v2 pretraining
This commit is contained in:
Patrick Esser
parent
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commit
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model:
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base_learning_rate: 1.0e-04
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target: ldm.models.diffusion.ddpm.LatentDiffusion
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params:
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linear_start: 0.001
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linear_end: 0.015
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num_timesteps_cond: 1
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log_every_t: 200
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timesteps: 1000
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first_stage_key: "jpg"
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cond_stage_key: "txt"
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image_size: 16
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channels: 16
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cond_stage_trainable: false # Note: different from the one we trained before
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conditioning_key: crossattn
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monitor: val/loss_simple_ema
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scale_factor: 0.22765929 # magic number
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scheduler_config: # 10000 warmup steps
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target: ldm.lr_scheduler.LambdaLinearScheduler
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params:
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warm_up_steps: [ 10000 ]
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cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
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f_start: [ 1.e-6 ]
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f_max: [ 1. ]
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f_min: [ 1. ]
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unet_config:
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target: ldm.modules.diffusionmodules.openaimodel.UNetModel
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params:
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image_size: 16 # not really needed
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in_channels: 16
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out_channels: 16
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model_channels: 320 # TODO: scale model here
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attention_resolutions: [ 4, 2, 1 ]
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num_res_blocks: 2
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channel_mult: [ 1, 2, 4, 4 ]
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num_heads: 8
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use_spatial_transformer: True
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transformer_depth: 1
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context_dim: 768
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use_checkpoint: True
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legacy: False
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first_stage_config:
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target: ldm.models.autoencoder.AutoencoderKL
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params:
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embed_dim: 16
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monitor: val/rec_loss
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ckpt_path: "models/first_stage_models/kl-f16/model.ckpt"
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ddconfig:
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double_z: True
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z_channels: 16
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resolution: 256
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in_channels: 3
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out_ch: 3
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ch: 128
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ch_mult: [ 1,1,2,2,4 ] # num_down = len(ch_mult)-1
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num_res_blocks: 2
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attn_resolutions: [ 16 ]
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dropout: 0.0
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lossconfig:
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target: torch.nn.Identity
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cond_stage_config:
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target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
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data:
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target: ldm.data.laion.WebDataModuleFromConfig
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params:
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tar_base: "pipe:aws s3 cp s3://s-datasets/laion5b/laion2B-data/"
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batch_size: 55
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num_workers: 4
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multinode: True
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min_size: 256
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train:
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shards: '{000000..231317}.tar -'
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shuffle: 10000
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image_key: jpg
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image_transforms:
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- target: torchvision.transforms.Resize
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params:
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size: 256
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interpolation: 3
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- target: torchvision.transforms.RandomCrop
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params:
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size: 256
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# NOTE use enough shards to avoid empty validation loops in workers
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validation:
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shards: '{231318..231349}.tar -'
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shuffle: 0
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image_key: jpg
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image_transforms:
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- target: torchvision.transforms.Resize
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params:
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size: 256
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interpolation: 3
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- target: torchvision.transforms.CenterCrop
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params:
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size: 256
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lightning:
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find_unused_parameters: false
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modelcheckpoint:
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params:
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every_n_train_steps: 5000
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callbacks:
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image_logger:
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target: main.ImageLogger
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params:
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batch_frequency: 5000
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max_images: 4
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increase_log_steps: False
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log_first_step: False
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log_images_kwargs:
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use_ema_scope: False
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inpaint: False
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plot_progressive_rows: False
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plot_diffusion_rows: False
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N: 4
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unconditional_guidance_scale: 3.0
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unconditional_guidance_label: [""]
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trainer:
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benchmark: True
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val_check_interval: 5000000 # really sorry
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num_sanity_val_steps: 0
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accumulate_grad_batches: 1
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@ -0,0 +1,42 @@
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#!/bin/bash
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# mpi version for node rank
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H=`hostname`
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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()]"`
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export NODE_RANK=${THEID}
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echo THEID=$THEID
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echo "##########################################"
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echo MASTER_ADDR=${MASTER_ADDR}
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echo MASTER_PORT=${MASTER_PORT}
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echo NODE_RANK=${NODE_RANK}
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echo WORLD_SIZE=${WORLD_SIZE}
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echo "##########################################"
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# debug environment worked great so we stick with it
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# no magic there, just a miniconda python=3.9, pytorch=1.12, cudatoolkit=11.3
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# env with pip dependencies from stable diffusion's requirements.txt
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eval "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
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#conda activate stable
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# torch 1.11 to avoid bug in ckpt restoring
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conda activate torch111
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cd /fsx/stable-diffusion/stable-diffusion
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CONFIG=configs/stable-diffusion/v2_pretraining.yaml
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# resume and set new seed to reshuffle data
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EXTRA="--seed 542 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/checkpoints/v2-256/216k-256.ckpt"
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#EXTRA="--seed 543 --resume_from_checkpoint ..."
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# reduce lr a bit
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#EXTRA="${EXTRA} model.params.scheduler_config.params.f_max=[0.75]"
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# custom logdir
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#EXTRA="${EXTRA} --logdir rlogs"
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# debugging
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#EXTRA="${EXTRA} -d True lightning.callbacks.image_logger.params.batch_frequency=50"
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# detect bad gpus early on
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/bin/bash /fsx/stable-diffusion/stable-diffusion/scripts/test_gpu.sh
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python main.py --base $CONFIG --gpus 0,1,2,3,4,5,6,7 -t --num_nodes ${WORLD_SIZE} --scale_lr False $EXTRA
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#!/bin/bash
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#SBATCH --partition=compute-od-gpu
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#SBATCH --job-name=stable-diffusion-v2-pretraining
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#SBATCH --nodes 32
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#SBATCH --ntasks-per-node 1
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#SBATCH --cpus-per-gpu=4
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#SBATCH --gres=gpu:8
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#SBATCH --exclusive
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#SBATCH --output=%x_%j.out
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#SBATCH --comment "Key=Monitoring,Value=ON"
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#SBATCH --no-requeue
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module load intelmpi
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source /opt/intel/mpi/latest/env/vars.sh
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export LD_LIBRARY_PATH=/opt/aws-ofi-nccl/lib:/opt/amazon/efa/lib64:/usr/local/cuda-11.0/efa/lib:/usr/local/cuda-11.0/lib:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0:/opt/nccl/build/lib:/opt/aws-ofi-nccl-install/lib:/opt/aws-ofi-nccl/lib:$LD_LIBRARY_PATH
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export NCCL_PROTO=simple
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export PATH=/opt/amazon/efa/bin:$PATH
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export LD_PRELOAD="/opt/nccl/build/lib/libnccl.so"
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export FI_EFA_FORK_SAFE=1
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export FI_LOG_LEVEL=1
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export FI_EFA_USE_DEVICE_RDMA=1 # use for p4dn
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export NCCL_DEBUG=info
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export PYTHONFAULTHANDLER=1
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export CUDA_LAUNCH_BLOCKING=0
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export OMPI_MCA_mtl_base_verbose=1
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export FI_EFA_ENABLE_SHM_TRANSFER=0
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export FI_PROVIDER=efa
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export FI_EFA_TX_MIN_CREDITS=64
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export NCCL_TREE_THRESHOLD=0
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# sent to sub script
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export HOSTNAMES=`scontrol show hostnames "$SLURM_JOB_NODELIST"`
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export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
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export MASTER_PORT=12802
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export COUNT_NODE=`scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l`
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export WORLD_SIZE=$COUNT_NODE
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echo go $COUNT_NODE
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echo $HOSTNAMES
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echo $WORLD_SIZE
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mpirun -n $COUNT_NODE -perhost 1 /fsx/stable-diffusion/stable-diffusion/scripts/slurm/v2_pretraining/launcher.sh
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