support unconditional guidance training
This commit is contained in:
Patrick Esser
parent
d762d5992a
commit
8ed76d2350
3 changed files with 110 additions and 0 deletions
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@ -74,6 +74,7 @@ class DDPM(pl.LightningModule):
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learn_logvar=False,
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logvar_init=0.,
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make_it_fit=False,
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ucg_training=None,
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):
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super().__init__()
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assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
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@ -117,6 +118,10 @@ class DDPM(pl.LightningModule):
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if self.learn_logvar:
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self.logvar = nn.Parameter(self.logvar, requires_grad=True)
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self.ucg_training = ucg_training or dict()
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if self.ucg_training:
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self.ucg_prng = np.random.RandomState()
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def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
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linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
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@ -389,6 +394,15 @@ class DDPM(pl.LightningModule):
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return loss, loss_dict
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def training_step(self, batch, batch_idx):
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for k in self.ucg_training:
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p = self.ucg_training[k]["p"]
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val = self.ucg_training[k]["val"]
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if val is None:
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val = ""
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for i in range(len(batch[k])):
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if self.ucg_prng.choice(2, p=[1-p, p]):
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batch[k][i] = val
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loss, loss_dict = self.shared_step(batch)
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self.log_dict(loss_dict, prog_bar=True,
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52
scripts/slurm/v1_iahr_torch111_ucg/launcher.sh
Executable file
52
scripts/slurm/v1_iahr_torch111_ucg/launcher.sh
Executable file
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@ -0,0 +1,52 @@
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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 "$(/fsx/stable-diffusion/debug/miniconda3/bin/conda shell.bash hook)"
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#conda activate stable
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conda activate torch111
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cd /fsx/stable-diffusion/stable-diffusion
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CONFIG="/fsx/stable-diffusion/stable-diffusion/configs/stable-diffusion/v1_improvedaesthetics.yaml"
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# resume and set new seed to reshuffle data
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#EXTRA="--seed 718 model.params.ckpt_path=/fsx/stable-diffusion/stable-diffusion/checkpoints2/v1pp/v1pp-flatline.ckpt"
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#EXTRA="--seed 718 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-22T07-45-07_v1_improvedaesthetics/checkpoints/last.ckpt"
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#EXTRA="--seed 719 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-22T12-32-32_v1_improvedaestheticsv1_iahr_torch111/checkpoints/last.ckpt"
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#EXTRA="--seed 720 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-23T07-52-21_v1_improvedaestheticsv1_iahr_torch111/checkpoints/last.ckpt"
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EXTRA="--seed 721 --resume_from_checkpoint /fsx/stable-diffusion/stable-diffusion/logs/2022-07-24T19-07-33_v1_improvedaestheticsv1_iahr_torch111/checkpoints/last.ckpt"
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# only images >= 512 and pwatermark <= 0.4999
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EXTRA="${EXTRA} data.params.min_size=512 data.params.max_pwatermark=0.4999"
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# unconditional guidance training
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EXTRA="${EXTRA} model.params.ucg_training.txt.p=0.1 model.params.ucg_training.txt.val=''"
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# postfix
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EXTRA="${EXTRA} -f v1_iahr_torch111_ucg"
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# time to decay
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#EXTRA="${EXTRA} model.params.scheduler_config.params.cycle_lengths=[300000] model.params.scheduler_config.params.warm_up_steps=[250000] model.params.scheduler_config.params.f_min=[1e-6]"
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# custom logdir
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#EXTRA="${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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/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 $EXTRA
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44
scripts/slurm/v1_iahr_torch111_ucg/sbatch.sh
Executable file
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scripts/slurm/v1_iahr_torch111_ucg/sbatch.sh
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@ -0,0 +1,44 @@
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#!/bin/bash
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#SBATCH --partition=compute-od-gpu
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#SBATCH --job-name=stable-diffusion-v1-iahr-torch111-ucg
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#SBATCH --nodes 32
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#SBATCH --ntasks-per-node 1
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#SBATCH --cpus-per-gpu=4
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#SBATCH --gres=gpu:8
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#SBATCH --exclusive
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#SBATCH --output=%x_%j.out
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#SBATCH --comment "Key=Monitoring,Value=ON"
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#SBATCH --no-requeue
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module load intelmpi
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source /opt/intel/mpi/latest/env/vars.sh
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export LD_LIBRARY_PATH=/opt/aws-ofi-nccl/lib:/opt/amazon/efa/lib64:/usr/local/cuda-11.0/efa/lib:/usr/local/cuda-11.0/lib:/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0:/opt/nccl/build/lib:/opt/aws-ofi-nccl-install/lib:/opt/aws-ofi-nccl/lib:$LD_LIBRARY_PATH
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#export 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
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#all/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/v1_iahr_torch111_ucg/launcher.sh
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