From 413d061959da01314c2726ed64e45cda62736305 Mon Sep 17 00:00:00 2001 From: Quentin Anthony Date: Sun, 31 Dec 2023 18:03:49 -0500 Subject: [PATCH 1/5] Update README and add llm directory site source --- README.md | 25 +- model-directory/index.html | 1878 ++++++++++++++++++++++++++++++++++++ 2 files changed, 1899 insertions(+), 4 deletions(-) create mode 100644 model-directory/index.html diff --git a/README.md b/README.md index 935e048..a4e1b84 100644 --- a/README.md +++ b/README.md @@ -6,17 +6,21 @@ All the practical details and utilities that go into working with real models! I ## Table of Contents - [The Cookbook](#the-cookbook) - * [Calculations](#calculations) - * [Benchmarks](#benchmarks) + * [Utilities](#utilites) + + [Calculations](#calculations) + + [Benchmarks](#benchmarks) * [Reading List](#reading-list) + [Basics](#basics) + [How to do LLM Calculations](#how-to-do-llm-calculations) + [Distributed Deep Learning](#distributed-deep-learning) + [Best Practices](#best-practices) + + [Data/Model Directories](#data-and-model-directories) * [Minimal Repositories for Educational Purposes](#minimal-repositories-for-educational-purposes) * [Contributing](#contributing) -## Calculations +## Utilities + +### Calculations For training/inference calculations (e.g. FLOPs, memory overhead, and parameter count) - **[calc](./calc/)** @@ -27,7 +31,7 @@ Useful external calculators include [Transformer Training and Inference VRAM Estimator](https://vram.asmirnov.xyz/) by Alexander Smirnov. A user-friendly tool to estimate VRAM overhead. -## Benchmarks +### Benchmarks For benchmarks (e.g. communication) - **[benchmarks](./benchmarks/)** @@ -58,12 +62,25 @@ For benchmarks (e.g. communication) [Efficient Training on Multiple GPUs](https://huggingface.co/docs/transformers/main/en/perf_train_gpu_many) by Hugging Face. Contains a detailed walk-through of model, tensor, and data parallelism along with the ZeRO optimizer. +Papers +- [Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM](https://arxiv.org/abs/2104.04473) +- [Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis](https://arxiv.org/abs/1802.09941) +- [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://arxiv.org/abs/1910.02054) +- [PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel](https://arxiv.org/abs/2304.11277) +- [PyTorch Distributed: Experiences on Accelerating Data Parallel Training](https://arxiv.org/abs/2006.15704) + ### Best Practices [ML-Engineering Repository](https://github.com/stas00/ml-engineering). Containing community notes and practical details of everything deep learning training led by Stas Bekman [Common HParam Settings](https://docs.google.com/spreadsheets/d/14vbBbuRMEHoqeuMHkTfw3uiZVmyXNuoSp8s-aHvfvZk/edit?usp=sharing) by Stella Biderman. Records common settings for model training hyperparameters and her current recommendations for training new models. +### Data and Model Directories + +[Directory of LLMs](https://docs.google.com/spreadsheets/d/1gc6yse74XCwBx028HV_cvdxwXkmXejVjkO-Mz2uwE0k/edit?usp=sharing) by Stella Biderman. Records details of trained LLMs including license, architecture type, and dataset. + +[Data Provenance Explorer](https://dataprovenance.org/) A tool for tracing and filtering on data provenance for the most popular open source finetuning data collections. + ## Minimal Repositories for Educational Purposes Large language models are frequently trained using very complex codebases due to the need to optimize things to work at scale and support a wide variety of configurable options. This can make them less useful pedagogical tools, so some people have developed striped-down so-called "Minimal Implementations" that are sufficient for smaller scale work and more pedagogically useful. diff --git a/model-directory/index.html b/model-directory/index.html new file mode 100644 index 0000000..8ffc96f --- /dev/null +++ b/model-directory/index.html @@ -0,0 +1,1878 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelDateParametersOrganizatonOrganization TypeAuthor LocationLanguageModel AccessibilityData AccessibilityArchitectureSources
GPT-2February 14, 20191.5BOpenAICompanyUSAEnglishOpen (MIT)Externally ReplicatedDecoder-only
Megatron-BERTSeptember 17, 20193.9BNVIDIACompanyUSAEnglishClosedClosedEncoder-onlyMegatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
Megatron-LMSeptember 17, 20198.3BNVIDIACompanyUSAEnglishClosedClosedDecoder-onlyMegatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
T5October 23, 201911.0BGoogleCompanyUSAEnglishOpen (Apache 2.0)Open (C4)Encoder-Decoder
MeenaJanuary 27, 20202.6BGoogleCompanyUSAEnglishClosedClosedEncoder-Decoder (Evolved)
Turing NLGFebruary 13, 202017.2BMicrosoftCompanyUSAEnglishClosedClosedDecoder-onlyTuring-NLG: A 17-billion-parameter language model by Microsoft
GPT-3May 28, 2020175.0BOpenAICompanyUSAEnglishAPIClosedDecoder-only
DeBERTaJune 5, 20201.5BMicrosoftCompanyUSAEnglishOpen (MIT)Closed + OWT + BCEncoder-onlyDeBERTa: Decoding-enhanced BERT with Disentangled Attention
mT5October 22, 202013.0BGoogleCompanyUSAMultilingualOpen (Apache 2.0)Open (mC4)Encoder-DecodermT5: A massively multilingual pre-trained text-to-text transformer
AlphaFold2November 30, 202093.0MDeepMindCompanyUSAProtein StructureOpen (Apache 2.0)OpenCustom
CPMDecember 1, 20202.6BTsinghua UniversityNon-profitChinaChineseNon-CommercialClosedDecoder-onlyCPM: A Large-scale Generative Chinese Pre-trained Language Model
AraGPT2December 31, 20201.5BAmerican University of BeirutNon-profitLebanonArabicOpen (Custom)Partially Released (OSCAR)Decoder-onlyAraGPT2: Pre-Trained Transformer for Arabic Language Generation, Checkpoint
Switch-CJanuary 11, 20211.5TGoogleCompanyUSAEnglishOpen (Apache 2.0)Open (C4)Encoder-Decoder MoESwitch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
Wu Dao 1.0March 20, 20212.6BBAAICompanyChinaChinese, EnglishAPIClosedDecoder-only MoE
GPT-NeoMarch 22, 20212.7BEleutherAINon-profitGermany, Canada, USAEnglishOpen (MIT)Open (Pile)Decoder-only
PanGu-αApril 26, 2021200.0BHuaweiCompanyChinaChineseClosedClosedDecoder-onlyPanGu-$α$: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation
ByT5May 28, 202113.0BGoogleCompanyUSAMultilingualOpen (Apache 2.0)Open (C4)Encoder-DecoderByT5: Towards a token-free future with pre-trained byte-to-byte models
Wu Dao 2.0June 1, 20211.8TBAAICompanyChinaChinese, EnglishAPIClosed + PileDecoder-only MoEChinese AI lab challenges Google, OpenAI with a model of 1.75 trillion parameters - PingWest
GPT-JJune 8, 20216.0BEleutherAINon-profitAustralia, USAEnglishOpen (Apache 2.0)Open (Pile)Decoder-only
CPM-2.1June 20, 202111.0BTsinghua UniversityNon-profitChinaChineseNon-CommercialClosedDecoder-only
CodexAugust 10, 2021UndisclosedOpenAICompanyUSAEnglishAPIClosedDecoder-only
Jurassic-1August 11, 2021178.0BAI21 LabsCompanyIsraelEnglishAPIOpen (Pile)Decoder-onlyWhite paper, data discovered via adversarial attacks and confirmed via personal communication
Wu Dao-GLM-XXLAugust 24, 202110.0BBAAICompanyChinaChineseClosedClosedEncoder-Decoder (Autoregressive)
Wu Dao-GLM-XXLAugust 24, 202110.0BBAAICompanyChinaEnglishClosedClosedEncoder-Decoder (Autoregressive)
CodeT5September 2, 202116.0BSalesforceCompanyUSACode, EnglishOpen (BSD 3-Clause)ClosedEncoder-DecoderarXiv
HyperCLOVASeptember 10, 2021204.0BNAVERCompanyKoreaKoreanClosedClosedDecoder-onlyarXiv
NTT DialogSeptember 17, 20211.6BNTTCompanyJapanEnglish, ChineseNon-CommercialClosedEncoder-Decoder
Plato-XLSeptember 20, 202111.0BBaiduCompanyChinaChinese, EnglishOpen (Apache 2.0)ClosedDecoder-only PrefixLM
Zidong TaichuSeptember 27, 2021100.0BChinese Academy of SciencesNon-profitChinaClosedClosedUndisclosedhttps://www.lwxsd.com/pcen/info_view.php?tab=mynews&VID=39572
Yuan 1.0October 10, 2021245.0BInspur AI ResearchCompanyChinaChineseLimited AccessPartially ReleasedDecoder-onlyarXiv
Megatron-TuringOctober 11, 2021530.0BMicrosoft, NVIDIACompanyUSAEnglishClosedClosed + PileDecoder-only
PAGnolOctober 16, 20211.5BLightOnCompanyFranceFrenchAPIClosed + OSCARDecoder-onlyPAGnol: An Extra-Large French Generative Model
Anthropic LMDecember 1, 202152.0BAnthropicCompanyUSAEnglishClosedClosedDecoder-only
ERNIE 3.0December 8, 2021260.0BBaiduCompanyChinaChinese, EnglishClosedClosedEncoder-Decoder w/ KGs
GopherDecember 8, 2021280.0BDeepMindCompanyUSAEnglishClosedClosed + C4Decoder-only
GLaMDecember 13, 20211.2TGoogleCompanyUSAEnglishClosedClosedDecoder-only MoEGLaM: Efficient Scaling of Language Models with Mixture-of-Experts
ExaoneDecember 14, 2021300.0BLGCompanyKoreaEnglish, KoreanChatBotClosedUndisclosed
XGLMDecember 20, 20217.5BMetaCompanyUSA, UK, GermanyMultilingualClosedClosedDecoder-onlyhttps://arxiv.org/abs/2112.10668
FairSeq DenseDecember 20, 202113.0BMetaCompanyUSA, UK, GermanyEnglishOpen (Ambiguous)ClosedDecoder-only
LaMDAJanuary 20, 2022137.0BGoogleCompanyUSAEnglishClosedClosed + C4Decoder-onlyLaMDA: Language Models for Dialog Applications
GPT-NeoX-20BFebruary 2, 202220.0BEleutherAINon-profitMultinationalEnglishOpen (Apache 2.0)Open (Pile)Decoder-only
GPT-SW3February 15, 20223.5BAI SwedenNon-profitSwedenSwedishLimited AccessClosed + Pile + OSCSAR + mC4Decoder-onlyGPT-SW3: An Autoregressive Language Model for the Nordic Languages
PolyCoderFebruary 26, 20222.7BCarnegie Mellon UniversityNon-profitUSACodeReleased (Unlicensed)Partially ReleasedDecoder-onlyNinedayWang/PolyCoder-2.7B · Hugging Face
CodeGenMarch 25, 202216.1BSalesforceCompanyUSACode, EnglishOpen (BSD 3-Clause)Closed + PileDecoder-onlyarXiv , https://huggingface.co/Salesforce/codegen-16B-multi
ChinchillaMarch 29, 202270.0BDeepMindCompanyUSAEnglishClosedClosed + C4Decoder-onlyTraining Compute-Optimal Large Language Models
PaLMApril 4, 2022540.0BGoogleCompanyUSAEnglishClosedClosedDecoder-onlyPaLM: Scaling Language Modeling with Pathways
NOORApril 11, 202213.0BTechnology Innovation Institute, LightOnMultilateral colaborationUAE, FranceArabicClosedClosedDecoder-onlyhttps://aclanthology.org/2022.bigscience-1.8.pdf
InCoder-6BApril 12, 20226.0BMetaCompanyUSACodeNon-CommercialClosedDecoder-only (Autoregressive Span Corruption)
Lyra-frApril 12, 202210.0BLightOnCompanyFranceFrenchAPIClosedDecoder-only
OPTMay 2, 2022175.0BMetaCompanyUSAEnglishNon-CommercialClosed + PileDecoder-onlyOPT: Open Pre-trained Transformer Language Models
UL2May 10, 202220.0BGoogleCompanyUSAEnglishOpen (Apache 2.0)Open (C4)Encoder-Decoder
YaLMMay 23, 2022100.0BYandexCompanyRussiaRussian, EnglishOpen (Apache 2.0)Closed + PileDecoder-only
BLOOMMay 26, 2022176.0BBigScienceMultilateral colaborationMultinationalMultilingualCommercial UseLimited Access + S2ORC + OSCARDecoder-only
NeMo MegatronSeptember 14, 202220.0BNVIDIACompanyUSAEnglishOpen (CC-BY-4.0)Open (Pile)Decoder-only
GLM-130BOctober 5, 2022130.0BTsinghua UniversityNon-profitChinaEnglishNon-CommercialClosed + PileDecoder-only (Autoregressive Span Corruption)
GenSLMsOctober 11, 202225.0BArgone National LabNon-profitUSAGenomicsOpen (MIT)Partially ReleasedCustomGenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics | bioRxiv
GalacticaNovember 16, 2022120.0BMetaCompanyUSAEnglishNon-CommercialClosed + S2ORCDecoder-only
GPT-3.5November 19, 2022UndisclosedOpenAICompanyUSAMultilingualAPIClosedUndisclosed
OpenFoldNovember 24, 202293.0MMultilateral ColaborationMultilateral colaborationUSAProtein StructureOpen (Apache 2.0)OpenCustom
PythiaDecember 10, 202212.0BEleutherAINon-profitMultinationalEnglishOpen (Apache 2.0)Open (Pile)Decoder-only
Pythia-dedupedDecember 10, 202212.0BEleutherAINon-profitMultinationalEnglishOpen (Apache 2.0)Open (Pile)Decoder-only
BioMedGPTDecember 15, 20222.7BStanford CRFMNon-ProfitUSAEnglish (Medical Texts)Commercial UseOpen (Pile)Decoder-only
PalmyraJanuary 5, 202320.0BWriterCompanyUSAEnglishOpen (Apache 2.0)ClosedDecoder-only
SantaCoderJanuary 9, 20231.1BBigCode ProjectCompanyMultinationalCodeCommercial UseOpen (Stack)Decoder-only (Autoregressive Span Corruption)
MusicLMJanuary 26, 20231.4BGoogleCompanyUSAMusicClosedPartially Released (FMA)Customhttps://arxiv.org/abs/2301.11325
Anthropic 175B LMFebruary 15, 2023175.0BAnthropicCompanyUSAClosedClosedUndisclosed
GPT-3-FinnishFebruary 16, 202313.0BUniversity of TurkuNon-profitFinlandFinnishOpen (Apache 2.0)Reproducible + ROOTSDecoder-only
LLaMAFebruary 24, 202365.0BMetaCompanyUSAEnglishNon-CommercialExternally ReplicatedDecoder-only
GPT-SW3 40BMarch 1, 202340.0BAI Sweden, RISENon-profitSwedenNordic Languages and EnglishLimited AccessClosed + Pile + mC4 + OSCARDecoder-only
Jurassic-2March 9, 2023UndisclosedAI21CompanyIsraelMultilingual (7)APIClosedUndisclosed
ChatGLMMarch 13, 20236.0BTsinghua UniversityNon-profitChinaEnglish, ChineseCommercial UseClosedDecoder-only (Autoregressive Span Corruption)
Claude-1March 14, 2023UndisclosedAnthropicCompanyUSAMultilingualChatBotClosedUndisclosed
GPT-4March 14, 2023UndisclosedOpenAICompanyUSAMultilingualChatBotClosedUndisclosed
ESM-2March 16, 202315.0BMetaCompanyUSAProteinOpen (MIT)OpenEncoder-only
CerebrasGPTMarch 28, 202313.0BCerebrasCompanyUSA, CanadaEnglishOpen (Apache 2.0)Open (Pile)Decoder-only
BloombergGPTMarch 30, 202350.6BBloombergCompanyUSAEnglishClosedClosed + Pile + C4Decoder-onlyBloombergGPT: A Large Language Model for Finance
RedPajamas-INCITEApril 17, 20237.0BTogetherAICompanyUSAEnglishOpen (Apache 2.0)Open (RP)Decoder-only
StableLMApril 19, 20237.0BStability AICompanyUSAEnglishOpen (CC-BY-SA-4.0)Closed + PileDecoder-only
CodeGen2May 3, 202316.0BSalesforceCompanyUSACodeOpen (Apache 2.0)Open (Stack)Decoder-only (Autoregressive Span Corruption)
MPT-7BMay 5, 20236.7BMosaicMLCompanyUSAEnglishOpen (Apache 2.0)Open (C4, RP, S2ORC, Stack, Pile)Decoder-only
StarCoderMay 9, 202315.0BBigCode ProjectCompanyMultinationalCodeCommercial UseOpen (Stack)Decoder-only (Autoregressive Span Corruption)
PaLM-2May 10, 2023UndisclosedGoogleCompanyUSAMultilingualChatBotClosedDecoder-only
CodeT5+May 13, 202316.0BSalesforceCompanyUSACode, EnglishOpen (BSD 3-Clause)ClosedEncoder-Decoder
OpenCALMMay 15, 20237.0BCyberAgentCompanyJapanJapaneseOpen (CC BY-SA 4.0)ClosedDecoder-only
RWKV v4May 22, 202313.0BEleutherAI and GenAI CommonsNon-profitMultinationalEnglishOpen (Apache 2.0)Open (Pile)RNN
Falcon-40BMay 25, 202340.0BTechnology Innovation Institute, LightOnMultilateral colaborationUAE, FranceEnglishOpen (Apache 2.0)Partially ReleasedDecoder-only
StellarXMay 27, 20234.0BArkane IndustriesCompanyUndisclosedCC-BY-NC-SA-4.0Open (RP)Decoder-onlyDampish/StellarX-4B-V0.2
Polyglot-KoJune 4, 202312.8BEleutherAINon-profitKoreaKoreanOpen (Apache 2.0)ClosedDecoder-only
PULI-GPTrioJune 8, 20237.7BHungarian Research Centre for LinguisticsNon-profitHungaryEnglish, Hungarian, ChineseCC-BY-NCClosedDecoder-onlyNYTK/PULI-GPTrio
Baichuan-1 7BJune 15, 20237.0BBaichuan AICompanyChinaChinese, EnglishCommercial UseClosedDecoder-onlybaichuan-inc/Baichuan-7B
Zidong Taichu 2.0June 16, 2023100.0BChinese Academy of SciencesNon-profitChinaClosedClosedUndisclosed
Phi-1June 20, 20231.3BMicrosoftCompanyUSAEnglishNon-CommercialClosedDecoder-only
MPT-30BJune 22, 202330.0BMosaicMLCompanyUSAEnglishOpen (Apache 2.0)Open (C4, RP, S2ORC, Stack, Pile)Decoder-only
Inflection-1June 23, 2023UndisclosedInflectionCompanyUSAChatBotClosedDecoder-only
ChatGLM2June 25, 20236.0BTsinghua UniversityNon-profitChinaEnglish, ChineseCommercial UseClosedDecoder-only (Autoregressive Span Corruption)
OpenLLaMAJune 30, 202313.0BUC BerkeleyNon-profitUSAEnglishOpen (Apache 2.0)Open (RP)Decoder-only
Codegen2.5July 6, 20237.0BSalesforceCompanyUSACodeOpen (Apache 2.0)Open (Stack)Decoder-only (Autoregressive Span Corruption)CodeGen2.5: Small, but mighty
Baichuan-1 13BJuly 10, 202313.0BBaichuan AICompanyChinaChinese, EnglishCommercial UseClosedDecoder-onlyhttps://huggingface.co/baichuan-inc/Baichuan-13B
Claude-2July 11, 2023UndisclosedAnthropicCompanyUSAMultilingualChatBotClosedUndisclosed
LLaMA 2July 18, 202370.0BMetaCompanyUSAEnglishCommercial UseClosedDecoder-only
ExaoneJuly 19, 2023UndisclosedLGCompanyKoreaEnglish, KoreanChatBotClosedUndisclosed
BTLM-3B-8k-base July 24, 20233.0BCerebrasCompanyUSA, Canada, UAEEnglishOpen (Apache 2.0)Open (RP)Decoder-only
bilingual-gpt-neox-4bJuly 30, 20233.8BRinnaCompanyJapanJapaneseOpen (MIT)Open (Pile + mC4 + RP)Decoder-only
JiangAugust 1, 202330.0BKDFCompanyChinaChineseOpen (Apache 2.0)ClosedDecoder-only
Baichuan-1 53BAugust 8, 202353.0BBaichuan AICompanyChinaChinese, EnglishCommercial UseClosedDecoder-only百川智能发布旗下第三代大模型产品Baichuan-53B
StableLM JapaneseAugust 10, 20237.0BStability AICompanyUSA, JapanJapaneseOpen (Apache 2.0)Open (Composite)Decoder-only
DeciCoderAugust 16, 20231.0BDeciCompanyIsraelCodeOpen (Apache 2.0)Open (StarCoder)Decoder-onlyDeci/DeciCoder-1b · Hugging Face
Dou BaoAugust 18, 2023UndisclosedByteDanceCompanyChinaAPIClosedUndisclosed
JaisAugust 30, 202313.0BTechnology Innovation Institute, LightOnMultilateral colaborationUSA, Canada, UAEArabicOpen (Apache 2.0)Closed + Pile + mC4Decoder-only
Falcon-180BSeptember 6, 2023180.0BTechnology Innovation Institute, LightOnMultilateral colaborationUAE, FranceEnglishCommercial UsePartially ReleasedDecoder-only
Persimmon-8BSeptember 7, 20238.0BAdept AICompanyUSAEnglishOpen (Apache 2.0)ClosedDecoder-onlyReleasing Persimmon-8B
XGenSeptember 7, 20237.0BSalesforceCompanyUSAEnglish, CodeOpen (Apache 2.0)Closed + RP + StackDecoder-onlySalesforce/xgen-7b-8k-base · Hugging Face
Phi-1.5September 11, 20231.3BMicrosoftCompanyUSAEnglishNon-CommercialClosedDecoder-only
FLM-101BSeptember 17, 2023101.0BMultilateral, lead by BAAIMultilateral colaborationChinaChinese, EnglishOpen (Apache 2.0)ClosedDecoder-onlyFLM-101B: An Open LLM and How to Train It with $100K Budget
Baichuan-2September 19, 202313.0BBaichuan AICompanyChinaEnglish, ChineseCommercial UseClosedDecoder-onlyBaichuan 2: Open Large-scale Language Models
InternLMSeptember 20, 202320.0BShanghai AI LaboratoryNon-profitChinaChinese, EnglishCommercial UseClosedDecoder-onlyhttps://huggingface.co/internlm/internlm-20b
Unnamed ModelSeptember 26, 20233.6BLine CorporationCompanyJapanJapaneseOpen (Apache 2.0)ClosedDecoder-only
Mistral-7B-v0.1September 27, 20237.0BMistral AICompanyFranceEnglishOpen (Apache 2.0)ClosedDecoder-onlyhttps://mistral.ai/news/announcing-mistral-7b/ , https://arxiv.org/abs/2310.06825
RWKV v4 WorldOctober 10, 20237.0BEleutherAI and GenAI CommonsNon-profitMultinationalMultilingualOpen (Apache 2.0)Open (incl. Pile)RNN
CodeFuseOctober 10, 202313.0BAnt GroupCompanyChinaChinese, English, CodeOpen (Custom)ClosedDecoder-only
Qwen-14BOctober 13, 202314.0BAlibaba GroupCompanyChinaMultilingualCommercial UseClosedDecoder-onlyarXiv , https://huggingface.co/Qwen/Qwen-14B/blob/main/LICENSE
ChatGLM3October 27, 20236.0BTsinghua UniversityNon-profitChinaEnglish, ChineseCommercial UseClosedDecoder-only (Autoregressive Span Corruption)
SkyworkOctober 30, 202313.0BKunlunCompanyChinaEnglish, ChineseCommercial UsePartially ReleasedDecoder-only
DeepSeek-CoderNovember 2, 202333.0BDeepSeekCompanyChinaCode, English, ChineseCommercial UseClosedDecoder-onlyDeepSeek Coder
FinGPT-3November 3, 202313.3BThe University of Turku, Hugging Face, the Finish National Library, and AMDMultilateral colaborationFinland, USAFinishOpen (Apache 2.0)Closed + mC4Decoder-only
Grok-0November 5, 202333.0BxAICompanyUSAEnglishClosedClosedUndisclosedhttps://x.ai/
Grok-1November 5, 2023UndisclosedxAICompanyUSAEnglishClosedClosedUndisclosedhttps://x.ai/
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["main"] + + # Allows you to run this workflow manually from the Actions tab + workflow_dispatch: + +# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages +permissions: + contents: read + pages: write + id-token: write + +# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued. +# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete. +concurrency: + group: "pages" + cancel-in-progress: false + +jobs: + # Single deploy job since we're just deploying + deploy: + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v4 + - name: Setup Pages + uses: actions/configure-pages@v4 + - name: Upload artifact + uses: actions/upload-pages-artifact@v3 + with: + # Upload entire repository + path: './model-directory' + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v4 From d080bfc9cc2844a20d7c14b7d799679321ab0216 Mon Sep 17 00:00:00 2001 From: Quentin Anthony Date: Sat, 27 Jan 2024 13:26:32 -0800 Subject: [PATCH 4/5] Update mem calculation to include per-gpu, any mixed precision, and fix a few errors --- calc/calc_transformer_mem.py | 264 +++++++++++++++++++++-------------- 1 file changed, 159 insertions(+), 105 deletions(-) diff --git a/calc/calc_transformer_mem.py b/calc/calc_transformer_mem.py index 9e1a419..0a38b46 100644 --- a/calc/calc_transformer_mem.py +++ b/calc/calc_transformer_mem.py @@ -15,10 +15,7 @@ def convert_params(params): def config_parser(): parser = argparse.ArgumentParser() - parser.add_argument("--params", "-p", - type=int, - default=20000000000, - help='Number of Parameters') + # Distributed Settings parser.add_argument("--num-gpus", type=int, default=1, @@ -39,6 +36,15 @@ def config_parser(): default=1, choices=[0,1,2,3], help='Stage of the ZeRO optimizer') + parser.add_argument("--zero-allgather-bucket-size", "-zbs", + type=int, + default=5e8, + help='Size of allgather buckets used by ZeRO') + parser.add_argument("--zero3-max-live-params", "-zmlp", + type=int, + default=1e9, + help='Maximum number of parameters ZeRO3 keeps in GPU memory') + # Training settings parser.add_argument("--checkpoint-activations", "-ca", action="store_true", help='Whether Megatron-style activation checkpointing is being used') @@ -46,6 +52,15 @@ def config_parser(): type=int, default=1, help='Batch size per GPU') + parser.add_argument("--sequence-length", "-s", + type=int, + default=2048, + help='Sequence length used for training') + parser.add_argument("--vocab-size", "-v", + type=int, + default=51200, + help='How many tokens are in the embedding layer') + # Model settings parser.add_argument("--hidden-size", "-hs", type=int, default=6144, @@ -54,180 +69,219 @@ def config_parser(): type=int, default=64, help='Number of attention heads used in model') - parser.add_argument("--kv-size-ratio", "-kv", - type=float, - default=1.0, - help='Ratio of total query heads to key/value heads. 1.0 for MHA, 1/num_attention_heads for MQA.') - parser.add_argument("--sequence-length", "-s", - type=int, - default=2048, - help='Sequence length used for training') parser.add_argument("--num-layers", "-l", type=int, default=44, help='Number of transformer layers used in model') - parser.add_argument("--fp32-model", - action="store_true", - help='Whether model is stored in fp32') - parser.add_argument("--fp32-grads", + parser.add_argument("--ffn-expansion-factor", "-ff", + type=int, + default=4, + help='How much the MLP hidden size expands') + # Inference settings + parser.add_argument("--infer", action="store_true", - help='Whether grads are stored in fp32') - parser.add_argument("--zero-allgather-bucket-size", "-zbs", + help="whether we're doing inference") + parser.add_argument("--kv-size-ratio", "-kv", + type=float, + default=1.0, + help='Ratio of total query heads to key/value heads. 1.0 for MHA, 1/num_attention_heads for MQA.') + # Precision settings + parser.add_argument("--disable-mixed-precision", + action="store_false", + help='Disables mixed precision training', + dest='is_mixed_precision') + parser.add_argument("--high-prec-bytes-per-val", type=int, - default=5e8, - help='Size of allgather buckets used by ZeRO') - parser.add_argument("--zero3-max-live-params", "-zmlp", + default=4, + help='The high-precision bytes per value (parameter, optimizer state, etc) in mixed precision') + parser.add_argument("--low-prec-bytes-per-val", type=int, - default=1e9, - help='Maximum number of parameters ZeRO3 keeps in GPU memory') - parser.add_argument("--misc-mem-gb", + default=2, + help='The low-precision bytes per value (parameter, optimizer state, etc) in mixed precision') + parser.add_argument("--bytes-per-grad-ele", type=int, - default=0, - help='Miscellaneous memory overhead by DL framework(s), communication libraries, etc') + default=4, + help='The precision of gradient elements as bytes per value') + # MoE Settings parser.add_argument("--num-experts", - type=int, - default=0, - help='Number of experts') - parser.add_argument("--ffn-expansion-factor", "-ff", - type=int, - default=4, - help='How much the MLP hidden size expands') + type=int, + default=0, + help='Number of experts') parser.add_argument("--expert-parallelism", "-ep", - type=int, - default=1, - help='How many ways are the experts sharded across ranks') - parser.add_argument("--vocab-size", "-v", - type=int, - default=51200, - help='How many tokens are in the embedding layer') - parser.add_argument("--infer", - action="store_true", - help="whether we're doing inference") + type=int, + default=1, + help='How many ways are the experts sharded across ranks') + # Miscellaneous memory (good for accounting for implementation-dependent fudge factors) + parser.add_argument("--misc-mem-gib", + type=int, + default=0, + help='Miscellaneous memory overhead per GPU by DL framework(s), communication libraries, etc') + return parser -# calculates the total memory necessary for training a model + +# Calculates the total memory necessary for model training or inference def calc_mem(args): dp_degree = args.num_gpus / (args.tensor_parallel_size * args.pipeline_parallel_size) - # 4 bytes in fp32, 2 bytes in fp16/bf16 - if args.fp32_model: - bytes_per_param = 4 - else: - bytes_per_param = 2 - - - # compute total parameters from the config + # Compute total parameters from the config embed_params = 2 * args.vocab_size * args.hidden_size positional_params = args.hidden_size * args.sequence_length ln_params = 8 * args.hidden_size * args.num_layers + (2 * args.hidden_size) attention_params = int(2 * (1 + args.kv_size_ratio) * args.num_layers * args.hidden_size * args.hidden_size) mlp_params = 2 * args.num_layers * args.hidden_size * args.ffn_expansion_factor * args.hidden_size total_params = embed_params + positional_params + ln_params + attention_params + mlp_params + + # --- MODEL MEMORY --- + # 4 bytes in fp32, 2 bytes in fp16/bf16, 1 byte in fp8 + if args.is_mixed_precision: + bytes_per_param = args.low_prec_bytes_per_val + else: + bytes_per_param = args.high_prec_bytes_per_val + + # Compute memory from param calculation and parallelism settings + model_mem = total_params * bytes_per_param + per_gpu_model_mem = model_mem if args.num_experts > 0: total_moe_params = embed_params + positional_params + ln_params + attention_params + (args.num_experts * mlp_params) # Split the model with 3D parallelism if args.num_experts == 0: - model_mem = (total_params * bytes_per_param) / (args.tensor_parallel_size * args.pipeline_parallel_size) + per_gpu_model_mem = (total_params * bytes_per_param) / (args.tensor_parallel_size * args.pipeline_parallel_size) else: EP_total_params = embed_params + positional_params + ln_params + attention_params + ((args.num_experts/args.expert_parallelism) * mlp_params) - model_mem = (EP_total_params * bytes_per_param) / (args.tensor_parallel_size * args.pipeline_parallel_size) + per_gpu_model_mem = (EP_total_params * bytes_per_param) / (args.tensor_parallel_size * args.pipeline_parallel_size) # ZeRO stage 3 shards the model parameters across GPUs (plus the gradients and optimizer states) if args.zero_stage == 3: - model_mem /= args.num_gpus + model_mem_per_gpu /= args.num_gpus - # 4 bytes in fp32, 2 bytes in fp16/bf16 - if args.fp32_grads: - bytes_per_grad_element = 4 - else: - bytes_per_grad_element = 2 + # --- GRADIENT MEMORY --- + # E.g. 4 bytes in fp32, 2 bytes in fp16/bf16, 1 byte in fp8 + # Gradient precision is sometimes configurable in training frameworks. + # Since high batch size means many accumulations, higher precision grads may reduce grad overflow. + bytes_per_grad_element = args.bytes_per_grad_ele if args.num_experts > 0: gradient_mem = EP_total_params * bytes_per_grad_element else: gradient_mem = total_params * bytes_per_grad_element + per_gpu_gradient_mem = gradient_mem # ZeRO stage 2 shards the gradients across GPUs (plus the optimizer states) if args.zero_stage >= 2: - gradient_mem /= args.num_gpus - gradient_mem /= args.pipeline_parallel_size + per_gpu_gradient_mem /= args.num_gpus + # --- OPTIMIZER MEMORY --- # For mixed-precision Adam/AdamW, the optimizer must store fp32 copies of the parameters, momentum, and variance (4 + 4 + 4 = 12 bytes per optimizer parameter) # Feel free to change the multiplier for your optimizer (examples include SGD (4 + 4 = 8) and 8-bit ADAM (2 + 2 + 2 = 6) if args.num_experts > 0: optimizer_mem = EP_total_params * 12 else: optimizer_mem = total_params * 12 + per_gpu_optimizer_mem = optimizer_mem # ZeRO stage 3 shards the optimizer states across GPUs if args.zero_stage >= 1: - optimizer_mem /= args.num_gpus + per_gpu_optimizer_mem /= args.num_gpus - communication_mem = 0 + # --- COMMUNICATION MEMORY --- + # Temporary GPU storage for communication buffers may become significant + per_gpu_communication_mem = 0 # The size of the communication buffer DeepSpeed uses to store ZeRO optimizer elements - if args.zero_stage >= 1: - communication_mem += args.zero_allgather_bucket_size * bytes_per_param + if args.zero_stage >= 1 and args.num_gpus > 1: + per_gpu_communication_mem += args.zero_allgather_bucket_size * bytes_per_param # The number of parameters ZeRO-3 keeps alive in GPU memory at a time - if args.zero_stage == 3: - communication_mem += args.zero3_max_live_params * bytes_per_param + if args.zero_stage == 3 and args.num_gpus > 1: + per_gpu_communication_mem += args.zero3_max_live_params * bytes_per_param - # Taken from Table 2 in https://arxiv.org/pdf/1910.02054.pdf - # We find these don't perfectly match with experiment, but are good approximations + # --- ACTIVATION MEMORY --- + # Taken from Table 2 in https://arxiv.org/pdf/1910.02054.pdf and generalized to any precision (instead of just fp16 from the paper) + # 3 cases: [training with activation checkpointing, training without activation checkpointing, inferencing] if not args.infer and args.checkpoint_activations: - activation_mem = bytes_per_param * args.sequence_length * args.batch_size_per_gpu * args.hidden_size * args.num_layers * (10 + (24 / args.tensor_parallel_size)) + activation_mem = args.sequence_length * args.batch_size_per_gpu * args.hidden_size * args.num_layers * ((16 * args.low_prec_bytes_per_val + 2)) elif not args.infer and not args.checkpoint_activations: - activation_mem = bytes_per_param * args.sequence_length * args.batch_size_per_gpu * args.hidden_size * args.num_layers * (10 + (24 / args.tensor_parallel_size) + 5 * ((args.num_attention_heads * args.sequence_length) / (args.hidden_size * args.tensor_parallel_size))) + activation_mem = args.sequence_length * args.batch_size_per_gpu * args.hidden_size * args.num_layers * ((16 * args.low_prec_bytes_per_val + 2) + (2 * args.low_prec_bytes_per_val + 1) * (args.num_attention_heads * args.sequence_length / args.hidden_size)) # If using inference, assume just a single layer's activation memory at peak elif args.infer: - activation_mem = bytes_per_param * args.sequence_length * args.batch_size_per_gpu * args.hidden_size * (10 + (24 / args.tensor_parallel_size) + 5 * ((args.num_attention_heads * args.sequence_length) / (args.hidden_size * args.tensor_parallel_size))) - + activation_mem = args.sequence_length * args.batch_size_per_gpu * args.hidden_size * ((16 * args.low_prec_bytes_per_val + 2)) + per_gpu_activation_mem = activation_mem # DeepSpeed's ZeRO-R partitions activation memory across tensor-parallel GPUs if args.partition_activations: - activation_mem /= args.tensor_parallel_size - + per_gpu_activation_mem = activation_mem / args.tensor_parallel_size + # --- KV CACHE MEMORY (IF INFERENCE) --- if args.infer: - if args.fp32_model: - bytes_per_param = 4 - else: - bytes_per_param = 2 - kv_cache_mem = bytes_per_param * 2 * args.num_layers * args.num_attention_heads * (args.hidden_size / args.num_attention_heads) * args.sequence_length - - # We include a "Miscellaneous Memory" term because we find some 3D-parallel frameworks add a constant memory overhead (~5GB in our experiments with Megatron-DeepSpeed) that we cannot explain. If you know the source of this, add a comment! - gradient_mem_gb = gradient_mem / 1024**3 - activation_mem_gb = activation_mem / 1024**3 - model_mem_gb = model_mem / 1024**3 - optimizer_mem_gb = optimizer_mem / 1024**3 - communication_mem_gb = communication_mem / 1024**3 - total_mem_gb = activation_mem_gb + gradient_mem_gb + model_mem_gb + optimizer_mem_gb + communication_mem_gb + args.misc_mem_gb + # See https://kipp.ly/transformer-inference-arithmetic/ for details + bytes_per_param = args.low_prec_bytes_per_val + per_gpu_kv_cache_mem = bytes_per_param * 2 * args.num_layers * args.num_attention_heads * (args.hidden_size / args.num_attention_heads) * args.sequence_length + kv_cache_mem = args.num_gpus * per_gpu_kv_cache_mem + + gradient_mem_gib = gradient_mem / 1024**3 + activation_mem_gib = activation_mem / 1024**3 + model_mem_gib = model_mem / 1024**3 + optimizer_mem_gib = optimizer_mem / 1024**3 + + per_gpu_gradient_mem_gib = per_gpu_gradient_mem / 1024**3 + per_gpu_activation_mem_gib = per_gpu_activation_mem / 1024**3 + per_gpu_model_mem_gib = per_gpu_model_mem / 1024**3 + per_gpu_optimizer_mem_gib = per_gpu_optimizer_mem / 1024**3 + per_gpu_communication_mem_gib = per_gpu_communication_mem / 1024**3 + + + # We include a "Miscellaneous Memory" per GPU term because we find some 3D-parallel frameworks add a constant memory overhead (~5GiB in our experiments with Megatron-DeepSpeed) that we cannot explain. If you know the source of this, add a comment! if args.infer: - kv_cache_mem_gb = kv_cache_mem / 1024**3 + kv_cache_mem_gib = kv_cache_mem / 1024**3 + per_gpu_kv_cache_mem_gib = per_gpu_kv_cache_mem / 1024**3 if args.infer: - total_mem_gb = activation_mem_gb + kv_cache_mem_gb + model_mem_gb + args.misc_mem_gb + per_gpu_mem_gib = per_gpu_activation_mem_gib + per_gpu_kv_cache_mem_gib + per_gpu_model_mem_gib + args.misc_mem_gib + single_replica_mem_gib = activation_mem_gib + kv_cache_mem_gib + model_mem_gib + args.misc_mem_gib * args.num_gpus else: - total_mem_gb = activation_mem_gb + gradient_mem_gb + model_mem_gb + optimizer_mem_gb + communication_mem_gb + args.misc_mem_gb + per_gpu_mem_gib = per_gpu_activation_mem_gib + per_gpu_gradient_mem_gib + per_gpu_model_mem_gib + per_gpu_optimizer_mem_gib + per_gpu_communication_mem_gib + args.misc_mem_gib + single_replica_mem_gib = activation_mem_gib + gradient_mem_gib + model_mem_gib + optimizer_mem_gib + args.misc_mem_gib * args.num_gpus + # Print number of forward-pass parameters, and account for experts if using MoE print(f'Calculating memory with training configuration: {vars(args)}\n') print(f'Number of Parameters: {convert_params(total_params)}') if args.num_experts > 0: print(f'Total Number of MoE Parameters: {convert_params(total_moe_params)}') - print(f'Activation Memory: {activation_mem_gb:.2f} GB') - print(f'Model Memory: {model_mem_gb:.2f} GB') + print() + + # Print per-GPU memory for each component + print(f'*** Per-GPU Memory') + print(f'Per-GPU Activation Memory: {per_gpu_activation_mem_gib:.2f} GiB') + print(f'Per-GPU Model Memory: {per_gpu_model_mem_gib:.2f} GiB') if args.infer: - print(f'KV Cache Memory: {kv_cache_mem_gb:.2f} GB') + print(f'Per-GPU KV Cache Memory: {per_gpu_kv_cache_mem_gib:.2f} GiB') else: - print(f'Gradient Memory: {gradient_mem_gb:.2f} GB') - print(f'Optimizer Memory: {optimizer_mem_gb:.2f} GB') - print(f'Communication Memory: {communication_mem_gb:.2f} GB') - print(f'Miscellaneous Memory: {args.misc_mem_gb:.2f} GB') - + print(f'Per-GPU Gradient Memory: {per_gpu_gradient_mem_gib:.2f} GiB') + print(f'Per-GPU Optimizer Memory: {per_gpu_optimizer_mem_gib:.2f} GiB') + print(f'Per-GPU Communication Memory: {per_gpu_communication_mem_gib:.2f} GiB') + print(f'Per-GPU Miscellaneous Memory: {args.misc_mem_gib:.2f} GiB') + # Aggregate Per-GPU Memory + if args.infer: + print(f'\nPer-GPU Memory Required for Inference: {per_gpu_mem_gib:.2f} GiB') + else: + print(f'\nPer-GPU Memory Required for Training: {per_gpu_mem_gib:.2f} GiB') + print() + + # Print total GPU memory required to store a complete model replica + print(f'*** Total GPU Memory for a Single Model Replica') + print(f'Total Activation Memory: {activation_mem_gib:.2f} GiB') + print(f'Total Model Memory: {model_mem_gib:.2f} GiB') + if args.infer: + print(f'Total KV Cache Memory: {kv_cache_mem_gib:.2f} GiB') + else: + print(f'Total Gradient Memory: {gradient_mem_gib:.2f} GiB') + print(f'Total Optimizer Memory: {optimizer_mem_gib:.2f} GiB') + print(f'Total Miscellaneous Memory: {args.num_gpus*args.misc_mem_gib:.2f} GiB') + # Aggregate GPU memory if args.infer: - print(f'Total Memory Required for Inference: {total_mem_gb:.2f} GB') + print(f'\nTotal GPU Memory Required to Store a Complete Model Replica for Inference: {single_replica_mem_gib:.2f} GiB') else: - print(f'Total Memory Required for Training: {total_mem_gb:.2f} GB') + print(f'\nTotal GPU Memory Required to Store a Complete Model Replica for Training: {single_replica_mem_gib:.2f} GiB') if __name__ == "__main__": - print('\nExample with pythia 6.9B: python transformer_mem.py --num-layers=32 --sequence-length=2048 --num-attention-heads=32 --hidden-size=4096 --batch-size-per-gpu=8 --checkpoint-activations --zero-stage=1 --partition-activations --pipeline-parallel-size=1 --tensor-parallel-size=2 --num-gpus=128 --params=6900000000') - print('Example with pythia 12B: python transformer_mem.py --num-layers=36 --sequence-length=2048 --num-attention-heads=40 --hidden-size=5120 --batch-size-per-gpu=8 --checkpoint-activations --zero-stage=1 --partition-activations --pipeline-parallel-size=1 --tensor-parallel-size=4 --num-gpus=256 --params=11849420800') - print('Example with default 20B: python transformer_mem.py --num-layers=44 --sequence-length=2048 --num-attention-heads=64 --hidden-size=6144 --batch-size-per-gpu=1 --checkpoint-activations --zero-stage=1 --partition-activations --pipeline-parallel-size=1 --tensor-parallel-size=1 --num-gpus=1 --params=20000000000\n') + print('\nExample with pythia 6.9B: python calc_transformer_mem.py --num-layers=32 --sequence-length=2048 --num-attention-heads=32 --hidden-size=4096 --batch-size-per-gpu=8 --checkpoint-activations --zero-stage=1 --partition-activations --pipeline-parallel-size=1 --tensor-parallel-size=2 --num-gpus=128') + print('Example with pythia 12B: python calc_transformer_mem.py --num-layers=36 --sequence-length=2048 --num-attention-heads=40 --hidden-size=5120 --batch-size-per-gpu=8 --checkpoint-activations --zero-stage=1 --partition-activations --pipeline-parallel-size=1 --tensor-parallel-size=4 --num-gpus=256') + print('Example with default 20B: python calc_transformer_mem.py --num-layers=44 --sequence-length=2048 --num-attention-heads=64 --hidden-size=6144 --batch-size-per-gpu=1 --checkpoint-activations --zero-stage=1 --partition-activations --pipeline-parallel-size=1 --tensor-parallel-size=1 --num-gpus=1\n') args = config_parser().parse_args() calc_mem(args) From 623b145fbc92e0eb69ac9ba476d3c3fa0c5e9614 Mon Sep 17 00:00:00 2001 From: Quentin Anthony Date: Sat, 27 Jan 2024 13:46:00 -0800 Subject: [PATCH 5/5] Update README and add swiglu to flops --- calc/README.md | 57 +++++++++++++++++++--------------- calc/calc_transformer_flops.py | 5 +++ 2 files changed, 37 insertions(+), 25 deletions(-) diff --git a/calc/README.md b/calc/README.md index 860633c..af2c8c0 100644 --- a/calc/README.md +++ b/calc/README.md @@ -20,8 +20,7 @@ Currently, scripts are entirely self-contained. This is for the dual purpose of: ``` Example with Fairseq-MoE 15B: python calc_transformer_flops.py -l 12 -hs 768 --moe -e 512 Example with GPT-3 175B: python calc_transformer_flops.py -l 96 -hs 12288 -usage: calc_transformer_flops.py [-h] [--vocab-size VOCAB_SIZE] [--hidden-size HIDDEN_SIZE] [--sequence-length SEQUENCE_LENGTH] [--num-layers NUM_LAYERS] [--moe] [--num-experts NUM_EXPERTS] [--expert-interval EXPERT_INTERVAL] - [--topk TOPK] [--batch-size BATCH_SIZE] [--tokens TOKENS] [--no-checkpoint-activations] +usage: calc_transformer_flops.py [-h] [--vocab-size VOCAB_SIZE] [--hidden-size HIDDEN_SIZE] [--sequence-length SEQUENCE_LENGTH] [--num-layers NUM_LAYERS] [--kv-size-ratio KV_SIZE_RATIO] [--moe] [--num-experts NUM_EXPERTS] [--expert-interval EXPERT_INTERVAL] [--topk TOPK] [--swiglu] [--batch-size BATCH_SIZE] [--tokens TOKENS] [--no-checkpoint-activations] options: -h, --help show this help message and exit @@ -33,12 +32,15 @@ options: Sequence length used for training --num-layers NUM_LAYERS, -l NUM_LAYERS Number of transformer layers used in model + --kv-size-ratio KV_SIZE_RATIO, -kv KV_SIZE_RATIO + Ratio of kv heads to query heads used in model. 1.0 for MHA --moe Whether our model is MoE --num-experts NUM_EXPERTS, -e NUM_EXPERTS Number of experts for MoE --expert-interval EXPERT_INTERVAL, -ei EXPERT_INTERVAL Expert interval for MoE --topk TOPK, -t TOPK Top k routing for MoE + --swiglu Use swiglu MLP. If set, ffn-hidden-size is defined as the inner dimension of each of the three MLP weights. --batch-size BATCH_SIZE, -b BATCH_SIZE Global batch size in units of samples --tokens TOKENS Number of tokens you are training over @@ -83,19 +85,15 @@ options: `calc_transformer_mem.py` calculates the amount of device memory required to train or infer a model. See [Transformers Math 101](https://blog.eleuther.ai/transformer-math/) for more details on how memory overhead is calculated. Take this estimation with a grain of salt, because every implementation is different and these calculations were written to match the GPT-NeoX library as close as possible. Even for other training and inference libraries, however, we expect our script to give approximate memory estimations within acceptable error. (Please see [LLM finetuning memory requirements](https://blog.scottlogic.com/2023/11/24/llm-mem.html) for a treatment of how specific memory costs may vary framework-to-framework). Other good resources that we consulted are [the ZeRO Paper](https://arxiv.org/abs/1910.02054) and [Reducing Activation Recomputation in Large Transformer Models](https://arxiv.org/pdf/2205.05198.pdf). ``` -Example with pythia 6.9B: python transformer_mem.py --num-layers=32 --sequence-length=2048 --num-attention-heads=32 --hidden-size=4096 --batch-size-per-gpu=8 --checkpoint-activations --zero-stage=1 --partition-activations --pipeline-parallel-size=1 --tensor-parallel-size=2 --num-gpus=128 --params=6900000000 -Example with pythia 12B: python transformer_mem.py --num-layers=36 --sequence-length=2048 --num-attention-heads=40 --hidden-size=5120 --batch-size-per-gpu=8 --checkpoint-activations --zero-stage=1 --partition-activations --pipeline-parallel-size=1 --tensor-parallel-size=4 --num-gpus=256 --params=11849420800 -Example with default 20B: python transformer_mem.py --num-layers=44 --sequence-length=2048 --num-attention-heads=64 --hidden-size=6144 --batch-size-per-gpu=1 --checkpoint-activations --zero-stage=1 --partition-activations --pipeline-parallel-size=1 --tensor-parallel-size=1 --num-gpus=1 --params=20000000000 +Example with pythia 6.9B: python calc_transformer_mem.py --num-layers=32 --sequence-length=2048 --num-attention-heads=32 --hidden-size=4096 --batch-size-per-gpu=8 --checkpoint-activations --zero-stage=1 --partition-activations --pipeline-parallel-size=1 --tensor-parallel-size=2 --num-gpus=128 +Example with pythia 12B: python calc_transformer_mem.py --num-layers=36 --sequence-length=2048 --num-attention-heads=40 --hidden-size=5120 --batch-size-per-gpu=8 --checkpoint-activations --zero-stage=1 --partition-activations --pipeline-parallel-size=1 --tensor-parallel-size=4 --num-gpus=256 +Example with default 20B: python calc_transformer_mem.py --num-layers=44 --sequence-length=2048 --num-attention-heads=64 --hidden-size=6144 --batch-size-per-gpu=1 --checkpoint-activations --zero-stage=1 --partition-activations --pipeline-parallel-size=1 --tensor-parallel-size=1 --num-gpus=1 -usage: calc_transformer_mem.py [-h] [--params PARAMS] [--num-gpus NUM_GPUS] [--tensor-parallel-size TENSOR_PARALLEL_SIZE] [--pipeline-parallel-size PIPELINE_PARALLEL_SIZE] [--partition-activations] [--zero-stage {0,1,2,3}] - [--checkpoint-activations] [--batch-size-per-gpu BATCH_SIZE_PER_GPU] [--hidden-size HIDDEN_SIZE] [--num-attention-heads NUM_ATTENTION_HEADS] [--sequence-length SEQUENCE_LENGTH] [--num-layers NUM_LAYERS] - [--fp32-model] [--fp32-grads] [--zero-allgather-bucket-size ZERO_ALLGATHER_BUCKET_SIZE] [--zero3-max-live-params ZERO3_MAX_LIVE_PARAMS] [--misc-mem-gb MISC_MEM_GB] [--num-experts NUM_EXPERTS] - [--ffn-expansion-factor FFN_EXPANSION_FACTOR] [--expert-parallelism EXPERT_PARALLELISM] [--vocab-size VOCAB_SIZE] +usage: calc_transformer_mem.py [-h] [--num-gpus NUM_GPUS] [--tensor-parallel-size TENSOR_PARALLEL_SIZE] [--pipeline-parallel-size PIPELINE_PARALLEL_SIZE] [--partition-activations] [--zero-stage {0,1,2,3}] [--zero-allgather-bucket-size ZERO_ALLGATHER_BUCKET_SIZE] [--zero3-max-live-params ZERO3_MAX_LIVE_PARAMS] [--checkpoint-activations] [--batch-size-per-gpu BATCH_SIZE_PER_GPU] [--sequence-length SEQUENCE_LENGTH] [--vocab-size VOCAB_SIZE] [--hidden-size HIDDEN_SIZE] [--num-attention-heads NUM_ATTENTION_HEADS] + [--num-layers NUM_LAYERS] [--ffn-expansion-factor FFN_EXPANSION_FACTOR] [--infer] [--kv-size-ratio KV_SIZE_RATIO] [--disable-mixed-precision] [--high-prec-bytes-per-val HIGH_PREC_BYTES_PER_VAL] [--low-prec-bytes-per-val LOW_PREC_BYTES_PER_VAL] [--bytes-per-grad-ele BYTES_PER_GRAD_ELE] [--num-experts NUM_EXPERTS] [--expert-parallelism EXPERT_PARALLELISM] [--misc-mem-gib MISC_MEM_GIB] options: -h, --help show this help message and exit - --params PARAMS, -p PARAMS - Number of Parameters --num-gpus NUM_GPUS Number of GPUs used for training --tensor-parallel-size TENSOR_PARALLEL_SIZE, -tp TENSOR_PARALLEL_SIZE Tensor parallel degree (1 if not used) @@ -105,34 +103,43 @@ options: Whether we use ZeRO-R to partition activation memory across tensor-parallel degree --zero-stage {0,1,2,3}, -z {0,1,2,3} Stage of the ZeRO optimizer + --zero-allgather-bucket-size ZERO_ALLGATHER_BUCKET_SIZE, -zbs ZERO_ALLGATHER_BUCKET_SIZE + Size of allgather buckets used by ZeRO + --zero3-max-live-params ZERO3_MAX_LIVE_PARAMS, -zmlp ZERO3_MAX_LIVE_PARAMS + Maximum number of parameters ZeRO3 keeps in GPU memory --checkpoint-activations, -ca Whether Megatron-style activation checkpointing is being used --batch-size-per-gpu BATCH_SIZE_PER_GPU, -b BATCH_SIZE_PER_GPU Batch size per GPU + --sequence-length SEQUENCE_LENGTH, -s SEQUENCE_LENGTH + Sequence length used for training + --vocab-size VOCAB_SIZE, -v VOCAB_SIZE + How many tokens are in the embedding layer --hidden-size HIDDEN_SIZE, -hs HIDDEN_SIZE Dimension of the model's hidden size --num-attention-heads NUM_ATTENTION_HEADS, -a NUM_ATTENTION_HEADS Number of attention heads used in model - --sequence-length SEQUENCE_LENGTH, -s SEQUENCE_LENGTH - Sequence length used for training --num-layers NUM_LAYERS, -l NUM_LAYERS Number of transformer layers used in model - --fp32-model Whether model is stored in fp32 - --fp32-grads Whether grads are stored in fp32 - --zero-allgather-bucket-size ZERO_ALLGATHER_BUCKET_SIZE, -zbs ZERO_ALLGATHER_BUCKET_SIZE - Size of allgather buckets used by ZeRO - --zero3-max-live-params ZERO3_MAX_LIVE_PARAMS, -zmlp ZERO3_MAX_LIVE_PARAMS - Maximum number of parameters ZeRO3 keeps in GPU memory - --misc-mem-gb MISC_MEM_GB - Miscellaneous memory overhead by DL framework(s), communication libraries, etc - --num-experts NUM_EXPERTS - Number of experts --ffn-expansion-factor FFN_EXPANSION_FACTOR, -ff FFN_EXPANSION_FACTOR How much the MLP hidden size expands + --infer whether we're doing inference + --kv-size-ratio KV_SIZE_RATIO, -kv KV_SIZE_RATIO + Ratio of total query heads to key/value heads. 1.0 for MHA, 1/num_attention_heads for MQA. + --disable-mixed-precision + Disables mixed precision training + --high-prec-bytes-per-val HIGH_PREC_BYTES_PER_VAL + The high-precision bytes per value (parameter, optimizer state, etc) in mixed precision + --low-prec-bytes-per-val LOW_PREC_BYTES_PER_VAL + The low-precision bytes per value (parameter, optimizer state, etc) in mixed precision + --bytes-per-grad-ele BYTES_PER_GRAD_ELE + The precision of gradient elements as bytes per value + --num-experts NUM_EXPERTS + Number of experts --expert-parallelism EXPERT_PARALLELISM, -ep EXPERT_PARALLELISM How many ways are the experts sharded across ranks - --vocab-size VOCAB_SIZE, -v VOCAB_SIZE - How many ways are the experts sharded across ranks + --misc-mem-gib MISC_MEM_GIB + Miscellaneous memory overhead per GPU by DL framework(s), communication libraries, etc ``` diff --git a/calc/calc_transformer_flops.py b/calc/calc_transformer_flops.py index 64d550d..edad7f9 100644 --- a/calc/calc_transformer_flops.py +++ b/calc/calc_transformer_flops.py @@ -51,6 +51,9 @@ def config_parser(): type=int, default=1, help='Top k routing for MoE') + parser.add_argument("--swiglu", + action="store_true", + help='Use swiglu MLP. If set, ffn-hidden-size is defined as the inner dimension of each of the three MLP weights.') parser.add_argument("--batch-size", "-b", type=int, default=1, @@ -85,6 +88,8 @@ def calc_params(args): attention_over_values_flops = iter_factor * 2 * args.num_layers * args.tokens * args.sequence_length * args.hidden_size linear_projection_flops = iter_factor * 2 * args.num_layers * args.tokens * args.hidden_size * args.hidden_size ffn_flops = iter_factor * 16 * args.num_layers * args.tokens * args.hidden_size * args.hidden_size + if args.swiglu: + ffn_flops = 3/2 * ffn_flops # no activation checkpointing for embeddings embedding_flops = 6 * args.tokens * args.hidden_size * args.vocab_size