LLMs on Polaris

Sam Foreman

foremans@anl.gov

ALCF

2024-07-17

LLMs on Polaris
 
🏡 Sam Foreman
SciFM Summer School 24

👤 Sam Foreman

• I’m a Computational Scientist in the Data Science Group at ALCF¹.
  • Personal Website: samforeman.me
  • Background: {ML, LLMs, AI4Science, HEP, Lattice QCD, MCMC, Generative Modeling, ...}

Ongoing / recent work:

• AI + Science
  • Building better sampling methods for Lattice QCD
  • GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics
  • Foundation models for long term climate forecasting

• Scaling Large Language Models
• Optimizing distibuted training across thousands of GPUs
• Building new parallelism techniques for efficient scaling
• Generative modeling (esp. for physical systems)

1. Mostly getting supercomputers to stop yelling at each other

Polaris @ ALCF

Refer to Getting Started for additional information.

• Login:

  ssh <username>@polaris.alcf.anl.gov

• Modules (+ using conda):

  module use /soft/modulefiles
  module load conda

Getting Started

• Running Jobs

  • example job scripts

• Proxy:

  # proxy settings
  export HTTP_PROXY="http://proxy.alcf.anl.gov:3128"
  export HTTPS_PROXY="http://proxy.alcf.anl.gov:3128"
  export http_proxy="http://proxy.alcf.anl.gov:3128"
  export https_proxy="http://proxy.alcf.anl.gov:3128"
  export ftp_proxy="http://proxy.alcf.anl.gov:3128"
  export no_proxy="admin,polaris-adminvm-01,localhost,*.cm.polaris.alcf.anl.gov,polaris-*,*.polaris.alcf.anl.gov,*.alcf.anl.gov"

• Getting Help:

  support@alcf.anl.gov

Polaris

• Polaris is a 560 node HPE Apollo 6500 Gen 10+ based system.

• Each node has a single 2.8 GHz AMD EPYC Milan 7543P 32 core CPU with:

  • 512 GB of DDR4 RAM
  • 4 (four) NVIDIA A100 GPUs connected via NVLink
  • 2 (a pair) of local 1.6TB of SSDs in RAID0 for the users use
  • 2 (a pair) of Slingshot 11 network adapters.

• There are two nodes per chassis, seven chassis per rack, and 40 racks for a total of 560 nodes.

Polaris Compute Nodes

Details

POLARIS COMPUTE	DESCRIPTION	PER NODE	AGGREGATE
Processor^{1}	2.8 GHz 7543P	1	560
Cores/Threads	AMD Zen 3 (Milan)	32/64	17,920/35,840
RAM^{2}	DDR4	512 GiB	280 TiB
GPUS	NVIDIA A100	4	2240
Local SSD	1.6 TB	2/3.2 TB	1120/1.8PB

1. 256MB shared L3 cache, 512KB L2 cache per core, 32 KB L1 cache per core
2. 8 memory channels rated at 204.8 GiB/s

Polaris A100 GPU Information

DESCRIPTION	A100 PCIe	A100 HGX (Polaris)
GPU Memory	40 GiB HBM2	160 GiB HBM2
GPU Memory BW	1.6 TB/s	6.4 TB/s
Interconnect	PCIe Gen4 64 GB/s	NVLink 600 GB/s
FP 64	9.7 TF	38.8 TF
FP64 Tensor Core	19.5 TF	78 TF
FP 32	19.5 TF	78 TF
BF16 Tensor Core	312 TF	1.3 PF
FP16 Tensor Core	312 TF	1.3 PF
INT8 Tensor Core	624 TOPS	2496 TOPS
Max TDP Power	250 W	400 W

Using Conda

• Additional information in our user guide

• We provide prebuilt conda environment containing GPU-supported builds of:

  • Pytorch - ALCF User Guides
  • DeepSped - ALCF User Guides
  • JAX - ALCF User Guides
  • Tensorflow - ALCF User Guides

• To activate / use: (either from an interactive job or inside a job script):

  $ module use /soft/modulefiles
  $ module load conda; conda activate base

Virtual Environments: venv

• To install additional libraries, we can create a virtual environment using venv

• Make sure you’re currently inside the base conda environment:

  • module load conda; conda activate base

• Now, create venv on top of base:

  $ python3 -m venv /path/to/venv --system-site-packages
  $ source /path/to/venv/bin/activate
  $ which python3
  /path/to/venv/bin/python3
  $ # Now you can `python3 -m pip install ...` etc

  🚧 Warning

  1. --system-site-packages tells the venv to use system packages
  2. You must replace the path /path/to/venv in the above commands with a suitably chosen directory which you are able to write to.

Note about venv’s

• The value of --system-site-packages can be changed by modifying its value in /path/to/venv/pyvenv.cfg

• To install a different version of a package that is already installed in the base environment:

  $ python3 -m pip install --ignore-installed ... # or -I

• The shared base environment is not writable

  • Impossible to remove or uninstall packages

• If you need additional flexibility, we can clone the base environment

Clone base conda environment

• If we need additional flexibility or to install packages which require a conda install, we can clone the base environment

  • requires copying the entirety of the base environment
  • large storage requirement, can get out of hand quickly

• The shared base environment is not writable

  • Impossible to remove or uninstall packages

• This can be done by:

  $ module load conda
  $ conda activate base
  (base) $ conda create --clone base --prefix="/path/to/envs/base-clone"

Containers on Polaris

• Polaris uses Nvidia A100 GPUs –>

  • We can take advantage of Nvidia optimized containers

• The container system on Polaris is singularity:

  module avail singularity # see available
  module load singularity  # load default version
  # To load a specific version:
  module load singularity/3.8.7

• Singularity: two options for creating containers:

  1. Using Docker on local machine and publishing to DockerHub
  2. Using a Singularity recipe file and building on a Polaris worker node

• See also: Containers - ALCF User Guides

Large Language Models

Status of Large Language Models

Figure 1: Large Language Models have (LLM)s have taken the NLP community world by storm¹

1. Hannibal046/Awesome-LLM

Emergent Abilities

Emergent abilities of Large Language Models Yao et al. (2023)

Training LLMs

Figure 2: Visualization from Yang et al. (2023)

Recent Work (2017 – Now)

Papers, 2017–*

Date	Paper	keywords	Institute	Publication
06/2017	Attention Is All You Need	Transformers	Google	NeurIPS
06/2018	Improving Language Understanding by Generative Pre-Training	GPT 1.0	OpenAI
10/2018	BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding	BERT	Google	NAACL
02/2019	Language Models are Unsupervised Multitask Learners	GPT 2.0	OpenAI
09/2019	Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism	Megatron-LM	NVIDIA
10/2019	Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer	T5	Google	JMLR
10/2019	ZeRO: Memory Optimizations Toward Training Trillion Parameter Models	ZeRO	Microsoft	SC
01/2020	Scaling Laws for Neural Language Models	Scaling Law	OpenAI
05/2020	Language models are few-shot learners	GPT 3.0	OpenAI	NeurIPS
01/2021	Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity	Switch Transformers	Google	JMLR
08/2021	Evaluating Large Language Models Trained on Code	Codex	OpenAI
08/2021	On the Opportunities and Risks of Foundation Models	Foundation Models	Stanford
09/2021	Finetuned Language Models are Zero-Shot Learners	FLAN	Google	ICLR
10/2021	Multitask Prompted Training Enables Zero-Shot Task Generalization	T0	HuggingFace et al.	ICLR
12/2021	GLaM: Efficient Scaling of Language Models with Mixture-of-Experts	GLaM	Google	ICML
12/2021	WebGPT: Browser-assisted question-answering with human feedback	WebGPT	OpenAI
12/2021	Improving language models by retrieving from trillions of tokens	Retro	DeepMind	ICML
12/2021	Scaling Language Models: Methods, Analysis & Insights from Training Gopher	Gopher	DeepMind
01/2022	Chain-of-Thought Prompting Elicits Reasoning in Large Language Models	COT	Google	NeurIPS
01/2022	LaMDA: Language Models for Dialog Applications	LaMDA	Google
01/2022	Solving Quantitative Reasoning Problems with Language Models	Minerva	Google	NeurIPS
01/2022	Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model	Megatron-Turing NLG	Microsoft&NVIDIA
03/2022	Training language models to follow instructions with human feedback	InstructGPT	OpenAI
04/2022	PaLM: Scaling Language Modeling with Pathways	PaLM	Google
04/2022	An empirical analysis of compute-optimal large language model training	Chinchilla	DeepMind	NeurIPS
05/2022	OPT: Open Pre-trained Transformer Language Models	OPT	Meta
05/2022	Unifying Language Learning Paradigms	UL2	Google
06/2022	Emergent Abilities of Large Language Models	Emergent Abilities	Google	TMLR
06/2022	Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models	BIG-bench	Google
06/2022	Language Models are General-Purpose Interfaces	METALM	Microsoft
09/2022	Improving alignment of dialogue agents via targeted human judgements	Sparrow	DeepMind
10/2022	Scaling Instruction-Finetuned Language Models	Flan-T5/PaLM	Google
10/2022	GLM-130B: An Open Bilingual Pre-trained Model	GLM-130B	Tsinghua	ICLR
11/2022	Holistic Evaluation of Language Models	HELM	Stanford
11/2022	BLOOM: A 176B-Parameter Open-Access Multilingual Language Model	BLOOM	BigScience
11/2022	Galactica: A Large Language Model for Science	Galactica	Meta
12/2022	OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization	OPT-IML	Meta
01/2023	The Flan Collection: Designing Data and Methods for Effective Instruction Tuning	Flan 2022 Collection	Google
02/2023	LLaMA: Open and Efficient Foundation Language Models	LLaMA	Meta
02/2023	Language Is Not All You Need: Aligning Perception with Language Models	Kosmos-1	Microsoft
03/2023	PaLM-E: An Embodied Multimodal Language Model	PaLM-E	Google
03/2023	GPT-4 Technical Report	GPT 4	OpenAI
04/2023	Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling	Pythia	EleutherAI et al.	ICML
05/2023	Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision	Dromedary	CMU et al.
05/2023	PaLM 2 Technical Report	PaLM 2	Google
05/2023	RWKV: Reinventing RNNs for the Transformer Era	RWKV	Bo Peng
05/2023	Direct Preference Optimization: Your Language Model is Secretly a Reward Model	DPO	Stanford
07/2023	Llama 2: Open Foundation and Fine-Tuned Chat Models	LLaMA 2	Meta

Life-Cycle of the LLM

1. Data collection + preprocessing

2. Pre-training

   • Architecture decisions:
     {model_size, hyperparameters,
parallelism, lr_schedule, ...}

3. Supervised Fine-Tuning

   • Instruction Tuning
   • Alignment

4. Deploy (+ monitor, re-evaluate, etc.)

Figure 3: Pre-training: Virtually all of the compute used during pretraining phase¹.

1. Figure from The Illustrated Transformer

Life-Cycle of the LLM: Pre-training

Figure 4: Pre-training: Virtually all of the compute used during pretraining phase

Life-Cycle of the LLM: Fine-Tuning

Figure 5: Fine-tuning¹: Fine-tuning actually updates the model’s weights to make the model better at a certain task.

1. Figure from The Illustrated Transformer

Forward Pass

Figure 6: Language Model trained for causal language modeling. Video from: 🤗 Generation with LLMs

Generating Text

Figure 7: Language Model trained for causal language modeling. Video from: 🤗 Generation with LLMs

Parallelism Overview

Modern parallelism techniques enable the training of large language models

See my slides on Parallel Training Techniques for additional details

Parallelism Concepts

• DataParallel (DP):

  • The same setup is replicated multiple times, and each being fed a slice of the data.

  • The processing is done in parallel and all setups are synchronized at the end of each training step.

• TensorParallel (TP):
  • Each tensor is split up into multiple chunks.
  • So, instead of having the whole tensor reside on a single gpu, each shard of the tensor resides on its designated gpu.
    • During processing each shard gets processed separately and in parallel on different GPUs and the results are synced at the end of the step.
    • This is what one may call horizontal parallelism, as he splitting happens on horizontal level.

🤗 Model Parallelism

Parallelism Concepts¹

• PipelineParallel (PP):
  • Model is split up vertically (layer-level) across multiple GPUs, so that only one or several layers of the model are places on a single gpu.
    • Each gpu processes in parallel different stages of the pipeline and working on a small chunk of the batch.
• Zero Redundancy Optimizer (ZeRO):
  • Also performs sharding of the tensors somewhat similar to TP, except the whole tensor gets reconstructed in time for a forward or backward computation, therefore the model doesn’t need to be modified.
  • It also supports various offloading techniques to compensate for limited GPU memory.
• Sharded DDP:
  • Another name for the foundational ZeRO concept as used by various other implementations of ZeRO.

1. 🤗 Model Parallelism

Data Parallelism

• Data Parallelism:
  • The simplest and most common parallelism technique. Workers maintain identical copies of the complete model and work on a subset of the data.
  • DDP supported in PyTorch native.
• ZeRO Data Parallel
  • ZeRO powered data parallelism is shown below¹

1. Blog Post

Tensor Parallelism¹

• In Tensor Paralleism each GPU processes only a slice of a tensor and only aggregates the full tensor for operations that require the whole thing.

  • The main building block of any transformer is a fully connected nn.Linear followed by a nonlinear activation GeLU.

    • Y = GeLU(XA), where X and Y are the input and output vectors, and A is the weight matrix.

  • If we look at the computation in matrix form, it’s easy to see how the matrix multiplication can be split between multiple GPUs:

1. Efficient Large-Scale Language Model Training on GPU Clusters

Tensor Parallelism

3D Parallelism

• DP + TP + PP (3D) Parallelism

Figure 8: 3D Parallelism illustration. Figure from: https://www.deepspeed.ai/

3D Parallelism

• DP + TP + PP (3D) Parallelism

Figure 9: Figure taken from 3D parallelism: Scaling to trillion-parameter models

🍋 ezpz

Clone Repo(s)

#[⭐][07:33:08 AM][foremans@x3101c0s13b0n0][~/tmp]
$ mkdir ~/tmp/polaris-talk

#[⭐][07:33:21 AM][foremans@x3101c0s13b0n0][~/tmp]
$ cd ~/tmp/polaris-talk

#[⭐][07:33:25 AM][foremans@x3101c0s13b0n0][~/tmp/polaris-talk]
$ NOW=$(tstamp) && mkdir "${NOW}" && cd "${NOW}" # && mkdir "core-dumps-${NOW}" && mv -v **core\.** "core-dumps-${NOW}" && mv "core-dumps-${NOW}" core-dumps

#[⭐][07:33:27 AM][foremans@x3101c0s13b0n0][~/tmp/polaris-talk/2024-07-17-073327]
$ pwd
/home/foremans/tmp/polaris-talk/2024-07-17-073327

#[⭐][07:33:31 AM][foremans@x3101c0s13b0n0][~/tmp/polaris-talk/2024-07-17-073327]
$ git clone https://github.com/saforem2/ezpz ezpz && git clone https://github.com/saforem2/wordplay wordplay
Cloning into 'ezpz'...
remote: Enumerating objects: 2134, done.`
remote: Counting objects: 100% (363/363), done.
remote: Compressing objects: 100% (169/169), done.
remote: Total 2134 (delta 197), reused 265 (delta 141), pack-reused 1771
Receiving objects: 100% (2134/2134), 4.27 MiB | 25.01 MiB/s, done.
Resolving deltas: 100% (1117/1117), done.
Cloning into 'wordplay'...
remote: Enumerating objects: 869, done.
remote: Counting objects: 100% (72/72), done.
remote: Compressing objects: 100% (37/37), done.
remote: Total 869 (delta 29), reused 56 (delta 23), pack-reused 797
Receiving objects: 100% (869/869), 14.36 MiB | 46.54 MiB/s, done.
Resolving deltas: 100% (395/395), done.

Setup Python

#[⭐][07:33:53 AM][foremans@x3101c0s13b0n0][~/tmp/polaris-talk/2024-07-17-073327]
$ source ezpz/src/ezpz/bin/utils.sh && ezpz_setup_python && ezpz_setup_alcf
Unable to detect PBS or SLURM working directory info...
Using /home/foremans/tmp/polaris-talk/2024-07-17-073327 as working directory...
Using WORKING_DIR: /home/foremans/tmp/polaris-talk/2024-07-17-073327
No conda_prefix OR virtual_env found in environment...
Setting up conda...
Lmod is automatically replacing "nvhpc/23.9" with "gcc-native/12.3".
Lmod is automatically replacing "PrgEnv-nvhpc/8.5.0" with "PrgEnv-gnu/8.5.0".
Due to MODULEPATH changes, the following have been reloaded:
  1) cray-mpich/8.1.28
Found conda at: /soft/applications/conda/2024-04-29/mconda3
No VIRTUAL_ENV found in environment!
    - Trying to setup from /soft/applications/conda/2024-04-29/mconda3
    - Using VENV_DIR=/home/foremans/tmp/polaris-talk/2024-07-17-073327/venvs/2024-04-29
    - Creating a new virtual env on top of 2024-04-29 in /home/foremans/tmp/polaris-talk/2024-07-17-073327/venvs/2024-04-29
[python] Using /home/foremans/tmp/polaris-talk/2024-07-17-073327/venvs/2024-04-29/bin/python3

[ezpz/bin/utils.sh]

[2024-07-17-073407]
    • USER=foremans
    • MACHINE=polaris
    • HOST=x3101c0s13b0n0

[ezpz_setup_host]
    • Using hostfile: /var/spool/pbs/aux/2024084.polaris-pbs-01.hsn.cm.polaris.alcf.anl.gov
    • Found in environment:
        • HOSTFILE: /var/spool/pbs/aux/2024084.polaris-pbs-01.hsn.cm.polaris.alcf.anl.gov
        • Writing PBS vars to: /home/foremans/.pbsenv

[ezpz_save_pbs_env]
    • Setting:
        • HOSTFILE: /var/spool/pbs/aux/2024084.polaris-pbs-01.hsn.cm.polaris.alcf.anl.gov
        • JOBENV_FILE: /home/foremans/.pbsenv

[HOSTS]
    • [host:0] - x3101c0s13b0n0.hsn.cm.polaris.alcf.anl.gov

[DIST INFO]
    • HOSTFILE=/var/spool/pbs/aux/2024084.polaris-pbs-01.hsn.cm.polaris.alcf.anl.gov
    • NHOSTS=1
    • NGPU_PER_HOST=4
    • NGPUS=4
    • DIST_LAUNCH=mpiexec --verbose --envall -n 4 -ppn 4 --hostfile /var/spool/pbs/aux/2024084.polaris-pbs-01.hsn.cm.polaris.alcf.anl.gov --cpu-bind depth -d 16

[LAUNCH]:
    • To launch across all available GPUs, use: launch
      launch = mpiexec --verbose --envall -n 4 -ppn 4 --hostfile /var/spool/pbs/aux/2024084.polaris-pbs-01.hsn.cm.polaris.alcf.anl.gov --cpu-bind depth -d 16

Install {ezpz, wordplay}

#[⭐][07:34:13 AM][foremans@x3101c0s13b0n0][~/tmp/polaris-talk/2024-07-17-073327]
$ python3 -m pip install -e ezpz wordplay --require-virtualenv
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Obtaining file:///home/foremans/tmp/polaris-talk/2024-07-17-073327/ezpz
  Installing build dependencies ... done
  Checking if build backend supports build_editable ... done
  Getting requirements to build editable ... done
  Installing backend dependencies ... done
  Preparing editable metadata (pyproject.toml) ... done

# ...[clipped]...

Successfully built ezpz
Installing collected packages: enum34, wordplay, pyinstrument, ezpz
  Attempting uninstall: ezpz
    Found existing installation: ezpz 0.1
    Not uninstalling ezpz at /home/foremans/.local/polaris/conda/2024-04-29/lib/python3.11/site-packages, outside environment /home/foremans/tmp/polaris-talk/2024-07-17-073327/venvs/2024-04-29
    Cant uninstall 'ezpz'. No files were found to uninstall.
Successfully installed enum34-1.1.10 ezpz pyinstrument-4.6.2 wordplay-1.0.0a4
[notice] A new release of pip is available: 24.0 -> 24.1.2
[notice] To update, run: pip install --upgrade pip
9.62s user 1.11s system 61% cpu 17.505s total

#[⭐][07:34:53 AM][foremans@x3101c0s13b0n0][~/tmp/polaris-talk/2024-07-17-073327]
$ python3 -m pip install --upgrade wandb
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Requirement already satisfied: wandb in /soft/applications/conda/2024-04-29/mconda3/lib/python3.11/site-packages (0.16.6)
Collecting wandb
  Downloading wandb-0.17.4-py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (10 kB)
Downloading wandb-0.17.4-py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.9 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 6.9/6.9 MB 2.1 MB/s eta 0:00:00
Installing collected packages: wandb
  Attempting uninstall: wandb
    Found existing installation: wandb 0.16.6
    Not uninstalling wandb at /soft/applications/conda/2024-04-29/mconda3/lib/python3.11/site-packages, outside environment /home/foremans/tmp/polaris-talk/2024-07-17-073327/venvs/2024-04-29
    Cant uninstall 'wandb'. No files were found to uninstall.
Successfully installed wandb-0.17.4
[notice] A new release of pip is available: 24.0 -> 24.1.2
[notice] To update, run: pip install --upgrade pip

Launch ezpz.test_dist

#(👻 2024-04-29)
#[⭐][07:34:07 AM][foremans@x3101c0s13b0n0][~/tmp/polaris-talk/2024-07-17-073327][⏱ 7s]
$ which launch
launch: aliased to mpiexec --verbose --envall -n 4 -ppn 4 --hostfile /var/spool/pbs/aux/2024084.polaris-pbs-01.hsn.cm.polaris.alcf.anl.gov --cpu-bind depth -d 16

#(👻 2024-04-29)
#[⭐][07:34:11 AM][foremans@x3101c0s13b0n0][~/tmp/polaris-talk/2024-07-17-073327]
$ which python3
/home/foremans/tmp/polaris-talk/2024-07-17-073327/venvs/2024-04-29/bin/python3

#(👻 2024-04-29)
#[⭐][07:35:21 AM][foremans@x3101c0s13b0n0][~/tmp/polaris-talk/2024-07-17-073327][⏱ 14s]
$ launch python3 -m ezpz.test_dist | tee ezpz-test-dist-DDP.log
Connected to tcp://x3101c0s13b0n0.hsn.cm.polaris.alcf.anl.gov:7919
Found executable /home/foremans/tmp/polaris-talk/2024-07-17-073327/venvs/2024-04-29/bin/python3
Launching application cff755ee-557e-4df2-a987-db85a8b7dbe7
[2024-07-17 07:35:30.304306][INFO][__init__:156] - Setting logging level to 'INFO' on 'RANK == 0'
[2024-07-17 07:35:30.307036][INFO][__init__:157] - Setting logging level to 'CRITICAL' on all others 'RANK != 0'
[2024-07-17 07:35:30.307494][INFO][__init__:160] - To disable this behavior, and log from ALL ranks (not recommended), set: 'export LOG_FROM_ALL_RANKS=1'  in your environment, and re-run.
[2024-07-17 07:35:32.116037][INFO][dist:358] - [device='cuda'][rank=2/3][local_rank=2/3][node=0/0]
[2024-07-17 07:35:32.116089][INFO][dist:358] - [device='cuda'][rank=3/3][local_rank=3/3][node=0/0]
[2024-07-17 07:35:32.116940][INFO][dist:358] - [device='cuda'][rank=1/3][local_rank=1/3][node=0/0]
[2024-07-17 07:35:32.122726][INFO][dist:95] -
[dist_info]:
  • DEVICE=cuda
  • DEVICE_ID=cuda:0
  • DISTRIBUTED_BACKEND=nccl
  • GPUS_PER_NODE=4
  • HOSTS=['x3101c0s13b0n0.hsn.cm.polaris.alcf.anl.gov']
  • HOSTFILE=/var/spool/pbs/aux/2024084.polaris-pbs-01.hsn.cm.polaris.alcf.anl.gov
  • HOSTNAME=x3101c0s13b0n0.hsn.cm.polaris.alcf.anl.gov
  • LOCAL_RANK=0
  • MACHINE=Polaris
  • NUM_NODES=1
  • NGPUS=4
  • NGPUS_AVAILABLE=4
  • NODE_ID=0
  • RANK=0
  • SCHEDULER=PBS
  • WORLD_SIZE_TOTAL=4
  • WORLD_SIZE_IN_USE=4
  • LAUNCH_CMD=mpiexec --verbose --envall -n 4 -ppn 4 --hostfile /var/spool/pbs/aux/2024084.polaris-pbs-01.hsn.cm.polaris.alcf.anl.gov --cpu-bind depth -d 16
[2024-07-17 07:35:32.124800][INFO][dist:725] - [0/4] Using device='cuda' with backend='DDP' + 'nccl' for distributed training.
[2024-07-17 07:35:32.129169][INFO][dist:358] - [device='cuda'][rank=0/3][local_rank=0/3][node=0/0]
[2024-07-17 07:35:32.129674][WARNING][dist:364] - Using [4 / 4] available "cuda" devices !!
[2024-07-17 07:35:32.130219][INFO][dist:874] - Setting up wandb from rank: 0
[2024-07-17 07:35:32.130638][INFO][dist:875] - Using: WB PROJECT: ezpz.test_dist
wandb: Using wandb-core as the SDK backend. Please refer to https://wandb.me/wandb-core for more information.
wandb: Currently logged in as: foremans (aurora_gpt). Use `wandb login --relogin` to force relogin
wandb: Tracking run with wandb version 0.17.4
wandb: Run data is saved locally in /home/foremans/tmp/polaris-talk/2024-07-17-073327/wandb/run-20240717_073532-p49rzxtv
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run vibrant-river-284
wandb: ⭐️ View project at https://wandb.ai/aurora_gpt/ezpz.test_dist
wandb: 🚀 View run at https://wandb.ai/aurora_gpt/ezpz.test_dist/runs/p49rzxtv
[2024-07-17 07:35:33.171085][INFO][dist:905] - W&B RUN: [vibrant-river-284](https://wandb.ai/aurora_gpt/ezpz.test_dist/runs/p49rzxtv)
[2024-07-17 07:35:33.182307][INFO][dist:312] - Updating wandb.run: vibrant-river-284 config with "DIST_INFO"
[2024-07-17 07:35:33.186499][INFO][dist:938] - Running on machine='Polaris'
[2024-07-17 07:35:33.187790][INFO][dist:95] -
[timers_import]:
  • os=1.082196831703186e-06
  • logging=4.507601261138916e-07
  • typing=2.9457733035087585e-06
  • pathlib=1.3122335076332092e-06
  • ezpz=6.109476089477539e-07
  • torch=2.9457733035087585e-06
  • torch_ddp=2.314336597919464e-06
  • wandb=1.842435449361801e-05
  • total=3.0086375772953033e-05

[2024-07-17 07:35:33.188979][INFO][dist:95] -

[CONFIG]:
  • warmup=0
  • log_freq=1
  • batch_size=64
  • input_size=128
  • output_size=128
  • dtype=torch.float32
  • device=cuda
  • world_size=4
  • train_iters=100

[2024-07-17 07:35:34.761945][INFO][test_dist:183] - model=Network(
  (layers): Sequential(
    (0): Linear(in_features=128, out_features=1024, bias=True)
    (1): Linear(in_features=1024, out_features=512, bias=True)
    (2): Linear(in_features=512, out_features=256, bias=True)
    (3): Linear(in_features=256, out_features=128, bias=True)
    (4): Linear(in_features=128, out_features=128, bias=True)
  )
)
[2024-07-17 07:35:36.943300][INFO][test_dist:274] - iter=1, loss=2152.41, sps=1.697e+04, dt=0.00377066, dtf=0.001003, dtb=0.002768
[2024-07-17 07:35:36.948048][INFO][test_dist:274] - iter=2, loss=1577.24, sps=3.611e+04, dt=0.00177221, dtf=0.0005256, dtb=0.001247
[2024-07-17 07:35:36.952085][INFO][test_dist:274] - iter=3, loss=1201.25, sps=3.59e+04, dt=0.00178271, dtf=0.0004875, dtb=0.001295
[2024-07-17 07:35:36.956071][INFO][test_dist:274] - iter=4, loss=1034.03, sps=3.704e+04, dt=0.0017279, dtf=0.0005082, dtb=0.00122
[2024-07-17 07:35:36.959944][INFO][test_dist:274] - iter=5, loss=875.796, sps=3.825e+04, dt=0.00167313, dtf=0.0005121, dtb=0.001161
[2024-07-17 07:35:36.963806][INFO][test_dist:274] - iter=6, loss=817.544, sps=3.804e+04, dt=0.00168248, dtf=0.0004651, dtb=0.001217
[2024-07-17 07:35:36.967806][INFO][test_dist:274] - iter=7, loss=734.838, sps=3.536e+04, dt=0.0018099, dtf=0.0004969, dtb=0.001313
[2024-07-17 07:35:36.971741][INFO][test_dist:274] - iter=8, loss=741.583, sps=3.682e+04, dt=0.00173809, dtf=0.0004537, dtb=0.001284
[2024-07-17 07:35:36.975672][INFO][test_dist:274] - iter=9, loss=738.157, sps=3.717e+04, dt=0.0017217, dtf=0.0004635, dtb=0.001258
[2024-07-17 07:35:36.979537][INFO][test_dist:274] - iter=10, loss=727.255, sps=3.857e+04, dt=0.00165911, dtf=0.0004897, dtb=0.001169
[2024-07-17 07:35:36.983367][INFO][test_dist:274] - iter=11, loss=715.534, sps=3.979e+04, dt=0.00160845, dtf=0.0004246, dtb=0.001184
[2024-07-17 07:35:36.987262][INFO][test_dist:274] - iter=12, loss=693.96, sps=3.791e+04, dt=0.00168827, dtf=0.0004543, dtb=0.001234
[2024-07-17 07:35:36.991156][INFO][test_dist:274] - iter=13, loss=693.518, sps=3.815e+04, dt=0.00167748, dtf=0.0004182, dtb=0.001259
[2024-07-17 07:35:36.994942][INFO][test_dist:274] - iter=14, loss=675.289, sps=4.003e+04, dt=0.00159879, dtf=0.0004048, dtb=0.001194
[2024-07-17 07:35:36.999681][INFO][test_dist:274] - iter=15, loss=677.706, sps=4.062e+04, dt=0.0015755, dtf=0.0004248, dtb=0.001151
[2024-07-17 07:35:37.003599][INFO][test_dist:274] - iter=16, loss=671.639, sps=3.754e+04, dt=0.00170499, dtf=0.000416, dtb=0.001289
[2024-07-17 07:35:37.007565][INFO][test_dist:274] - iter=17, loss=652.219, sps=3.704e+04, dt=0.00172777, dtf=0.0004208, dtb=0.001307
[2024-07-17 07:35:37.011753][INFO][test_dist:274] - iter=18, loss=633.308, sps=3.191e+04, dt=0.00200554, dtf=0.0004193, dtb=0.001586
[2024-07-17 07:35:37.015595][INFO][test_dist:274] - iter=19, loss=635.459, sps=3.845e+04, dt=0.0016645, dtf=0.0004236, dtb=0.001241
[2024-07-17 07:35:37.019356][INFO][test_dist:274] - iter=20, loss=626.979, sps=4.033e+04, dt=0.00158685, dtf=0.0004225, dtb=0.001164
[2024-07-17 07:35:37.023081][INFO][test_dist:274] - iter=21, loss=612.352, sps=4.105e+04, dt=0.00155914, dtf=0.0004169, dtb=0.001142
[2024-07-17 07:35:37.026861][INFO][test_dist:274] - iter=22, loss=609.89, sps=4.004e+04, dt=0.00159827, dtf=0.0004155, dtb=0.001183
[2024-07-17 07:35:37.030555][INFO][test_dist:274] - iter=23, loss=602.673, sps=4.258e+04, dt=0.00150295, dtf=0.0004166, dtb=0.001086
[2024-07-17 07:35:37.034382][INFO][test_dist:274] - iter=24, loss=613.106, sps=3.918e+04, dt=0.00163367, dtf=0.0004164, dtb=0.001217
[2024-07-17 07:35:37.038129][INFO][test_dist:274] - iter=25, loss=644.755, sps=4.173e+04, dt=0.00153368, dtf=0.0004175, dtb=0.001116
[2024-07-17 07:35:37.041943][INFO][test_dist:274] - iter=26, loss=789.106, sps=4.049e+04, dt=0.00158053, dtf=0.0004397, dtb=0.001141
[2024-07-17 07:35:37.045705][INFO][test_dist:274] - iter=27, loss=691.36, sps=4.166e+04, dt=0.00153641, dtf=0.0004157, dtb=0.001121
[2024-07-17 07:35:37.049496][INFO][test_dist:274] - iter=28, loss=657.228, sps=4.018e+04, dt=0.00159288, dtf=0.0004209, dtb=0.001172
[2024-07-17 07:35:37.053229][INFO][test_dist:274] - iter=29, loss=633.212, sps=4.19e+04, dt=0.0015274, dtf=0.0004288, dtb=0.001099
[2024-07-17 07:35:37.057013][INFO][test_dist:274] - iter=30, loss=640.29, sps=4.012e+04, dt=0.00159538, dtf=0.0004144, dtb=0.001181
[2024-07-17 07:35:37.060722][INFO][test_dist:274] - iter=31, loss=604.287, sps=4.21e+04, dt=0.00152018, dtf=0.000398, dtb=0.001122
[2024-07-17 07:35:37.064489][INFO][test_dist:274] - iter=32, loss=640.15, sps=4.079e+04, dt=0.00156912, dtf=0.0004007, dtb=0.001168
[2024-07-17 07:35:37.068206][INFO][test_dist:274] - iter=33, loss=585.789, sps=4.238e+04, dt=0.00151007, dtf=0.0004199, dtb=0.00109
[2024-07-17 07:35:37.071974][INFO][test_dist:274] - iter=34, loss=591.99, sps=4.053e+04, dt=0.00157917, dtf=0.000434, dtb=0.001145
[2024-07-17 07:35:37.075702][INFO][test_dist:274] - iter=35, loss=618.223, sps=4.168e+04, dt=0.00153538, dtf=0.0004152, dtb=0.00112
[2024-07-17 07:35:37.079496][INFO][test_dist:274] - iter=36, loss=572.365, sps=3.998e+04, dt=0.0016008, dtf=0.0004108, dtb=0.00119
[2024-07-17 07:35:37.083250][INFO][test_dist:274] - iter=37, loss=573.749, sps=4.276e+04, dt=0.00149675, dtf=0.0004123, dtb=0.001084
[2024-07-17 07:35:37.086969][INFO][test_dist:274] - iter=38, loss=580.662, sps=4.136e+04, dt=0.00154751, dtf=0.0004129, dtb=0.001135
[2024-07-17 07:35:37.090636][INFO][test_dist:274] - iter=39, loss=568.836, sps=4.311e+04, dt=0.0014847, dtf=0.000409, dtb=0.001076
[2024-07-17 07:35:37.094396][INFO][test_dist:274] - iter=40, loss=551.294, sps=4.145e+04, dt=0.00154388, dtf=0.0004118, dtb=0.001132
[2024-07-17 07:35:37.098103][INFO][test_dist:274] - iter=41, loss=573.647, sps=4.352e+04, dt=0.00147048, dtf=0.0003977, dtb=0.001073
[2024-07-17 07:35:37.101867][INFO][test_dist:274] - iter=42, loss=545.584, sps=4.257e+04, dt=0.00150354, dtf=0.000433, dtb=0.001071
[2024-07-17 07:35:37.105639][INFO][test_dist:274] - iter=43, loss=544.877, sps=4.322e+04, dt=0.00148085, dtf=0.0004117, dtb=0.001069
[2024-07-17 07:35:37.109471][INFO][test_dist:274] - iter=44, loss=559.886, sps=4.028e+04, dt=0.00158879, dtf=0.0004254, dtb=0.001163
[2024-07-17 07:35:37.113186][INFO][test_dist:274] - iter=45, loss=534.895, sps=4.311e+04, dt=0.00148444, dtf=0.0004153, dtb=0.001069
[2024-07-17 07:35:37.116972][INFO][test_dist:274] - iter=46, loss=536.457, sps=4.099e+04, dt=0.00156151, dtf=0.0004113, dtb=0.00115
[2024-07-17 07:35:37.120710][INFO][test_dist:274] - iter=47, loss=548.508, sps=4.183e+04, dt=0.00152993, dtf=0.0004151, dtb=0.001115
[2024-07-17 07:35:37.124552][INFO][test_dist:274] - iter=48, loss=532.186, sps=4.051e+04, dt=0.0015798, dtf=0.0004379, dtb=0.001142
[2024-07-17 07:35:37.128266][INFO][test_dist:274] - iter=49, loss=519.254, sps=4.272e+04, dt=0.0014981, dtf=0.0004164, dtb=0.001082
[2024-07-17 07:35:37.131975][INFO][test_dist:274] - iter=50, loss=535.535, sps=4.16e+04, dt=0.00153862, dtf=0.0004304, dtb=0.001108
[2024-07-17 07:35:37.135717][INFO][test_dist:274] - iter=51, loss=520.722, sps=4.136e+04, dt=0.00154757, dtf=0.0004158, dtb=0.001132
[2024-07-17 07:35:37.139451][INFO][test_dist:274] - iter=52, loss=513.063, sps=4.147e+04, dt=0.00154317, dtf=0.0004138, dtb=0.001129
[2024-07-17 07:35:37.143231][INFO][test_dist:274] - iter=53, loss=514.546, sps=4.038e+04, dt=0.0015848, dtf=0.0004149, dtb=0.00117
[2024-07-17 07:35:37.146971][INFO][test_dist:274] - iter=54, loss=506.488, sps=4.137e+04, dt=0.00154701, dtf=0.0004132, dtb=0.001134
[2024-07-17 07:35:37.150659][INFO][test_dist:274] - iter=55, loss=503.01, sps=4.319e+04, dt=0.0014817, dtf=0.000415, dtb=0.001067
[2024-07-17 07:35:37.154441][INFO][test_dist:274] - iter=56, loss=506.116, sps=4.06e+04, dt=0.00157637, dtf=0.0004211, dtb=0.001155
[2024-07-17 07:35:37.158180][INFO][test_dist:274] - iter=57, loss=485.523, sps=4.287e+04, dt=0.00149301, dtf=0.000414, dtb=0.001079
[2024-07-17 07:35:37.161931][INFO][test_dist:274] - iter=58, loss=489.076, sps=4.185e+04, dt=0.00152915, dtf=0.0004162, dtb=0.001113
[2024-07-17 07:35:37.165759][INFO][test_dist:274] - iter=59, loss=484.844, sps=4.134e+04, dt=0.00154802, dtf=0.0004119, dtb=0.001136
[2024-07-17 07:35:37.169483][INFO][test_dist:274] - iter=60, loss=496.104, sps=4.209e+04, dt=0.00152069, dtf=0.0003993, dtb=0.001121
[2024-07-17 07:35:37.173190][INFO][test_dist:274] - iter=61, loss=467.599, sps=4.221e+04, dt=0.00151621, dtf=0.0004142, dtb=0.001102
[2024-07-17 07:35:37.176950][INFO][test_dist:274] - iter=62, loss=480.055, sps=4.187e+04, dt=0.00152868, dtf=0.0004138, dtb=0.001115
[2024-07-17 07:35:37.181194][INFO][test_dist:274] - iter=63, loss=483.146, sps=3.656e+04, dt=0.00175062, dtf=0.0006253, dtb=0.001125
[2024-07-17 07:35:37.185018][INFO][test_dist:274] - iter=64, loss=479.273, sps=4.099e+04, dt=0.00156151, dtf=0.0004447, dtb=0.001117
[2024-07-17 07:35:37.188752][INFO][test_dist:274] - iter=65, loss=464.753, sps=4.189e+04, dt=0.00152781, dtf=0.0004161, dtb=0.001112
[2024-07-17 07:35:37.192464][INFO][test_dist:274] - iter=66, loss=462.583, sps=4.188e+04, dt=0.00152824, dtf=0.0004138, dtb=0.001114
[2024-07-17 07:35:37.196126][INFO][test_dist:274] - iter=67, loss=461.665, sps=4.272e+04, dt=0.00149801, dtf=0.0004293, dtb=0.001069
[2024-07-17 07:35:37.199838][INFO][test_dist:274] - iter=68, loss=465.25, sps=4.118e+04, dt=0.00155412, dtf=0.0004298, dtb=0.001124
[2024-07-17 07:35:37.203602][INFO][test_dist:274] - iter=69, loss=460.897, sps=4.01e+04, dt=0.00159593, dtf=0.0004131, dtb=0.001183
[2024-07-17 07:35:37.207372][INFO][test_dist:274] - iter=70, loss=456.136, sps=4.106e+04, dt=0.00155887, dtf=0.00041, dtb=0.001149
[2024-07-17 07:35:37.211089][INFO][test_dist:274] - iter=71, loss=447.565, sps=4.158e+04, dt=0.00153923, dtf=0.0004113, dtb=0.001128
[2024-07-17 07:35:37.214861][INFO][test_dist:274] - iter=72, loss=444.733, sps=4.05e+04, dt=0.00158026, dtf=0.0004127, dtb=0.001168
[2024-07-17 07:35:37.218601][INFO][test_dist:274] - iter=73, loss=459.152, sps=4.123e+04, dt=0.00155234, dtf=0.0004201, dtb=0.001132
[2024-07-17 07:35:37.222334][INFO][test_dist:274] - iter=74, loss=444.6, sps=4.226e+04, dt=0.00151444, dtf=0.0004371, dtb=0.001077
[2024-07-17 07:35:37.226042][INFO][test_dist:274] - iter=75, loss=439.884, sps=4.29e+04, dt=0.001492, dtf=0.0004154, dtb=0.001077
[2024-07-17 07:35:37.229838][INFO][test_dist:274] - iter=76, loss=438.578, sps=4.086e+04, dt=0.00156632, dtf=0.0004418, dtb=0.001125
[2024-07-17 07:35:37.233560][INFO][test_dist:274] - iter=77, loss=431.993, sps=4.327e+04, dt=0.00147909, dtf=0.0004096, dtb=0.00107
[2024-07-17 07:35:37.237367][INFO][test_dist:274] - iter=78, loss=422.338, sps=4.057e+04, dt=0.00157754, dtf=0.0004468, dtb=0.001131
[2024-07-17 07:35:37.241117][INFO][test_dist:274] - iter=79, loss=427.973, sps=4.288e+04, dt=0.00149254, dtf=0.000415, dtb=0.001077
[2024-07-17 07:35:37.244895][INFO][test_dist:274] - iter=80, loss=418.703, sps=4.06e+04, dt=0.00157617, dtf=0.0004137, dtb=0.001162
[2024-07-17 07:35:37.248740][INFO][test_dist:274] - iter=81, loss=427.645, sps=4.031e+04, dt=0.00158766, dtf=0.000415, dtb=0.001173
[2024-07-17 07:35:37.252447][INFO][test_dist:274] - iter=82, loss=417.629, sps=4.227e+04, dt=0.00151406, dtf=0.0004149, dtb=0.001099
[2024-07-17 07:35:37.256190][INFO][test_dist:274] - iter=83, loss=411.667, sps=4.189e+04, dt=0.00152778, dtf=0.0004357, dtb=0.001092
[2024-07-17 07:35:37.259935][INFO][test_dist:274] - iter=84, loss=409.366, sps=4.144e+04, dt=0.0015445, dtf=0.0004575, dtb=0.001087
[2024-07-17 07:35:37.263677][INFO][test_dist:274] - iter=85, loss=409.511, sps=4.232e+04, dt=0.00151228, dtf=0.0004035, dtb=0.001109
[2024-07-17 07:35:37.267463][INFO][test_dist:274] - iter=86, loss=409.593, sps=4.101e+04, dt=0.00156049, dtf=0.0004028, dtb=0.001158
[2024-07-17 07:35:37.271174][INFO][test_dist:274] - iter=87, loss=408.794, sps=4.3e+04, dt=0.00148828, dtf=0.0004006, dtb=0.001088
[2024-07-17 07:35:37.274926][INFO][test_dist:274] - iter=88, loss=403.151, sps=4.091e+04, dt=0.00156441, dtf=0.000415, dtb=0.001149
[2024-07-17 07:35:37.278633][INFO][test_dist:274] - iter=89, loss=402.182, sps=4.26e+04, dt=0.00150243, dtf=0.0004147, dtb=0.001088
[2024-07-17 07:35:37.282372][INFO][test_dist:274] - iter=90, loss=387.829, sps=4.216e+04, dt=0.00151793, dtf=0.0004411, dtb=0.001077
[2024-07-17 07:35:37.286102][INFO][test_dist:274] - iter=91, loss=393.108, sps=4.308e+04, dt=0.00148558, dtf=0.0004167, dtb=0.001069
[2024-07-17 07:35:37.289904][INFO][test_dist:274] - iter=92, loss=389.039, sps=4.103e+04, dt=0.00155996, dtf=0.0004359, dtb=0.001124
[2024-07-17 07:35:37.293618][INFO][test_dist:274] - iter=93, loss=383.54, sps=4.322e+04, dt=0.00148092, dtf=0.0004147, dtb=0.001066
[2024-07-17 07:35:37.297401][INFO][test_dist:274] - iter=94, loss=384.459, sps=4.1e+04, dt=0.00156106, dtf=0.0004164, dtb=0.001145
[2024-07-17 07:35:37.301172][INFO][test_dist:274] - iter=95, loss=376.397, sps=4.191e+04, dt=0.0015272, dtf=0.0004129, dtb=0.001114
[2024-07-17 07:35:37.304924][INFO][test_dist:274] - iter=96, loss=389.544, sps=4.091e+04, dt=0.00156433, dtf=0.0004139, dtb=0.00115
[2024-07-17 07:35:37.308641][INFO][test_dist:274] - iter=97, loss=365.041, sps=4.343e+04, dt=0.00147362, dtf=0.0004165, dtb=0.001057
[2024-07-17 07:35:37.312398][INFO][test_dist:274] - iter=98, loss=358.427, sps=4.134e+04, dt=0.00154796, dtf=0.0004143, dtb=0.001134
[2024-07-17 07:35:37.561881][INFO][test_dist:274] - iter=99, loss=375.596, sps=258.9, dt=0.247161, dtf=0.1969, dtb=0.05026

                            train/dt [2024-07-17-073537]
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     └┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬┘
     1.0              25.5              50.0              74.5             99.0
train/dt                                iter
[2024-07-17 07:35:37.589287][INFO][plot:156] - Appending plot to: /home/foremans/tmp/polaris-talk/2024-07-17-073327/test-dist-plots/train/dt.txt
text saved in /home/foremans/tmp/polaris-talk/2024-07-17-073327/test-dist-plots/train/dt.txt
                            train/dtf [2024-07-17-073537]
     ┌─────────────────────────────────────────────────────────────────────────┐
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     └┬─────────────────┬─────────────────┬─────────────────┬─────────────────┬┘
     1.0              25.5              50.0              74.5             99.0
train/dtf                               iter
[2024-07-17 07:35:37.603242][INFO][plot:156] - Appending plot to: /home/foremans/tmp/polaris-talk/2024-07-17-073327/test-dist-plots/train/dtf.txt
text saved in /home/foremans/tmp/polaris-talk/2024-07-17-073327/test-dist-plots/train/dtf.txt
                             train/dtb [2024-07-17-073537]
      ┌────────────────────────────────────────────────────────────────────────┐
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      └┬─────────────────┬─────────────────┬────────────────┬─────────────────┬┘
      1.0              25.5              50.0             74.5             99.0
train/dtb                                iter
[2024-07-17 07:35:37.615896][INFO][plot:156] - Appending plot to: /home/foremans/tmp/polaris-talk/2024-07-17-073327/test-dist-plots/train/dtb.txt
text saved in /home/foremans/tmp/polaris-talk/2024-07-17-073327/test-dist-plots/train/dtb.txt
                            train/loss [2024-07-17-073537]
      ┌────────────────────────────────────────────────────────────────────────┐
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      └┬─────────────────┬─────────────────┬────────────────┬─────────────────┬┘
      1.0              25.5              50.0             74.5             99.0
train/loss                               iter
[2024-07-17 07:35:37.655339][INFO][plot:156] - Appending plot to: /home/foremans/tmp/polaris-talk/2024-07-17-073327/test-dist-plots/train/loss.txt
text saved in /home/foremans/tmp/polaris-talk/2024-07-17-073327/test-dist-plots/train/loss.txt
                           train/iter [2024-07-17-073537]
    ┌──────────────────────────────────────────────────────────────────────────┐
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    └┬─────────────────┬──────────────────┬─────────────────┬─────────────────┬┘
    1.0              25.5               50.0              74.5             99.0
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[2024-07-17 07:35:37.669214][INFO][plot:156] - Appending plot to: /home/foremans/tmp/polaris-talk/2024-07-17-073327/test-dist-plots/train/iter.txt
text saved in /home/foremans/tmp/polaris-talk/2024-07-17-073327/test-dist-plots/train/iter.txt
                             train/sps [2024-07-17-073537]
       ┌───────────────────────────────────────────────────────────────────────┐
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       1.0              25.5             50.0              74.5            99.0
train/sps                                iter
[2024-07-17 07:35:37.681268][INFO][plot:156] - Appending plot to: /home/foremans/tmp/polaris-talk/2024-07-17-073327/test-dist-plots/train/sps.txt
text saved in /home/foremans/tmp/polaris-talk/2024-07-17-073327/test-dist-plots/train/sps.txt

PyInstrument Profile

Recorded: 07:35:34  Samples:  2227
Duration: 2.948     CPU time: 5.441
PyInstrument: v4.6.2
Program: /home/foremans/tmp/polaris-talk/2024-07-17-073327/ezpz/src/ezpz/test_dist.py
2.948 <module>  ezpz/test_dist.py:1
└─ 2.946 main  ezpz/test_dist.py:217
   ├─ 2.043 build_model_and_optimizer  ezpz/test_dist.py:171
   │  └─ 2.011 Adam.__init__  torch/optim/adam.py:15
   │        [129 frames hidden]  torch, wandb, transformers, jax, func...
   ├─ 0.326 _forward_step  ezpz/test_dist.py:231
   │  ├─ 0.279 DistributedDataParallel._wrapped_call_impl  torch/nn/modules/module.py:1528
   │  │     [13 frames hidden]  torch, wandb, <built-in>
   │  │        0.273 Network._call_impl  torch/nn/modules/module.py:1534
   │  │        └─ 0.076 Network.forward  ezpz/test_dist.py:164
   │  │           └─ 0.076 Sequential._wrapped_call_impl  torch/nn/modules/module.py:1528
   │  │                 [7 frames hidden]  torch, <built-in>
   │  └─ 0.046 calc_loss  ezpz/test_dist.py:168
   ├─ 0.254 _backward_step  ezpz/test_dist.py:236
   │  ├─ 0.177 Tensor.backward  torch/_tensor.py:466
   │  │     [4 frames hidden]  torch, <built-in>
   │  └─ 0.077 wrapper  torch/optim/optimizer.py:374
   │        [5 frames hidden]  torch
   ├─ 0.119 tplot_dict  ezpz/plot.py:136
   │  └─ 0.069 show  plotext/_core.py:292
   │        [5 frames hidden]  plotext
   ├─ 0.102 Logger.info  logging/__init__.py:1479
   │     [6 frames hidden]  logging, rich
   │        0.102 RichHandler.emit  rich/logging.py:126
   │        └─ 0.100 Console.print  ezpz/log/console.py:79
   │           └─ 0.100 Console.print  rich/console.py:1624
   │                 [5 frames hidden]  rich
   └─ 0.099 Run.wrapper  wandb/sdk/wandb_run.py:418
         [13 frames hidden]  wandb, json
[2024-07-17 07:35:37.876629][INFO][profile:115] - Saving pyinstrument profile output to: /home/foremans/tmp/polaris-talk/2024-07-17-073327/ezpz_pyinstrument_profiles
[2024-07-17 07:35:37.877255][INFO][profile:123] - PyInstrument profile saved (as html) to:  /home/foremans/tmp/polaris-talk/2024-07-17-073327/ezpz_pyinstrument_profiles/pyinstrument-profile-2024-07-17-073537.html
[2024-07-17 07:35:37.877936][INFO][profile:131] - PyInstrument profile saved (as text) to:  /home/foremans/tmp/polaris-talk/2024-07-17-073327/ezpz_pyinstrument_profiles/pyinstrument-profile-2024-07-17-073537.txt
[2024-07-17 07:35:38.391628][INFO][profile:143] - Finished with pyinstrument profiler. Took: 2.94768s
[2024-07-17 07:35:38.392519][INFO][test_dist:318] - [0] runtime=8.075730s
wandb: 🚀 View run vibrant-river-284 at: https://wandb.ai/aurora_gpt/ezpz.test_dist/runs/p49rzxtv
wandb: Find logs at: wandb/run-20240717_073532-p49rzxtv/logs
Application cff755ee resources: utime=25s stime=23s maxrss=1434396KB inblock=32 oublock=4320 minflt=670179 majflt=864 nvcsw=195893 nivcsw=1331214

Example: ezpz 🍋

Figure 10: Example: using 🍋 ezpz.test_dist to train a small model using DDP

Link⁸ to video

1. idk why it doesn’t render correctly in the slide (seems like refreshing helps?)

Example: wordplay 🎮💬

Prepare Data

#[⭐][07:41:20 AM][foremans@x3101c0s13b0n0][~/tmp/polaris-talk/2024-07-17-073327][⏱ 29s]
$ python3 wordplay/data/shakespeare_char/prepare.py
Using HF_DATASETS_CACHE=/home/foremans/tmp/polaris-talk/2024-07-17-073327/wordplay/data/shakespeare_char/.cache/huggingface
length of dataset in characters: 1,115,394
all the unique characters:
 !$&\',-.3:;?ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz
vocab size: 65
train has 1,003,854 tokens
val has 111,540 tokens

Launch Training (DDP)

#(👻 2024-04-29)
#[⭐][07:42:02 AM][foremans@x3101c0s13b0n0][~/tmp/polaris-talk/2024-07-17-073327]
$ launch python3 -m wordplay train.backend=DDP train.eval_interval=100 data=shakespeare train.dtype=bf16 model.batch_size=64 model.block_size=1024 train.max_iters=1000 train.log_interval=10 train.compile=false | tee wordplay-gpt2-DDP.log
[2024-07-17 07:42:11.746540][INFO][__init__:156] - Setting logging level to 'INFO' on 'RANK == 0'
[2024-07-17 07:42:11.748763][INFO][__init__:157] - Setting logging level to 'CRITICAL' on all others 'RANK != 0'
[2024-07-17 07:42:11.749453][INFO][__init__:160] - To disable this behavior, and log from ALL ranks (not recommended), set: 'export LOG_FROM_ALL_RANKS=1'  in your environment, and re-run.
[2024-07-17 07:42:11.772718][INFO][configs:81] - Setting HF_DATASETS_CACHE to /home/foremans/tmp/polaris-talk/2024-07-17-073327/wordplay/.cache/huggingface/datasets
[2024-07-17 07:42:15.341532][INFO][dist:358] - [device='cuda'][rank=2/3][local_rank=2/3][node=0/0]
[2024-07-17 07:42:15.342381][INFO][dist:358] - [device='cuda'][rank=1/3][local_rank=1/3][node=0/0]
[2024-07-17 07:42:15.342430][INFO][dist:358] - [device='cuda'][rank=3/3][local_rank=3/3][node=0/0]
[2024-07-17 07:42:15.348657][INFO][dist:95] -

[dist_info]:
  • DEVICE=cuda
  • DEVICE_ID=cuda:0
  • DISTRIBUTED_BACKEND=nccl
  • GPUS_PER_NODE=4
  • HOSTS=['x3101c0s13b0n0.hsn.cm.polaris.alcf.anl.gov']
  • HOSTFILE=/var/spool/pbs/aux/2024084.polaris-pbs-01.hsn.cm.polaris.alcf.anl.gov
  • HOSTNAME=x3101c0s13b0n0.hsn.cm.polaris.alcf.anl.gov
  • LOCAL_RANK=0
  • MACHINE=Polaris
  • NUM_NODES=1
  • NGPUS=4
  • NGPUS_AVAILABLE=4
  • NODE_ID=0
  • RANK=0
  • SCHEDULER=PBS
  • WORLD_SIZE_TOTAL=4
  • WORLD_SIZE_IN_USE=4
  • LAUNCH_CMD=mpiexec --verbose --envall -n 4 -ppn 4 --hostfile /var/spool/pbs/aux/2024084.polaris-pbs-01.hsn.cm.polaris.alcf.anl.gov --cpu-bind depth -d 16


[2024-07-17 07:42:15.351446][INFO][dist:725] - [0/4] Using device='cuda' with backend='DDP' + 'nccl' for distributed training.
[2024-07-17 07:42:15.356169][INFO][dist:358] - [device='cuda'][rank=0/3][local_rank=0/3][node=0/0]
[2024-07-17 07:42:15.356692][WARNING][dist:364] - Using [4 / 4] available "cuda" devices !!
[2024-07-17 07:42:15.359571][INFO][configs:317] - Loading val from /home/foremans/tmp/polaris-talk/2024-07-17-073327/wordplay/data/shakespeare_char/val.bin
[2024-07-17 07:42:15.360138][INFO][configs:317] - Loading train from /home/foremans/tmp/polaris-talk/2024-07-17-073327/wordplay/data/shakespeare_char/train.bin
[2024-07-17 07:42:15.361154][INFO][configs:442] - Tokens per iteration: 262,144
[2024-07-17 07:42:15.361574][INFO][configs:465] - Using self.ptdtype=torch.float16 on self.device_type='cuda'
[2024-07-17 07:42:15.362002][INFO][configs:471] - Initializing a new model from scratch
[2024-07-17 07:42:15.362529][INFO][dist:874] - Setting up wandb from rank: 0
[2024-07-17 07:42:15.362896][INFO][dist:875] - Using: WB PROJECT: WordPlay
[2024-07-17 07:42:16.451786][INFO][dist:905] - W&B RUN: [still-frog-17](https://wandb.ai/aurora_gpt/WordPlay/runs/6by9vpcj)
[2024-07-17 07:42:16.464106][INFO][dist:312] - Updating wandb.run: still-frog-17 config with "DIST_INFO"
[2024-07-17 07:42:16.469424][INFO][dist:938] - Running on machine='Polaris'
[2024-07-17 07:42:16.471151][WARNING][__main__:89] - {
    "train": {
        "framework": "pytorch",
        "backend": "DDP",
        "device": null,
        "seed": null,
        "port": null,
        "ds_config_path": null,
        "precision": null,
        "ngpus": null,
        "use_wandb": true,
        "eval_interval": 100,
        "log_interval": 10,
        "eval_iters": 200,
        "eval_only": false,
        "always_save_checkpoint": false,
        "init_from": "scratch",
        "wandb_project": "WordPlay",
        "max_iters": 1000,
        "warmup_iters": 100,
        "dtype": "bf16",
        "compile": false
    },
    "model": {
        "n_layer": 12,
        "n_head": 12,
        "n_embd": 768,
        "batch_size": 64,
        "block_size": 1024,
        "activation": "gelu",
        "dropout": 0.0,
        "bias": false,
        "vocab_size": 65
    },
    "data": {
        "dataset": "shakespeare_char",
        "out_dir": "out-shakespeare-char",
        "root_path": null
    },
    "optimizer": {
        "gas": 1,
        "name": "AdamW",
        "learning_rate": 0.0006,
        "weight_decay": 0.1,
        "beta1": 0.9,
        "beta2": 0.95,
        "grad_clip": 1.0,
        "decay_lr": true,
        "lr_decay_iters": 600000,
        "min_lr": 6e-05
    }
}
[2024-07-17 07:42:16.474305][WARNING][__main__:90] - Output dir: /home/foremans/tmp/polaris-talk/outputs/runs/pytorch/DDP/2024-07-17/07-42-13
[2024-07-17 07:42:16.474922][INFO][trainer:246] - Initializing a new model from scratch
[2024-07-17 07:42:17.258904][INFO][model:255] - number of parameters: 85.00M
[2024-07-17 07:42:17.290004][INFO][trainer:264] - Model size: num_params=85003776
[2024-07-17 07:42:17.292626][INFO][model:445] - num decayed parameter tensors: 50, with 85,771,008 parameters
[2024-07-17 07:42:17.293296][INFO][model:449] - num non-decayed parameter tensors: 25, with 19,200 parameters
[2024-07-17 07:42:17.515324][CRITICAL][trainer:316] - "devid='cuda:1'"
[2024-07-17 07:42:17.515340][CRITICAL][trainer:316] - "devid='cuda:2'"
[2024-07-17 07:42:17.515465][CRITICAL][trainer:316] - "devid='cuda:3'"
[2024-07-17 07:42:18.431814][INFO][model:465] - using fused AdamW: True
[2024-07-17 07:42:18.432620][CRITICAL][trainer:316] - "devid='cuda:0'"
[2024-07-17 07:42:19.951020][INFO][trainer:356] - • self.model=GPT(
  (transformer): ModuleDict(
    (wte): Embedding(65, 768)
    (wpe): Embedding(1024, 768)
    (drop): Dropout(p=0.0, inplace=False)
    (h): ModuleList(
      (0-11): 12 x Block(
        (ln_1): LayerNorm()
        (attn): CausalSelfAttention(
          (c_attn): Linear(in_features=768, out_features=2304, bias=False)
          (c_proj): Linear(in_features=768, out_features=768, bias=False)
          (attn_dropout): Dropout(p=0.0, inplace=False)
          (resid_dropout): Dropout(p=0.0, inplace=False)
        )
        (ln_2): LayerNorm()
        (mlp): MLP(
          (c_fc): Linear(in_features=768, out_features=3072, bias=False)
          (act_fn): GELU(approximate='none')
          (c_proj): Linear(in_features=3072, out_features=768, bias=False)
          (dropout): Dropout(p=0.0, inplace=False)
        )
      )
    )
    (ln_f): LayerNorm()
  )
  (lm_head): Linear(in_features=768, out_features=65, bias=False)
)
[2024-07-17 07:42:19.955340][INFO][trainer:357] - • self.grad_scaler=<torch.cuda.amp.grad_scaler.GradScaler object at 0x145a38f0f090>
[2024-07-17 07:42:19.956897][INFO][trainer:358] - • self.model_engine=DistributedDataParallel(
  (module): GPT(
    (transformer): ModuleDict(
      (wte): Embedding(65, 768)
      (wpe): Embedding(1024, 768)
      (drop): Dropout(p=0.0, inplace=False)
      (h): ModuleList(
        (0-11): 12 x Block(
          (ln_1): LayerNorm()
          (attn): CausalSelfAttention(
            (c_attn): Linear(in_features=768, out_features=2304, bias=False)
            (c_proj): Linear(in_features=768, out_features=768, bias=False)
            (attn_dropout): Dropout(p=0.0, inplace=False)
            (resid_dropout): Dropout(p=0.0, inplace=False)
          )
          (ln_2): LayerNorm()
          (mlp): MLP(
            (c_fc): Linear(in_features=768, out_features=3072, bias=False)
            (act_fn): GELU(approximate='none')
            (c_proj): Linear(in_features=3072, out_features=768, bias=False)
            (dropout): Dropout(p=0.0, inplace=False)
          )
        )
      )
      (ln_f): LayerNorm()
    )
    (lm_head): Linear(in_features=768, out_features=65, bias=False)
  )
)
[2024-07-17 07:42:19.961066][INFO][trainer:359] - • self.optimizer=AdamW (
Parameter Group 0
    amsgrad: False
    betas: (0.9, 0.95)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: True
    lr: 0.0006
    maximize: False
    weight_decay: 0.1

Parameter Group 1
    amsgrad: False
    betas: (0.9, 0.95)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: True
    lr: 0.0006
    maximize: False
    weight_decay: 0.0
)
[2024-07-17 07:42:19.988827][INFO][trainer:802] - Startup time: 6.7125
                Training Legend
┏━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃    abbr     ┃ desc                           ┃
┡━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│    step     │ Current training iteration     │
│    loss     │ Loss value                     │
│     dt      │ Elapsed time per training step │
│     dtf     │ Elapsed time per forward step  │
│     dtb     │ Elapsed time per backward step │
│     sps     │ Samples per second             │
│ sps_per_gpu │ Samples per second (per GPU)   │
│     tps     │ Tokens per second              │
│ tps_per_gpu │ Tokens per second (per GPU)    │
│     mfu     │ Model flops utilization        │
│ train_loss  │ Training loss value            │
│  val_loss   │ Validation loss value          │
└─────────────┴────────────────────────────────┘
[2024-07-17 07:42:21.451865][INFO][trainer:820] - ['prompt']: 'What is an LLM?'
[2024-07-17 07:42:21.452667][INFO][trainer:824] - ['response']:
What is an LLM?eelEl\'$nltPwBSWal,;PWw bbu\'HiyP\'FWwF &AhW:ygrn kk-\'\'KFlMwnlEfflkc,elpWaWtgml$Pgglhllw lglhFllzczPAFHpeAAPPSltgkrWPPhlEMgcrN ggPWt-WPSSzHSkkrzzk.FFrtSSkgMll&gFXr,hghaueaVPW-pHFF-gg,,,FF,,kbApgg gg\'aWWzzkk\'a\'CggHl$bGeA,FFk,,SF;UF,,aZ ;gglee$,k.US&kg:S,,zVzzc
[2024-07-17 07:43:01.573073][INFO][trainer:885] - step=10 loss=3.154310 dt=0.282833 dtf=0.005247 dtb=0.011417 sps=14.142633 sps_per_gpu=3.535658 tps=926851.609409 tps_per_gpu=231712.902352 mfu=46.288281 train_loss=4.125778 val_loss=4.128809
[2024-07-17 07:43:04.402750][INFO][trainer:885] - step=20 loss=2.660851 dt=0.306263 dtf=0.005233 dtb=0.011419 sps=13.060678 sps_per_gpu=3.265170 tps=855944.613638 tps_per_gpu=213986.153409 mfu=45.934162 train_loss=4.125778 val_loss=4.128809
[2024-07-17 07:43:07.237507][INFO][trainer:885] - step=30 loss=2.543283 dt=0.283021 dtf=0.005238 dtb=0.011245 sps=14.133211 sps_per_gpu=3.533303 tps=926234.088226 tps_per_gpu=231558.522057 mfu=45.966490 train_loss=4.125778 val_loss=4.128809
[2024-07-17 07:43:10.077248][INFO][trainer:885] - step=40 loss=2.503963 dt=0.285001 dtf=0.005213 dtb=0.011471 sps=14.035061 sps_per_gpu=3.508765 tps=919801.749941 tps_per_gpu=229950.437485 mfu=45.963461 train_loss=4.125778 val_loss=4.128809
[2024-07-17 07:43:12.917039][INFO][trainer:885] - step=50 loss=2.477469 dt=0.283532 dtf=0.005166 dtb=0.011294 sps=14.107763 sps_per_gpu=3.526941 tps=924566.380009 tps_per_gpu=231141.595002 mfu=45.984530 train_loss=4.125778 val_loss=4.128809
[2024-07-17 07:43:15.760749][INFO][trainer:885] - step=60 loss=2.471083 dt=0.284630 dtf=0.005140 dtb=0.011224 sps=14.053326 sps_per_gpu=3.513332 tps=920998.786204 tps_per_gpu=230249.696551 mfu=45.985675 train_loss=4.125778 val_loss=4.128809
[2024-07-17 07:43:18.602785][INFO][trainer:885] - step=70 loss=2.458894 dt=0.283926 dtf=0.005219 dtb=0.010383 sps=14.088155 sps_per_gpu=3.522039 tps=923281.352698 tps_per_gpu=230820.338174 mfu=45.998106 train_loss=4.125778 val_loss=4.128809
[2024-07-17 07:43:21.451433][INFO][trainer:885] - step=80 loss=2.489088 dt=0.285537 dtf=0.005183 dtb=0.011373 sps=14.008683 sps_per_gpu=3.502171 tps=918073.060430 tps_per_gpu=229518.265108 mfu=45.983282 train_loss=4.125778 val_loss=4.128809
[2024-07-17 07:43:24.302241][INFO][trainer:885] - step=90 loss=2.471990 dt=0.300767 dtf=0.005445 dtb=0.010290 sps=13.299337 sps_per_gpu=3.324834 tps=871585.359388 tps_per_gpu=217896.339847 mfu=45.737774 train_loss=4.125778 val_loss=4.128809
[2024-07-17 07:43:27.153275][INFO][trainer:885] - step=100 loss=2.445556 dt=0.285869 dtf=0.005182 dtb=0.011251 sps=13.992403 sps_per_gpu=3.498101 tps=917006.151328 tps_per_gpu=229251.537832 mfu=45.743655 train_loss=4.125778 val_loss=4.128809
[2024-07-17 07:43:28.182553][INFO][trainer:820] - ['prompt']: 'What is an LLM?'
[2024-07-17 07:43:28.183179][INFO][trainer:824] - ['response']:

What is an LLM?

Goupay my winghimithell bls ger t bon sinthard ht omind be,
And lereind h py balithand frd oforondof wimon me hageas thinero mand,
Thacanes,
An frift ghik med d herthecke ntore thack couthen ale, t thit ang d m t h chy me fache ag, wit my hathan glat ng
[2024-07-17 07:44:06.025837][INFO][trainer:760] - Saving checkpoint to: /home/foremans/tmp/polaris-talk/outputs/runs/pytorch/DDP/2024-07-17/07-42-13
[2024-07-17 07:44:06.026607][INFO][trainer:761] - Saving model to: /home/foremans/tmp/polaris-talk/outputs/runs/pytorch/DDP/2024-07-17/07-42-13/model.pth
[2024-07-17 07:44:07.682968][INFO][configs:141] - Appending /home/foremans/tmp/polaris-talk/outputs/runs/pytorch/DDP/2024-07-17/07-42-13 to /home/foremans/tmp/polaris-talk/2024-07-17-073327/wordplay/src/ckpts/checkpoints.log
[2024-07-17 07:44:10.519506][INFO][trainer:885] - step=110 loss=2.433923 dt=0.285038 dtf=0.005757 dtb=0.011762 sps=14.033209 sps_per_gpu=3.508302 tps=919680.367894 tps_per_gpu=229920.091974 mfu=45.762304 train_loss=2.439494 val_loss=2.478951
[2024-07-17 07:44:13.362148][INFO][trainer:885] - step=120 loss=2.429014 dt=0.284445 dtf=0.005222 dtb=0.011486 sps=14.062460 sps_per_gpu=3.515615 tps=921597.361532 tps_per_gpu=230399.340383 mfu=45.788661 train_loss=2.439494 val_loss=2.478951
[2024-07-17 07:44:16.210694][INFO][trainer:885] - step=130 loss=2.402059 dt=0.285559 dtf=0.005199 dtb=0.011765 sps=14.007633 sps_per_gpu=3.501908 tps=918004.211586 tps_per_gpu=229501.052897 mfu=45.794438 train_loss=2.439494 val_loss=2.478951
[2024-07-17 07:44:19.061546][INFO][trainer:885] - step=140 loss=2.374062 dt=0.285476 dtf=0.005239 dtb=0.011453 sps=14.011662 sps_per_gpu=3.502916 tps=918268.297093 tps_per_gpu=229567.074273 mfu=45.800956 train_loss=2.439494 val_loss=2.478951
[2024-07-17 07:44:21.917283][INFO][trainer:885] - step=150 loss=2.365385 dt=0.285846 dtf=0.005125 dtb=0.011320 sps=13.993568 sps_per_gpu=3.498392 tps=917082.475791 tps_per_gpu=229270.618948 mfu=45.800900 train_loss=2.439494 val_loss=2.478951
[2024-07-17 07:44:24.771924][INFO][trainer:885] - step=160 loss=2.317337 dt=0.280788 dtf=0.005173 dtb=0.011249 sps=14.245602 sps_per_gpu=3.561401 tps=933599.792506 tps_per_gpu=233399.948127 mfu=45.883340 train_loss=2.439494 val_loss=2.478951
[2024-07-17 07:44:27.626812][INFO][trainer:885] - step=170 loss=2.256231 dt=0.284973 dtf=0.005141 dtb=0.011299 sps=14.036416 sps_per_gpu=3.509104 tps=919890.544506 tps_per_gpu=229972.636126 mfu=45.889069 train_loss=2.439494 val_loss=2.478951
[2024-07-17 07:44:30.480952][INFO][trainer:885] - step=180 loss=2.216419 dt=0.286555 dtf=0.005180 dtb=0.011402 sps=13.958906 sps_per_gpu=3.489726 tps=914810.852170 tps_per_gpu=228702.713043 mfu=45.868857 train_loss=2.439494 val_loss=2.478951
[2024-07-17 07:44:33.337342][INFO][trainer:885] - step=190 loss=2.145123 dt=0.291456 dtf=0.005409 dtb=0.019347 sps=13.724205 sps_per_gpu=3.431051 tps=899429.467247 tps_per_gpu=224857.366812 mfu=45.773849 train_loss=2.439494 val_loss=2.478951
[2024-07-17 07:44:36.194584][INFO][trainer:885] - step=200 loss=2.068149 dt=0.285703 dtf=0.005153 dtb=0.011286 sps=14.000555 sps_per_gpu=3.500139 tps=917540.393411 tps_per_gpu=229385.098353 mfu=45.778791 train_loss=2.439494 val_loss=2.478951
[2024-07-17 07:44:37.224149][INFO][trainer:820] - ['prompt']: 'What is an LLM?'
[2024-07-17 07:44:37.224745][INFO][trainer:824] - ['response']:

What is an LLM?

LORTESS LA:
No, sighappat selace? don downd sourciceans note cancen up sof liond
This and my man, werame, of re thee
Thise not will I on land brond sul me a fingore?

FLER:
Tisint your not nare lame o igen,-to brorst.

SamERS:
Sin:
I\'l hell she lor hen w
[2024-07-17 07:45:14.409129][INFO][trainer:760] - Saving checkpoint to: /home/foremans/tmp/polaris-talk/outputs/runs/pytorch/DDP/2024-07-17/07-42-13
[2024-07-17 07:45:14.409820][INFO][trainer:761] - Saving model to: /home/foremans/tmp/polaris-talk/outputs/runs/pytorch/DDP/2024-07-17/07-42-13/model.pth
[2024-07-17 07:45:16.366935][INFO][configs:141] - Appending /home/foremans/tmp/polaris-talk/outputs/runs/pytorch/DDP/2024-07-17/07-42-13 to /home/foremans/tmp/polaris-talk/2024-07-17-073327/wordplay/src/ckpts/checkpoints.log
[2024-07-17 07:45:19.245061][INFO][trainer:885] - step=210 loss=1.982169 dt=0.283305 dtf=0.005223 dtb=0.011284 sps=14.119042 sps_per_gpu=3.529760 tps=925305.515083 tps_per_gpu=231326.378771 mfu=45.822019 train_loss=2.045786 val_loss=2.148510
[2024-07-17 07:45:22.092430][INFO][trainer:885] - step=220 loss=1.897731 dt=0.284759 dtf=0.005217 dtb=0.011187 sps=14.046945 sps_per_gpu=3.511736 tps=920580.608106 tps_per_gpu=230145.152026 mfu=45.837327 train_loss=2.045786 val_loss=2.148510
[2024-07-17 07:45:24.942639][INFO][trainer:885] - step=230 loss=1.817213 dt=0.285266 dtf=0.005208 dtb=0.011446 sps=14.022003 sps_per_gpu=3.505501 tps=918945.985503 tps_per_gpu=229736.496376 mfu=45.842940 train_loss=2.045786 val_loss=2.148510
[2024-07-17 07:45:27.797910][INFO][trainer:885] - step=240 loss=1.779287 dt=0.285465 dtf=0.005189 dtb=0.011220 sps=14.012250 sps_per_gpu=3.503062 tps=918306.793546 tps_per_gpu=229576.698387 mfu=45.844800 train_loss=2.045786 val_loss=2.148510
[2024-07-17 07:45:30.653597][INFO][trainer:885] - step=250 loss=1.704220 dt=0.289284 dtf=0.005471 dtb=0.010346 sps=13.827253 sps_per_gpu=3.456813 tps=906182.836379 tps_per_gpu=226545.709095 mfu=45.785926 train_loss=2.045786 val_loss=2.148510
[2024-07-17 07:45:33.512769][INFO][trainer:885] - step=260 loss=1.671318 dt=0.287679 dtf=0.005125 dtb=0.011250 sps=13.904380 sps_per_gpu=3.476095 tps=911237.442617 tps_per_gpu=227809.360654 mfu=45.758182 train_loss=2.045786 val_loss=2.148510
[2024-07-17 07:45:36.373461][INFO][trainer:885] - step=270 loss=1.650952 dt=0.298661 dtf=0.005118 dtb=0.011520 sps=13.393107 sps_per_gpu=3.348277 tps=877730.651421 tps_per_gpu=219432.662855 mfu=45.565875 train_loss=2.045786 val_loss=2.148510
[2024-07-17 07:45:39.236930][INFO][trainer:885] - step=280 loss=1.573242 dt=0.285970 dtf=0.005171 dtb=0.011290 sps=13.987477 sps_per_gpu=3.496869 tps=916683.279847 tps_per_gpu=229170.819962 mfu=45.587333 train_loss=2.045786 val_loss=2.148510
[2024-07-17 07:45:42.100605][INFO][trainer:885] - step=290 loss=1.533265 dt=0.286487 dtf=0.005432 dtb=0.011288 sps=13.962259 sps_per_gpu=3.490565 tps=915030.617828 tps_per_gpu=228757.654457 mfu=45.598392 train_loss=2.045786 val_loss=2.148510
[2024-07-17 07:45:44.964424][INFO][trainer:885] - step=300 loss=1.492064 dt=0.288480 dtf=0.005355 dtb=0.011480 sps=13.865774 sps_per_gpu=3.466443 tps=908707.340870 tps_per_gpu=227176.835218 mfu=45.576766 train_loss=2.045786 val_loss=2.148510
[2024-07-17 07:45:45.995833][INFO][trainer:820] - ['prompt']: 'What is an LLM?'
[2024-07-17 07:45:45.996497][INFO][trainer:824] - ['response']:

What is an LLM?

RICHMORD:
Char stire? how in those are name the range hone.

GLOUCESTER:
Nay, in lond's time the palt are worder more
That wilt in the purpose be a pey
And thou thine onter hands, and the which broth.

ELBOWINCA:
At lie my lord with the me an arms be a s
[2024-07-17 07:46:23.549987][INFO][trainer:760] - Saving checkpoint to: /home/foremans/tmp/polaris-talk/outputs/runs/pytorch/DDP/2024-07-17/07-42-13
[2024-07-17 07:46:23.550696][INFO][trainer:761] - Saving model to: /home/foremans/tmp/polaris-talk/outputs/runs/pytorch/DDP/2024-07-17/07-42-13/model.pth
[2024-07-17 07:46:25.496559][INFO][configs:141] - Appending /home/foremans/tmp/polaris-talk/outputs/runs/pytorch/DDP/2024-07-17/07-42-13 to /home/foremans/tmp/polaris-talk/2024-07-17-073327/wordplay/src/ckpts/checkpoints.log
[2024-07-17 07:46:28.374854][INFO][trainer:885] - step=310 loss=1.444200 dt=0.299907 dtf=0.005333 dtb=0.010637 sps=13.337481 sps_per_gpu=3.334370 tps=874085.133345 tps_per_gpu=218521.283336 mfu=45.384395 train_loss=1.495372 val_loss=1.713714
[2024-07-17 07:46:31.223079][INFO][trainer:885] - step=320 loss=1.429350 dt=0.285238 dtf=0.005245 dtb=0.011485 sps=14.023353 sps_per_gpu=3.505838 tps=919034.479880 tps_per_gpu=229758.619970 mfu=45.435743 train_loss=1.495372 val_loss=1.713714
[2024-07-17 07:46:34.074957][INFO][trainer:885] - step=330 loss=1.362220 dt=0.285027 dtf=0.005165 dtb=0.011407 sps=14.033736 sps_per_gpu=3.508434 tps=919714.904826 tps_per_gpu=229928.726207 mfu=45.485355 train_loss=1.495372 val_loss=1.713714
[2024-07-17 07:46:36.929464][INFO][trainer:885] - step=340 loss=1.350888 dt=0.284436 dtf=0.005199 dtb=0.011287 sps=14.062893 sps_per_gpu=3.515723 tps=921625.744709 tps_per_gpu=230406.436177 mfu=45.539549 train_loss=1.495372 val_loss=1.713714

wordplay 🎮💬

• Link¹ to video

Example: Training a LLM to talk like Shakespeare using saforem2/wordplay 🎮💬

1. idk why it doesn’t render correctly in the slide (seems like refreshing helps?)

❤️ Thank you!

• Organizers

• Feel free to reach out!

🙏 Acknowledgements

This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357.

Extras

Transformer Architecture

Figure 11: Vaswani et al. (2017)

References

Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. “Attention Is All You Need.” https://arxiv.org/abs/1706.03762.

Yang, Jingfeng, Hongye Jin, Ruixiang Tang, Xiaotian Han, Qizhang Feng, Haoming Jiang, Bing Yin, and Xia Hu. 2023. “Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond.” https://arxiv.org/abs/2304.13712.

Yao, Shunyu, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan. 2023. “Tree of Thoughts: Deliberate Problem Solving with Large Language Models.” https://arxiv.org/abs/2305.10601.