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from dataclasses import dataclass, field\nimport os\nimport time\n\nimport einops\nfrom flax.training import orbax_utils\nfrom flax.training.train_state import TrainState\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax\nfrom orbax.checkpoint import PyTreeCheckpointer\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\n\nfrom models.tokenizer import TokenizerVQVAE\nfrom utils.dataloader import get_dataloader\nfrom utils.parameter_utils import count_parameters_by_component\n\nts = int(time.time())\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 300_000\n seed: int = 0\n seq_len: int = 16\n image_channels: int = 3\n image_height: int = 90\n image_width: int = 160\n data_dir: str = "data_tfrecords/coinrun"\n checkpoint: str = ""\n # Optimization\n vq_beta: float = 0.25\n batch_size: int = 48\n min_lr: float = 3e-4\n max_lr: float = 3e-4\n warmup_steps: int = 10000\n # Tokenizer\n model_dim: int = 512\n latent_dim: int = 32\n num_latents: int = 1024\n patch_size: int = 4\n num_blocks: int = 8\n num_heads: int = 8\n dropout: float = 0.0\n codebook_dropout: float = 0.01\n # Logging\n log: bool = False\n entity: str = ""\n project: str = ""\n name: str = "train_tokenizer"\n tags: list[str] = field(default_factory=lambda: ["tokenizer"])\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = ""\n log_checkpoint_interval: int = 10000\n log_gradients: bool = False\n\n\nargs = tyro.cli(Args)\n\n\ndef tokenizer_loss_fn(params, state, inputs):\n # --- Compute loss ---\n outputs = state.apply_fn(\n params,\n inputs,\n training=True,\n rngs={"params": inputs["rng"], "dropout": inputs["dropout_rng"]},\n )\n mse = jnp.square(inputs["videos"] - outputs["recon"]).mean()\n q_loss = jnp.square(jax.lax.stop_gradient(outputs["emb"]) - outputs["z"]).mean()\n commitment_loss = jnp.square(\n outputs["emb"] - jax.lax.stop_gradient(outputs["z"])\n ).mean()\n loss = mse + q_loss + args.vq_beta * commitment_loss\n\n # --- Compute validation metrics ---\n gt = inputs["videos"].clip(0, 1).reshape(-1, *inputs["videos"].shape[2:])\n recon = outputs["recon"].clip(0, 1).reshape(-1, *outputs["recon"].shape[2:])\n psnr = pix.psnr(gt, recon).mean()\n ssim = pix.ssim(gt, recon).mean()\n _, index_counts = jnp.unique_counts(\n jnp.ravel(outputs["indices"]), size=args.num_latents, fill_value=0\n )\n codebook_usage = (index_counts != 0).mean()\n metrics = dict(\n loss=loss,\n mse=mse,\n q_loss=q_loss,\n commitment_loss=commitment_loss,\n psnr=psnr,\n ssim=ssim,\n codebook_usage=codebook_usage,\n )\n return loss, (outputs["recon"], metrics)\n\n\n@jax.jit\ndef train_step(state, inputs):\n grad_fn = jax.value_and_grad(tokenizer_loss_fn, has_aux=True, allow_int=True)\n (loss, (recon, metrics)), grads = grad_fn(state.params, state, inputs)\n state = state.apply_gradients(grads=grads)\n if args.log_gradients:\n metrics["encoder_gradients_std/"] = jax.tree.map(\n lambda x: x.std(), grads["params"]["encoder"]\n )\n metrics["vq_gradients_std/"] = jax.tree.map(\n lambda x: x.std(), grads["params"]["vq"]\n )\n metrics["decoder_gradients_std/"] = jax.tree.map(\n lambda x: x.std(), grads["params"]["decoder"]\n )\n return state, loss, recon, metrics\n\n\nif __name__ == "__main__":\n jax.distributed.initialize()\n num_devices = jax.device_count()\n if num_devices == 0:\n raise ValueError("No JAX devices found.")\n print(f"Running on {num_devices} devices.")\n\n if args.batch_size % num_devices != 0:\n raise ValueError(\n f"Global batch size {args.batch_size} must be divisible by "\n f"number of devices {num_devices}."\n )\n\n per_device_batch_size_for_init = args.batch_size // num_devices\n\n rng = jax.random.PRNGKey(args.seed)\n if args.log and jax.process_index() == 0:\n wandb.init(\n entity=args.entity,\n project=args.project,\n name=args.name,\n tags=args.tags,\n group="debug",\n config=args\n )\n\n # --- Initialize model ---\n tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.model_dim,\n latent_dim=args.latent_dim,\n num_latents=args.num_latents,\n patch_size=args.patch_size,\n num_blocks=args.num_blocks,\n num_heads=args.num_heads,\n dropout=args.dropout,\n codebook_dropout=args.codebook_dropout,\n )\n rng, _rng = jax.random.split(rng)\n image_shape = (args.image_height, args.image_width, args.image_channels)\n inputs = dict(\n videos=jnp.zeros(\n (per_device_batch_size_for_init, args.seq_len, *image_shape),\n dtype=jnp.float32,\n ),\n )\n init_params = tokenizer.init(_rng, inputs)\n\n param_counts = count_parameters_by_component(init_params)\n print("Parameter counts:")\n print(param_counts)\n\n\n\n # --- Initialize optimizer ---\n lr_schedule = optax.warmup_cosine_decay_schedule(\n args.min_lr, args.max_lr, args.warmup_steps, args.num_steps\n )\n tx = optax.adamw(learning_rate=lr_schedule, b1=0.9, b2=0.9, weight_decay=1e-4)\n train_state = TrainState.create(apply_fn=tokenizer.apply, params=init_params, tx=tx)\n\n # FIXME: switch to create_hybrid_device_mesh for runs spanning multiple nodes\n device_mesh_arr = create_device_mesh((num_devices,))\n mesh = Mesh(devices=device_mesh_arr, axis_names=("data",))\n\n replicated_sharding = NamedSharding(mesh, PartitionSpec())\n train_state = jax.device_put(train_state, replicated_sharding)\n\n # --- Load checkpoint ---\n step = 0\n if args.checkpoint:\n restore_target = {"model": train_state}\n restore_args = orbax_utils.restore_args_from_target(restore_target)\n train_state.params["params"].update(\n PyTreeCheckpointer()\n .restore(args.checkpoint, item=restore_target, restore_args=restore_args)[\n "model"\n ]\n .params["params"]\n )\n # Assume checkpoint is of the form tokenizer_<timestamp>_<step>\n step += int(args.checkpoint.split("_")[-1])\n\n # --- TRAIN LOOP ---\n tfrecord_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith(".tfrecord")\n ]\n dataloader = get_dataloader(\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n tfrecord_files,\n args.seq_len,\n args.batch_size,\n *image_shape,\n )\n print(f"Starting training from step {step}...")\n while step < args.num_steps:\n # for videos in dataloader:\n # npy_path = "overfit_dir/single_sample_corner.npy"\n # npy_path = "overfit_dir/single_batch_12_elems.npy"\n npy_path = "/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/overfit_dir/single_sample_corner.npy"\n videos = np.load(npy_path)\n print("batch shape: ", videos.shape)\n while(True):\n # --- Train step ---\n rng, _rng, _rng_dropout = jax.random.split(rng, 3)\n\n videos_sharding = NamedSharding(\n mesh, PartitionSpec("data", None, None, None, None)\n )\n videos = jax.make_array_from_process_local_data(videos_sharding, videos)\n\n inputs = dict(videos=videos, rng=_rng, dropout_rng=_rng_dropout)\n start_time = time.time()\n train_state, loss, recon, metrics = train_step(train_state, inputs)\n jax.block_until_ready(loss)\n elapsed_time = (time.time() - start_time) * 1000\n print(f"Step {step}, loss: {loss}, step time: {elapsed_time}ms")\n step += 1\n\n # --- Logging ---\n if args.log:\n if step % args.log_interval == 0 and jax.process_index() == 0:\n wandb.log(\n {\n "loss": loss,\n "step": step,\n "step_time_ms": elapsed_time,\n **metrics,\n }\n )\n if step % args.log_image_interval == 0:\n gt_seq = inputs["videos"][0]\n recon_seq = recon[0].clip(0, 1)\n comparison_seq = jnp.concatenate((gt_seq, recon_seq), axis=1)\n comparison_seq = einops.rearrange(\n comparison_seq * 255, "t h w c -> h (t w) c"\n )\n # NOTE: Process-dependent control flow deliberately happens\n # after indexing operation since it must not contain code\n # sections that lead to cross-accelerator communication.\n if jax.process_index() == 0:\n log_images = dict(\n image=wandb.Image(np.asarray(gt_seq[0])),\n recon=wandb.Image(np.asarray(recon_seq[0])),\n true_vs_recon=wandb.Image(\n np.asarray(comparison_seq.astype(np.uint8))\n ),\n )\n wandb.log(log_images)\n if step % args.log_checkpoint_interval == 0:\n ckpt = {"model": train_state}\n orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n save_args = orbax_utils.save_args_from_target(ckpt)\n orbax_checkpointer.save(\n os.path.join(os.getcwd(), args.ckpt_dir, f"tokenizer_{ts}_{step}"),\n ckpt,\n save_args=save_args,\n )\n if step >= args.num_steps:\n break\n
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5:23:08 PM [info] Activating crowd-code\n5:23:08 PM [info] Welcome back tum_ind3695. Your user-id is '507ab0ec0dfe0c18ad7778dd15e072f92367194c94623114de802c8ed9c52e20'. Happy coding!\n5:23:08 PM [info] Recording started\n
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]633;E;2025-06-27 17:23:11 gs;dcbf4775-e574-4e9d-b507-67c2737583de]633;COn branch main\r\nYour branch is ahead of 'origin/main' by 1 commit.\r\n (use "git push" to publish your local commits)\r\n\r\nnothing to commit, working tree clean\r\n]0;tum_ind3695@hkn1993:~/projects/jafar_run_overfit/slurm]633;D;0
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/bin/python3 /hkfs/home/project/hk-project-pai00039/tum_ind3695/.cursor-server/extensions/ms-python.python-2025.6.1-linux-x64/python_files/printEnvVariablesToFile.py /hkfs/home/project/hk-project-pai00039/tum_ind3695/.cursor-server/extensions/ms-python.python-2025.6.1-linux-x64/python_files/deactivate/bash/envVars.txt
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]633;E;2025-06-27 17:23:11 /bin/python3 /hkfs/home/project/hk-project-pai00039/tum_ind3695/.cursor-server/extensions/ms-python.python-2025.6.1-linux-x64/python_files/printEnvVariablesToFile.py /hkfs/home/project/hk-project-pai00039/tum_ind3695/.cursor-server/extensions/ms-python.python-2025.6.1-linux-x64/python_files/deactivate/bash/envVars.txt;0b224146-4490-4cce-ac02-48c0ad2ab6c3]633;C]0;tum_ind3695@hkn1993:/hkfs/home/project/hk-project-pai00039/tum_ind3695/.cursor-server/extensions/ms-python.python-2025.6.1-linux-x64/python_files/deactivate/bash]633;D;0
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]633;E;2025-06-27 17:23:14 git push;dcbf4775-e574-4e9d-b507-67c2737583de]633;C
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To github.com:p-doom/slurm.git\r\n [31m! [rejected] [m main -> main (fetch first)\r\n[31merror: failed to push some refs to 'github.com:p-doom/slurm.git'\r\n[m[33mhint: Updates were rejected because the remote contains work that you do not[m\r\n[33mhint: have locally. This is usually caused by another repository pushing to[m\r\n[33mhint: the same ref. If you want to integrate the remote changes, use[m\r\n[33mhint: 'git pull' before pushing again.[m\r\n[33mhint: See the 'Note about fast-forwards' in 'git push --help' for details.[m\r\n]0;tum_ind3695@hkn1993:~/projects/jafar_run_overfit/slurm]633;D;1
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]633;E;2025-06-27 17:23:20 git pull;dcbf4775-e574-4e9d-b507-67c2737583de]633;C
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remote: Enumerating objects: 16, done.[K\r\nremote: Counting objects: 6% (1/16)[K\rremote: Counting objects: 12% (2/16)[K\rremote: Counting objects: 18% (3/16)[K\rremote: Counting objects: 25% (4/16)[K\rremote: Counting objects: 31% (5/16)[K\rremote: Counting objects: 37% (6/16)[K\rremote: Counting objects: 43% (7/16)[K\rremote: Counting objects: 50% (8/16)[K\rremote: Counting objects: 56% (9/16)[K\rremote: Counting objects: 62% (10/16)[K\rremote: Counting objects: 68% (11/16)[K\rremote: Counting objects: 75% (12/16)[K\rremote: Counting objects: 81% (13/16)[K\rremote: Counting objects: 87% (14/16)[K\rremote: Counting objects: 93% (15/16)[K\rremote: Counting objects: 100% (16/16)[K\rremote: Counting objects: 100% (16/16), done.[K\r\nremote: Compressing objects: 25% (1/4)[K\rremote: Compressing objects: 50% (2/4)[K\rremote: Compressing objects: 75% (3/4)[K\rremote: Compressing objects: 100% (4/4)[K\rremote: Compressing objects: 100% (4/4), done.[K\r\nremote: Total 12 (delta 7), reused 12 (delta 7), pack-reused 0 (from 0)[K\r\nUnpacking objects: 8% (1/12)\rUnpacking objects: 16% (2/12)\rUnpacking objects: 25% (3/12)\rUnpacking objects: 33% (4/12)\rUnpacking objects: 41% (5/12)\rUnpacking objects: 50% (6/12)\rUnpacking objects: 58% (7/12)\rUnpacking objects: 66% (8/12)\rUnpacking objects: 75% (9/12)\rUnpacking objects: 83% (10/12)\rUnpacking objects: 91% (11/12)\rUnpacking objects: 100% (12/12)\rUnpacking objects: 100% (12/12), 1.70 KiB | 30.00 KiB/s, done.\r\nFrom github.com:p-doom/slurm\r\n dbca399..06dec29 main -> origin/main\r\n[33mhint: You have divergent branches and need to specify how to reconcile them.[m\r\n[33mhint: You can do so by running one of the following commands sometime before[m\r\n[33mhint: your next pull:[m\r\n[33mhint: [m\r\n[33mhint: git config pull.rebase false # merge[m\r\n[33mhint: git config pull.rebase true # rebase[m\r\n[33mhint: git config pull.ff only # fast-forward only[m\r\n[33mhint: [m\r\n[33mhint: You can replace "git config" with "git config --global" to set a default[m\r\n[33mhint: preference for all repositories. You can also pass --rebase, --no-rebase,[m\r\n[33mhint: or --ff-only on the command line to override the configured default per[m\r\n[33mhint: invocation.[m\r\nfatal: Need to specify how to reconcile divergent branches.\r\n]0;tum_ind3695@hkn1993:~/projects/jafar_run_overfit/slurm]633;D;128
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git config pull.rebase false
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]633;E;2025-06-27 17:23:26 git config pull.rebase false ;dcbf4775-e574-4e9d-b507-67c2737583de]633;C]0;tum_ind3695@hkn1993:~/projects/jafar_run_overfit/slurm]633;D;0
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]633;E;2025-06-27 17:23:28 git pull;dcbf4775-e574-4e9d-b507-67c2737583de]633;C
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hint: Waiting for your editor to close the file... [?1049h[22;0;0t[>4;2m[?1h=[?2004h[?1004h[1;24r[?12h[?12l[22;2t[22;1t[27m[23m[29m[m[H[2J[?25l[24;1H"~/projects/jafar_run_overfit/slurm/.git/MERGE_MSG" 6L, 273B[2;1H▽[6n[2;1H [3;1HPzz\[0%m[6n[3;1H [1;1H[>c]10;?]11;?[1;1H[38;5;130mMerge branch 'main' of github.com:p-doom/slurm[m\r\n[34m# Please enter a commit message to explain why this merge is necessary,[m[2;72H[K[3;1H[34m# especially if it merges an updated upstream into a topic branch.[m[3;67H[K[4;1H[34m#\r\n# Lines starting with '#' will be ignored, and an empty message aborts\r\n# the commit.[m\r\n[94m~ [8;1H~ [9;1H~ [10;1H~ [11;1H~ [12;1H~ [13;1H~ [14;1H~ [15;1H~ [16;1H~ [17;1H~ [18;1H~ [19;1H~ [20;1H~ [21;1H~ [22;1H~ [23;1H~ [m[24;158H1,1[11CAll[1;1H[?25hP+q436f\P+q6b75\P+q6b64\P+q6b72\P+q6b6c\P+q2332\P+q2334\P+q2569\P+q2a37\P+q6b31\[?12$p[?25l[24;148H/[1;1H[24;149H3[1;1H[24;150H3[1;1H[24;151H3[1;1H[24;152H3[1;1H[24;153H/[1;1H[24;154H3[1;1H[24;155H3[1;1H[24;156H3[1;1H[24;157H3[1;1H[24;148H [1;1H[?25h[?25l[24;148H/[1;1H[24;149Hf[1;1H[24;150Ha[1;1H[24;151Hf[1;1H[24;152Ha[1;1H[24;153H/[1;1H[24;154Hf[1;1H[24;155Ha[1;1H[24;156Hf[1;1H[24;157Ha[1;1H[24;148H [1;1H[?25h
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