p-doom/crowd-code-dataset-0.2 huggingface dataset:
[content] /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py (python)


    def __call__(self, x: jax.Array) -> jax.Array:
        x = self.input_norm1(x)
        x = self.input_dense(x)
        x = self.pos_encoding(x)
        x = self.input_norm2(x)

        for block in self.blocks:
            x = block(x)

        x = self.output_dense
[content] /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py (python)
, dtype=jnp.float32)[:, None]
        div_term = jnp.exp(
            jnp.arange(0, self.d_model, 2) * (-math.log(10000.0) / self.d_model)
        )
        pe = pe.at[:, 0::2].set(jnp.sin(position * div_term))
        p
[content] /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py (python)
"""
        Args:
            x: The input tensor of shape (Batch, Time, Space, Dimension).

        Returns:
            The input tensor with positional encodings added.
        """
        assert x.ndim == 4, f"Input must be 4-dimensional, but got shape {x.shape}"

        num_timesteps = x.shape[1]
        num_spatial_patches =
[tab] slurm/dev/mihir/horeka/yolo-runs/train_dynamics_maskgit_speedrun.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --time=48:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --job-name=train_dyn_yolorun_maskgit

# Log the sbatch script
cat $0

module unload mpi/openmpi/5.0
module unload devel/cuda/12.4
# source .venv/bin/activate

array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=$ws_dir/checkpoints/maskgit-speedrun/tokenizer/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/train_tokenizer_lr_sweep_1e-4

env | grep SLURM

srun python train_dynamics.py \
    --save_ckpt \
    --num_steps=3000 \
    --warmup_steps=0 \
    --wsd_decay_steps=0 \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=1 \
    --init_lr=1e-4 \
    --max_lr=1e-4 \
    --log_image_interval=100 \
    --log \
    --log_checkpoint_interval=100 \
    --name=dynamics-maskgit-speedrun-$slurm_job_id \
    --tags dynamics \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir \
    --dyna_dim=128 \
    --dyna_num_blocks=2 \
    --dyna_num_heads=4
[tab] slurm/dev/mihir/horeka/yolo-runs/train_tokenizer_maskgit_speedrun.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --time=48:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --job-name=train_tokenizer_yolorun_maskgit

# Log the sbatch script
cat $0

module unload mpi/openmpi/5.0
module unload devel/cuda/12.4
# source .venv/bin/activate

array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=$ws_dir/checkpoints/maskgit-speedrun/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/train_tokenizer_lr_sweep_1e-4

env | grep SLURM

srun python train_tokenizer.py \
    --save_ckpt \
    --num_steps=3000 \
    --warmup_steps=0 \
    --wsd_decay_steps=0 \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=1 \
    --init_lr=1e-4 \
    --max_lr=1e-4 \
    --log_image_interval=500 \
    --log \
    --log_checkpoint_interval=500 \
    --name=tokenizer-speedrun-$slurm_job_id \
    --tags tokenizer \
    --entity instant-uv \
    --project jafar \
    --model_dim 384 \
    --latent_dim 32 \
    --num_latents 1024 \
    --patch_size 4 \
    --num_blocks 8 \
    --num_heads 8 \
    --data_dir $array_records_dir
[tab] slurm/dev/mihir/horeka/yolo-runs/train_dynamics_new_arch_speedrun.sbatch (shellscript)
#!/usr/bin/env bash

#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --time=48:00:00
#SBATCH --partition=accelerated
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
#SBATCH --job-name=train_dyn_yolorun_new_arch

# Log the sbatch script
cat $0

module unload mpi/openmpi/5.0
module unload devel/cuda/12.4
# source .venv/bin/activate

array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked

job_name=$SLURM_JOB_NAME
slurm_job_id=$SLURM_JOB_ID

CHECKPOINT_DIR=$ws_dir/checkpoints/$job_name/shift-spatial/$slurm_job_id
mkdir -p $CHECKPOINT_DIR

tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/train_tokenizer_lr_sweep_1e-4

env | grep SLURM

srun python train_dynamics.py \
    --save_ckpt \
    --num_steps=1000 \
    --warmup_steps=0 \
    --wsd_decay_steps=0 \
    --ckpt_dir $CHECKPOINT_DIR \
    --batch_size=1 \
    --init_lr=1e-3 \
    --max_lr=1e-3 \
    --log_image_interval=100 \
    --log \
    --log_checkpoint_interval=100 \
    --name=dynamics-new-arch-speedrun-shift-temporal-$slurm_job_id \
    --tags dynamics \
    --entity instant-uv \
    --project jafar \
    --tokenizer_checkpoint=$tokenizer_ckpt_dir \
    --data_dir $array_records_dir \
    --dyna_dim=128 \
    --dyna_num_blocks=2 \
    --dyna_num_heads=4