File size: 4,740 Bytes
b910c09 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 | #!/usr/bin/env bash
set -euo pipefail
ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
ENV_DIR="${PFT_TRAIN_ENV_DIR:-/tmp/pft-venv-node-full}"
PYTHON_BIN="${PYTHON_BIN:-/home/dyvm6xra/dyvm6xrauser11/miniforge3/bin/python}"
PYTHON="${ENV_DIR}/bin/python"
PIP="${ENV_DIR}/bin/pip"
MODE="${1:-dummy}"
shift || true
EXTRA_ARGS=("$@")
usage() {
cat <<'EOF'
Usage:
scripts/run_train_official.sh [dummy|imnet-pft-b|imnet-pft-xl] [extra hydra overrides...]
Modes:
dummy
Official Patch Forcing B training stack on dummy256 data.
Good for verifying the training pipeline on a single GPU.
imnet-pft-b
Full official ImageNet-256 Patch Forcing B training command.
Requires configs/data/imagenet256.yaml to be filled in.
imnet-pft-xl
Full official ImageNet-256 Patch Forcing XL training command.
Requires configs/data/imagenet256.yaml to be filled in.
Examples:
scripts/run_train_official.sh dummy
scripts/run_train_official.sh dummy train_params.max_steps=100 data.params.batch_size=4
scripts/run_train_official.sh imnet-pft-b
Recommended flow:
1. On the login node, request a GPU shell:
/home/dyvm6xra/dyvm6xrauser11/workspace/cz/debug_apply.sh 1 patch-forcing --debug
2. On the allocated compute node, run this script.
EOF
}
if [[ "${MODE}" == "-h" || "${MODE}" == "--help" ]]; then
usage
exit 0
fi
require_gpu() {
if ! command -v nvidia-smi >/dev/null 2>&1; then
echo "nvidia-smi not found. Run this script on a GPU compute node."
exit 1
fi
nvidia-smi >/dev/null
}
ensure_env() {
if [[ ! -x "${PYTHON}" ]]; then
rm -rf "${ENV_DIR}"
"${PYTHON_BIN}" -m venv "${ENV_DIR}"
fi
if ! "${PYTHON}" - <<'PY' >/dev/null 2>&1
import accelerate, cv2, diffusers, hydra, jutils, lightning, matplotlib, pandas, tensorboard
import timm, torch, torch_fidelity, torchvision, wandb, webdataset
PY
then
env HTTPS_PROXY= HTTP_PROXY= ALL_PROXY= https_proxy= http_proxy= all_proxy= \
"${PIP}" install torch==2.8.0+cu128 torchvision==0.23.0+cu128 --index-url https://download.pytorch.org/whl/cu128
env HTTPS_PROXY= HTTP_PROXY= ALL_PROXY= https_proxy= http_proxy= all_proxy= \
"${PIP}" install \
hydra-core lightning accelerate tensorboard webdataset opencv-python h5py pandas \
wandb torch-fidelity scipy requests packaging omegaconf PyYAML tqdm einops jaxtyping \
termcolor matplotlib ipython
env HTTPS_PROXY= HTTP_PROXY= ALL_PROXY= https_proxy= http_proxy= all_proxy= \
"${PIP}" install --no-deps timm diffusers git+https://github.com/joh-schb/jutils.git#egg=jutils
fi
}
prepare_sd_ae() {
mkdir -p "${ROOT_DIR}/checkpoints"
if [[ ! -f "${ROOT_DIR}/checkpoints/sd_ae_full.ckpt" ]]; then
env HTTPS_PROXY= HTTP_PROXY= ALL_PROXY= https_proxy= http_proxy= all_proxy= \
curl -L --retry 5 -C - \
-o "${ROOT_DIR}/checkpoints/sd_ae_full.ckpt" \
https://huggingface.co/stabilityai/sd-vae-ft-ema-original/resolve/main/vae-ft-ema-560000-ema-pruned.ckpt
fi
if [[ ! -f "${ROOT_DIR}/checkpoints/sd_ae.ckpt" ]]; then
"${PYTHON}" - <<'PY'
import torch
src = "checkpoints/sd_ae_full.ckpt"
dst = "checkpoints/sd_ae.ckpt"
ckpt = torch.load(src, map_location="cpu", weights_only=False)
state_dict = ckpt["state_dict"] if "state_dict" in ckpt else ckpt
state_dict = {k: v for k, v in state_dict.items() if not k.startswith("model_ema.")}
torch.save(state_dict, dst)
print(f"Saved converted SD autoencoder weights to {dst}")
PY
fi
}
check_imagenet_cfg() {
if grep -q "tar_base: ..." "${ROOT_DIR}/configs/data/imagenet256.yaml"; then
echo "configs/data/imagenet256.yaml is still unconfigured."
echo "Fill tar_base and shard patterns before running ${MODE}."
exit 1
fi
}
build_train_cmd() {
case "${MODE}" in
dummy)
cat <<'EOF'
train.py experiment=imnet-pft-b data=dummy256 autoencoder=sd_ae model.params.compile=false train_params.max_steps=100 train_params.val_check_interval=1000 train_params.limit_val_batches=0 data.params.batch_size=4 data.params.num_workers=0 name=debug/train-official
EOF
;;
imnet-pft-b)
check_imagenet_cfg
cat <<'EOF'
train.py experiment=imnet-pft-b
EOF
;;
imnet-pft-xl)
check_imagenet_cfg
cat <<'EOF'
train.py experiment=imnet-pft-xl
EOF
;;
*)
echo "Unknown mode: ${MODE}"
usage
exit 1
;;
esac
}
require_gpu
cd "${ROOT_DIR}"
ensure_env
prepare_sd_ae
TRAIN_CMD="$(build_train_cmd)"
echo "Using Python: ${PYTHON}"
echo "Mode : ${MODE}"
echo "Train cmd : ${TRAIN_CMD} ${EXTRA_ARGS[*]:-}"
env HTTPS_PROXY= HTTP_PROXY= ALL_PROXY= https_proxy= http_proxy= all_proxy= \
LD_LIBRARY_PATH="${LD_LIBRARY_PATH:-}" \
"${PYTHON}" ${TRAIN_CMD} "${EXTRA_ARGS[@]}"
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