprintf(stderr, "WARNING: failed to allocate %.2f MiB of pinned memory: %s\n", size/1024.0/1024.0, cudaGetErrorString(err)); return nullptr; } diff --git a/ggml-metal.m b/ggml-metal.m index 3d22b0b27e444..a9fdd39035aa3 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -345,10 +345,10 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ } } - GGML_METAL_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false"); - GGML_METAL_LOG_INFO("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); + GGML_METAL_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false"); + GGML_METAL_LOG_INFO("%s: recommendedMaxWorkingSetSize = %8.2f MiB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); if (ctx->device.maxTransferRate != 0) { - GGML_METAL_LOG_INFO("%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0); + GGML_METAL_LOG_INFO("%s: maxTransferRate = %8.2f MiB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0); } else { GGML_METAL_LOG_INFO("%s: maxTransferRate = built-in GPU\n", __func__); } @@ -541,11 +541,11 @@ bool ggml_metal_add_buffer( ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil]; if (ctx->buffers[ctx->n_buffers].metal == nil) { - GGML_METAL_LOG_ERROR("%s: error: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_aligned / 1024.0 / 1024.0); + GGML_METAL_LOG_ERROR("%s: error: failed to allocate '%-16s' buffer, size = %8.2f MiB\n", __func__, name, size_aligned / 1024.0 / 1024.0); return false; } - GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MB", __func__, name, size_aligned / 1024.0 / 1024.0); + GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MiB", __func__, name, size_aligned / 1024.0 / 1024.0); ++ctx->n_buffers; } else { @@ -565,11 +565,11 @@ bool ggml_metal_add_buffer( ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil]; if (ctx->buffers[ctx->n_buffers].metal == nil) { - GGML_METAL_LOG_ERROR("%s: error: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0); + GGML_METAL_LOG_ERROR("%s: error: failed to allocate '%-16s' buffer, size = %8.2f MiB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0); return false; } - GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i); + GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MiB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i); if (i + size_step < size) { GGML_METAL_LOG_INFO("\n"); } diff --git a/llama.cpp b/llama.cpp index 3f6b7fe761b64..8500b20cbaf09 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1087,9 +1087,9 @@ enum e_model { MODEL_70B, }; -static const size_t kB = 1024; -static const size_t MB = 1024*kB; -static const size_t GB = 1024*MB; +static const size_t kiB = 1024; +static const size_t MiB = 1024*kiB; +static const size_t GiB = 1024*MiB; struct llama_hparams { bool vocab_only; @@ -1488,7 +1488,7 @@ static bool llama_kv_cache_init( vram_kv_cache += ggml_nbytes(cache.k); } if (vram_kv_cache > 0) { - LLAMA_LOG_INFO("%s: VRAM kv self = %.2f MB\n", __func__, vram_kv_cache / 1024.0 / 1024.0); + LLAMA_LOG_INFO("%s: VRAM kv self = %.2f MiB\n", __func__, vram_kv_cache / 1024.0 / 1024.0); } } #endif @@ -2543,8 +2543,8 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { LLAMA_LOG_INFO("%s: model type = %s\n", __func__, llama_model_type_name(model.type)); LLAMA_LOG_INFO("%s: model ftype = %s\n", __func__, llama_model_ftype_name(model.ftype).c_str()); LLAMA_LOG_INFO("%s: model params = %.2f B\n", __func__, ml.n_elements*1e-9); - if (ml.n_bytes < GB) { - LLAMA_LOG_INFO("%s: model size = %.2f MiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements); + if (ml.n_bytes < GiB) { + LLAMA_LOG_INFO("%s: model size = %.2f MiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements); } else { LLAMA_LOG_INFO("%s: model size = %.2f GiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements); } @@ -2582,7 +2582,7 @@ static void llm_load_tensors( ml.calc_sizes(ctx_size, mmapped_size); - LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MB\n", __func__, ctx_size/1024.0/1024.0); + LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MiB\n", __func__, ctx_size/1024.0/1024.0); // create the ggml context { @@ -3231,7 +3231,7 @@ static void llm_load_tensors( ctx_size + mmapped_size - vram_weights; // weights in VRAM not in memory - LLAMA_LOG_INFO("%s: mem required = %7.2f MB\n", __func__, mem_required / 1024.0 / 1024.0); + LLAMA_LOG_INFO("%s: mem required = %7.2f MiB\n", __func__, mem_required / 1024.0 / 1024.0); #if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer)); @@ -3250,7 +3250,7 @@ static void llm_load_tensors( #endif // GGML_USE_CUBLAS LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers); - LLAMA_LOG_INFO("%s: VRAM used: %.2f MB\n", __func__, vram_weights / 1024.0 / 1024.0); + LLAMA_LOG_INFO("%s: VRAM used: %.2f MiB\n", __func__, vram_weights / 1024.0 / 1024.0); #else (void) n_gpu_layers; #endif // defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) @@ -7962,7 +7962,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s workers.clear(); } - LLAMA_LOG_INFO("size = %8.2f MB -> %8.2f MB | hist: ", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0); + LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB | hist: ", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0); int64_t tot_count = 0; for (size_t i = 0; i < hist_cur.size(); i++) { hist_all[i] += hist_cur[i]; @@ -8502,7 +8502,7 @@ struct llama_context * llama_new_context_with_model( { const size_t memory_size = ggml_nbytes(ctx->kv_self.k) + ggml_nbytes(ctx->kv_self.v); - LLAMA_LOG_INFO("%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0); + LLAMA_LOG_INFO("%s: kv self size = %7.2f MiB\n", __func__, memory_size / 1024.0 / 1024.0); } // resized during inference @@ -8547,7 +8547,7 @@ struct llama_context * llama_new_context_with_model( // measure memory requirements for the graph size_t alloc_size = ggml_allocr_alloc_graph(ctx->alloc, gf) + tensor_alignment; - LLAMA_LOG_INFO("%s: compute buffer total size = %.2f MB\n", __func__, (ctx->buf_compute.size + alloc_size) / 1024.0 / 1024.0); + LLAMA_LOG_INFO("%s: compute buffer total size = %.2f MiB\n", __func__, (ctx->buf_compute.size + alloc_size) / 1024.0 / 1024.0); // recreate allocator with exact memory requirements ggml_allocr_free(ctx->alloc); @@ -8561,7 +8561,7 @@ struct llama_context * llama_new_context_with_model( #endif #ifdef GGML_USE_CUBLAS ggml_cuda_set_scratch_size(alloc_size); - LLAMA_LOG_INFO("%s: VRAM scratch buffer: %.2f MB\n", __func__, alloc_size / 1024.0 / 1024.0); + LLAMA_LOG_INFO("%s: VRAM scratch buffer: %.2f MiB\n", __func__, alloc_size / 1024.0 / 1024.0); // calculate total VRAM usage auto add_tensor = [](const ggml_tensor * t, size_t & size) { @@ -8581,10 +8581,10 @@ struct llama_context * llama_new_context_with_model( size_t ctx_vram_size = alloc_size + kv_vram_size; size_t total_vram_size = model_vram_size + ctx_vram_size; - LLAMA_LOG_INFO("%s: total VRAM used: %.2f MB (model: %.2f MB, context: %.2f MB)\n", __func__, + LLAMA_LOG_INFO("%s: total VRAM used: %.2f MiB (model: %.2f MiB, context: %.2f MiB)\n", __func__, total_vram_size / 1024.0 / 1024.0, model_vram_size / 1024.0 / 1024.0, - ctx_vram_size / 1024.0 / 1024.0); + ctx_vram_size / 1024.0 / 1024.0); #endif } @@ -8605,7 +8605,7 @@ struct llama_context * llama_new_context_with_model( const size_t max_size = ggml_get_max_tensor_size(ctx->model.ctx); - LLAMA_LOG_INFO("%s: max tensor size = %8.2f MB\n", __func__, max_size/1024.0/1024.0); + LLAMA_LOG_INFO("%s: max tensor size = %8.2f MiB\n", __func__, max_size/1024.0/1024.0); #define LLAMA_METAL_CHECK_BUF(result) \ if (!(result)) { \ From b83e149ec6264d078e6a47412e7347bf5c2bfcc9 Mon Sep 17 00:00:00 2001 From: Andrew Godfrey Date: Fri, 17 Nov 2023 00:01:15 -0800 Subject: [PATCH 23/39] cuda : get_row_rounding F32 (#4095) * Fix #4017 * Update ggml-cuda.cu Co-authored-by: Jared Van Bortel * Update ggml-cuda.cu Co-authored-by: Jared Van Bortel --------- Co-authored-by: Jared Van Bortel --- ggml-cuda.cu | 2 ++ 1 file changed, 2 insertions(+) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 9aa61fe4da7b0..874ad9ac4e8ec 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -6356,6 +6356,7 @@ static int64_t get_row_rounding(ggml_type type) { case GGML_TYPE_Q8_0: return max_compute_capability >= CC_RDNA2 ? 128 : 64; case GGML_TYPE_F16: + case GGML_TYPE_F32: return 1; case GGML_TYPE_Q2_K: return max_compute_capability >= CC_RDNA2 ? 128 : 32; @@ -6378,6 +6379,7 @@ static int64_t get_row_rounding(ggml_type type) { case GGML_TYPE_Q8_0: return 64; case GGML_TYPE_F16: + case GGML_TYPE_F32: return 1; case GGML_TYPE_Q2_K: case GGML_TYPE_Q3_K: From 947f64f1630bb8b0b363a3bb5e29e11425312d57 Mon Sep 17 00:00:00 2001 From: Andrew Godfrey Date: Fri, 17 Nov 2023 02:23:11 -0800 Subject: [PATCH 24/39] finetune : zero the loraB initial vectors (#4082) * finetune : zero the loraB initial vectors Without this, the first iteration is starting out far from the base model, instead of exactly on it. Zeroing loraB is what the paper recommends. loralib also zeroes at least one of the init vector pairs (though it departs from the paper in using a different distribution for the other vector, in some cases). * tabs to spaces * Use ggml_set_zero instead of adding a new function --- examples/finetune/finetune.cpp | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index 5a6cf22ce1b95..7fecce2541c99 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -548,35 +548,35 @@ static void randomize_lora(struct my_llama_lora * lora, int seed, float mean, fl struct random_normal_distribution * rnd = init_random_normal_distribution(seed, mean, std, min, max); randomize_tensor_normal(lora->tok_embeddings_a, rnd); - randomize_tensor_normal(lora->tok_embeddings_b, rnd); + ggml_set_zero(lora->tok_embeddings_b); randomize_tensor_normal(lora->norm_a, rnd); - randomize_tensor_normal(lora->norm_b, rnd); + ggml_set_zero(lora->norm_b); randomize_tensor_normal(lora->output_a, rnd); - randomize_tensor_normal(lora->output_b, rnd); + ggml_set_zero(lora->output_b); for (uint32_t i = 0; i < n_layer; ++i) { auto & layer = lora->layers[i]; randomize_tensor_normal(layer.attention_norm_a, rnd); - randomize_tensor_normal(layer.attention_norm_b, rnd); + ggml_set_zero(layer.attention_norm_b); randomize_tensor_normal(layer.wq_a, rnd); - randomize_tensor_normal(layer.wq_b, rnd); + ggml_set_zero(layer.wq_b); randomize_tensor_normal(layer.wk_a, rnd); - randomize_tensor_normal(layer.wk_b, rnd); + ggml_set_zero(layer.wk_b); randomize_tensor_normal(layer.wv_a, rnd); - randomize_tensor_normal(layer.wv_b, rnd); + ggml_set_zero(layer.wv_b); randomize_tensor_normal(layer.wo_a, rnd); - randomize_tensor_normal(layer.wo_b, rnd); + ggml_set_zero(layer.wo_b); randomize_tensor_normal(layer.ffn_norm_a, rnd); - randomize_tensor_normal(layer.ffn_norm_b, rnd); + ggml_set_zero(layer.ffn_norm_b); randomize_tensor_normal(layer.w1_a, rnd); - randomize_tensor_normal(layer.w1_b, rnd); + ggml_set_zero(layer.w1_b); randomize_tensor_normal(layer.w2_a, rnd); - randomize_tensor_normal(layer.w2_b, rnd); + ggml_set_zero(layer.w2_b); randomize_tensor_normal(layer.w3_a, rnd); - randomize_tensor_normal(layer.w3_b, rnd); + ggml_set_zero(layer.w3_b); } free_random_normal_distribution(rnd); From 3e916a07ac093045d88ef0c4fa78647ae0efc010 Mon Sep 17 00:00:00 2001 From: gwjr <502526+gwjr@users.noreply.github.com> Date: Fri, 17 Nov 2023 14:48:19 +0000 Subject: [PATCH 25/39] finetune : speed-up ggml_compute_forward_out_prod_f32 via BLAS (#4079) * Remove logically superfluous assertions and order by dimension * Use cblas_sgemm() to implement ggml_compute_forward_out_prod() * Remove ggml_compute_forward_out_prod_use_blas(), fix compiling errors on cmake/zig, remove trailing whitespace * Add openBLAS support for sgemm() in compute_forward_out_prod() --- ggml.c | 69 +++++++++++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 61 insertions(+), 8 deletions(-) diff --git a/ggml.c b/ggml.c index ada1067da56d4..c7086ba844c60 100644 --- a/ggml.c +++ b/ggml.c @@ -9611,10 +9611,12 @@ static void ggml_compute_forward_out_prod_f32( const int ith = params->ith; const int nth = params->nth; + GGML_ASSERT(ne0 == ne00); + GGML_ASSERT(ne1 == ne10); + GGML_ASSERT(ne2 == ne02); GGML_ASSERT(ne02 == ne12); - GGML_ASSERT(ne03 == ne13); - GGML_ASSERT(ne2 == ne12); GGML_ASSERT(ne3 == ne13); + GGML_ASSERT(ne03 == ne13); // we don't support permuted src0 or src1 GGML_ASSERT(nb00 == sizeof(float)); @@ -9625,18 +9627,25 @@ static void ggml_compute_forward_out_prod_f32( // GGML_ASSERT(nb1 <= nb2); // GGML_ASSERT(nb2 <= nb3); - GGML_ASSERT(ne0 == ne00); - GGML_ASSERT(ne1 == ne10); - GGML_ASSERT(ne2 == ne02); - GGML_ASSERT(ne3 == ne03); - // nb01 >= nb00 - src0 is not transposed // compute by src0 rows // TODO: #if defined(GGML_USE_CUBLAS) ggml_cuda_out_prod - // TODO: #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST) + // TODO: #if defined(GGML_USE_CLBLAST) + +#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) + bool use_blas = ggml_is_matrix(src0) && + ggml_is_matrix(src1) && + ggml_is_contiguous(src0) && + (ggml_is_contiguous(src1) || ggml_is_transposed(src1)); +#endif if (params->type == GGML_TASK_INIT) { +#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) // gemm beta will zero dst + if (use_blas) { + return; + } +#endif ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); return; } @@ -9645,6 +9654,50 @@ static void ggml_compute_forward_out_prod_f32( return; } +#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) + if (use_blas) { + if (params->ith != 0) { // All threads other than the first do no work. + return; + } + // Arguments to ggml_compute_forward_out_prod (expressed as major,minor) + // src0: (k,n) + // src1: (k,m) + // dst: (m,n) + // + // Arguments to sgemm (see https://github.com/Reference-LAPACK/lapack/blob/master/BLAS/SRC/sgemm.f) + // Also expressed as (major,minor) + // a: (m,k): so src1 transposed + // b: (k,n): so src0 + // c: (m,n) + // + // However, if ggml_is_transposed(src1) is true, then + // src1->data already contains a transposed version, so sgemm mustn't + // transpose it further. + + int n = src0->ne[0]; + int k = src0->ne[1]; + int m = src1->ne[0]; + + int transposeA, lda; + + if (!ggml_is_transposed(src1)) { + transposeA = CblasTrans; + lda = m; + } else { + transposeA = CblasNoTrans; + lda = k; + } + + float * a = (float *) ((char *) src1->data); + float * b = (float *) ((char *) src0->data); + float * c = (float *) ((char *) dst->data); + + cblas_sgemm(CblasRowMajor, transposeA, CblasNoTrans, m, n, k, 1.0, a, lda, b, n, 0.0, c, n); + + return; + } +#endif + // dst[:,:,:,:] = 0 // for i2,i3: // for i1: From e85bb1a8e736228a1f0d965777de5f77f22834b8 Mon Sep 17 00:00:00 2001 From: slaren Date: Fri, 17 Nov 2023 16:17:37 +0100 Subject: [PATCH 26/39] llama : add functions to get the model's metadata (#4013) * llama : add functions to get the model's metadata * format -> std::to_string * better documentation --- ggml.c | 25 +++++++++++ ggml.h | 1 + llama.cpp | 131 +++++++++++++++++++++++++++++++++++++++++++++++++++--- llama.h | 17 +++++++ 4 files changed, 167 insertions(+), 7 deletions(-) diff --git a/ggml.c b/ggml.c index c7086ba844c60..f92292b39c635 100644 --- a/ggml.c +++ b/ggml.c @@ -18452,24 +18452,29 @@ int gguf_find_key(const struct gguf_context * ctx, const char * key) { } const char * gguf_get_key(const struct gguf_context * ctx, int key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); return ctx->kv[key_id].key.data; } enum gguf_type gguf_get_kv_type(const struct gguf_context * ctx, int key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); return ctx->kv[key_id].type; } enum gguf_type gguf_get_arr_type(const struct gguf_context * ctx, int key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_ARRAY); return ctx->kv[key_id].value.arr.type; } const void * gguf_get_arr_data(const struct gguf_context * ctx, int key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_ARRAY); return ctx->kv[key_id].value.arr.data; } const char * gguf_get_arr_str(const struct gguf_context * ctx, int key_id, int i) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_ARRAY); struct gguf_kv * kv = &ctx->kv[key_id]; struct gguf_str * str = &((struct gguf_str *) kv->value.arr.data)[i]; @@ -18477,70 +18482,90 @@ const char * gguf_get_arr_str(const struct gguf_context * ctx, int key_id, int i } int gguf_get_arr_n(const struct gguf_context * ctx, int key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_ARRAY); return ctx->kv[key_id].value.arr.n; } uint8_t gguf_get_val_u8(const struct gguf_context * ctx, int key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_UINT8); return ctx->kv[key_id].value.uint8; } int8_t gguf_get_val_i8(const struct gguf_context * ctx, int key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_INT8); return ctx->kv[key_id].value.int8; } uint16_t gguf_get_val_u16(const struct gguf_context * ctx, int key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_UINT16); return ctx->kv[key_id].value.uint16; } int16_t gguf_get_val_i16(const struct gguf_context * ctx, int key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_INT16); return ctx->kv[key_id].value.int16; } uint32_t gguf_get_val_u32(const struct gguf_context * ctx, int key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_UINT32); return ctx->kv[key_id].value.uint32; } int32_t gguf_get_val_i32(const struct gguf_context * ctx, int key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_INT32); return ctx->kv[key_id].value.int32; } float gguf_get_val_f32(const struct gguf_context * ctx, int key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_FLOAT32); return ctx->kv[key_id].value.float32; } uint64_t gguf_get_val_u64(const struct gguf_context * ctx, int key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_UINT64); return ctx->kv[key_id].value.uint64; } int64_t gguf_get_val_i64(const struct gguf_context * ctx, int key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_INT64); return ctx->kv[key_id].value.int64; } double gguf_get_val_f64(const struct gguf_context * ctx, int key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_FLOAT64); return ctx->kv[key_id].value.float64; } bool gguf_get_val_bool(const struct gguf_context * ctx, int key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_BOOL); return ctx->kv[key_id].value.bool_; } const char * gguf_get_val_str(const struct gguf_context * ctx, int key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); GGML_ASSERT(ctx->kv[key_id].type == GGUF_TYPE_STRING); return ctx->kv[key_id].value.str.data; } +const void * gguf_get_val_data(const struct gguf_context * ctx, int key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); + GGML_ASSERT(ctx->kv[key_id].type != GGUF_TYPE_ARRAY); + GGML_ASSERT(ctx->kv[key_id].type != GGUF_TYPE_STRING); + return &ctx->kv[key_id].value; +} + int gguf_get_n_tensors(const struct gguf_context * ctx) { return ctx->header.n_tensors; } diff --git a/ggml.h b/ggml.h index 8e6b646066b7a..f2fce0f22d357 100644 --- a/ggml.h +++ b/ggml.h @@ -2045,6 +2045,7 @@ extern "C" { GGML_API double gguf_get_val_f64 (const struct gguf_context * ctx, int key_id); GGML_API bool gguf_get_val_bool(const struct gguf_context * ctx, int key_id); GGML_API const char * gguf_get_val_str (const struct gguf_context * ctx, int key_id); + GGML_API const void * gguf_get_val_data(const struct gguf_context * ctx, int key_id); GGML_API int gguf_get_arr_n (const struct gguf_context * ctx, int key_id); GGML_API const void * gguf_get_arr_data(const struct gguf_context * ctx, int key_id); GGML_API const char * gguf_get_arr_str (const struct gguf_context * ctx, int key_id, int i); diff --git a/llama.cpp b/llama.cpp index 8500b20cbaf09..3cc3fc9f0f3fb 100644 --- a/llama.cpp +++ b/llama.cpp @@ -604,6 +604,60 @@ static int8_t llama_rope_scaling_type_from_string(const std::string & name) { return LLAMA_ROPE_SCALING_UNSPECIFIED; } +static std::string gguf_data_to_str(enum gguf_type type, const void * data, int i) { + switch (type) { + case GGUF_TYPE_UINT8: return std::to_string(((const uint8_t *)data)[i]); + case GGUF_TYPE_INT8: return std::to_string(((const int8_t *)data)[i]); + case GGUF_TYPE_UINT16: return std::to_string(((const uint16_t *)data)[i]); + case GGUF_TYPE_INT16: return std::to_string(((const int16_t *)data)[i]); + case GGUF_TYPE_UINT32: return std::to_string(((const uint32_t *)data)[i]); + case GGUF_TYPE_INT32: return std::to_string(((const int32_t *)data)[i]); + case GGUF_TYPE_UINT64: return std::to_string(((const uint64_t *)data)[i]); + case GGUF_TYPE_INT64: return std::to_string(((const int64_t *)data)[i]); + case GGUF_TYPE_FLOAT32: return std::to_string(((const float *)data)[i]); + case GGUF_TYPE_FLOAT64: return std::to_string(((const double *)data)[i]); + case GGUF_TYPE_BOOL: return ((const bool *)data)[i] ? "true" : "false"; + default: return format("unknown type %d", type); + } +} + +static std::string gguf_kv_to_str(struct gguf_context * ctx_gguf, int i) { + const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i); + + switch (type) { + case GGUF_TYPE_STRING: + return gguf_get_val_str(ctx_gguf, i); + case GGUF_TYPE_ARRAY: + { + const enum gguf_type arr_type = gguf_get_arr_type(ctx_gguf, i); + int arr_n = gguf_get_arr_n(ctx_gguf, i); + const void * data = gguf_get_arr_data(ctx_gguf, i); + std::stringstream ss; + ss << "["; + for (int j = 0; j < arr_n; j++) { + if (arr_type == GGUF_TYPE_STRING) { + std::string val = gguf_get_arr_str(ctx_gguf, i, j); + // escape quotes + replace_all(val, "\\", "\\\\"); + replace_all(val, "\"", "\\\""); + ss << '"' << val << '"'; + } else if (arr_type == GGUF_TYPE_ARRAY) { + ss << "???"; + } else { + ss << gguf_data_to_str(arr_type, data, j); + } + if (j < arr_n - 1) { + ss << ", "; + } + } + ss << "]"; + return ss.str(); + } + default: + return gguf_data_to_str(type, gguf_get_val_data(ctx_gguf, i), 0); + } +} + // // ggml helpers // @@ -1327,6 +1381,9 @@ struct llama_model { int n_gpu_layers; + // gguf metadata + std::unordered_map gguf_kv; + // context struct ggml_context * ctx = NULL; @@ -1785,10 +1842,10 @@ struct llama_model_loader { case GGML_TYPE_Q5_K: ftype = LLAMA_FTYPE_MOSTLY_Q5_K_M; break; case GGML_TYPE_Q6_K: ftype = LLAMA_FTYPE_MOSTLY_Q6_K; break; default: - { - LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max)); - ftype = LLAMA_FTYPE_ALL_F32; - } break; + { + LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max)); + ftype = LLAMA_FTYPE_ALL_F32; + } break; } // this is a way to mark that we have "guessed" the file type @@ -1802,10 +1859,20 @@ struct llama_model_loader { } for (int i = 0; i < n_kv; i++) { - const char * name = gguf_get_key(ctx_gguf, i); - const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i); + const char * name = gguf_get_key(ctx_gguf, i); + const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i); + const std::string type_name = + type == GGUF_TYPE_ARRAY + ? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(ctx_gguf, i)), gguf_get_arr_n(ctx_gguf, i)) + : gguf_type_name(type); + + std::string value = gguf_kv_to_str(ctx_gguf, i); + const size_t MAX_VALUE_LEN = 40; + if (value.size() > MAX_VALUE_LEN) { + value = format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str()); + } - LLAMA_LOG_INFO("%s: - kv %3d: %42s %-8s\n", __func__, i, name, gguf_type_name(type)); + LLAMA_LOG_INFO("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str()); } // print type counts @@ -2100,6 +2167,17 @@ static void llm_load_hparams( auto & hparams = model.hparams; + // get metadata as string + for (int i = 0; i < gguf_get_n_kv(ctx); i++) { + enum gguf_type type = gguf_get_kv_type(ctx, i); + if (type == GGUF_TYPE_ARRAY) { + continue; + } + const char * name = gguf_get_key(ctx, i); + const std::string value = gguf_kv_to_str(ctx, i); + model.gguf_kv.emplace(name, value); + } + // get general kv GGUF_GET_KEY(ctx, model.name, gguf_get_val_str, GGUF_TYPE_STRING, false, kv(LLM_KV_GENERAL_NAME)); @@ -8671,6 +8749,45 @@ float llama_rope_freq_scale_train(const struct llama_model * model) { return model->hparams.rope_freq_scale_train; } +int llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size) { + const auto & it = model->gguf_kv.find(key); + if (it == model->gguf_kv.end()) { + if (buf_size > 0) { + buf[0] = '\0'; + } + return -1; + } + return snprintf(buf, buf_size, "%s", it->second.c_str()); +} + +int llama_model_meta_count(const struct llama_model * model) { + return (int)model->gguf_kv.size(); +} + +int llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) { + if (i < 0 || i >= (int)model->gguf_kv.size()) { + if (buf_size > 0) { + buf[0] = '\0'; + } + return -1; + } + auto it = model->gguf_kv.begin(); + std::advance(it, i); + return snprintf(buf, buf_size, "%s", it->first.c_str()); +} + +int llama_model_meta_val_str_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) { + if (i < 0 || i >= (int)model->gguf_kv.size()) { + if (buf_size > 0) { + buf[0] = '\0'; + } + return -1; + } + auto it = model->gguf_kv.begin(); + std::advance(it, i); + return snprintf(buf, buf_size, "%s", it->second.c_str()); +} + int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) { return snprintf(buf, buf_size, "%s %s %s", llama_model_arch_name(model->arch).c_str(), diff --git a/llama.h b/llama.h index 0a5d6c60ff15b..70e8fda4bf1b3 100644 --- a/llama.h +++ b/llama.h @@ -301,6 +301,23 @@ extern "C" { // Get the model's RoPE frequency scaling factor LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model); + // Functions to access the model's GGUF metadata scalar values + // - The functions return the length of the string on success, or -1 on failure + // - The output string is always null-terminated and cleared on failure + // - GGUF array values are not supported by these functions + + // Get metadata value as a string by key name + LLAMA_API int llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size); + + // Get the number of metadata key/value pairs + LLAMA_API int llama_model_meta_count(const struct llama_model * model); + + // Get metadata key name by index + LLAMA_API int llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size); + + // Get metadata value as a string by index + LLAMA_API int llama_model_meta_val_str_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size); + // Get a string describing the model type LLAMA_API int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size); From ba4cf5c0bf37a729d29e899dadf14541cddd23d4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ji=C5=99=C3=AD=20Podiv=C3=ADn?= <66251151+jpodivin@users.noreply.github.com> Date: Fri, 17 Nov 2023 16:19:16 +0100 Subject: [PATCH 27/39] train : move number of gpu layers argument parsing to common/train.cpp (#4074) - introduces help entry for the argument - cuts '--gpu-layers' form in order to simplify usage and documentation. Signed-off-by: Jiri Podivin Co-authored-by: Jiri Podivin --- common/train.cpp | 12 ++++++++++++ examples/finetune/finetune.cpp | 11 ----------- 2 files changed, 12 insertions(+), 11 deletions(-) diff --git a/common/train.cpp b/common/train.cpp index 964b156b5abe4..773e2c59cc669 100644 --- a/common/train.cpp +++ b/common/train.cpp @@ -1136,6 +1136,7 @@ void print_common_train_usage(int /*argc*/, char ** /*argv*/, const struct train fprintf(stderr, " --adam-beta2 N AdamW beta2 in interval [0,1). How much to smooth the second moment of gradients. (default %f)\n", params->adam_beta2); fprintf(stderr, " --adam-gclip N AdamW gradient clipping. Disabled when zero. (default %f)\n", params->adam_gclip); fprintf(stderr, " --adam-epsf N AdamW epsilon for convergence test. Disabled when <= zero. (default %f)\n", params->adam_eps_f); + fprintf(stderr, " -ngl N, --n-gpu-layers N Number of model layers to offload to GPU (default %d)", params->n_gpu_layers); fprintf(stderr, "\n"); } @@ -1355,6 +1356,17 @@ bool consume_common_train_arg( return true; } params->adam_gclip = std::stof(argv[i]); + } else if (arg == "-ngl" || arg == "--n-gpu-layers") { + if (++i >= argc) { + *invalid_param = true; + return true; + } +#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD + params->n_gpu_layers = std::stoi(argv[i]); +#else + fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); + fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); +#endif } else if (arg == "-h" || arg == "--help") { params->print_usage = true; return true; diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index 7fecce2541c99..af46e44a6e216 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -1460,17 +1460,6 @@ static bool train_params_parse(int argc, char ** argv, struct train_params * par } params->n_rank_w3 = std::stoi(argv[i]); params->custom_n_rank_w3 = true; - } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") { - if (++i >= argc) { - invalid_param = true; - break; - } -#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD - params->common.n_gpu_layers = std::stoi(argv[i]); -#else - fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); - fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); -#endif } else { fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); train_print_usage(argc, argv, &default_params); From f7d5e975424ff0eea55ca5a9181ac8e15553c1fc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ji=C5=99=C3=AD=20Podiv=C3=ADn?= <66251151+jpodivin@users.noreply.github.com> Date: Fri, 17 Nov 2023 16:20:53 +0100 Subject: [PATCH 28/39] py : remove superfluous import statements (#4076) Signed-off-by: Jiri Podivin Co-authored-by: Jiri Podivin --- convert-baichuan-hf-to-gguf.py | 2 -- convert-llama-ggml-to-gguf.py | 1 - examples/finetune/convert-finetune-checkpoint-to-gguf.py | 2 -- tests/test-tokenizer-0-falcon.py | 2 -- tests/test-tokenizer-0-llama.py | 2 -- 5 files changed, 9 deletions(-) diff --git a/convert-baichuan-hf-to-gguf.py b/convert-baichuan-hf-to-gguf.py index 789602351ca9d..3785a7d265524 100755 --- a/convert-baichuan-hf-to-gguf.py +++ b/convert-baichuan-hf-to-gguf.py @@ -6,11 +6,9 @@ import argparse import json import os -import struct import sys from pathlib import Path from typing import TYPE_CHECKING, Any -import itertools import numpy as np import torch from sentencepiece import SentencePieceProcessor # type: ignore[import] diff --git a/convert-llama-ggml-to-gguf.py b/convert-llama-ggml-to-gguf.py index d898d81c4c445..0c12356707341 100755 --- a/convert-llama-ggml-to-gguf.py +++ b/convert-llama-ggml-to-gguf.py @@ -2,7 +2,6 @@ from __future__ import annotations import argparse -import math import struct import sys from enum import IntEnum diff --git a/examples/finetune/convert-finetune-checkpoint-to-gguf.py b/examples/finetune/convert-finetune-checkpoint-to-gguf.py index c8e14da87e9e8..c89090918da97 100644 --- a/examples/finetune/convert-finetune-checkpoint-to-gguf.py +++ b/examples/finetune/convert-finetune-checkpoint-to-gguf.py @@ -3,9 +3,7 @@ import argparse import gguf -import os import struct -import sys import numpy as np from pathlib import Path diff --git a/tests/test-tokenizer-0-falcon.py b/tests/test-tokenizer-0-falcon.py index cf65a3f65d72c..65e1c0dbf700c 100644 --- a/tests/test-tokenizer-0-falcon.py +++ b/tests/test-tokenizer-0-falcon.py @@ -1,7 +1,5 @@ # tests with BPE tokenizer -import os -import sys import argparse from transformers import AutoTokenizer diff --git a/tests/test-tokenizer-0-llama.py b/tests/test-tokenizer-0-llama.py index 078f680b165ca..21df8e6e4898d 100644 --- a/tests/test-tokenizer-0-llama.py +++ b/tests/test-tokenizer-0-llama.py @@ -1,7 +1,5 @@ # tests with SPM tokenizer -import os -import sys import argparse from sentencepiece import SentencePieceProcessor From c7cce1246e248124117ae5bc058923e3ade95f11 Mon Sep 17 00:00:00 2001 From: Huawei Lin Date: Fri, 17 Nov 2023 10:22:56 -0500 Subject: [PATCH 29/39] llava : fix compilation warning that fread return value is not used (#4069) --- examples/llava/llava.cpp | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index d10bcf2d22465..0cae8c4b10a3a 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -127,7 +127,14 @@ static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long fclose(file); return false; } - fread(buffer, 1, fileSize, file); // Read the file into the buffer + errno = 0; + size_t ret = fread(buffer, 1, fileSize, file); // Read the file into the buffer + if (ferror(file)) { + die_fmt("read error: %s", strerror(errno)); + } + if (ret != (size_t) fileSize) { + die("unexpectedly reached end of file"); + } fclose(file); // Close the file *bytesOut = buffer; From 9e87ef60e18d69338c5efea314aa7e718bf2040a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jannis=20Sch=C3=B6nleber?= Date: Fri, 17 Nov 2023 16:24:07 +0100 Subject: [PATCH 30/39] common : improve yaml log escaping (#4080) * logging: improve escaping in yaml output * logging: include review feedback --- common/common.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/common/common.cpp b/common/common.cpp index e119317d6097e..7ee29f5ba4923 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1194,6 +1194,7 @@ void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const cha if (!data_str.empty() && (std::isspace(data_str[0]) || std::isspace(data_str.back()))) { data_str = std::regex_replace(data_str, std::regex("\n"), "\\n"); data_str = std::regex_replace(data_str, std::regex("\""), "\\\""); + data_str = std::regex_replace(data_str, std::regex(R"(\\[^n"])"), R"(\$&)"); data_str = "\"" + data_str + "\""; fprintf(stream, "%s: %s\n", prop_name, data_str.c_str()); return; From 11173c92d6eaa2bd1308c2389f44f838480836ac Mon Sep 17 00:00:00 2001 From: John <78893154+cmp-nct@users.noreply.github.com> Date: Fri, 17 Nov 2023 16:24:30 +0100 Subject: [PATCH 31/39] py : Falcon HF compatibility (#4104) Falcon HF compatibility --- convert-hf-to-gguf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index e7db7591260af..3a618fd4dc0f6 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -193,7 +193,7 @@ def _get_model_architecture(self) -> gguf.MODEL_ARCH: return gguf.MODEL_ARCH.MPT if arch in ("BaichuanForCausalLM", "BaiChuanForCausalLM"): return gguf.MODEL_ARCH.BAICHUAN - if arch == "FalconForCausalLM": + if arch in ("FalconForCausalLM", "RWForCausalLM"): return gguf.MODEL_ARCH.FALCON if arch == "GPTBigCodeForCausalLM": return gguf.MODEL_ARCH.STARCODER From 2ab0707acbf3a3ca9e5bc5959c7920c22eba2257 Mon Sep 17 00:00:00 2001 From: Don Mahurin Date: Fri, 17 Nov 2023 07:32:34 -0800 Subject: [PATCH 32/39] convert : use 'model' value if it exists. This allows karpathy/tinyllamas to load (#4089) Co-authored-by: Don Mahurin <@> --- convert.py | 1 + 1 file changed, 1 insertion(+) diff --git a/convert.py b/convert.py index 3d6216f1d4e7a..5b6344aa8fc85 100755 --- a/convert.py +++ b/convert.py @@ -690,6 +690,7 @@ def lazy_load_torch_file(outer_fp: IO[bytes], path: Path) -> ModelPlus: data_base_path=pickle_paths[0][:-4], zip_file=zf) model = unpickler.load() + if 'model' in model: model = model['model'] as_dict = dict(model.items()) return ModelPlus(model=as_dict, paths=[path], format='torch', vocab=None) From 2fa02b4b3d86182381311c98b75065ee1b7c2930 Mon Sep 17 00:00:00 2001 From: zakkor Date: Fri, 17 Nov 2023 17:36:44 +0200 Subject: [PATCH 33/39] examples : add tokenize (#4039) --- examples/CMakeLists.txt | 1 + examples/tokenize/CMakeLists.txt | 5 ++++ examples/tokenize/tokenize.cpp | 44 ++++++++++++++++++++++++++++++++ 3 files changed, 50 insertions(+) create mode 100644 examples/tokenize/CMakeLists.txt create mode 100644 examples/tokenize/tokenize.cpp diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 75b8df676c52b..71bcb6893e20d 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -24,6 +24,7 @@ else() add_subdirectory(llama-bench) add_subdirectory(llava) add_subdirectory(main) + add_subdirectory(tokenize) add_subdirectory(parallel) add_subdirectory(perplexity) add_subdirectory(quantize) diff --git a/examples/tokenize/CMakeLists.txt b/examples/tokenize/CMakeLists.txt new file mode 100644 index 0000000000000..5e6654d7e5988 --- /dev/null +++ b/examples/tokenize/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET tokenize) +add_executable(${TARGET} tokenize.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/tokenize/tokenize.cpp b/examples/tokenize/tokenize.cpp new file mode 100644 index 0000000000000..72b60f9a45808 --- /dev/null +++ b/examples/tokenize/tokenize.cpp @@ -0,0 +1,44 @@ +#include "common.h" +#include "llama.h" + +#include +#include +#include +#include + +int main(int argc, char ** argv) { + if (argc < 3 || argv[1][0] == '-') { + printf("usage: %s MODEL_PATH PROMPT [--ids]\n" , argv[0]); + return 1; + } + + auto model_path = argv[1]; + auto prompt = argv[2]; + + const bool printing_ids = argc > 3 && std::string(argv[3]) == "--ids"; + + llama_backend_init(false); + + llama_model_params model_params = llama_model_default_params(); + model_params.vocab_only = true; + llama_model * model = llama_load_model_from_file(model_path, model_params); + + llama_context_params ctx_params = llama_context_default_params(); + llama_context * ctx = llama_new_context_with_model(model, ctx_params); + + const bool add_bos = true; + + std::vector tokens; + + tokens = ::llama_tokenize(model, prompt, add_bos, true); + + for (int i = 0; i < (int) tokens.size(); i++) { + if (printing_ids) { + printf("%d\n", tokens[i]); + } else { + printf("%6d -> '%s'\n", tokens[i], llama_token_to_piece(ctx, tokens[i]).c_str()); + } + } + + return 0; +} From 5ad387e994dde77a47ec547a4a65f7611dc325f4 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 17 Nov 2023 18:01:38 +0200 Subject: [PATCH 34/39] tokenize : fix trailing whitespace --- examples/tokenize/tokenize.cpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/tokenize/tokenize.cpp b/examples/tokenize/tokenize.cpp index 72b60f9a45808..17166836ac127 100644 --- a/examples/tokenize/tokenize.cpp +++ b/examples/tokenize/tokenize.cpp @@ -12,8 +12,8 @@ int main(int argc, char ** argv) { return 1; } - auto model_path = argv[1]; - auto prompt = argv[2]; + const char * model_path = argv[1]; + const char * prompt = argv[2]; const bool printing_ids = argc > 3 && std::string(argv[3]) == "--ids"; @@ -36,7 +36,7 @@ int main(int argc, char ** argv) { if (printing_ids) { printf("%d\n", tokens[i]); } else { - printf("%6d -> '%s'\n", tokens[i], llama_token_to_piece(ctx, tokens[i]).c_str()); + printf("%6d -> '%s'\n", tokens[i], llama_token_to_piece(ctx, tokens[i]).c_str()); } } From 8e9361089dd31ae9ae59452a8ee409fd51a16371 Mon Sep 17 00:00:00 2001 From: Roger Meier Date: Fri, 17 Nov 2023 17:11:23 +0100 Subject: [PATCH 35/39] build : support ppc64le build for make and CMake (#3963) * build: support ppc64le build for make and CMake * build: keep __POWER9_VECTOR__ ifdef and extend with __powerpc64__ Co-authored-by: Georgi Gerganov --------- Co-authored-by: Georgi Gerganov --- CMakeLists.txt | 8 ++++++-- Makefile | 8 ++++++++ ggml-quants.c | 2 +- 3 files changed, 15 insertions(+), 3 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index db1f42f1eda6a..f32df5fe52335 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -574,8 +574,12 @@ elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GE endif() elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64") message(STATUS "PowerPC detected") - add_compile_options(-mcpu=native -mtune=native) - #TODO: Add targets for Power8/Power9 (Altivec/VSX) and Power10(MMA) and query for big endian systems (ppc64/le/be) + if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64le") + add_compile_options(-mcpu=powerpc64le) + else() + add_compile_options(-mcpu=native -mtune=native) + #TODO: Add targets for Power8/Power9 (Altivec/VSX) and Power10(MMA) and query for big endian systems (ppc64/le/be) + endif() else() message(STATUS "Unknown architecture") endif() diff --git a/Makefile b/Makefile index 36d08811e32b6..7150dbaf60294 100644 --- a/Makefile +++ b/Makefile @@ -342,6 +342,12 @@ ifneq ($(filter ppc64%,$(UNAME_M)),) endif endif +ifneq ($(filter ppc64le%,$(UNAME_M)),) + MK_CFLAGS += -mcpu=powerpc64le + MK_CXXFLAGS += -mcpu=powerpc64le + CUDA_POWER_ARCH = 1 +endif + else MK_CFLAGS += -march=rv64gcv -mabi=lp64d MK_CXXFLAGS += -march=rv64gcv -mabi=lp64d @@ -392,6 +398,8 @@ else endif #LLAMA_CUDA_NVCC ifdef CUDA_DOCKER_ARCH NVCCFLAGS += -Wno-deprecated-gpu-targets -arch=$(CUDA_DOCKER_ARCH) +else ifdef CUDA_POWER_ARCH + NVCCFLAGS += else NVCCFLAGS += -arch=native endif # CUDA_DOCKER_ARCH diff --git a/ggml-quants.c b/ggml-quants.c index cf2860b8cbd59..7285d5f7fbcc0 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -19,7 +19,7 @@ #ifdef __wasm_simd128__ #include #else -#ifdef __POWER9_VECTOR__ +#if defined(__POWER9_VECTOR__) || defined(__powerpc64__) #include #undef bool #define bool _Bool From bbecf3f415797f812893947998bda4f866fa900e Mon Sep 17 00:00:00 2001 From: slaren Date: Fri, 17 Nov 2023 20:39:11 +0100 Subject: [PATCH 36/39] llama : increase max nodes (#4115) --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 3cc3fc9f0f3fb..56d8e765cc112 100644 --- a/llama.cpp +++ b/llama.cpp @@ -91,7 +91,7 @@ #define LLAMA_ATTRIBUTE_FORMAT(...) #endif -#define LLAMA_MAX_NODES 4096 +#define LLAMA_MAX_NODES 8192 // // logging From 45ad1b97f89ae64f14977c4b054e9e8aff8b145d Mon Sep 17 00:00:00 2001 From: Concedo <39025047+LostRuins@users.noreply.github.com> Date: Sat, 18 Nov 2023 11:02:35 +0800 Subject: [PATCH 37/39] max nodes 8192 --- otherarch/llama_v3.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/otherarch/llama_v3.cpp b/otherarch/llama_v3.cpp index 0f2313aecd470..03a6438e27051 100644 --- a/otherarch/llama_v3.cpp +++ b/otherarch/llama_v3.cpp @@ -3457,7 +3457,7 @@ struct llama_v3_context * llama_v3_new_context_with_model( #ifdef LLAMA_V3_USE_ALLOCATOR { static const size_t tensor_alignment = 32; - static const size_t GGML_MAX_NODES = 4096; + static const size_t GGML_MAX_NODES = 8192; // the compute buffer is used to store the tensor and graph structs, while the allocator buffer is used for the tensor data ctx->buf_compute.resize(ggml_tensor_overhead()*GGML_MAX_NODES + ggml_graph_overhead()); From 35ce2b054fec25a9a6a4fd9c6a369011300ea5da Mon Sep 17 00:00:00 2001 From: Concedo <39025047+LostRuins@users.noreply.github.com> Date: Sat, 18 Nov 2023 11:05:04 +0800 Subject: [PATCH 38/39] typo fixes --- klite.embd | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/klite.embd b/klite.embd index 57e6845326dc1..ace5d1eac0324 100644 --- a/klite.embd +++ b/klite.embd @@ -1936,7 +1936,7 @@ Current version: 96 "prefmodel2":adventuremodels2, "prompt":"", "adventure_context_mod":false, - "memory": instructstartplaceholder+"\nSimulate a text adventure game.\nUser actions will be on their own seperate line prefixed with a >\n\nThe game will feature a brief introduction text about who the main character is and the setting of the world. Followed by a brief description of the current task that must be overcome. Afterwards it will be up to the user to input the desired instruction and outputs will describe the impact of this action.\n\nAll outputs should consider how likely it is that the players action succeeds and succeed or fail accordingly. The game should be challenging and action failures should be funny.\n"+instructendplaceholder, + "memory": instructstartplaceholder+"\nSimulate a text adventure game.\nUser actions will be on their own separate line prefixed with a >\n\nThe game will feature a brief introduction text about who the main character is and the setting of the world. Followed by a brief description of the current task that must be overcome. Afterwards it will be up to the user to input the desired instruction and outputs will describe the impact of this action.\n\nAll outputs should consider how likely it is that the players action succeeds and succeed or fail accordingly. The game should be challenging and action failures should be funny.\n"+instructendplaceholder, "authorsnote": "", "worldinfo": [] }, @@ -2000,11 +2000,11 @@ Current version: 96 { "title":"Post Apocalypse", "author":"Concedo", - "desc":"The year is 2038. A full scale global thermonuclear exchange has wiped out nearly all of the world population, and left most cities as radioactive wastelands. Running out of supplies, you must leave your bunker and scavange to find a new home in the ruins of civilization.", + "desc":"The year is 2038. A full scale global thermonuclear exchange has wiped out nearly all of the world population, and left most cities as radioactive wastelands. Running out of supplies, you must leave your bunker and scavenge to find a new home in the ruins of civilization.", "opmode":2, "prefmodel1":adventuremodels1, "prefmodel2":adventuremodels2, - "prompt":`The year is 2038. A full scale global thermonuclear exchange has wiped out nearly all of the world population, and left most cities as radioactive wastelands. Running out of supplies, you must leave your bunker and scavange to find a new home in the ruins of civilization.\n\nEmerging from your shelter, you squint as the harsh sunlight blinds you. For a moment, you're disoriented, your eyes struggling to adjust to the brightness of the new world outside. As your vision clears, you step forward, and take in the barren wasteland that stretches out before you.`, + "prompt":`The year is 2038. A full scale global thermonuclear exchange has wiped out nearly all of the world population, and left most cities as radioactive wastelands. Running out of supplies, you must leave your bunker and scavenge to find a new home in the ruins of civilization.\n\nEmerging from your shelter, you squint as the harsh sunlight blinds you. For a moment, you're disoriented, your eyes struggling to adjust to the brightness of the new world outside. As your vision clears, you step forward, and take in the barren wasteland that stretches out before you.`, "adventure_context_mod":false, "adventure_is_action":true, "memory": `[Interactive Fiction: Game Mode Enabled]\n[You are playing a choose-your-own-adventure game. Please input action.]\n`, @@ -2029,7 +2029,7 @@ Current version: 96 { "title":"Dr. Katharine", "author":"Concedo", - "desc":"DISCLAIMER: This scenario is purely for ENTERTAINMENT and should NOT be used as substitute for actual therapy. Dr. Katharine is a therapist. As a mental health professional, she is very knowledgable in psychotherapy, and is ready to help you work through any personal issues you may have.", + "desc":"DISCLAIMER: This scenario is purely for ENTERTAINMENT and should NOT be used as substitute for actual therapy. Dr. Katharine is a therapist. As a mental health professional, she is very knowledgeable in psychotherapy, and is ready to help you work through any personal issues you may have.", "opmode":3, "chatname": "You", "chatopponent": "Dr. Katharine", @@ -2038,7 +2038,7 @@ Current version: 96 "prefmodel1":chatmodels1, "prefmodel2":chatmodels2, "prompt":"\nDr. Katharine: Good Afternoon. My focus is on providing evidence-based treatment that helps individuals manage their symptoms, improve their relationships, and live more fulfilling lives.\nDr. Katharine: I would like to know a bit more about your specific needs. What do you want to talk about today?", - "memory":`[Dr. Katharine is a professional therapist. She is very knowledgable in psychotherapy, and holds a medical license to provide advice. As a mental health professional, Dr. Katherine has been helping individuals with their personal issues for over 20 years. She is patient and understanding, compassionate and acknowledges her clients feelings and thoughts without judgement.]\n[The following is a transcript of your therapy session.]\n\nDr. Katharine: Please have a seat.\nYou: Hello Doctor, and thank you for letting me be treated by you. How should I start?`, + "memory":`[Dr. Katharine is a professional therapist. She is very knowledgeable in psychotherapy, and holds a medical license to provide advice. As a mental health professional, Dr. Katherine has been helping individuals with their personal issues for over 20 years. She is patient and understanding, compassionate and acknowledges her clients feelings and thoughts without judgement.]\n[The following is a transcript of your therapy session.]\n\nDr. Katharine: Please have a seat.\nYou: Hello Doctor, and thank you for letting me be treated by you. How should I start?`, "authorsnote": "", "worldinfo": [] }, @@ -11990,7 +11990,7 @@ Current version: 96 - + From 22c56f9221e6aaba6a274505961c2c39cd58c85f Mon Sep 17 00:00:00 2001 From: Concedo <39025047+LostRuins@users.noreply.github.com> Date: Sat, 18 Nov 2023 12:55:59 +0800 Subject: [PATCH 39/39] default to multiuser --- koboldcpp.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/koboldcpp.py b/koboldcpp.py index af3e22edc5b35..1daa5a1f7b85c 100755 --- a/koboldcpp.py +++ b/koboldcpp.py @@ -1021,7 +1021,7 @@ def show_new_gui(): port_var = ctk.StringVar(value=defaultport) host_var = ctk.StringVar(value="") - multiuser_var = ctk.IntVar() + multiuser_var = ctk.IntVar(value=1) horde_name_var = ctk.StringVar(value="koboldcpp") horde_gen_var = ctk.StringVar(value=maxhordelen) horde_context_var = ctk.StringVar(value=maxhordectx)