[tab] tmp.json (json)
{"messages":[{"content":"You are a helpful assistant that can interact multiple times with a computer shell to solve programming tasks.
Your response must contain exactly ONE bash code block with ONE command (or commands connected with && or ||).

Format your response as shown in <format_example>.

<format_example>
```bash
your_command_here
```
<\/format_example>

Failure to follow these rules will cause your response to be rejected.","role":"system"},{"content":"```bash
cat -n train_lam.py
```
","role":"assistant"},{"content":"<stdout>
     1	from dataclasses import dataclass
     2	import os
     3	import time
     4	
     5	import einops
     6	from flax.training import orbax_utils
     7	from flax.training.train_state import TrainState
     8	from jax.sharding import Mesh, PartitionSpec, NamedSharding
     9	from jax.experimental.mesh_utils import create_device_mesh
    10	import optax
    11	import orbax
    12	from orbax.checkpoint import PyTreeCheckpointer
    13	import numpy as np
    14	import dm_pix as pix
    15	import jax
    16	import jax.numpy as jnp
    17	import tyro
    18	import wandb
    19	
    20	from models.lam import LatentActionModel
    21	from utils.dataloader import get_dataloader
    22	
    23	ts = int(time.time())
    24	
    25	
    26	@dataclass
    27	class Args:
    28	    # Experiment
    29	    num_steps: int = 200_000
    30	    seed: int = 0
    31	    seq_len: int = 16
    32	    image_channels: int = 3
    33	    image_resolution: int = 64
    34	    data_dir: str = \"data_tfrecords\/coinrun\"
    35	    checkpoint: str = \"\"
    36	    # Optimization
    37	    batch_size: int = 36
    38	    vq_beta: float = 0.25
    39	    min_lr: float = 3e-6
    40	    max_lr: float = 3e-5
    41	    warmup_steps: int = 5000
    42	    vq_reset_thresh: int = 50
    43	    # LAM
    44	    model_dim: int = 512
    45	    latent_dim: int = 32
    46	    num_latents: int = 6
    47	    patch_size: int = 16
    48	    num_blocks: int = 8
    49	    num_heads: int = 8
    50	    dropout: float = 0.0
    51	    codebook_dropout: float = 0.0
    52	    # Logging
    53	    log: bool = False
    54	    entity: str = \"\"
    55	    project: str = \"\"
    56	    log_interval: int = 5
    57	    log_image_interval: int = 250
    58	    ckpt_dir: str = \"\"
    59	    log_checkpoint_interval: int = 10000
    60	
    61	
    62	args = tyro.cli(Args)
    63	
    64	
    65	def lam_loss_fn(params, state, inputs):
    66	    # --- Compute loss ---
    67	    outputs = state.apply_fn(
    68	        params, inputs, training=True, rngs={\"dropout\": inputs[\"rng\"]}
    69	    )
    70	    gt_future_frames = inputs[\"videos\"][:, 1:]
    71	    mse = jnp.square(gt_future_frames - outputs[\"recon\"]).mean()
    72	    q_loss = jnp.square(jax.lax.stop_gradient(outputs[\"emb\"]) - outputs[\"z\"]).mean()
    73	    commitment_loss = jnp.square(
    74	        outputs[\"emb\"] - jax.lax.stop_gradient(outputs[\"z\"])
    75	    ).mean()
    76	    loss = mse + q_loss + args.vq_beta * commitment_loss
    77	
    78	    # --- Compute validation metrics ---
    79	    gt = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])
    80	    recon = outputs[\"recon\"].clip(0, 1).reshape(-1, *outputs[\"recon\"].shape[2:])
    81	    psnr = pix.psnr(gt, recon).mean()
    82	    ssim = pix.ssim(gt, recon).mean()
    83	    count_fn = jax.vmap(lambda i: (outputs[\"indices\"] == i).sum())
    84	    index_counts = count_fn(jnp.arange(args.num_latents))
    85	    metrics = dict(
    86	        loss=loss,
    87	        mse=mse,
    88	        q_loss=q_loss,
    89	        commitment_loss=commitment_loss,
    90	        psnr=psnr,
    91	        ssim=ssim,
    92	        codebook_usage=(index_counts != 0).mean(),
    93	    )
    94	    return loss, (outputs[\"recon\"], index_counts, metrics)
    95	
    96	
    97	@jax.jit
    98	def train_step(state, inputs, action_last_active):
    99	    # --- Update model ---
   100	    rng, inputs[\"rng\"] = jax.random.split(inputs[\"rng\"])
   101	    grad_fn = jax.value_and_grad(lam_loss_fn, has_aux=True, allow_int=True)
   102	    (loss, (recon, idx_counts, metrics)), grads = grad_fn(state.params, state, inputs)
   103	    state = state.apply_gradients(grads=grads)
   104	
   105	    # --- Reset inactive latent actions ---
   106	    codebook = state.params[\"params\"][\"vq\"][\"codebook\"]
   107	    num_codes = len(codebook)
   108	    active_codes = idx_counts != 0.0
   109	    action_last_active = jnp.where(active_codes, 0, action_last_active + 1)
   110	    p_code = active_codes \/ active_codes.sum()
   111	    reset_idxs = jax.random.choice(rng, num_codes, shape=(num_codes,), p=p_code)
   112	    do_reset = action_last_active >= args.vq_reset_thresh
   113	    new_codebook = jnp.where(
   114	        jnp.expand_dims(do_reset, -1), codebook[reset_idxs], codebook
   115	    )
   116	    state.params[\"params\"][\"vq\"][\"codebook\"] = new_codebook
   117	    action_last_active = jnp.where(do_reset, 0, action_last_active)
   118	    return state, loss, recon, action_last_active, metrics
   119	
   120	
   121	if __name__ == \"__main__\":
   122	    jax.distributed.initialize()
   123	    num_devices = jax.device_count()
   124	    if num_devices == 0:
   125	        raise ValueError(\"No JAX devices found.\")
   126	    print(f\"Running on {num_devices} devices.\")
   127	
   128	    if args.batch_size % num_devices != 0:
   129	        raise ValueError(
   130	            f\"Global batch size {args.batch_size} must be divisible by \"
   131	            f\"number of devices {num_devices}.\"
   132	        )
   133	
   134	    per_device_batch_size_for_init = args.batch_size \/\/ num_devices
   135	
   136	    rng = jax.random.PRNGKey(args.seed)
   137	    if args.log and jax.process_index() == 0:
   138	        wandb.init(entity=args.entity, project=args.project, group=\"debug\", config=args)
   139	
   140	    # --- Initialize model ---
   141	    lam = LatentActionModel(
   142	        in_dim=args.image_channels,
   143	        model_dim=args.model_dim,
   144	        latent_dim=args.latent_dim,
   145	        num_latents=args.num_latents,
   146	        patch_size=args.patch_size,
   147	        num_blocks=args.num_blocks,
   148	        num_heads=args.num_heads,
   149	        dropout=args.dropout,
   150	        codebook_dropout=args.codebook_dropout,
   151	    )
   152	    # Track when each action was last sampled
   153	    action_last_active = jnp.zeros(args.num_latents)
   154	    image_shape = (args.image_resolution, args.image_resolution, args.image_channels)
   ","role":"user"},{"content":"```bash
\/usr\/bin\/python3 \/ictstr01\/home\/aih\/franz.srambical\/.cursor-server\/extensions\/ms-python.python-2024.12.3-linux-x64\/python_files\/printEnvVariablesToFile.py \/ictstr01\/home\/aih\/franz.srambical\/.cursor-server\/extensions\/ms-python.python-2024.12.3-linux-x64\/python_files\/deactivate\/bash\/envVars.txt
```
","role":"assistant"},{"content":"```bash
cat -n train_lam.py | sed -n '1,11p'
```
","role":"assistant"},{"content":"<stdout>
     1	from dataclasses import dataclass
     2	import os
     3	import time
     4	
     5	import einops
     6	from flax.training import orbax_utils
     7	from flax.training.train_state import TrainState
     8	from jax.sharding import Mesh, PartitionSpec, NamedSharding
     9	from jax.experimental.mesh_utils import create_device_mesh
    10	import optax
    11	import orbax
<\/stdout>","role":"user"},{"content":"```bash
cat -n train_lam.py | sed -n '199,219p'
```
","role":"assistant"},{"content":"<stdout>
   199	        for x in os.listdir(args.data_dir)
   200	        if x.endswith(\".tfrecord\")
   201	    ]
   202	    dataloader = get_dataloader(
   203	        # NOTE: We deliberately pass the global batch size
   204	        # The dataloader shards the dataset across all processes
   205	        tfrecord_files,
   206	        args.seq_len,
   207	        args.batch_size,
   208	        *image_shape,
   209	        seed=args.seed,
   210	    )
   211	    print(f\"Starting training from step {step}...\")
   212	    while step < args.num_steps:
   213	        for videos in dataloader:
   214	            # --- Train step ---
   215	            rng, _rng = jax.random.split(rng)
   216	
   217	            videos_sharding = NamedSharding(
   218	                mesh, PartitionSpec(\"data\", None, None, None, None)
   219	            )
<\/stdout>","role":"user"},{"content":"```bash
cat -n train_dynamics.py
```
","role":"assistant"}]}