patch-forcing/scripts/run_train_official.sh

#!/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[@]}"