p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] slurm/jobs/mihir/berlin/diffusion/coinrun/sample_coinrun.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=01:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=/fast/project/HFMI_ProFOUND/mihir/logs/sample/coinrun/diffusion/sample_coinrun_%j.log
#SBATCH --error=/fast/project/HFMI_ProFOUND/mihir/logs/sample/coinrun/diffusion/sample_coinrun_%j.log
#SBATCH --job-name=sample_coinrun

# Activate virtual environment
source .venv/bin/activate

array_records_dir=/fast/project/HFMI_ProFOUND/mihir/data/data_coinrun/coinrun_episodes_50m/val

CHECKPOINT_PATH=/fast/project/HFMI_ProFOUND/mihir/checkpoints/dyn/train_dyn_dit/32748

echo "Sampling from checkpoint: $CHECKPOINT_PATH"

srun python jasmine/sample.py \
    --checkpoint "$CHECKPOINT_PATH" \
    --data_dir=$array_records_dir \
    --seq_len=32 \
    --window_size=16 \
    --batch_size=4 \
    --start_frame=4 \
    --image_height=64 \
    --image_width=64 \
    --dyna_type=diffusion \
    --diffusion_denoise_steps=128 \
    --print-action-indices \
    --output_dir "gifs/diffusion/coinrun/from_tokenizer_checkpoint" \
    --tokenizer_type=mae \
[tab] slurm/jobs/mihir/berlin/diffusion/coinrun/train_tokenizer_mae copy.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:1
#SBATCH --mem=100GB
#SBATCH --output=/fast/project/HFMI_ProFOUND/mihir/logs/train/tokenizer/mae/%x_%j.log
#SBATCH --error=/fast/project/HFMI_ProFOUND/mihir/logs/train/tokenizer/mae/%x_%j.log
#SBATCH --job-name=coinrun_train_tokenizer_mae

cat $0

source .venv/bin/activate

array_records_dir_train=/fast/project/HFMI_ProFOUND/mihir/data/data_coinrun/coinrun_episodes_50m/train
array_records_dir_val=/fast/project/HFMI_ProFOUND/mihir/data/data_coinrun/coinrun_episodes_50m/val

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/fast/project/HFMI_ProFOUND/mihir/checkpoints/tokenizer/mae/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

srun python jasmine/train_tokenizer.py \
    --save_ckpt \
    --tokenizer_type=mae \
    --ckpt_dir $CHECKPOINT_DIR \
    --name=train_tokenizer_mae_coinrun_$slurm_job_id \
    --tags tokenizer mae coinrun \
    --entity instant-uv \
    --project jafar \
    --data_dir $array_records_dir_train \
    --val_data_dir $array_records_dir_val \
[tab] slurm/jobs/mihir/berlin/diffusion/coinrun/train_tokenizer_mae_no_bottleneck.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:1
#SBATCH --mem=100GB
#SBATCH --output=/fast/project/HFMI_ProFOUND/mihir/logs/train/tokenizer/mae/%x_%j.log
#SBATCH --error=/fast/project/HFMI_ProFOUND/mihir/logs/train/tokenizer/mae/%x_%j.log
#SBATCH --job-name=coinrun_train_tokenizer_mae

cat $0

source .venv/bin/activate

array_records_dir_train=/fast/project/HFMI_ProFOUND/mihir/data/data_coinrun/coinrun_episodes_50m/train
array_records_dir_val=/fast/project/HFMI_ProFOUND/mihir/data/data_coinrun/coinrun_episodes_50m/val

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/fast/project/HFMI_ProFOUND/mihir/checkpoints/tokenizer/mae/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

srun python jasmine/train_tokenizer.py \
    --save_ckpt \
    --tokenizer_type=mae \
    --ckpt_dir $CHECKPOINT_DIR \
    --name=train_tokenizer_mae_coinrun_$slurm_job_id \
    --tags tokenizer mae coinrun \
    --entity instant-uv \
    --project jafar \
    --data_dir $array_records_dir_train \
    --val_data_dir $array_records_dir_val \
[tab] slurm/jobs/mihir/berlin/diffusion/coinrun/train_tokenizer_mae_no_bottleneck copy.sh (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:1
#SBATCH --mem=100GB
#SBATCH --output=/fast/project/HFMI_ProFOUND/mihir/logs/train/tokenizer/mae/%x_%j.log
#SBATCH --error=/fast/project/HFMI_ProFOUND/mihir/logs/train/tokenizer/mae/%x_%j.log
#SBATCH --job-name=coinrun_train_tokenizer_mae

cat $0

source .venv/bin/activate

array_records_dir_train=/fast/project/HFMI_ProFOUND/mihir/data/data_coinrun/coinrun_episodes_50m/train
array_records_dir_val=/fast/project/HFMI_ProFOUND/mihir/data/data_coinrun/coinrun_episodes_50m/val

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=/fast/project/HFMI_ProFOUND/mihir/checkpoints/tokenizer/mae/$job_name/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

srun python jasmine/train_tokenizer.py \
    --save_ckpt \
    --tokenizer_type=mae \
    --latent_dim=512 \
    --ckpt_dir $CHECKPOINT_DIR \
    --name=train_tokenizer_mae_coinrun_$slurm_job_id \
    --tags tokenizer mae coinrun \
    --entity instant-uv \
    --project jafar \
    --data_dir $array_records_dir_train \
    --val_data_dir $array_records_dir_val \
[content] /fast/home/mihir.mahajan/Projects/jasmine/jasmine/genie.py (python)
# Create a mask for the frames to update
frame_mask = jnp.arange(min(window_size, seq_len)) <= frame_i_window
frame_mask = frame_mask.reshape(1, -1, 1, 1)  # Broadcast to BSNL shape

# Update only the frames that should be corrupted
tok_latents_BSNL = jnp.where(
    frame_mask,
    currupted_latents_BSNL,
    tok_latents_BSNL
)
[content] /fast/home/mihir.mahajan/Projects/jasmine/jasmine/genie.py (python)
frame_mask = jnp.arange(min(window_size, seq_len)) <= frame_i_window
frame_mask = frame_mask.reshape(1, -1, 1, 1)  # Broadcast to BSNL shape

# Update only the frames that should be corrupted
tok_latents_BSNL = jnp.where(
    frame_mask,
    currupted_latents_BSNL,
    tok_latents_BSNL
)