test gpu scripts and other launchers and configs

This commit is contained in:
Patrick Esser 2022-07-22 09:56:22 +00:00
parent 099376de22
commit eee8df53b5
6 changed files with 250 additions and 1 deletions

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model:
base_learning_rate: 1.0e-04
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "jpg"
cond_stage_key: "txt"
image_size: 64
channels: 4
cond_stage_trainable: false # Note: different from the one we trained before
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
scheduler_config: # 10000 warmup steps
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
f_start: [ 1.e-6 ]
f_max: [ 1. ]
f_min: [ 1. ]
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
image_size: 32 # unused
in_channels: 4
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
legacy: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
data:
target: ldm.data.laion.WebDataModuleFromConfig
params:
tar_base: "pipe:aws s3 cp s3://s-datasets/laion-high-resolution/"
batch_size: 4
num_workers: 4
multinode: True
train:
shards: '{00000..17279}.tar -'
shuffle: 10000
image_key: jpg
image_transforms:
- target: torchvision.transforms.Resize
params:
size: 512
interpolation: 3
- target: torchvision.transforms.RandomCrop
params:
size: 512
# NOTE use enough shards to avoid empty validation loops in workers
validation:
shards: '{17280..17535}.tar -'
shuffle: 0
image_key: jpg
image_transforms:
- target: torchvision.transforms.Resize
params:
size: 512
interpolation: 3
- target: torchvision.transforms.CenterCrop
params:
size: 512
lightning:
find_unused_parameters: False
modelcheckpoint:
params:
every_n_train_steps: 5000
callbacks:
image_logger:
target: main.ImageLogger
params:
batch_frequency: 5000
max_images: 4
increase_log_steps: False
log_first_step: False
log_images_kwargs:
use_ema_scope: False
inpaint: False
plot_progressive_rows: False
plot_diffusion_rows: False
N: 4
unconditional_guidance_scale: 3.0
unconditional_guidance_label: [""]
trainer:
benchmark: True
val_check_interval: 5000000 # really sorry
num_sanity_val_steps: 0
accumulate_grad_batches: 2

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@ -24,7 +24,8 @@ CONFIG="/fsx/stable-diffusion/stable-diffusion/configs/stable-diffusion/v1_impro
# resume and set new seed to reshuffle data # resume and set new seed to reshuffle data
#EXTRA="--seed 714 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/logs/2022-07-11T20-16-11_txt2img-1p4B-multinode-clip-encoder-high-res-512_improvedaesthetic/checkpoints/last.ckpt" #EXTRA="--seed 714 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/logs/2022-07-11T20-16-11_txt2img-1p4B-multinode-clip-encoder-high-res-512_improvedaesthetic/checkpoints/last.ckpt"
#EXTRA="--seed 715 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/logs/2022-07-14T23-26-13_v1_improvedaesthetics/checkpoints/last.ckpt" #EXTRA="--seed 715 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/logs/2022-07-14T23-26-13_v1_improvedaesthetics/checkpoints/last.ckpt"
EXTRA="--seed 716 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-18T23-11-11_v1_improvedaesthetics/checkpoints/last.ckpt" #EXTRA="--seed 716 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-18T23-11-11_v1_improvedaesthetics/checkpoints/last.ckpt"
EXTRA="--seed 717 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-19T19-03-04_v1_improvedaesthetics/checkpoints/last.ckpt"
# time to decay # time to decay
EXTRA="${EXTRA} model.params.scheduler_config.params.cycle_lengths=[50000] model.params.scheduler_config.params.f_min=[1e-6]" EXTRA="${EXTRA} model.params.scheduler_config.params.cycle_lengths=[50000] model.params.scheduler_config.params.f_min=[1e-6]"

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#!/bin/bash
# mpi version for node rank
H=`hostname`
THEID=`echo -e $HOSTNAMES | python3 -c "import sys;[sys.stdout.write(str(i)) for i,line in enumerate(next(sys.stdin).split(' ')) if line.strip() == '$H'.strip()]"`
export NODE_RANK=${THEID}
echo THEID=$THEID
echo "##########################################"
echo MASTER_ADDR=${MASTER_ADDR}
echo MASTER_PORT=${MASTER_PORT}
echo NODE_RANK=${NODE_RANK}
echo WORLD_SIZE=${WORLD_SIZE}
echo "##########################################"
# debug environment worked great so we stick with it
# no magic there, just a miniconda python=3.9, pytorch=1.12, cudatoolkit=11.3
# env with pip dependencies from stable diffusion's requirements.txt
eval "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
conda activate torch111
cd /fsx/stable-diffusion/stable-diffusion
CONFIG="/fsx/stable-diffusion/stable-diffusion/configs/stable-diffusion/v1_laionhr.yaml"
# resume and set new seed to reshuffle data
EXTRA="--seed 718 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/checkpoints2/v1pp/v1pp-flatline.ckpt"
# time to decay
#EXTRA="${EXTRA} model.params.scheduler_config.params.cycle_lengths=[50000] model.params.scheduler_config.params.f_min=[1e-6]"
# custom logdir
#EXTRA="${EXTRA} --logdir rlogs"
# debugging
#EXTRA="${EXTRA} -d True lightning.callbacks.image_logger.params.batch_frequency=50"
python main.py --base $CONFIG --gpus 0,1,2,3,4,5,6,7 -t --num_nodes ${WORLD_SIZE} --scale_lr False $EXTRA

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#!/bin/bash
#SBATCH --partition=compute-od-gpu
#SBATCH --job-name=stable-diffusion-v1-laionhr-torch111
#SBATCH --nodes 20
#SBATCH --ntasks-per-node 1
#SBATCH --cpus-per-gpu=4
#SBATCH --gres=gpu:8
#SBATCH --exclusive
#SBATCH --output=%x_%j.out
#SBATCH --comment "Key=Monitoring,Value=ON"
module load intelmpi
source /opt/intel/mpi/latest/env/vars.sh
export LD_LIBRARY_PATH=/opt/aws-ofi-nccl/lib:/opt/amazon/efa/lib64:/usr/local/cuda-11.0/efa/lib:/usr/local/cuda-11.0/lib:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0:/opt/nccl/build/lib:/opt/aws-ofi-nccl-inst
all/lib:/opt/aws-ofi-nccl/lib:$LD_LIBRARY_PATH
export NCCL_PROTO=simple
export PATH=/opt/amazon/efa/bin:$PATH
export LD_PRELOAD="/opt/nccl/build/lib/libnccl.so"
export FI_EFA_FORK_SAFE=1
export FI_LOG_LEVEL=1
export FI_EFA_USE_DEVICE_RDMA=1 # use for p4dn
export NCCL_DEBUG=info
export PYTHONFAULTHANDLER=1
export CUDA_LAUNCH_BLOCKING=0
export OMPI_MCA_mtl_base_verbose=1
export FI_EFA_ENABLE_SHM_TRANSFER=0
export FI_PROVIDER=efa
export FI_EFA_TX_MIN_CREDITS=64
export NCCL_TREE_THRESHOLD=0
# sent to sub script
export HOSTNAMES=`scontrol show hostnames "$SLURM_JOB_NODELIST"`
export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
export MASTER_PORT=12802
export COUNT_NODE=`scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l`
export WORLD_SIZE=$COUNT_NODE
echo go $COUNT_NODE
echo $HOSTNAMES
echo $WORLD_SIZE
mpirun -n $COUNT_NODE -perhost 1 /fsx/stable-diffusion/stable-diffusion/scripts/slurm/v1_laionhr_torch111/launcher.sh

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scripts/test_gpu.py Normal file
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import socket
try:
import torch
n_gpus = torch.cuda.device_count()
print(f"checking {n_gpus} gpus.")
for i_gpu in range(n_gpus):
print(i_gpu)
device = torch.device(f"cuda:{i_gpu}")
torch.backends.cuda.matmul.allow_tf32 = False
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.allow_tf32 = True
data = torch.randn([4, 640, 32, 32], dtype=torch.float, device=device, requires_grad=True)
net = torch.nn.Conv2d(640, 640, kernel_size=[3, 3], padding=[1, 1], stride=[1, 1], dilation=[1, 1], groups=1)
net = net.to(device=device).float()
out = net(data)
out.backward(torch.randn_like(out))
torch.cuda.synchronize()
except RuntimeError as err:
import requests
import datetime
import os
device = os.environ.get("CUDA_VISIBLE_DEVICES", "?")
hostname = socket.gethostname()
ts = datetime.datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S')
resp = requests.get('http://169.254.169.254/latest/meta-data/instance-id')
print(f'ERROR at {ts} on {hostname}/{resp.text} (CUDA_VISIBLE_DEVICES={device}): {type(err).__name__}: {err}', flush=True)
raise err
else:
print(f"checked {socket.gethostname()}")

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scripts/test_gpu.sh Normal file
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#!/bin/bash
eval "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
conda activate stable
cd /fsx/stable-diffusion/stable-diffusion
python scripts/test_gpu.py