diff --git a/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-043437fb-fde6-4180-b927-bb5cfebf6b0e1759058799627-2025_09_28-13.27.24.771/source.csv b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-043437fb-fde6-4180-b927-bb5cfebf6b0e1759058799627-2025_09_28-13.27.24.771/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..5743c1e260911f0b4ec021cd7d7a7850e641c671 --- /dev/null +++ b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-043437fb-fde6-4180-b927-bb5cfebf6b0e1759058799627-2025_09_28-13.27.24.771/source.csv @@ -0,0 +1,6192 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +2,334,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"1:27:24 PM [info] Activating crowd-code\n1:27:24 PM [info] Recording started\n1:27:24 PM [info] Initializing git provider using file system watchers...\n",Log,tab +3,412,"extension-output-pdoom-org.crowd-code-#1-crowd-code",150,0,"1:27:25 PM [info] Git repository found\n1:27:25 PM [info] Git provider initialized successfully\n1:27:25 PM [info] Initial git state: [object Object]\n",Log,content +4,152506,"TERMINAL",0,0,"git branch",,terminal_command +5,152573,"TERMINAL",0,0,"]633;C[?1h=\r",,terminal_output +6,153100,"TERMINAL",0,0," action-mapper\r\n add-noise-to-combat-exposure-bias\r\n add-wandb-name-and-tags\r\n before-nnx\r\n causal-mem-reduce\r\n causal-spatiotemporal-kv-cache\r\n causal-st-transformer\r\n causal-transformer-dynamics-model\r\n causal-transformer-nnx-no-kv-cache\r\n coinrun-gt-actions\r\n convert-to-jax-array-in-iter\r\n correct-batched-sampling\r\n dev\r\n dont-let-tf-see-gpu\r\n feat/darkness-filter\r\n feat/explicit-image-dims\r\n fix-action-padding-lam-future-information-access\r\n fix-sampling\r\n fix-transformer-forwardpass\r\n fix/dyn-restore-after-nnx-upgrade\r\n fix/spatiotemporal-pe-once-in-STTransformer\r\n generate-minatar-breakout-dataset\r\n grad-norm-log-and-clip\r\n grain-dataloader\r\n gt-actions\r\n hotfix/eval-full-frame-fix\r\n hotfix/fix-val-loss-maskgit-masking\r\n hotfix/full-frame-eval-only-calculate-last-frame-metrics\r\n hotfix/sampling-shapes-error\r\n input_pipeline/add-npy2array_record\r\n logging-variants\r\n lr-schedules\r\n main\r\n maskgit-different-maskprob-per-sample\r\n maskgit-sampling-iterative-unmasking-fix\r\n:",,terminal_output +7,154537,"TERMINAL",0,0,"\r metrics-logging-for-dynamics-model\r\n:",,terminal_output +8,155111,"TERMINAL",0,0,"\r monkey-patch\r\n:\r new-arch-sampling\r\n:\r preprocess_video\r\n:\r refactor-full-frame-val-loss\r\n:\r refactor-tmp\r\n:",,terminal_output +9,155173,"TERMINAL",0,0,"\r remove-restore-branching\r\n:",,terminal_output +10,155526,"TERMINAL",0,0,"\r revised-dataloader\r\n:",,terminal_output +11,155707,"TERMINAL",0,0,"\r runner\r\n:",,terminal_output +12,155849,"TERMINAL",0,0,"\r runner-grain\r\n:",,terminal_output +13,156095,"TERMINAL",0,0,"\r sample-ali-branch\r\n:",,terminal_output +14,156190,"TERMINAL",0,0,"\r sample-from-different-topologies\r\n:",,terminal_output +15,156402,"TERMINAL",0,0,"\r sampling-script-add-metrics\r\n:",,terminal_output +16,156588,"TERMINAL",0,0,"\r sampling-startframe-indexing-fix\r\n:",,terminal_output +17,157549,"TERMINAL",0,0,"\rM dont-let-tf-see-gpu\r\n\r:",,terminal_output +18,158763,"TERMINAL",0,0,"\rM dev\r\n\r:\rM correct-batched-sampling\r\n\r:\rM convert-to-jax-array-in-iter\r\n\r:\rM coinrun-gt-actions\r\n\r:\rM causal-transformer-nnx-no-kv-cache\r\n\r:\rM causal-transformer-dynamics-model\r\n\r:\rM causal-st-transformer\r\n\r:\rM causal-spatiotemporal-kv-cache\r\n\r:\rM causal-mem-reduce\r\n\r:\rM before-nnx\r\n\r:\rM add-wandb-name-and-tags\r\n\r:\rM add-noise-to-combat-exposure-bias\r\n\r:\rM action-mapper\r\n\r:\r\r:\r\r:\r\r:\r\r:\r\r:\r\r:\r\r:\r\r:\r\r:\r\r:\r\r:\r\r:\r\r:",,terminal_output +19,168386,"TERMINAL",0,0,"\r\r:",,terminal_output +20,169178,"TERMINAL",0,0,"\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jasmine",,terminal_output +21,172190,"TERMINAL",0,0,"git diff",,terminal_command +22,172248,"TERMINAL",0,0,"]633;C[?1h=\r",,terminal_output +23,172338,"TERMINAL",0,0,"\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jasmine",,terminal_output +24,174928,"TERMINAL",0,0,"git checkout add-noise-to-combat-exposure-bias",,terminal_command +25,174968,"TERMINAL",0,0,"]633;C",,terminal_output +26,175274,"TERMINAL",0,0,"Switched to branch 'add-noise-to-combat-exposure-bias'\r\nYour branch is ahead of 'origin/add-noise-to-combat-exposure-bias' by 1 commit.\r\n (use ""git push"" to publish your local commits)\r\n]0;tum_cte0515@hkn1993:~/Projects/jasmine",,terminal_output +27,175299,"",0,0,"Switched from branch 'zloss-runs' to 'add-noise-to-combat-exposure-bias'",,git_branch_checkout +28,180632,"TERMINAL",0,0,"git log",,terminal_command +29,180686,"TERMINAL",0,0,"]633;C",,terminal_output +30,180817,"TERMINAL",0,0,"[?1h=\rcommit 28c431527970770c7a4c95d76f8919a8f4041418 (HEAD -> add-noise-to-combat-exposure-bias)\r\nAuthor: Mihir Mahajan \r\nDate: Sat Sep 27 19:13:23 2025 +0200\r\n\r\n max noise to 1 in sampling logic\r\n\r\ncommit fa9afacf62a0974e93a32bfeb9b120f4fce42993 (origin/add-noise-to-combat-exposure-bias)\r\nAuthor: Mihir Mahajan \r\nDate: Thu Sep 25 16:25:36 2025 +0200\r\n\r\n fix noise augmentation logic\r\n\r\ncommit 69d3b80a13d11d464da81a745f04da3cbb2c5585\r\nAuthor: Mihir Mahajan \r\nDate: Thu Sep 25 11:08:55 2025 +0200\r\n\r\n added noise level to sample.py\r\n\r\ncommit 82cb201ccccac9d9e90c160b948196128d5f6ec8\r\nMerge: 3d8ba61 8993b1d\r\nAuthor: Mihir Mahajan \r\nDate: Thu Sep 25 11:02:45 2025 +0200\r\n\r\n Merge branch 'main' into add-noise-to-combat-exposure-bias\r\n\r\ncommit 3d8ba61c33be90eab3955303e9e4472d6c44b78b\r\nAuthor: Mihir Mahajan \r\nDate: Wed Sep 24 18:39:12 2025 +0200\r\n\r\n first attempt to implementing noise level feature\r\n\r\ncommit 8993b1d39267b826933c22e1911d50f2702dd634 (main)\r\nAuthor: Franz Srambical <79149449+emergenz@users.noreply.github.com>\r\nDate: Wed Sep 24 17:04:15 2025 +0200\r\n\r\n:",,terminal_output +31,182188,"TERMINAL",0,0,"\r\r:",,terminal_output +32,182488,"TERMINAL",0,0,"\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jasmine",,terminal_output +33,184138,"TERMINAL",0,0,"git push",,terminal_command +34,184201,"TERMINAL",0,0,"]633;C",,terminal_output +35,185629,"TERMINAL",0,0,"Enumerating objects: 7, done.\r\nCounting objects: 14% (1/7)\rCounting objects: 28% (2/7)\rCounting objects: 42% (3/7)\rCounting objects: 57% (4/7)\rCounting objects: 71% (5/7)\rCounting objects: 85% (6/7)\rCounting objects: 100% (7/7)\rCounting objects: 100% (7/7), done.\r\nDelta compression using up to 152 threads\r\nCompressing objects: 25% (1/4)\rCompressing objects: 50% (2/4)\rCompressing objects: 75% (3/4)\rCompressing objects: 100% (4/4)\rCompressing objects: 100% (4/4), done.\r\nWriting objects: 25% (1/4)\rWriting objects: 50% (2/4)\rWriting objects: 75% (3/4)\rWriting objects: 100% (4/4)\rWriting objects: 100% (4/4), 374 bytes | 374.00 KiB/s, done.\r\nTotal 4 (delta 3), reused 0 (delta 0), pack-reused 0\r\n",,terminal_output +36,185736,"TERMINAL",0,0,"remote: Resolving deltas: 0% (0/3)\rremote: Resolving deltas: 33% (1/3)\rremote: Resolving deltas: 66% (2/3)\rremote: Resolving deltas: 100% (3/3)\rremote: Resolving deltas: 100% (3/3), completed with 3 local objects.\r\n",,terminal_output +37,186264,"TERMINAL",0,0,"To github.com:p-doom/jasmine.git\r\n fa9afac..28c4315 add-noise-to-combat-exposure-bias -> add-noise-to-combat-exposure-bias\r\n",,terminal_output +38,186279,"TERMINAL",0,0,"]0;tum_cte0515@hkn1993:~/Projects/jasmine",,terminal_output +39,191827,"TERMINAL",0,0,"--time=01:00:00 --partition=accelerated --nodes=1 --gres=gpu:1 --cpus-per-task=8",,terminal_command +40,191850,"TERMINAL",0,0,"]633;C]0;tum_cte0515@hkn1993:~/Projects/jasmine",,terminal_output +41,202738,"jasmine/models/dynamics.py",0,0,"from typing import Dict\n\nimport jax\nimport jax.numpy as jnp\nimport flax.nnx as nnx\n\nfrom utils.nn import STTransformer, Transformer\n\n\nclass DynamicsMaskGIT(nnx.Module):\n """"""\n MaskGIT dynamics model\n\n Dimension keys:\n B: batch size\n T: sequence length\n N: number of patches per frame\n L: latent dimension\n V: vocabulary size (number of latents)\n """"""\n\n def __init__(\n self,\n model_dim: int,\n ffn_dim: int,\n num_latents: int,\n latent_action_dim: int,\n num_blocks: int,\n num_heads: int,\n dropout: float,\n max_noise_level: float,\n noise_buckets: int,\n mask_limit: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n rngs: nnx.Rngs,\n ):\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.num_latents = num_latents\n self.latent_action_dim = latent_action_dim\n self.num_blocks = num_blocks\n self.num_heads = num_heads\n self.dropout = dropout\n self.max_noise_level = max_noise_level\n self.noise_buckets = noise_buckets\n self.mask_limit = mask_limit\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n\n self.transformer = STTransformer(\n self.model_dim,\n self.model_dim,\n self.ffn_dim,\n self.num_latents,\n self.num_blocks,\n self.num_heads,\n self.dropout,\n self.param_dtype,\n self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)\n self.mask_token = nnx.Param(\n nnx.initializers.lecun_uniform()(rngs.params(), (1, 1, 1, self.model_dim))\n )\n self.action_up = nnx.Linear(\n self.latent_action_dim,\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.noise_level_embed = nnx.Embed(\n self.noise_buckets, self.model_dim, rngs=rngs\n )\n\n def __call__(\n self,\n batch: Dict[str, jax.Array],\n ) -> tuple[jax.Array, jax.Array]:\n # --- Mask videos ---\n video_tokens_BTN = batch[""video_tokens""]\n latent_actions_BTm11L = batch[""latent_actions""]\n vid_embed_BTNM = self.patch_embed(video_tokens_BTN)\n\n B, T, N, M = vid_embed_BTNM.shape\n rng, _rng_prob, *_rngs_mask = jax.random.split(batch[""mask_rng""], B + 2)\n mask_prob = jax.random.uniform(_rng_prob, shape=(B,), minval=self.mask_limit)\n per_sample_shape = vid_embed_BTNM.shape[1:-1]\n mask = jax.vmap(\n lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),\n in_axes=(0, 0),\n )(jnp.asarray(_rngs_mask), mask_prob)\n mask = mask.at[:, 0].set(False)\n vid_embed_BTNM = jnp.where(\n jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM\n )\n\n # --- Sample noise ---\n rng, _rng_noise = jax.random.split(rng)\n noise_level_B = jax.random.uniform(\n _rng_noise, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B / self.max_noise_level) * self.noise_buckets\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n vid_embed_BTNM = (\n jnp.sqrt(1 - noise_level_B111) * vid_embed_BTNM\n + jnp.sqrt(noise_level_B111) * noise_BTNM\n )\n\n # --- Predict transition ---\n act_embed_BTm11M = self.action_up(latent_actions_BTm11L)\n padded_act_embed_BT1M = jnp.pad(\n act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))\n )\n vid_embed_BTNp2M = jnp.concatenate(\n [padded_act_embed_BT1M, noise_level_embed_BT1M, vid_embed_BTNM], axis=2\n )\n logits_BTNp2V = self.transformer(vid_embed_BTNp2M)\n logits_BTNV = logits_BTNp2V[:, :, 2:]\n return logits_BTNV, mask\n\n\nclass DynamicsCausal(nnx.Module):\n """"""Causal dynamics model""""""\n\n def __init__(\n self,\n model_dim: int,\n ffn_dim: int,\n num_latents: int,\n latent_action_dim: int,\n num_blocks: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n decode: bool,\n rngs: nnx.Rngs,\n ):\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.num_latents = num_latents\n self.latent_action_dim = latent_action_dim\n self.num_blocks = num_blocks\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.decode = decode\n\n self.transformer = Transformer(\n self.model_dim,\n self.model_dim,\n self.ffn_dim,\n self.num_latents,\n self.num_blocks,\n self.num_heads,\n self.dropout,\n self.param_dtype,\n self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=self.decode,\n rngs=rngs,\n )\n self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)\n self.action_up = nnx.Linear(\n self.latent_action_dim,\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n def __call__(\n self,\n batch: Dict[str, jax.Array],\n ) -> tuple[jax.Array, jax.Array]:\n video_tokens_BTN = batch[""video_tokens""]\n latent_actions_BTm11L = batch[""latent_actions""]\n vid_embed_BTNM = self.patch_embed(video_tokens_BTN)\n act_embed_BTm11M = self.action_up(latent_actions_BTm11L)\n padded_act_embed_BT1M = jnp.pad(\n act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))\n )\n vid_embed_BTNp1M = jnp.concatenate(\n [padded_act_embed_BT1M, vid_embed_BTNM], axis=2\n )\n logits_BTNp1V = self.transformer(vid_embed_BTNp1M)\n logits_BTNV = logits_BTNp1V[:, :, :-1]\n return logits_BTNV, jnp.ones_like(video_tokens_BTN)\n",python,tab +42,205211,"jasmine/models/dynamics.py",0,0,"",python,tab +43,211182,"jasmine/models/dynamics.py",2273,0,"",python,selection_mouse +44,211184,"jasmine/models/dynamics.py",2272,0,"",python,selection_command +45,211969,"jasmine/models/dynamics.py",0,0,"",python,selection_command +46,212233,"jasmine/models/dynamics.py",0,23,"from typing import Dict",python,selection_command +47,212544,"jasmine/models/dynamics.py",0,6768,"from typing import Dict\n\nimport jax\nimport jax.numpy as jnp\nimport flax.nnx as nnx\n\nfrom utils.nn import STTransformer, Transformer\n\n\nclass DynamicsMaskGIT(nnx.Module):\n """"""\n MaskGIT dynamics model\n\n Dimension keys:\n B: batch size\n T: sequence length\n N: number of patches per frame\n L: latent dimension\n V: vocabulary size (number of latents)\n """"""\n\n def __init__(\n self,\n model_dim: int,\n ffn_dim: int,\n num_latents: int,\n latent_action_dim: int,\n num_blocks: int,\n num_heads: int,\n dropout: float,\n max_noise_level: float,\n noise_buckets: int,\n mask_limit: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n rngs: nnx.Rngs,\n ):\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.num_latents = num_latents\n self.latent_action_dim = latent_action_dim\n self.num_blocks = num_blocks\n self.num_heads = num_heads\n self.dropout = dropout\n self.max_noise_level = max_noise_level\n self.noise_buckets = noise_buckets\n self.mask_limit = mask_limit\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n\n self.transformer = STTransformer(\n self.model_dim,\n self.model_dim,\n self.ffn_dim,\n self.num_latents,\n self.num_blocks,\n self.num_heads,\n self.dropout,\n self.param_dtype,\n self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)\n self.mask_token = nnx.Param(\n nnx.initializers.lecun_uniform()(rngs.params(), (1, 1, 1, self.model_dim))\n )\n self.action_up = nnx.Linear(\n self.latent_action_dim,\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.noise_level_embed = nnx.Embed(\n self.noise_buckets, self.model_dim, rngs=rngs\n )\n\n def __call__(\n self,\n batch: Dict[str, jax.Array],\n ) -> tuple[jax.Array, jax.Array]:\n # --- Mask videos ---\n video_tokens_BTN = batch[""video_tokens""]\n latent_actions_BTm11L = batch[""latent_actions""]\n vid_embed_BTNM = self.patch_embed(video_tokens_BTN)\n\n B, T, N, M = vid_embed_BTNM.shape\n rng, _rng_prob, *_rngs_mask = jax.random.split(batch[""mask_rng""], B + 2)\n mask_prob = jax.random.uniform(_rng_prob, shape=(B,), minval=self.mask_limit)\n per_sample_shape = vid_embed_BTNM.shape[1:-1]\n mask = jax.vmap(\n lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),\n in_axes=(0, 0),\n )(jnp.asarray(_rngs_mask), mask_prob)\n mask = mask.at[:, 0].set(False)\n vid_embed_BTNM = jnp.where(\n jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM\n )\n\n # --- Sample noise ---\n rng, _rng_noise = jax.random.split(rng)\n noise_level_B = jax.random.uniform(\n _rng_noise, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B / self.max_noise_level) * self.noise_buckets\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n vid_embed_BTNM = (\n jnp.sqrt(1 - noise_level_B111) * vid_embed_BTNM\n + jnp.sqrt(noise_level_B111) * noise_BTNM\n )\n\n # --- Predict transition ---\n act_embed_BTm11M = self.action_up(latent_actions_BTm11L)\n padded_act_embed_BT1M = jnp.pad(\n act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))\n )\n vid_embed_BTNp2M = jnp.concatenate(\n [padded_act_embed_BT1M, noise_level_embed_BT1M, vid_embed_BTNM], axis=2\n )\n logits_BTNp2V = self.transformer(vid_embed_BTNp2M)\n logits_BTNV = logits_BTNp2V[:, :, 2:]\n return logits_BTNV, mask\n\n\nclass DynamicsCausal(nnx.Module):\n """"""Causal dynamics model""""""\n\n def __init__(\n self,\n model_dim: int,\n ffn_dim: int,\n num_latents: int,\n latent_action_dim: int,\n num_blocks: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n decode: bool,\n rngs: nnx.Rngs,\n ):\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.num_latents = num_latents\n self.latent_action_dim = latent_action_dim\n self.num_blocks = num_blocks\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.decode = decode\n\n self.transformer = Transformer(\n self.model_dim,\n self.model_dim,\n self.ffn_dim,\n self.num_latents,\n self.num_blocks,\n self.num_heads,\n self.dropout,\n self.param_dtype,\n self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=self.decode,\n rngs=rngs,\n )\n self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)\n self.action_up = nnx.Linear(\n self.latent_action_dim,\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n def __call__(\n self,\n batch: Dict[str, jax.Array],\n ) -> tuple[jax.Array, jax.Array]:\n video_tokens_BTN = batch[""video_tokens""]\n latent_actions_BTm11L = batch[""latent_actions""]\n vid_embed_BTNM = self.patch_embed(video_tokens_BTN)\n act_embed_BTm11M = self.action_up(latent_actions_BTm11L)\n padded_act_embed_BT1M = jnp.pad(\n act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))\n )\n vid_embed_BTNp1M = jnp.concatenate(\n [padded_act_embed_BT1M, vid_embed_BTNM], axis=2\n )\n logits_BTNp1V = self.transformer(vid_embed_BTNp1M)\n logits_BTNV = logits_BTNp1V[:, :, :-1]\n return logits_BTNV, jnp.ones_like(video_tokens_BTN)\n",python,selection_command +48,547761,"jasmine/models/dynamics.py",6707,0,"",python,selection_mouse +49,547795,"jasmine/models/dynamics.py",6706,0,"",python,selection_command +50,551560,"jasmine/models/dynamics.py",1324,0,"",python,selection_mouse 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jnp.sqrt(noise_level_B111) * noise_BTNM\n )\n",python,selection_mouse +750,1113649,"jasmine/models/dynamics.py",4125,120," jnp.sqrt(1 - noise_level_B111) * vid_embed_BTNM\n + jnp.sqrt(noise_level_B111) * noise_BTNM\n )\n",python,selection_mouse +751,1113702,"jasmine/models/dynamics.py",4124,121," jnp.sqrt(1 - noise_level_B111) * vid_embed_BTNM\n + jnp.sqrt(noise_level_B111) * noise_BTNM\n )\n",python,selection_mouse +752,1113766,"jasmine/models/dynamics.py",4123,122," jnp.sqrt(1 - noise_level_B111) * vid_embed_BTNM\n + jnp.sqrt(noise_level_B111) * noise_BTNM\n )\n",python,selection_mouse +753,1113767,"jasmine/models/dynamics.py",4096,149," vid_embed_BTNM = (\n jnp.sqrt(1 - noise_level_B111) * vid_embed_BTNM\n + jnp.sqrt(noise_level_B111) * noise_BTNM\n )\n",python,selection_mouse +754,1113783,"jasmine/models/dynamics.py",4095,150," vid_embed_BTNM = (\n jnp.sqrt(1 - noise_level_B111) * vid_embed_BTNM\n + jnp.sqrt(noise_level_B111) * noise_BTNM\n )\n",python,selection_mouse 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.venv/bin/activate\n\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/train\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/val\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\n# lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3512576\n# tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3512502\n\nlam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3518963\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3518963\n\nenv | grep SLURM\n\nexport PYTHONUNBUFFERED=1\n\nsrun python jasmine/train_dynamics.py \\n --save_ckpt \\n --image_height=10 \\n --image_width=10 \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=120 \\n --init_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=250 \\n --log_checkpoint_interval=250 \\n --dyna_type=maskgit \\n --log \\n --name=breakout-dyn-noise-lvl-default-$slurm_job_id \\n --tags dyn breakout noise-lvl default \\n --entity instant-uv \\n --project jafar \\n --patch_size 4 \\n --lam_patch_size 4 \\n --warmup_steps 100 \\n --wsd_decay_steps 1000 \\n --num_steps 5000 \\n --data_dir $array_records_dir_train \\n --val_data_dir $array_records_dir_val \\n --tokenizer_checkpoint $tokenizer_checkpoint \\n --lam_checkpoint $lam_checkpoint \\n --val_interval 500 \\n --eval_full_frame \\n",shellscript,tab +928,1194171,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",2426,0,"",shellscript,selection_command 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+957,1252567,"TERMINAL",0,0,"\r\n[?2004l\r]0;tum_cte0515@hkn0702:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0702 jasmine]$ ",,terminal_output +958,1257984,"TERMINAL",0,0,"\r(jasmine) [tum_cte0515@hkn0702 jasmine]$ \r(jasmine) [tum_cte0515@hkn0702 jasmine]$ \r(jasmine) [tum_cte0515@hkn0702 jasmine]$ \r(jasmine) [tum_cte0515@hkn0702 jasmine]$ \r(jasmine) [tum_cte0515@hkn0702 jasmine]$ \r(jasmine) [tum_cte0515@hkn0702 jasmine]$ \r(jasmine) [tum_cte0515@hkn0702 jasmine]$ \r(jasmine) [tum_cte0515@hkn0702 jasmine]$ \r(jasmine) [tum_cte0515@hkn0702 jasmine]$ ",,terminal_output +959,1259458,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",0,0,"",shellscript,tab +960,1259473,"TERMINAL",0,0,"\r(jasmine) [tum_cte0515@hkn0702 jasmine]$ \r(jasmine) [tum_cte0515@hkn0702 jasmine]$ ",,terminal_output +961,1263253,"TERMINAL",0,0,"s",,terminal_output +962,1263476,"TERMINAL",0,0,"h",,terminal_output +963,1263662,"TERMINAL",0,0," ",,terminal_output +964,1263860,"TERMINAL",0,0,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",,terminal_output +965,1264062,"TERMINAL",0,0,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=1\r\n#SBATCH --time=48:00:00\r\n#SBATCH --partition=accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\r\n#SBATCH --job-name=train_dyn_default_breakout_longer\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\n# source .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/train\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/val\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\n# lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3512576\r\n# tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3512502\r\n\r\nlam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3518963\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3518963\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python jasmine/train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=10 \\r\n --image_width=10 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=120 \\r\n --init_lr=0 \\r\n --max_lr=3e-5 \\r\n --log_image_interval=250 \\r\n --log_checkpoint_interval=250 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=breakout-dyn-noise-lvl-default-$slurm_job_id \\r\n --tags dyn breakout noise-lvl default \\r\n --entity instant-uv \\r\n --project jafar \\r\n --patch_size 4 \\r\n --lam_patch_size 4 \\r\n --warmup_steps 100 \\r\n --wsd_decay_steps 1000 \\r\n --num_steps 5000 \\r\n --data_dir $array_records_dir_train \\r\n --val_data_dir $array_records_dir_val \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --lam_checkpoint $lam_checkpoint \\r\n --val_interval 500 \\r\n --eval_full_frame \\r\n",,terminal_output +966,1264341,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=3602395\r\nSLURM_JOB_GPUS=0\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\r\nSLURMD_NODENAME=hkn0702\r\nSLURM_JOB_START_TIME=1759060062\r\nSLURM_STEP_NODELIST=hkn0702\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1759070862\r\nSLURM_PMI2_SRUN_PORT=38045\r\nSLURM_CPUS_ON_NODE=8\r\nSLURM_JOB_CPUS_PER_NODE=8\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=accelerated\r\nSLURM_TRES_PER_TASK=cpu=8\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=1\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3527669\r\nSLURM_PTY_PORT=34957\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.201\r\nSLURM_PTY_WIN_ROW=36\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e8.hkn0702\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.201\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=156\r\nSLURM_NODELIST=hkn0702\r\nSLURM_SRUN_COMM_PORT=35753\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1993.localdomain\r\nSLURM_JOB_ID=3527669\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0702\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_STEP_LAUNCHER_PORT=35753\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0702\r\n",,terminal_output +967,1264646,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +968,1266633,"jasmine/models/dynamics.py",0,0,"",python,tab +969,1277673,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +970,1283809,"TERMINAL",0,0,"Counting all components: ['dynamics', 'lam', 'tokenizer']\r\n",,terminal_output +971,1284206,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +972,1285249,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.22.0\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20250928_134848-3q3izg97\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run breakout-dyn-noise-lvl-default-3527669\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/3q3izg97\r\n",,terminal_output +973,1285642,"TERMINAL",0,0,"Parameter counts:\r\n{'dynamics': 26561024, 'lam': 16900976, 'tokenizer': 33750256, 'total': 77212256}\r\n",,terminal_output +974,1289920,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +975,1353197,"jasmine/models/dynamics.py",0,0,"",python,tab +976,1363676,"TERMINAL",0,0,"Total memory size: 2.7 GB, Output size: 0.9 GB, Temp size: 1.9 GB, Argument size: 0.9 GB, Host temp size: 0.0 GB.\r\nFLOPs: 4.797e+11, Bytes: 5.190e+10 (48.3 GB), Intensity: 9.2 FLOPs/byte\r\nStarting training from step 0...\r\n",,terminal_output +977,1364179,"TERMINAL",0,0,"\r\nMemstats: After params initialized:\r\n\tUsing (GB) 1.1 / 38.7 (2.842377%) on cuda:0\r\n",,terminal_output +978,1364361,"jasmine/models/dynamics.py",4172,0,"",python,selection_mouse +979,1364513,"jasmine/models/dynamics.py",4172,1,"0",python,selection_mouse +980,1364688,"jasmine/models/dynamics.py",4170,3,"0.0",python,selection_mouse +981,1364734,"jasmine/models/dynamics.py",4169,4,"=0.0",python,selection_mouse +982,1364790,"jasmine/models/dynamics.py",4164,9,"a_min=0.0",python,selection_mouse +983,1365419,"jasmine/models/dynamics.py",4165,0,"",python,selection_mouse +984,1365550,"jasmine/models/dynamics.py",4164,5,"a_min",python,selection_mouse +985,1365798,"jasmine/models/dynamics.py",4164,6,"a_min=",python,selection_mouse +986,1365863,"jasmine/models/dynamics.py",4164,7,"a_min=0",python,selection_mouse +987,1365980,"jasmine/models/dynamics.py",4164,8,"a_min=0.",python,selection_mouse +988,1366495,"jasmine/models/dynamics.py",4172,0,"",python,selection_mouse +989,1366496,"jasmine/models/dynamics.py",4172,1,"0",python,selection_mouse +990,1366691,"jasmine/models/dynamics.py",4171,2,".0",python,selection_mouse +991,1366713,"jasmine/models/dynamics.py",4170,3,"0.0",python,selection_mouse +992,1366734,"jasmine/models/dynamics.py",4169,4,"=0.0",python,selection_mouse +993,1366772,"jasmine/models/dynamics.py",4164,9,"a_min=0.0",python,selection_mouse +994,1367158,"jasmine/models/dynamics.py",4166,0,"",python,selection_mouse +995,1372687,"jasmine/models/dynamics.py",4154,0,"",python,selection_mouse +996,1372869,"jasmine/models/dynamics.py",4146,16,"noise_level_B111",python,selection_mouse +997,1373745,"jasmine/models/dynamics.py",4276,0,"",python,selection_mouse +998,1373890,"jasmine/models/dynamics.py",4265,16,"noise_level_B111",python,selection_mouse +999,1384307,"jasmine/models/dynamics.py",3648,0,"",python,selection_mouse +1000,1384415,"jasmine/models/dynamics.py",3635,18,"noise_bucket_idx_B",python,selection_mouse +1001,1430982,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_50k.sh",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=06:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\n#SBATCH --job-name=train_dyn_default_breakout_longer\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\n# source .venv/bin/activate\n\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/train\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/val\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\n# lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3512576\n# tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3512502\n\nlam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3518963\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3518963\n\nenv | grep SLURM\n\nsrun python jasmine/train_dynamics.py \\n --save_ckpt \\n --image_height=10 \\n --image_width=10 \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=120 \\n --init_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --dyna_type=maskgit \\n --log \\n --name=breakout-dyn-50ksteps-3e-5-noise-lvl-$slurm_job_id \\n --tags dyn breakout 50ksteps 3e-5 noise-lvl \\n --entity instant-uv \\n --project jafar \\n --patch_size 4 \\n --lam_patch_size 4 \\n --warmup_steps 1000 \\n --wsd_decay_steps 10000 \\n --num_steps 50000 \\n --data_dir $array_records_dir_train \\n --val_data_dir $array_records_dir_val \\n --tokenizer_checkpoint $tokenizer_checkpoint \\n --lam_checkpoint $lam_checkpoint \\n --val_interval 1000 \\n --eval_full_frame \\n",shellscript,tab +1002,1432505,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_gt_actions_50k.sh",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=06:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\n#SBATCH --job-name=train_dyn_default_gt_actions_breakout_longer\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\n# source .venv/bin/activate\n\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect_big/train\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect_big/val\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn-gt-actions/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3518963\n\nenv | grep SLURM\n\nsrun python jasmine/train_dynamics.py \\n --save_ckpt \\n --image_height=10 \\n --image_width=10 \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=120 \\n --init_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=250 \\n --log_checkpoint_interval=250 \\n --dyna_type=maskgit \\n --log \\n --name=breakout-dyn-50ksteps-noise-lvl-gt-actions-lr-3e-5-$slurm_job_id \\n --tags dyn breakout 50ksteps noise-lvl 3e-5 \\n --entity instant-uv \\n --project jafar \\n --patch_size 4 \\n --lam_patch_size 4 \\n --warmup_steps 100 \\n --wsd_decay_steps 1000 \\n --num_steps 50000 \\n --use_gt_actions \\n --data_dir $array_records_dir_train \\n --val_data_dir $array_records_dir_val \\n --tokenizer_checkpoint $tokenizer_checkpoint \\n --val_interval 750 \\n --eval_full_frame \\n",shellscript,tab +1003,1433550,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_gt_actions_smaller_lr_50k.sh",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=06:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\n#SBATCH --job-name=train_dyn_default_gt_actions_breakout_longer_smaller_lr\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\n# source .venv/bin/activate\n\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect_big/train\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect_big/val\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn-gt-actions/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3518963\n\nenv | grep SLURM\n\nsrun python jasmine/train_dynamics.py \\n --save_ckpt \\n --image_height=10 \\n --image_width=10 \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=120 \\n --init_lr=0 \\n --max_lr=1e-5 \\n --log_image_interval=250 \\n --log_checkpoint_interval=250 \\n --dyna_type=maskgit \\n --log \\n --name=breakout-dyn-50ksteps-noise-lvl-gt-actions-lr-1e-5-$slurm_job_id \\n --tags dyn breakout 50ksteps noise-lvl 1e-5 \\n --entity instant-uv \\n --project jafar \\n --patch_size 4 \\n --lam_patch_size 4 \\n --warmup_steps 100 \\n --wsd_decay_steps 1000 \\n --num_steps 50000 \\n --use_gt_actions \\n --data_dir $array_records_dir_train \\n --val_data_dir $array_records_dir_val \\n --tokenizer_checkpoint $tokenizer_checkpoint \\n --val_interval 750 \\n --eval_full_frame \\n",shellscript,tab +1004,1434461,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_gt_actions.sh",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\n#SBATCH --job-name=train_dyn_default_breakout_longer\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\n# source .venv/bin/activate\n\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect_big/train\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect_big/val\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn-gt-actions/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3518963\n\nenv | grep SLURM\n\nexport PYTHONUNBUFFERED=1\n\nsrun python jasmine/train_dynamics.py \\n --save_ckpt \\n --image_height=10 \\n --image_width=10 \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=120 \\n --init_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --dyna_type=maskgit \\n --log \\n --name=breakout-dyn-default-noise-lvl-gt-actions-$slurm_job_id \\n --tags dyn breakout default noise-lvl \\n --entity instant-uv \\n --project jafar \\n --patch_size 4 \\n --lam_patch_size 4 \\n --warmup_steps 100 \\n --wsd_decay_steps 1000 \\n --num_steps 5000 \\n --use_gt_actions \\n --data_dir $array_records_dir_train \\n --val_data_dir $array_records_dir_val \\n --tokenizer_checkpoint $tokenizer_checkpoint \\n --val_interval 1000 \\n --eval_full_frame \\n",shellscript,tab +1005,1436849,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_gt_actions.sh",85,0,"",shellscript,selection_mouse +1006,1437426,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_gt_actions.sh",84,0,"",shellscript,selection_mouse +1007,1438415,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_gt_actions.sh",83,1,"",shellscript,content +1008,1438517,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_gt_actions.sh",82,1,"",shellscript,content +1009,1439260,"TERMINAL",0,0,"WARNING:absl:[process=0][thread=MainThread][operation_id=1] _SignalingThread.join() waiting for signals ([, ]) blocking the main thread will slow down blocking save times. This is likely due to main thread calling result() on a CommitFuture.\r\nWARNING:absl:[process=0][thread=MainThread][operation_id=1] _SignalingThread.join() waiting for signals ([, ]) blocking the main thread will slow down blocking save times. This is likely due to main thread calling result() on a CommitFuture.\r\n",,terminal_output +1010,1439873,"TERMINAL",0,0,"Saved checkpoint at step 250\r\n",,terminal_output +1011,1441373,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_gt_actions.sh",82,0,"0",shellscript,content +1012,1441374,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_gt_actions.sh",83,0,"",shellscript,selection_keyboard +1013,1441433,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_gt_actions.sh",83,0,"6",shellscript,content +1014,1441434,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_gt_actions.sh",84,0,"",shellscript,selection_keyboard +1015,1443650,"TERMINAL",0,0,"^Csrun: interrupt (one more within 1 sec to abort)\r\nsrun: StepId=3527669.0 task 0: running\r\n",,terminal_output +1016,1443883,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3527669.0\r\nsrun: forcing job termination\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-6:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-4:\r\nProcess SpawnProcess-7:\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\nKeyboardInterrupt\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\nKeyboardInterrupt\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\nKeyboardInterrupt\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\nslurmstepd: error: *** STEP 3527669.0 ON hkn0702 CANCELLED AT 2025-09-28T13:51:28 ***\r\n",,terminal_output +1017,1444073,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3527669.0\r\nsrun: job abort in progress\r\n",,terminal_output +1018,1444239,"TERMINAL",0,0,"]0;tum_cte0515@hkn0702:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0702 jasmine]$ ",,terminal_output +1019,1460767,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_50k.sh",0,0,"",shellscript,tab +1020,1462285,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_gt_actions_50k.sh",0,0,"",shellscript,tab +1021,1463229,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_gt_actions_smaller_lr_50k.sh",0,0,"",shellscript,tab +1022,1463770,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_gt_actions.sh",0,0,"",shellscript,tab +1023,1465087,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",0,0,"",shellscript,tab +1024,1466594,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",84,0,"",shellscript,selection_mouse +1025,1467503,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",83,1,"",shellscript,content +1026,1467594,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",82,1,"",shellscript,content +1027,1468168,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",82,0,"0",shellscript,content +1028,1468169,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",83,0,"",shellscript,selection_keyboard +1029,1468543,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",83,0,"6",shellscript,content +1030,1468544,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",84,0,"",shellscript,selection_keyboard +1031,1470697,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_smaller_lr_50k.sh",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=06:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\n#SBATCH --job-name=train_dyn_default_breakout_longer_smaller_lr\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\n# source .venv/bin/activate\n\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/train\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/val\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\n# lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3512576\n# tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3512502\n\nlam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3518963\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3518963\n\nenv | grep SLURM\n\nsrun python jasmine/train_dynamics.py \\n --save_ckpt \\n --image_height=10 \\n --image_width=10 \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=120 \\n --init_lr=0 \\n --max_lr=1e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --dyna_type=maskgit \\n --log \\n --name=breakout-dyn-50ksteps-noise-lvl-$slurm_job_id \\n --tags dyn breakout 50ksteps noise-lvl 1e-5 \\n --entity instant-uv \\n --project jafar \\n --patch_size 4 \\n --lam_patch_size 4 \\n --warmup_steps 1000 \\n --wsd_decay_steps 10000 \\n --num_steps 50000 \\n --data_dir $array_records_dir_train \\n --val_data_dir $array_records_dir_val \\n --tokenizer_checkpoint $tokenizer_checkpoint \\n --lam_checkpoint $lam_checkpoint \\n --val_interval 1000 \\n --eval_full_frame \\n",shellscript,tab +1032,1474613,"TERMINAL",0,0,"s",,terminal_output +1033,1474790,"TERMINAL",0,0,"y",,terminal_output +1034,1474843,"TERMINAL",0,0,"n",,terminal_output +1035,1475035,"TERMINAL",0,0,"c",,terminal_output 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1hkn08073527676 accelerat train_dy tum_cte0 R\t0:01\t 1hkn08073527669 accelerat interact tum_cte0 R\t4:37\t 1hkn0702",,terminal_output +1084,1495389,"TERMINAL",0,0,"202222228",,terminal_output +1085,1496481,"TERMINAL",0,0,"13333339",,terminal_output +1086,1497408,"TERMINAL",0,0,"244444440",,terminal_output +1087,1497779,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn0702:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0702 jasmine]$ ",,terminal_output +1088,1505553,"TERMINAL",0,0,"s",,terminal_output +1089,1505749,"TERMINAL",0,0,"c",,terminal_output +1090,1505866,"TERMINAL",0,0,"a",,terminal_output +1091,1506026,"TERMINAL",0,0,"c",,terminal_output +1092,1506372,"TERMINAL",0,0,"",,terminal_output +1093,1506578,"TERMINAL",0,0,"n",,terminal_output +1094,1506683,"TERMINAL",0,0,"c",,terminal_output +1095,1506745,"TERMINAL",0,0,"e",,terminal_output +1096,1506808,"TERMINAL",0,0,"l",,terminal_output +1097,1506976,"TERMINAL",0,0," ",,terminal_output 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accelerat train_dy tum_cte0 R\t0:28\t 1hkn05343527675 accelerat train_dy tum_cte0 R\t0:28\t 1hkn08073527676 accelerat train_dy tum_cte0 R\t0:28\t 1hkn08073527669 accelerat interact tum_cte0 R\t5:04\t 1hkn0702",,terminal_output +1109,1523465,"TERMINAL",0,0,"73030303030306",,terminal_output +1110,1523543,"TERMINAL",0,0,"Every 1.0s: squeu... hkn0702.localdomain: Sun Sep 28 13:52:48 2025JOBID PARTITION NAME USER ST\tTIME NODESNODELIST(REASON)3527671 accelerat train_dy tum_cte0 R\t0:30\t 1hkn07023527672 accelerat train_dy tum_cte0 R\t0:30\t 1hkn07023527673 accelerat train_dy tum_cte0 R\t0:30\t 1hkn07023527674 accelerat train_dy tum_cte0 R\t0:30\t 1hkn05343527675 accelerat train_dy tum_cte0 R\t0:30\t 1hkn08073527676 accelerat train_dy tum_cte0 R\t0:30\t 1hkn08073527669 accelerat interact tum_cte0 R\t5:06\t 1hkn0702Every 1.0s: squeue --mehkn0702.localdomain: Sun Sep 28 13:52:48 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3527671 accelerat train_dy tum_cte0 R\t0:30\t 1 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this hook\r\n\r\nreformatted jasmine/models/dynamics.py\r\n\r\nAll done! ✨ 🍰 ✨\r\n1 file reformatted.\r\n\r\n]0;tum_cte0515@hkn0702:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0702 jasmine]$ ",,terminal_output +1206,1555542,"TERMINAL",0,0,"git commit -am ""made noise augmentation safer""",,terminal_output +1207,1555762,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +1208,1556489,"TERMINAL",0,0,"black....................................................................",,terminal_output +1209,1556602,"TERMINAL",0,0,"Passed\r\n",,terminal_output +1210,1557010,"TERMINAL",0,0,"[add-noise-to-combat-exposure-bias 57dee33] made noise augmentation safer\r\n 1 file changed, 11 insertions(+), 7 deletions(-)\r\n]0;tum_cte0515@hkn0702:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0702 jasmine]$ ",,terminal_output +1211,1559277,"jasmine/models/dynamics.py",0,0,"",python,tab +1212,1559394,"jasmine/models/dynamics.py",3455,607," # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n",python,content +1213,1563699,"TERMINAL",0,0,"\r(jasmine) [tum_cte0515@hkn0702 jasmine]$ ",,terminal_output +1214,1565249,"jasmine/models/dynamics.py",0,0,"",python,tab +1215,1580171,"jasmine/models/dynamics.py",4679,0,"",python,selection_mouse +1216,1580279,"jasmine/models/dynamics.py",4666,22,"noise_level_embed_BT1M",python,selection_mouse +1217,1619264,"jasmine/models/dynamics.py",3765,0,"",python,selection_command +1218,1625454,"jasmine/models/dynamics.py",3347,0,"",python,selection_mouse 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+1298,3554437,"TERMINAL",0,0,"c",,terminal_output +1299,3554619,"TERMINAL",0,0,"le",,terminal_output +1300,3554822,"TERMINAL",0,0,"a",,terminal_output +1301,3555059,"TERMINAL",0,0,"r\r\n[?2004l\r]0;tum_cte0515@hkn0702:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0702 jasmine]$ ",,terminal_output +1302,3711861,"jasmine/genie.py",0,0,"from typing import Dict\n\nimport einops\nimport jax\nimport jax.numpy as jnp\nimport flax.nnx as nnx\nimport orbax.checkpoint as ocp\n\nfrom models.dynamics import DynamicsMaskGIT, DynamicsCausal\nfrom models.lam import LatentActionModel\nfrom models.tokenizer import TokenizerVQVAE\n\n\nclass Genie(nnx.Module):\n """"""Genie model""""""\n\n def __init__(\n self,\n in_dim: int,\n tokenizer_dim: int,\n tokenizer_ffn_dim: int,\n latent_patch_dim: int,\n num_patch_latents: int,\n patch_size: int,\n tokenizer_num_blocks: int,\n tokenizer_num_heads: int,\n lam_dim: int,\n lam_ffn_dim: int,\n latent_action_dim: int,\n num_actions: int,\n lam_patch_size: int,\n lam_num_blocks: int,\n lam_num_heads: int,\n lam_co_train: bool,\n use_gt_actions: bool,\n dyna_type: str,\n dyna_dim: int,\n dyna_ffn_dim: int,\n dyna_num_blocks: int,\n dyna_num_heads: int,\n max_noise_level: float,\n noise_buckets: int,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n decode: bool,\n rngs: nnx.Rngs,\n dropout: float = 0.0,\n mask_limit: float = 0.0,\n ):\n # --- Tokenizer ---\n self.in_dim = in_dim\n self.tokenizer_dim = tokenizer_dim\n self.tokenizer_ffn_dim = tokenizer_ffn_dim\n self.latent_patch_dim = latent_patch_dim\n self.num_patch_latents = num_patch_latents\n self.patch_size = patch_size\n self.tokenizer_num_blocks = tokenizer_num_blocks\n self.tokenizer_num_heads = tokenizer_num_heads\n # --- LAM ---\n self.lam_dim = lam_dim\n self.lam_ffn_dim = lam_ffn_dim\n self.latent_action_dim = latent_action_dim\n self.num_actions = num_actions\n self.lam_patch_size = lam_patch_size\n self.lam_num_blocks = lam_num_blocks\n self.lam_num_heads = lam_num_heads\n self.lam_co_train = lam_co_train\n self.use_gt_actions = use_gt_actions\n # --- Dynamics ---\n self.dyna_type = dyna_type\n self.dyna_dim = dyna_dim\n self.dyna_ffn_dim = dyna_ffn_dim\n self.dyna_num_blocks = dyna_num_blocks\n self.dyna_num_heads = dyna_num_heads\n self.max_noise_level = max_noise_level\n self.noise_buckets = noise_buckets\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.dropout = dropout\n self.mask_limit = mask_limit\n self.decode = decode\n\n self.tokenizer = TokenizerVQVAE(\n in_dim=self.in_dim,\n model_dim=self.tokenizer_dim,\n ffn_dim=self.tokenizer_ffn_dim,\n latent_dim=self.latent_patch_dim,\n num_latents=self.num_patch_latents,\n patch_size=self.patch_size,\n num_blocks=self.tokenizer_num_blocks,\n num_heads=self.tokenizer_num_heads,\n dropout=0.0,\n codebook_dropout=0.0,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n if self.use_gt_actions:\n self.action_embed = nnx.Embed(\n self.num_actions, self.latent_action_dim, rngs=rngs\n )\n self.lam = None\n else:\n self.lam = LatentActionModel(\n in_dim=self.in_dim,\n model_dim=self.lam_dim,\n ffn_dim=self.lam_ffn_dim,\n latent_dim=self.latent_patch_dim,\n num_latents=self.num_actions,\n patch_size=self.lam_patch_size,\n num_blocks=self.lam_num_blocks,\n num_heads=self.lam_num_heads,\n dropout=0.0,\n codebook_dropout=0.0,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n self.action_embed = None\n if self.dyna_type == ""maskgit"":\n self.dynamics = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n mask_limit=self.mask_limit,\n max_noise_level=self.max_noise_level,\n noise_buckets=self.noise_buckets,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n elif self.dyna_type == ""causal"":\n self.dynamics = DynamicsCausal(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n max_noise_level=self.max_noise_level,\n noise_buckets=self.noise_buckets,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=decode,\n rngs=rngs,\n )\n else:\n raise ValueError(f""Invalid dynamics type: {self.dyna_type}"")\n\n def __call__(\n self,\n batch: Dict[str, jax.Array],\n ) -> Dict[str, jax.Array]:\n videos_BTHWC = batch[""videos""]\n tokenizer_outputs = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_indices_BTN = tokenizer_outputs[""indices""]\n latent_actions_BTm11L = None\n action_embeddings_BTm11L = None\n if self.use_gt_actions:\n assert self.action_embed is not None\n action_indices_E = None\n action_embeddings_BT1L = self.action_embed(batch[""actions""]).reshape(\n *batch[""actions""].shape[:2], 1, self.latent_action_dim\n )\n action_embeddings_BTm11L = action_embeddings_BT1L[:, :-1]\n else:\n assert self.lam is not None\n lam_outputs = self.lam.vq_encode(videos_BTHWC, training=False)\n z_q_BTm11L = lam_outputs[""z_q""]\n action_indices_E = lam_outputs[""indices""]\n latent_actions_BTm11L = jax.lax.cond(\n self.lam_co_train,\n lambda: z_q_BTm11L,\n lambda: jax.lax.stop_gradient(z_q_BTm11L),\n )\n outputs = dict(\n video_tokens=jax.lax.stop_gradient(token_indices_BTN),\n latent_actions=(\n action_embeddings_BTm11L\n if self.use_gt_actions\n else latent_actions_BTm11L\n ),\n )\n outputs[""mask_rng""] = batch[""rng""]\n dyna_logits_BTNV, dyna_mask = self.dynamics(outputs)\n outputs[""token_logits""] = dyna_logits_BTNV\n outputs[""mask""] = dyna_mask\n mle_indices_BTN = jnp.argmax(outputs[""token_logits""], axis=-1)\n H, W = batch[""videos""].shape[2:4]\n outputs[""recon""] = self.tokenizer.decode(mle_indices_BTN, (H, W))\n if action_indices_E is not None:\n outputs[""lam_indices""] = action_indices_E\n return outputs\n\n def sample(\n self,\n batch: Dict[str, jax.Array],\n seq_len: int,\n temperature: float = 1,\n sample_argmax: bool = False,\n maskgit_steps: int = 25,\n ) -> tuple[jax.Array, jax.Array]:\n if self.dyna_type == ""maskgit"":\n return self.sample_maskgit(\n batch, seq_len, 0.0, maskgit_steps, temperature, sample_argmax\n )\n elif self.dyna_type == ""causal"":\n return self.sample_causal(batch, seq_len, temperature, sample_argmax)\n else:\n raise ValueError(f""Dynamics model type unknown: {self.dyna_type}"")\n\n def sample_maskgit(\n self,\n batch: Dict[str, jax.Array],\n seq_len: int,\n noise_level: float,\n steps: int = 25,\n temperature: float = 1,\n sample_argmax: bool = False,\n ) -> tuple[jax.Array, jax.Array]:\n """"""\n Autoregressively samples up to `seq_len` future frames, following Figure 8 of the paper.\n\n - Input frames are tokenized once.\n - Future frames are generated autoregressively in token space.\n - All frames are detokenized in a single pass.\n\n Note:\n - For interactive or step-wise sampling, detokenization should occur after each action.\n - To maintain consistent tensor shapes across timesteps, all current and future frames are decoded at every step.\n - Temporal causal structure is preserved by\n a) reapplying the mask before each decoding step.\n b) a temporal causal mask is applied within each ST-transformer block.\n\n Dimension keys:\n B: batch size\n T: number of input (conditioning) frames\n N: number of patches per frame\n M: model dimension\n S: sequence length\n H: height\n W: width\n E: B * (S - 1)\n P: S * N\n """"""\n assert isinstance(self.dynamics, DynamicsMaskGIT)\n assert (\n noise_level < self.max_noise_level\n ), ""Noise level must me smaller than max_noise_level.""\n # --- Encode videos and actions ---\n videos_BTHWC = batch[""videos""]\n tokenizer_out = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_idxs_BTN = tokenizer_out[""indices""]\n B, T, N = token_idxs_BTN.shape\n pad_shape = (B, seq_len - T, N)\n pad = jnp.zeros(pad_shape, dtype=token_idxs_BTN.dtype)\n token_idxs_BSN = jnp.concatenate([token_idxs_BTN, pad], axis=1)\n init_logits_BSNV = jnp.zeros(\n shape=(*token_idxs_BSN.shape, self.num_patch_latents)\n )\n noise_level = jnp.array(noise_level)\n if self.use_gt_actions:\n assert self.action_embed is not None\n latent_actions_BT1L = self.action_embed(batch[""actions""]).reshape(\n *batch[""actions""].shape[:2], 1, self.latent_action_dim\n )\n latent_actions_BTm11L = latent_actions_BT1L[:, :-1]\n action_tokens_EL = latent_actions_BTm11L.reshape(-1, self.latent_action_dim)\n else:\n assert self.lam is not None\n latent_actions_E = batch[""latent_actions""]\n action_tokens_EL = self.lam.vq.get_codes(latent_actions_E)\n\n # --- Extract submodule state ---\n dynamics_state = nnx.state(self.dynamics)\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def maskgit_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array],\n step: jax.Array,\n ) -> tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array]:\n rng, token_idxs_BSN, logits_BSNV, mask_BSN, action_tokens_EL = carry\n S, N = token_idxs_BSN.shape[1:]\n L = action_tokens_EL.shape[-1]\n\n # We need to reconstruct the submodule inside scan body to prevent trace context mismatches\n dynamics_maskgit = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n max_noise_level=self.max_noise_level,\n noise_buckets=self.noise_buckets,\n mask_limit=self.mask_limit,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=nnx.Rngs(0),\n )\n nnx.update(dynamics_maskgit, dynamics_state)\n\n # --- Construct + encode video ---\n vid_embed_BSNM = dynamics_maskgit.patch_embed(token_idxs_BSN)\n mask_token_111M = dynamics_maskgit.mask_token.value\n mask_expanded_BSN1 = mask_BSN[..., None]\n vid_embed_BSNM = jnp.where(\n mask_expanded_BSN1, mask_token_111M, vid_embed_BSNM\n )\n\n # --- Predict transition ---\n action_tokens_BSm1L = jnp.reshape(action_tokens_EL, (B, S - 1, L))\n act_embed_BSm1M = dynamics_maskgit.action_up(action_tokens_BSm1L)\n act_embed_BSM = jnp.pad(act_embed_BSm1M, ((0, 0), (1, 0), (0, 0)))\n act_embed_BS1M = jnp.reshape(\n act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1])\n )\n # TODO mihir\n\n rng, _rng_noise = jax.random.split(rng)\n noise_level_111 = noise_level.reshape(1, 1, 1)\n noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\n noise_bucket_idx_B11 = jnp.floor(\n (noise_level_B11 / self.max_noise_level) * self.noise_buckets\n ).astype(jnp.int32)\n noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(\n noise_bucket_idx_B11\n )\n noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\n vid_embed_BSNM += jnp.expand_dims(noise_level_B11, -1)\n\n vid_embed_BSNp2M = jnp.concatenate(\n [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\n )\n unmasked_ratio = jnp.cos(jnp.pi * (step + 1) / (steps * 2))\n step_temp = temperature * (1.0 - unmasked_ratio)\n final_logits_BSNp2V = (\n dynamics_maskgit.transformer(vid_embed_BSNp2M) / step_temp\n )\n final_logits_BSNV = final_logits_BSNp2V[:, :, 2:]\n\n # --- Sample new tokens for final frame ---\n if sample_argmax:\n sampled_token_idxs_BSN = jnp.argmax(final_logits_BSNV, axis=-1)\n else:\n rng, _rng = jax.random.split(rng)\n sampled_token_idxs_BSN = jax.random.categorical(_rng, final_logits_BSNV)\n gather_fn = jax.vmap(jax.vmap(jax.vmap(lambda x, y: x[y])))\n final_token_probs_BSN = gather_fn(\n jax.nn.softmax(final_logits_BSNV), sampled_token_idxs_BSN\n )\n final_token_probs_BSN += ~mask_BSN\n # Update masked tokens and logits only\n token_idxs_BSN = jnp.where(mask_BSN, sampled_token_idxs_BSN, token_idxs_BSN)\n logits_BSNV = jnp.where(\n jnp.expand_dims(mask_BSN, -1), final_logits_BSNV, logits_BSNV\n )\n\n # --- Update mask ---\n num_unmasked_tokens = jnp.round(N * (1.0 - unmasked_ratio)).astype(int)\n final_token_probs_flat_BP = einops.rearrange(\n final_token_probs_BSN, ""b s n -> b (s n)""\n )\n idx_mask_P = (\n jnp.arange(final_token_probs_flat_BP.shape[-1])\n <= N - num_unmasked_tokens\n )\n sorted_idxs_BP = jnp.argsort(final_token_probs_flat_BP, axis=-1)\n mask_update_fn = jax.vmap(lambda msk, ids: msk.at[ids].set(idx_mask_P))\n mask_flat_BP = einops.rearrange(mask_BSN, ""b s n -> b (s n)"")\n new_mask_flat_BP = mask_update_fn(mask_flat_BP, sorted_idxs_BP)\n new_mask_BSN = einops.rearrange(new_mask_flat_BP, ""b (s n) -> b s n"", n=N)\n\n new_carry = (\n rng,\n token_idxs_BSN,\n logits_BSNV,\n new_mask_BSN,\n action_tokens_EL,\n )\n return new_carry\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def generation_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array], step_t: jax.Array\n ) -> tuple[jax.Array, jax.Array, jax.Array]:\n rng, current_token_idxs_BSN, current_logits_BSNV = carry\n rng, step_rng = jax.random.split(rng)\n\n # Mask current frame (i.e., t == step_t)\n mask_S = jnp.arange(seq_len) == step_t\n mask_BSN = jnp.broadcast_to(mask_S[None, :, None], (B, seq_len, N)).astype(\n bool\n )\n masked_token_idxs_BSN = current_token_idxs_BSN * ~mask_BSN\n masked_logits_BSNV = current_logits_BSNV * jnp.expand_dims(~mask_BSN, -1)\n\n # --- Initialize and run MaskGIT loop ---\n init_carry_maskgit = (\n step_rng,\n masked_token_idxs_BSN,\n masked_logits_BSNV,\n mask_BSN,\n action_tokens_EL,\n )\n final_carry_maskgit = maskgit_step_fn(init_carry_maskgit, jnp.arange(steps))\n updated_token_idxs_BSN = final_carry_maskgit[1]\n updated_logits_BSNV = final_carry_maskgit[2]\n new_carry = (rng, updated_token_idxs_BSN, updated_logits_BSNV)\n return new_carry\n\n # --- Run the autoregressive generation using jax.lax.scan ---\n initial_carry = (batch[""rng""], token_idxs_BSN, init_logits_BSNV)\n timesteps_to_scan = jnp.arange(T, seq_len)\n final_carry = generation_step_fn(initial_carry, timesteps_to_scan)\n final_token_idxs_BSN = final_carry[1]\n final_logits_BSNV = final_carry[2]\n\n # --- Decode all tokens at once at the end ---\n H, W = batch[""videos""].shape[2:4]\n final_frames_BSHWC = self.tokenizer.decode(\n final_token_idxs_BSN,\n video_hw=(H, W),\n )\n return final_frames_BSHWC, final_logits_BSNV\n\n def sample_causal(\n self,\n batch: Dict[str, jax.Array],\n seq_len: int,\n temperature: float = 1,\n sample_argmax: bool = False,\n ) -> tuple[jax.Array, jax.Array]:\n """"""\n Autoregressively samples up to `seq_len` future frames, following Figure 8 of the paper.\n\n - Input frames are tokenized once.\n - Future frames are generated autoregressively in token space.\n - All frames are detokenized in a single pass.\n\n Note:\n - For interactive or step-wise sampling, detokenization should occur after each action.\n - To maintain consistent tensor shapes across timesteps, all current and future frames are decoded at every step.\n - Temporal causal structure is preserved by\n a) reapplying the mask before each decoding step.\n b) a temporal causal mask is applied within each ST-transformer block.\n\n Dimension keys:\n B: batch size\n T: number of input (conditioning) frames\n N: number of patches per frame\n M: model dimension\n S: sequence length\n H: height\n W: width\n E: B * (S - 1)\n """"""\n assert isinstance(self.dynamics, DynamicsCausal)\n # --- Encode videos and actions ---\n videos_BTHWC = batch[""videos""]\n tokenizer_out = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_idxs_BTN = tokenizer_out[""indices""]\n B, T, N = token_idxs_BTN.shape\n pad_shape = (B, seq_len - T, N)\n pad = jnp.zeros(pad_shape, dtype=token_idxs_BTN.dtype)\n token_idxs_BSN = jnp.concatenate([token_idxs_BTN, pad], axis=1)\n logits_BSNV = jnp.zeros((*token_idxs_BSN.shape, self.num_patch_latents))\n dynamics_state = nnx.state(self.dynamics)\n\n if self.use_gt_actions:\n assert self.action_embed is not None\n latent_actions_BT1L = self.action_embed(batch[""actions""]).reshape(\n *batch[""actions""].shape[:2], 1, self.latent_action_dim\n )\n latent_actions_BTm11L = latent_actions_BT1L[:, :-1]\n action_tokens_EL = latent_actions_BTm11L.reshape(-1, self.latent_action_dim)\n else:\n assert self.lam is not None\n latent_actions_E = batch[""latent_actions""]\n action_tokens_EL = self.lam.vq.get_codes(latent_actions_E)\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def causal_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array],\n step_n: jax.Array,\n ) -> tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array]:\n rng, token_idxs_BSN, logits_BSNV, action_tokens_EL, step_t = carry\n S, N = token_idxs_BSN.shape[1:]\n L = action_tokens_EL.shape[-1]\n\n # We need to reconstruct the submodule inside scan body to prevent trace context mismatches\n dynamics_causal = DynamicsCausal(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=self.decode,\n rngs=nnx.Rngs(0),\n )\n nnx.update(dynamics_causal, dynamics_state)\n\n # --- Construct + encode video ---\n vid_embed_BSNM = dynamics_causal.patch_embed(token_idxs_BSN)\n\n # --- Predict transition ---\n action_tokens_BSm1L = jnp.reshape(action_tokens_EL, (B, S - 1, L))\n act_embed_BSm1M = dynamics_causal.action_up(action_tokens_BSm1L)\n act_embed_BSM = jnp.pad(act_embed_BSm1M, ((0, 0), (1, 0), (0, 0)))\n act_embed_BS1M = jnp.reshape(\n act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1])\n )\n vid_embed_BSNp1M = jnp.concatenate([act_embed_BS1M, vid_embed_BSNM], axis=2)\n final_logits_BTNp1V = (\n dynamics_causal.transformer(vid_embed_BSNp1M, (step_t, step_n))\n / temperature\n )\n final_logits_BV = final_logits_BTNp1V[:, step_t, step_n, :]\n\n # --- Sample new tokens for final frame ---\n if sample_argmax:\n sampled_token_idxs_B = jnp.argmax(final_logits_BV, axis=-1)\n else:\n rng, _rng = jax.random.split(rng)\n sampled_token_idxs_B = jax.random.categorical(_rng, final_logits_BV)\n # Update next tokens only\n token_idxs_BSN = token_idxs_BSN.at[:, step_t, step_n].set(\n sampled_token_idxs_B\n )\n logits_BSNV = logits_BSNV.at[:, step_t, step_n].set(final_logits_BV)\n\n new_carry = (rng, token_idxs_BSN, logits_BSNV, action_tokens_EL, step_t)\n return new_carry\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def generation_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array], step_t: jax.Array\n ) -> tuple[jax.Array, jax.Array, jax.Array]:\n rng, current_token_idxs_BSN, current_logits_BSNV = carry\n rng, step_rng = jax.random.split(rng)\n\n # --- Initialize and run causal loop ---\n init_carry_causal = (\n step_rng,\n current_token_idxs_BSN,\n current_logits_BSNV,\n action_tokens_EL,\n step_t,\n )\n final_carry_causal = causal_step_fn(init_carry_causal, jnp.arange(N))\n updated_token_idxs_BSN = final_carry_causal[1]\n updated_logits_BSNV = final_carry_causal[2]\n new_carry = (rng, updated_token_idxs_BSN, updated_logits_BSNV)\n return new_carry\n\n # --- Run the autoregressive generation using jax.lax.scan ---\n initial_carry = (batch[""rng""], token_idxs_BSN, logits_BSNV)\n timesteps_to_scan = jnp.arange(T, seq_len)\n final_carry = generation_step_fn(initial_carry, timesteps_to_scan)\n final_token_idxs_BSN = final_carry[1]\n final_logits_BSNV = final_carry[2]\n\n # --- Decode all tokens at once at the end ---\n H, W = batch[""videos""].shape[2:4]\n final_frames_BSHWC = self.tokenizer.decode(\n final_token_idxs_BSN,\n video_hw=(H, W),\n )\n return final_frames_BSHWC, final_logits_BSNV\n\n def vq_encode(self, batch: Dict[str, jax.Array], training: bool) -> jax.Array:\n # --- Preprocess videos ---\n assert self.lam is not None\n video_BTHWC = batch[""videos""]\n lam_output: Dict[str, jax.Array] = self.lam.vq_encode(\n video_BTHWC, training=training\n )\n lam_indices_E = lam_output[""indices""]\n return lam_indices_E\n\n\n# FIXME (f.srambical): add conversion script for old checkpoints\ndef restore_genie_components(\n optimizer: nnx.ModelAndOptimizer,\n sharding: jax.sharding.NamedSharding,\n rng: jax.Array,\n args,\n) -> nnx.ModelAndOptimizer:\n """"""Restore pre-trained Genie components""""""\n rng_tokenizer, rng_lam = jax.random.split(rng)\n rngs_tokenizer = nnx.Rngs(rng_tokenizer)\n rngs_lam = nnx.Rngs(rng_lam)\n\n tx = optimizer.tx\n model = optimizer.model\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n\n checkpoint_options = ocp.CheckpointManagerOptions(\n step_format_fixed_length=6,\n )\n tokenizer_checkpoint_manager = ocp.CheckpointManager(\n directory=args.tokenizer_checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n dummy_tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.tokenizer_dim,\n ffn_dim=args.tokenizer_ffn_dim,\n latent_dim=args.latent_patch_dim,\n num_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n num_blocks=args.tokenizer_num_blocks,\n num_heads=args.tokenizer_num_heads,\n dropout=args.dropout,\n codebook_dropout=args.dropout,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs_tokenizer,\n )\n dummy_tokenizer_optimizer = nnx.ModelAndOptimizer(dummy_tokenizer, tx)\n dummy_tokenizer_optimizer_state = nnx.state(dummy_tokenizer_optimizer)\n abstract_sharded_tokenizer_optimizer_state = _create_abstract_sharded_pytree(\n dummy_tokenizer_optimizer_state, sharding\n )\n restored_tokenizer = tokenizer_checkpoint_manager.restore(\n step=tokenizer_checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore( # type: ignore\n abstract_sharded_tokenizer_optimizer_state # type: ignore\n ),\n ),\n )[""model_state""]\n nnx.update(dummy_tokenizer_optimizer.model, restored_tokenizer.model)\n model.tokenizer = dummy_tokenizer_optimizer.model\n tokenizer_checkpoint_manager.close()\n\n if args.lam_checkpoint:\n lam_checkpoint_manager = ocp.CheckpointManager(\n directory=args.lam_checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n dummy_lam = LatentActionModel(\n in_dim=args.image_channels,\n model_dim=args.lam_dim,\n ffn_dim=args.lam_ffn_dim,\n latent_dim=args.latent_patch_dim,\n num_latents=args.num_actions,\n patch_size=args.lam_patch_size,\n num_blocks=args.lam_num_blocks,\n num_heads=args.lam_num_heads,\n dropout=args.dropout,\n codebook_dropout=args.dropout,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs_lam,\n )\n dummy_lam_optimizer = nnx.ModelAndOptimizer(dummy_lam, tx)\n dummy_lam_optimizer_state = nnx.state(dummy_lam_optimizer)\n abstract_sharded_lam_optimizer_state = _create_abstract_sharded_pytree(\n dummy_lam_optimizer_state, sharding\n )\n restored_lam_optimizer = lam_checkpoint_manager.restore(\n step=lam_checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore( # type: ignore\n abstract_sharded_lam_optimizer_state # type: ignore\n ),\n ),\n )[""model_state""]\n nnx.update(dummy_lam_optimizer.model, restored_lam_optimizer.model)\n model.lam = dummy_lam_optimizer.model\n # Remove the LAM decoder to save memory and avoid unnecessary computation.\n del model.lam.decoder\n lam_checkpoint_manager.close()\n\n # Reinitialize the optimizer states\n optimizer = nnx.ModelAndOptimizer(model, tx)\n return optimizer\n\n\ndef _create_abstract_sharded_pytree(\n pytree_template: nnx.GraphState, sharding_spec: jax.sharding.NamedSharding\n) -> jax.Array:\n """"""Replaces arrays in a pytree with ShapeDtypeStructs having the given sharding.""""""\n\n def map_fn(leaf_template):\n if hasattr(leaf_template, ""shape"") and hasattr(leaf_template, ""dtype""):\n return jax.ShapeDtypeStruct(\n leaf_template.shape, leaf_template.dtype, sharding=sharding_spec\n )\n return leaf_template\n\n return jax.tree_util.tree_map(map_fn, pytree_template)\n",python,tab +1303,3716643,"jasmine/genie.py",9621,0,"",python,selection_mouse +1304,3716644,"jasmine/genie.py",9620,0,"",python,selection_command +1305,3821596,"jasmine/genie.py",9125,0,"",python,selection_mouse +1306,3822263,"jasmine/genie.py",9302,0,"",python,selection_mouse +1307,3822272,"jasmine/genie.py",9301,0,"",python,selection_command +1308,3822838,"jasmine/genie.py",9436,0,"",python,selection_mouse +1309,3822839,"jasmine/genie.py",9435,0,"",python,selection_command +1310,3827755,"jasmine/genie.py",0,0,"",python,tab +1311,3855214,"jasmine/genie.py",7628,0,"",python,selection_mouse +1312,3855228,"jasmine/genie.py",7627,0,"",python,selection_command +1313,3871471,"jasmine/train_dynamics.py",0,0,"import os\n\n\nos.environ.setdefault(""XLA_PYTHON_CLIENT_MEM_FRACTION"", ""0.98"")\n\nfrom dataclasses import dataclass, field\nimport itertools\nfrom typing import cast, Optional\n\nimport einops\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\nimport flax.nnx as nnx\n\nfrom genie import Genie, restore_genie_components\nfrom utils.dataloader import get_dataloader\nfrom utils.train_utils import (\n get_lr_schedule,\n count_parameters_by_component,\n print_mem_stats,\n print_compiled_memory_stats,\n print_compiled_cost_analysis,\n)\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n batch_size: int = 36\n init_lr: float = 0.0\n max_lr: float = 3e-5\n decay_end: float = 0.0\n wsd_decay_steps: int = (\n 10000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n )\n warmup_steps: int = 5000\n lr_schedule: str = ""wsd"" # supported options: wsd, cos\n # Tokenizer\n tokenizer_dim: int = 512\n tokenizer_ffn_dim: int = 2048\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 4\n tokenizer_num_heads: int = 8\n tokenizer_checkpoint: str = """"\n # LAM\n lam_dim: int = 512\n lam_ffn_dim: int = 2048\n latent_action_dim: int = 32\n num_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 4\n lam_num_heads: int = 8\n lam_checkpoint: str = """"\n # Dynamics\n dyna_type: str = ""maskgit"" # supported options: maskgit, causal\n dyna_dim: int = 512\n dyna_ffn_dim: int = 2048\n dyna_num_blocks: int = 6\n dyna_num_heads: int = 8\n max_noise_level: float = 0.7\n noise_buckets: int = 10\n dropout: float = 0.0\n mask_limit: float = 0.5\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\n use_flash_attention: bool = True\n use_gt_actions: bool = False\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n name: str = ""train_dynamics""\n tags: list[str] = field(default_factory=lambda: [""dynamics""])\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 25000\n log_checkpoint_keep_period: int = 20000\n log_gradients: bool = False\n val_data_dir: str = """"\n val_interval: int = 20_000\n val_steps: int = 50\n eval_full_frame: bool = False\n val_maskgit_steps: int = 25\n val_temperature: float = 1\n val_sample_argmax: bool = False\n wandb_id: str = """"\n\n\ndef build_model(args: Args, rng: jax.Array) -> tuple[Genie, jax.Array]:\n rng, _rng = jax.random.split(rng)\n rngs = nnx.Rngs(_rng)\n genie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n tokenizer_ffn_dim=args.tokenizer_ffn_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n lam_ffn_dim=args.lam_ffn_dim,\n latent_action_dim=args.latent_action_dim,\n num_actions=args.num_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n lam_co_train=not args.lam_checkpoint,\n use_gt_actions=args.use_gt_actions,\n # Dynamics\n dyna_type=args.dyna_type,\n dyna_dim=args.dyna_dim,\n dyna_ffn_dim=args.dyna_ffn_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n dropout=args.dropout,\n max_noise_level=args.max_noise_level,\n noise_buckets=args.noise_buckets,\n mask_limit=args.mask_limit,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n decode=False,\n rngs=rngs,\n )\n if args.use_gt_actions:\n assert (\n not args.lam_checkpoint\n ), ""Cannot use LAM when using ground-truth actions.""\n else:\n assert genie.lam is not None\n del genie.lam.decoder\n return genie, rng\n\n\ndef build_optimizer(genie: Genie, args: Args) -> nnx.ModelAndOptimizer:\n lr_schedule = get_lr_schedule(\n args.lr_schedule,\n args.init_lr,\n args.max_lr,\n args.decay_end,\n args.num_steps,\n args.warmup_steps,\n args.wsd_decay_steps,\n )\n tx = optax.adamw(\n learning_rate=lr_schedule,\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.param_dtype, # moments in full precision\n )\n optimizer = nnx.ModelAndOptimizer(genie, tx)\n return optimizer\n\n\ndef build_mesh_and_sharding(\n num_devices: int,\n) -> tuple[Mesh, NamedSharding, NamedSharding, NamedSharding]:\n device_mesh_arr = create_device_mesh((num_devices,))\n mesh = Mesh(devices=device_mesh_arr, axis_names=(""data"",))\n replicated_sharding = NamedSharding(mesh, PartitionSpec())\n videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n actions_sharding = NamedSharding(mesh, PartitionSpec(""data"", None))\n return mesh, replicated_sharding, videos_sharding, actions_sharding\n\n\ndef shard_optimizer_states(\n optimizer: nnx.ModelAndOptimizer, replicated_sharding: NamedSharding\n) -> None:\n model_state = nnx.state(optimizer.model)\n model_sharded_state = jax.lax.with_sharding_constraint(\n model_state, replicated_sharding\n )\n nnx.update(optimizer.model, model_sharded_state)\n optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)\n optimizer_sharded_state = jax.lax.with_sharding_constraint(\n optimizer_state, replicated_sharding\n )\n nnx.update(optimizer, optimizer_sharded_state)\n\n\ndef build_dataloader(args: Args, data_dir: str) -> grain.DataLoaderIterator:\n image_shape = (args.image_height, args.image_width, args.image_channels)\n array_record_files = [\n os.path.join(data_dir, x)\n for x in os.listdir(data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n return grain_iterator\n\n\ndef build_checkpoint_manager(args: Args) -> Optional[ocp.CheckpointManager]:\n if args.restore_ckpt or args.save_ckpt:\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n if args.val_data_dir:\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(\n ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler\n ),\n )\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(\n ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler\n ),\n )\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n return checkpoint_manager\n else:\n return None\n\n\ndef restore_or_initialize_components(\n args: Args,\n checkpoint_manager: Optional[ocp.CheckpointManager],\n optimizer: nnx.ModelAndOptimizer,\n train_iterator: grain.DataLoaderIterator,\n rng: jax.Array,\n replicated_sharding: NamedSharding,\n val_iterator: Optional[grain.DataLoaderIterator],\n restore_step: Optional[int] = None,\n) -> tuple[\n int,\n nnx.ModelAndOptimizer,\n grain.DataLoaderIterator,\n grain.DataLoaderIterator,\n jax.Array,\n]:\n step = 0\n if checkpoint_manager and restore_step is None:\n restore_step = checkpoint_manager.latest_step()\n if args.restore_ckpt:\n assert checkpoint_manager is not None\n abstract_optimizer = nnx.eval_shape(lambda: optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n if val_iterator:\n restore_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointRestore(train_iterator), # type: ignore\n val_dataloader_state=grain.checkpoint.CheckpointRestore(val_iterator), # type: ignore\n )\n else:\n restore_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointRestore(train_iterator), # type: ignore\n )\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(), args=restore_args\n )\n restored_optimizer_state = restored[""model_state""]\n nnx.update(optimizer, restored_optimizer_state)\n train_iterator = restored[""train_dataloader_state""]\n if val_iterator:\n val_iterator = restored[""val_dataloader_state""]\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored dataloader and model state from step {step}"")\n else:\n # Restore from pre-trained tokenizer (and LAM)\n rng, _rng = jax.random.split(rng)\n optimizer = restore_genie_components(optimizer, replicated_sharding, _rng, args)\n return step, optimizer, train_iterator, val_iterator, rng\n\n\ndef _calculate_step_metrics(\n outputs: dict[str, jax.Array],\n gt: jax.Array,\n num_actions: int,\n num_patch_latents: int,\n) -> tuple[jax.Array, dict]:\n mask = outputs[""mask""]\n outputs[""token_logits""] = outputs[""token_logits""].astype(jnp.float32)\n ce_loss = optax.softmax_cross_entropy_with_integer_labels(\n outputs[""token_logits""], outputs[""video_tokens""]\n )\n ce_loss = (mask * ce_loss).sum() / mask.sum()\n acc = outputs[""token_logits""].argmax(-1) == outputs[""video_tokens""]\n acc = (mask * acc).sum() / mask.sum()\n select_probs = jax.nn.softmax(outputs[""token_logits""])\n gt_val = gt.clip(0, 1).reshape(-1, *gt.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt_val, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt_val, recon)).mean()\n _, index_counts_tokenizer = jnp.unique_counts(\n jnp.ravel(outputs[""video_tokens""]),\n size=num_patch_latents,\n fill_value=0,\n )\n codebook_usage_tokenizer = (index_counts_tokenizer != 0).mean()\n metrics = dict(\n cross_entropy_loss=ce_loss,\n masked_token_accuracy=acc,\n select_logit=outputs[""token_logits""].max(-1).mean(),\n select_p=select_probs.max(-1).mean(),\n entropy=jax.scipy.special.entr(select_probs).sum(-1).mean(),\n psnr=psnr,\n ssim=ssim,\n codebook_usage_tokenizer=codebook_usage_tokenizer,\n )\n if ""lam_indices"" in outputs.keys():\n _, index_counts_lam = jnp.unique_counts(\n jnp.ravel(outputs[""lam_indices""]),\n size=num_actions,\n fill_value=0,\n )\n codebook_usage_lam = (index_counts_lam != 0).mean()\n metrics[""codebook_usage_lam""] = codebook_usage_lam\n return ce_loss, metrics\n\n\ndef main(args: Args) -> None:\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 rng = jax.random.key(args.seed)\n\n # --- Initialize model ---\n genie, rng = build_model(args, rng)\n _, params, _ = nnx.split(genie, nnx.Param, ...)\n param_counts = count_parameters_by_component(params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\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 if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n optimizer = build_optimizer(genie, args)\n del genie\n\n # FIXME: switch to create_hybrid_device_mesh for runs spanning multiple nodes\n _, replicated_sharding, videos_sharding, actions_sharding = build_mesh_and_sharding(\n num_devices\n )\n\n shard_optimizer_states(optimizer, replicated_sharding)\n\n # --- Initialize checkpoint manager ---\n checkpoint_manager = build_checkpoint_manager(args)\n\n # --- Create DataLoaderIterator from dataloader ---\n train_iterator = build_dataloader(args, args.data_dir)\n val_iterator = None\n if args.val_data_dir:\n val_iterator = build_dataloader(args, args.val_data_dir)\n\n # --- Restore checkpoint ---\n step, optimizer, train_iterator, val_iterator, rng = (\n restore_or_initialize_components(\n args,\n checkpoint_manager,\n optimizer,\n train_iterator,\n rng,\n replicated_sharding,\n val_iterator,\n )\n )\n\n # --- Define loss and train step (close over args) ---\n def dynamics_loss_fn(\n model: Genie,\n inputs: dict,\n ) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs)\n ce_loss, metrics = _calculate_step_metrics(\n outputs, gt, args.num_actions, args.num_patch_latents\n )\n return ce_loss, (outputs[""recon""], metrics)\n\n @nnx.jit(donate_argnums=0)\n def train_step(\n optimizer: nnx.ModelAndOptimizer, inputs: dict\n ) -> tuple[jax.Array, jax.Array, dict]:\n def loss_fn(model: Genie) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n model.train()\n return dynamics_loss_fn(model, inputs)\n\n (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(\n optimizer.model\n )\n optimizer.update(grads)\n if args.log_gradients:\n metrics[""gradients_std/""] = jax.tree.map(\n lambda x: x.std(), grads[""params""][""dynamics""]\n )\n return loss, recon, metrics\n\n @nnx.jit\n def val_step(genie: Genie, inputs: dict) -> dict:\n """"""Evaluate model and compute metrics""""""\n genie.eval()\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n (loss, (recon, metrics)) = dynamics_loss_fn(genie, inputs)\n val_output = {""loss"": loss, ""recon"": recon, ""metrics"": metrics}\n\n # --- Evaluate full frame prediction (sampling) ---\n if args.eval_full_frame:\n inputs[""videos""] = gt.astype(args.dtype)\n tokenizer_outputs = genie.tokenizer.vq_encode(\n inputs[""videos""], training=False\n )\n tokens_full_frame = tokenizer_outputs[""indices""]\n lam_indices_E = None\n if not args.use_gt_actions:\n lam_indices_E = genie.vq_encode(inputs, training=False)\n inputs[""latent_actions""] = lam_indices_E\n inputs[""videos""] = inputs[""videos""][\n :, :-1\n ] # remove last frame for generation\n recon_full_frame, logits_full_frame = genie.sample(\n inputs,\n args.seq_len,\n args.val_temperature,\n args.val_sample_argmax,\n args.val_maskgit_steps,\n )\n # Calculate metrics for the last frame only\n step_outputs = {\n ""recon"": recon_full_frame[:, -1],\n ""token_logits"": logits_full_frame[:, -1],\n ""video_tokens"": tokens_full_frame[:, -1],\n ""mask"": jnp.ones_like(tokens_full_frame[:, -1]),\n }\n if lam_indices_E is not None:\n lam_indices_B = lam_indices_E.reshape((-1, args.seq_len - 1))[:, -1]\n step_outputs[""lam_indices""] = lam_indices_B\n\n loss_full_frame, metrics_full_frame = _calculate_step_metrics(\n step_outputs, gt[:, -1], args.num_actions, args.num_patch_latents\n )\n val_output.update(\n {\n ""loss_full_frame"": loss_full_frame,\n ""recon_full_frame"": recon_full_frame,\n ""metrics_full_frame"": metrics_full_frame,\n }\n )\n return val_output\n\n def calculate_validation_metrics(val_dataloader, genie, rng):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n loss_full_frame_per_step = []\n metrics_full_frame_per_step = []\n batch = None\n recon = None\n recon_full_frame = None\n for batch in val_dataloader:\n rng, _rng_mask = jax.random.split(rng, 2)\n batch[""rng""] = _rng_mask\n val_outputs = val_step(genie, batch)\n loss_per_step.append(val_outputs[""loss""])\n metrics_per_step.append(val_outputs[""metrics""])\n recon = val_outputs[""recon""]\n if args.eval_full_frame:\n loss_full_frame_per_step.append(val_outputs[""loss_full_frame""])\n metrics_full_frame_per_step.append(val_outputs[""metrics_full_frame""])\n recon_full_frame = val_outputs[""recon_full_frame""]\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(\n f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}""\n )\n\n val_metrics = {\n f""val_{key}"": np.mean([float(m[key]) for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n val_metrics[""val_loss""] = np.mean(loss_per_step)\n if args.eval_full_frame:\n val_metrics_full_frame = {\n f""val_full_frame_{key}"": np.mean(\n [float(m[key]) for m in metrics_full_frame_per_step]\n )\n for key in metrics_full_frame_per_step[0].keys()\n }\n val_metrics.update(val_metrics_full_frame)\n val_metrics[""val_full_frame_loss""] = np.mean(loss_full_frame_per_step)\n return val_metrics, batch, recon, recon_full_frame\n\n # --- TRAIN LOOP ---\n dataloader_train = (\n {\n ""videos"": jax.make_array_from_process_local_data(\n videos_sharding, local_data=elem[""videos""]\n ),\n ""actions"": (\n jax.make_array_from_process_local_data(\n actions_sharding, elem[""actions""]\n )\n if args.use_gt_actions\n else None\n ),\n }\n for elem in train_iterator\n )\n dataloader_val = None\n if val_iterator:\n dataloader_val = (\n {\n ""videos"": jax.make_array_from_process_local_data(\n videos_sharding, elem[""videos""]\n ),\n ""actions"": (\n jax.make_array_from_process_local_data(\n actions_sharding, elem[""actions""]\n )\n if args.use_gt_actions\n else None\n ),\n }\n for elem in val_iterator\n )\n if jax.process_index() == 0:\n first_batch = next(dataloader_train)\n first_batch[""rng""] = rng # type: ignore\n compiled = train_step.lower(optimizer, first_batch).compile()\n print_compiled_memory_stats(compiled.memory_analysis())\n print_compiled_cost_analysis(compiled.cost_analysis())\n # Do not skip the first batch during training\n dataloader_train = itertools.chain([first_batch], dataloader_train)\n print(f""Starting training from step {step}..."")\n first_step = step\n while step < args.num_steps:\n for batch in dataloader_train:\n # --- Train step ---\n rng, _rng_mask = jax.random.split(rng, 2)\n batch[""rng""] = _rng_mask\n loss, recon, metrics = train_step(optimizer, batch)\n if step == first_step:\n print_mem_stats(""After params initialized"")\n step += 1\n\n # --- Validation loss ---\n val_results = {}\n if dataloader_val and step % args.val_interval == 0:\n rng, _rng_mask_val = jax.random.split(rng, 2)\n print(""Calculating validation metrics..."")\n val_metrics, val_gt_batch, val_recon, val_recon_full_frame = (\n calculate_validation_metrics(\n dataloader_val, optimizer.model, _rng_mask_val\n )\n )\n print(f""Step {step}, validation loss: {val_metrics['val_loss']}"")\n val_results = {\n ""metrics"": val_metrics,\n ""gt_batch"": val_gt_batch,\n ""recon"": val_recon,\n ""full_frame"": val_recon_full_frame,\n }\n\n # --- Logging ---\n if args.log:\n if step % args.log_interval == 0 and jax.process_index() == 0:\n log_dict = {""loss"": loss, ""step"": step, **metrics}\n if val_results:\n log_dict.update(val_results[""metrics""])\n wandb.log(log_dict)\n if step % args.log_image_interval == 0:\n gt_seq = batch[""videos""][0].astype(jnp.float32) / 255.0\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 if val_results:\n val_results[""gt_seq_val""] = (\n val_results[""gt_batch""][""videos""][0].astype(jnp.float32)\n / 255.0\n )\n val_results[""recon_seq_val""] = val_results[""recon""][0].clip(\n 0, 1\n )\n val_comparison_seq = jnp.concatenate(\n (val_results[""gt_seq_val""], val_results[""recon_seq_val""]),\n axis=1,\n )\n val_results[""val_comparison_seq""] = einops.rearrange(\n val_comparison_seq * 255, ""t h w c -> h (t w) c""\n )\n if args.eval_full_frame:\n val_results[""full_frame_seq_val""] = val_results[\n ""full_frame""\n ][0].clip(0, 1)\n val_results[""val_full_frame_comparison_seq""] = (\n jnp.concatenate(\n (\n val_results[""gt_seq_val""],\n val_results[""full_frame_seq_val""],\n ),\n axis=1,\n )\n )\n val_results[""val_full_frame_comparison_seq""] = (\n einops.rearrange(\n val_results[""val_full_frame_comparison_seq""] * 255,\n ""t h w c -> h (t w) c"",\n )\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[args.seq_len - 1])),\n recon=wandb.Image(np.asarray(recon_seq[args.seq_len - 1])),\n true_vs_recon=wandb.Image(\n np.asarray(comparison_seq.astype(np.uint8))\n ),\n )\n if val_results:\n log_images.update(\n dict(\n val_image=wandb.Image(\n np.asarray(\n val_results[""gt_seq_val""][args.seq_len - 1]\n )\n ),\n val_recon=wandb.Image(\n np.asarray(\n val_results[""recon_seq_val""][\n args.seq_len - 1\n ]\n )\n ),\n val_true_vs_recon=wandb.Image(\n np.asarray(\n val_results[""val_comparison_seq""].astype(\n np.uint8\n )\n )\n ),\n )\n )\n if args.eval_full_frame:\n log_images.update(\n dict(\n val_full_frame=wandb.Image(\n np.asarray(\n val_results[""full_frame_seq_val""][\n args.seq_len - 1\n ]\n )\n ),\n val_true_vs_full_frame=wandb.Image(\n np.asarray(\n val_results[\n ""val_full_frame_comparison_seq""\n ].astype(np.uint8)\n )\n ),\n )\n )\n wandb.log(log_images)\n # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n assert checkpoint_manager is not None\n optimizer_state = nnx.state(optimizer)\n if val_iterator:\n ckpt_manager_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n train_iterator # type: ignore\n ),\n val_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n val_iterator # type: ignore\n ),\n )\n else:\n ckpt_manager_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n train_iterator # type: ignore\n ),\n 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+1800,4677482,"jasmine/models/dynamics.py",4372,0,"",python,selection_mouse +1801,4681540,"jasmine/models/dynamics.py",4359,0,"",python,selection_mouse +1802,4681683,"jasmine/models/dynamics.py",4350,14,"vid_embed_BTNM",python,selection_mouse +1803,4685629,"jasmine/models/dynamics.py",4731,0,"",python,selection_mouse +1804,4685756,"jasmine/models/dynamics.py",4721,22,"noise_level_embed_BT1M",python,selection_mouse +1805,4688000,"jasmine/models/dynamics.py",4360,0,"",python,selection_mouse +1806,4688153,"jasmine/models/dynamics.py",4350,14,"vid_embed_BTNM",python,selection_mouse +1807,4693463,"jasmine/models/dynamics.py",4360,0,"",python,selection_mouse +1808,4697376,"jasmine/models/dynamics.py",4342,82," vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1809,4697594,"jasmine/models/dynamics.py",4341,83,"\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1810,4698129,"jasmine/models/dynamics.py",4272,152," sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1811,4698131,"jasmine/models/dynamics.py",4221,203," sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1812,4698154,"jasmine/models/dynamics.py",4151,273," one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1813,4698180,"jasmine/models/dynamics.py",4108,316," # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1814,4698242,"jasmine/models/dynamics.py",4107,317,"\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1815,4698243,"jasmine/models/dynamics.py",4046,378," noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1816,4698281,"jasmine/models/dynamics.py",3966,458," noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1817,4698305,"jasmine/models/dynamics.py",3888,536," noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1818,4698328,"jasmine/models/dynamics.py",3821,603," noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1819,4698389,"jasmine/models/dynamics.py",3793,631," ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1820,4698390,"jasmine/models/dynamics.py",3721,703," (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1821,4698418,"jasmine/models/dynamics.py",3681,743," noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1822,4698463,"jasmine/models/dynamics.py",3594,830," # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1823,4698567,"jasmine/models/dynamics.py",3510,914," # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1824,4698567,"jasmine/models/dynamics.py",3439,985," noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1825,4698640,"jasmine/models/dynamics.py",3429,995," )\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1826,4698771,"jasmine/models/dynamics.py",3349,1075," _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1827,4698914,"jasmine/models/dynamics.py",3305,1119," noise_level_B = jax.random.uniform(\n _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1828,4699057,"jasmine/models/dynamics.py",3238,1186," rng, _rng_noise_lvl, _rng_noise = jax.random.split(rng, 3)\n noise_level_B = jax.random.uniform(\n _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1829,4699226,"jasmine/models/dynamics.py",3207,1217," # --- Sample noise ---\n rng, _rng_noise_lvl, _rng_noise = jax.random.split(rng, 3)\n noise_level_B = jax.random.uniform(\n _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1830,4699584,"jasmine/models/dynamics.py",3238,1186," rng, _rng_noise_lvl, _rng_noise = jax.random.split(rng, 3)\n noise_level_B = jax.random.uniform(\n _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,selection_command +1831,4703119,"jasmine/models/dynamics.py",3238,0,"",python,selection_command +1832,4704766,"jasmine/models/dynamics.py",2259,0,"",python,selection_mouse +1833,4705412,"jasmine/models/dynamics.py",2282,0,"\n rng, _rng_noise_lvl, _rng_noise = jax.random.split(rng, 3)\n noise_level_B = jax.random.uniform(\n _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM",python,content +1834,4705445,"jasmine/models/dynamics.py",2291,0,"",python,selection_command +1835,4706022,"jasmine/models/dynamics.py",2358,0,"",python,selection_command +1836,4706515,"jasmine/models/dynamics.py",2402,0,"",python,selection_command +1837,4706567,"jasmine/models/dynamics.py",2482,0,"",python,selection_command +1838,4706578,"jasmine/models/dynamics.py",2492,0,"",python,selection_command +1839,4706598,"jasmine/models/dynamics.py",2563,0,"",python,selection_command 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+2271,4799992,"jasmine/models/dynamics.py",5544,119," sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))",python,selection_command +2272,4800538,"jasmine/models/dynamics.py",5474,189," one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))",python,selection_command +2273,4800542,"jasmine/models/dynamics.py",5431,232," # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))",python,selection_command +2274,4800546,"jasmine/models/dynamics.py",5430,233,"\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))",python,selection_command +2275,4800628,"jasmine/models/dynamics.py",5369,294," noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))",python,selection_command +2276,4800629,"jasmine/models/dynamics.py",5289,374," noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))",python,selection_command +2277,4800720,"jasmine/models/dynamics.py",5211,452," noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))",python,selection_command +2278,4800895,"jasmine/models/dynamics.py",5144,519," noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))",python,selection_command +2279,4801053,"jasmine/models/dynamics.py",5116,547," ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))",python,selection_command +2280,4801203,"jasmine/models/dynamics.py",5044,619," (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))",python,selection_command +2281,4801359,"jasmine/models/dynamics.py",5004,659," noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))",python,selection_command +2282,4801512,"jasmine/models/dynamics.py",4917,746," # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))",python,selection_command +2283,4801776,"jasmine/models/dynamics.py",4833,830," # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))",python,selection_command +2284,4801924,"jasmine/models/dynamics.py",4762,901," noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))",python,selection_command +2285,4802105,"jasmine/models/dynamics.py",4752,911," )\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))",python,selection_command +2286,4802232,"jasmine/models/dynamics.py",4672,991," _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))",python,selection_command +2287,4802460,"jasmine/models/dynamics.py",4628,1035," noise_level_B = jax.random.uniform(\n _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))",python,selection_command +2288,4805922,"jasmine/models/dynamics.py",4561,1102," rng, _rng_noise_lvl, _rng_noise = jax.random.split(rng, 3)\n noise_level_B = jax.random.uniform(\n _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))",python,selection_command +2289,4811155,"jasmine/models/dynamics.py",4561,1103,"",python,content +2290,4811666,"jasmine/models/dynamics.py",4530,0,"",python,selection_command +2291,4811978,"jasmine/models/dynamics.py",4561,0,"",python,selection_command +2292,4812943,"jasmine/models/dynamics.py",4562,0,"",python,selection_command +2293,4813379,"jasmine/models/dynamics.py",4563,0,"",python,selection_command +2294,4813886,"jasmine/models/dynamics.py",4564,0,"",python,selection_command +2295,4813899,"jasmine/models/dynamics.py",4565,0,"",python,selection_command +2296,4813953,"jasmine/models/dynamics.py",4566,0,"",python,selection_command +2297,4813959,"jasmine/models/dynamics.py",4567,0,"",python,selection_command +2298,4814019,"jasmine/models/dynamics.py",4568,0,"",python,selection_command 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+2428,4832628,"jasmine/models/dynamics.py",4568,0,"",python,selection_keyboard +2429,4833046,"jasmine/models/dynamics.py",4568,0," ",python,content +2430,4833047,"jasmine/models/dynamics.py",4569,0,"",python,selection_keyboard +2431,4833289,"jasmine/models/dynamics.py",4568,0,"",python,selection_command +2432,4833452,"jasmine/models/dynamics.py",4611,0,"",python,selection_command +2433,4833839,"jasmine/models/dynamics.py",4568,0,"",python,selection_command +2434,4834259,"jasmine/models/dynamics.py",4611,0,"",python,selection_command +2435,4834498,"jasmine/models/dynamics.py",4673,0,"\n ",python,content +2436,4834710,"jasmine/models/dynamics.py",4674,8,"",python,content +2437,4837634,"jasmine/models/dynamics.py",4890,0,"",python,selection_mouse +2438,4837636,"jasmine/models/dynamics.py",4889,0,"",python,selection_command +2439,4839740,"jasmine/models/dynamics.py",3934,0,"",python,selection_mouse +2440,4841632,"jasmine/models/dynamics.py",3934,1,"_",python,content +2441,4841888,"jasmine/models/dynamics.py",3935,0,"",python,selection_command +2442,4842067,"jasmine/models/dynamics.py",3936,0,"",python,selection_command +2443,4842257,"jasmine/models/dynamics.py",3937,0,"",python,selection_command +2444,4842621,"jasmine/models/dynamics.py",3937,1,"_",python,content +2445,4842897,"jasmine/models/dynamics.py",3938,0,"",python,selection_command +2446,4843068,"jasmine/models/dynamics.py",3939,0,"",python,selection_command +2447,4843368,"jasmine/models/dynamics.py",3940,0,"",python,selection_command +2448,4843743,"jasmine/models/dynamics.py",3940,1,"_",python,content +2449,4846637,"jasmine/models/dynamics.py",3932,0,"",python,selection_mouse +2450,4846822,"jasmine/models/dynamics.py",3932,3,", _",python,selection_mouse +2451,4846845,"jasmine/models/dynamics.py",3932,4,", _,",python,selection_mouse +2452,4846857,"jasmine/models/dynamics.py",3932,5,", _, ",python,selection_mouse +2453,4846891,"jasmine/models/dynamics.py",3932,6,", _, _",python,selection_mouse +2454,4846892,"jasmine/models/dynamics.py",3932,7,", _, _,",python,selection_mouse +2455,4846906,"jasmine/models/dynamics.py",3932,8,", _, _, ",python,selection_mouse +2456,4846972,"jasmine/models/dynamics.py",3932,9,", _, _, _",python,selection_mouse +2457,4848432,"jasmine/models/dynamics.py",3932,9,"",python,content +2458,4849530,"jasmine/models/dynamics.py",3955,0,"",python,selection_mouse +2459,4849531,"jasmine/models/dynamics.py",3954,0,"",python,selection_command +2460,4850058,"jasmine/models/dynamics.py",3955,0,"",python,selection_command +2461,4850838,"jasmine/models/dynamics.py",3955,0,"[]",python,content +2462,4850839,"jasmine/models/dynamics.py",3956,0,"",python,selection_keyboard +2463,4851033,"jasmine/models/dynamics.py",3956,0,"0",python,content +2464,4851035,"jasmine/models/dynamics.py",3957,0,"",python,selection_keyboard +2465,4851515,"jasmine/models/dynamics.py",3956,0,"",python,selection_command +2466,4866796,"jasmine/models/dynamics.py",3579,0,"",python,selection_mouse +2467,4868998,"jasmine/models/dynamics.py",3549,69," return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2468,4869304,"jasmine/models/dynamics.py",3450,168," noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2469,4869795,"jasmine/models/dynamics.py",3449,169,"\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2470,4869863,"jasmine/models/dynamics.py",3380,238," sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2471,4869871,"jasmine/models/dynamics.py",3329,289," sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2472,4869885,"jasmine/models/dynamics.py",3259,359," one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2473,4869956,"jasmine/models/dynamics.py",3216,402," # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2474,4869958,"jasmine/models/dynamics.py",3215,403,"\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2475,4869981,"jasmine/models/dynamics.py",3154,464," noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2476,4870036,"jasmine/models/dynamics.py",3074,544," noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2477,4870047,"jasmine/models/dynamics.py",2996,622," noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2478,4870066,"jasmine/models/dynamics.py",2929,689," noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2479,4870149,"jasmine/models/dynamics.py",2901,717," ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2480,4870149,"jasmine/models/dynamics.py",2829,789," (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2481,4870150,"jasmine/models/dynamics.py",2789,829," noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2482,4870181,"jasmine/models/dynamics.py",2702,916," # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2483,4870229,"jasmine/models/dynamics.py",2618,1000," # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2484,4870252,"jasmine/models/dynamics.py",2547,1071," noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2485,4870273,"jasmine/models/dynamics.py",2537,1081," )\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2486,4870379,"jasmine/models/dynamics.py",2457,1161," _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2487,4870530,"jasmine/models/dynamics.py",2413,1205," noise_level_B = jax.random.uniform(\n _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2488,4870706,"jasmine/models/dynamics.py",2346,1272," rng, _rng_noise_lvl, _rng_noise = jax.random.split(rng, 3)\n noise_level_B = jax.random.uniform(\n _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2489,4870813,"jasmine/models/dynamics.py",2304,1314," B, T, N, M = vid_embed_BTNM.shape\n rng, _rng_noise_lvl, _rng_noise = jax.random.split(rng, 3)\n noise_level_B = jax.random.uniform(\n _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2490,4870952,"jasmine/models/dynamics.py",2242,1376," def _apply_noise_augmentation(self, vid_embed_BTNM, rng):\n B, T, N, M = vid_embed_BTNM.shape\n rng, _rng_noise_lvl, _rng_noise = jax.random.split(rng, 3)\n noise_level_B = jax.random.uniform(\n _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2491,4871127,"jasmine/models/dynamics.py",2241,1377,"\n def _apply_noise_augmentation(self, vid_embed_BTNM, rng):\n B, T, N, M = vid_embed_BTNM.shape\n rng, _rng_noise_lvl, _rng_noise = jax.random.split(rng, 3)\n noise_level_B = jax.random.uniform(\n _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_command +2492,4871296,"jasmine/models/dynamics.py",2241,0,"",python,selection_command +2493,4873786,"jasmine/models/dynamics.py",6694,0,"",python,selection_mouse +2494,4873788,"jasmine/models/dynamics.py",6693,0,"",python,selection_command +2495,4874417,"jasmine/models/dynamics.py",6694,0,"\n\n def _apply_noise_augmentation(self, vid_embed_BTNM, rng):\n B, T, N, M = vid_embed_BTNM.shape\n rng, _rng_noise_lvl, _rng_noise = jax.random.split(rng, 3)\n noise_level_B = jax.random.uniform(\n _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,content +2496,4874466,"jasmine/models/dynamics.py",6695,0,"",python,selection_command +2497,4875287,"jasmine/models/dynamics.py",6701,0,"",python,selection_command +2498,4875790,"jasmine/models/dynamics.py",6763,0,"",python,selection_command +2499,4875830,"jasmine/models/dynamics.py",6805,0,"",python,selection_command +2500,4875852,"jasmine/models/dynamics.py",6872,0,"",python,selection_command 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+3070,5678773,"jasmine/genie.py",13620,0,"",python,selection_mouse +3071,5680195,"jasmine/genie.py",13649,0,"",python,selection_mouse +3072,5681762,"jasmine/genie.py",13617,0,"",python,selection_mouse +3073,5682646,"jasmine/genie.py",13582,67,"",python,content +3074,5692971,"jasmine/genie.py",13583,0,"",python,selection_command +3075,5693104,"jasmine/genie.py",13631,0,"",python,selection_command +3076,5693631,"jasmine/genie.py",13712,0,"",python,selection_command +3077,5693981,"jasmine/genie.py",13712,13," )",python,selection_command +3078,5694202,"jasmine/genie.py",13631,94," [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\n )",python,selection_command +3079,5694352,"jasmine/genie.py",13583,142," vid_embed_BSNp2M = jnp.concatenate(\n [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\n )",python,selection_command +3080,5694485,"jasmine/genie.py",13582,143,"\n vid_embed_BSNp2M = jnp.concatenate(\n [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\n )",python,selection_command +3081,5694619,"jasmine/genie.py",13498,227," noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\n\n vid_embed_BSNp2M = jnp.concatenate(\n [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\n )",python,selection_command +3082,5694766,"jasmine/genie.py",13484,241," )\n noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\n\n vid_embed_BSNp2M = jnp.concatenate(\n [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\n )",python,selection_command +3083,5694904,"jasmine/genie.py",13447,278," noise_bucket_idx_B11\n )\n noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\n\n vid_embed_BSNp2M = jnp.concatenate(\n [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\n )",python,selection_command +3084,5695437,"jasmine/genie.py",13374,351," noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(\n noise_bucket_idx_B11\n )\n noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\n\n vid_embed_BSNp2M = jnp.concatenate(\n [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\n )",python,selection_command +3085,5695466,"jasmine/genie.py",13342,383," ).astype(jnp.int32)\n noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(\n noise_bucket_idx_B11\n )\n noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\n\n vid_embed_BSNp2M = jnp.concatenate(\n [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\n )",python,selection_command +3086,5695480,"jasmine/genie.py",13264,461," (noise_level_B11 * self.noise_buckets) * self.max_noise_level\n ).astype(jnp.int32)\n noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(\n noise_bucket_idx_B11\n )\n noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\n\n vid_embed_BSNp2M = jnp.concatenate(\n [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\n )",python,selection_command +3087,5695540,"jasmine/genie.py",13218,507," noise_bucket_idx_B11 = jnp.floor(\n (noise_level_B11 * self.noise_buckets) * self.max_noise_level\n ).astype(jnp.int32)\n noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(\n noise_bucket_idx_B11\n )\n noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\n\n vid_embed_BSNp2M = jnp.concatenate(\n [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\n )",python,selection_command +3088,5695587,"jasmine/genie.py",13151,574," noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\n noise_bucket_idx_B11 = jnp.floor(\n (noise_level_B11 * self.noise_buckets) * self.max_noise_level\n ).astype(jnp.int32)\n noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(\n noise_bucket_idx_B11\n )\n noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\n\n vid_embed_BSNp2M = jnp.concatenate(\n [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\n )",python,selection_command +3089,5695593,"jasmine/genie.py",13092,633," noise_level_111 = noise_level.reshape(1, 1, 1)\n noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\n noise_bucket_idx_B11 = jnp.floor(\n (noise_level_B11 * self.noise_buckets) * self.max_noise_level\n ).astype(jnp.int32)\n noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(\n noise_bucket_idx_B11\n )\n noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\n\n vid_embed_BSNp2M = jnp.concatenate(\n [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\n )",python,selection_command +3090,5695656,"jasmine/genie.py",13040,685," rng, _rng_noise = jax.random.split(rng)\n noise_level_111 = noise_level.reshape(1, 1, 1)\n noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\n noise_bucket_idx_B11 = jnp.floor(\n (noise_level_B11 * self.noise_buckets) * self.max_noise_level\n ).astype(jnp.int32)\n noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(\n noise_bucket_idx_B11\n )\n noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\n\n vid_embed_BSNp2M = jnp.concatenate(\n [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\n )",python,selection_command +3091,5696036,"jasmine/genie.py",13092,633," noise_level_111 = noise_level.reshape(1, 1, 1)\n noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\n noise_bucket_idx_B11 = jnp.floor(\n (noise_level_B11 * self.noise_buckets) * self.max_noise_level\n ).astype(jnp.int32)\n noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(\n noise_bucket_idx_B11\n )\n noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\n\n vid_embed_BSNp2M = jnp.concatenate(\n [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\n )",python,selection_command +3092,5696367,"jasmine/genie.py",13040,685," rng, _rng_noise = jax.random.split(rng)\n noise_level_111 = noise_level.reshape(1, 1, 1)\n noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\n noise_bucket_idx_B11 = jnp.floor(\n (noise_level_B11 * self.noise_buckets) * self.max_noise_level\n ).astype(jnp.int32)\n noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(\n noise_bucket_idx_B11\n )\n noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\n\n vid_embed_BSNp2M = jnp.concatenate(\n [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\n )",python,selection_command +3093,5696579,"jasmine/genie.py",13040,0,"",python,selection_command +3094,5733587,"jasmine/genie.py",13039,0,"",python,selection_mouse +3095,5733589,"jasmine/genie.py",13038,0,"",python,selection_command +3096,5749586,"jasmine/genie.py",13321,0,"",python,selection_mouse +3097,5750815,"jasmine/genie.py",13320,0,"",python,selection_command +3098,5751345,"jasmine/genie.py",13319,1,"",python,content +3099,5751921,"jasmine/genie.py",13319,0,"/",python,content +3100,5751922,"jasmine/genie.py",13320,0,"",python,selection_keyboard +3101,5752384,"jasmine/genie.py",13319,0,"",python,selection_command +3102,5756523,"jasmine/models/dynamics.py",0,0,"",python,tab +3103,6074623,"jasmine/train_dynamics.py",0,0,"",python,tab +3104,6075393,"jasmine/genie.py",0,0,"",python,tab +3105,6076576,"jasmine/genie.py",13242,0,"",python,selection_mouse +3106,6077075,"jasmine/genie.py",13241,0,"",python,selection_mouse +3107,6077231,"jasmine/genie.py",13230,20,"noise_bucket_idx_B11",python,selection_mouse +3108,6077842,"jasmine/genie.py",13287,0,"",python,selection_mouse +3109,6077977,"jasmine/genie.py",13281,15,"noise_level_B11",python,selection_mouse +3110,6082582,"jasmine/train_dynamics.py",0,0,"",python,tab +3111,6295115,"jasmine/train_dynamics.py",22462,0,"",python,selection_mouse +3112,6303321,"jasmine/genie.py",0,0,"",python,tab +3113,6313581,"jasmine/genie.py",13281,93,"noise_level_B11 * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n",python,selection_command +3114,6314485,"jasmine/genie.py",13372,0,"",python,selection_command +3115,6314731,"jasmine/genie.py",13296,0,"",python,selection_command +3116,6314908,"jasmine/genie.py",13250,0,"",python,selection_command +3117,6315037,"jasmine/genie.py",13183,0,"",python,selection_command +3118,6315182,"jasmine/genie.py",13124,0,"",python,selection_command +3119,6315325,"jasmine/genie.py",13072,0,"",python,selection_command +3120,6315889,"jasmine/genie.py",13040,51," rng, _rng_noise = jax.random.split(rng)",python,selection_command +3121,6316151,"jasmine/genie.py",13040,110," rng, _rng_noise = jax.random.split(rng)\n noise_level_111 = noise_level.reshape(1, 1, 1)",python,selection_command +3122,6316636,"jasmine/genie.py",13040,177," rng, _rng_noise = jax.random.split(rng)\n noise_level_111 = noise_level.reshape(1, 1, 1)\n noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))",python,selection_command +3123,6316711,"jasmine/genie.py",13040,223," rng, _rng_noise = jax.random.split(rng)\n noise_level_111 = noise_level.reshape(1, 1, 1)\n noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\n noise_bucket_idx_B11 = jnp.floor(",python,selection_command +3124,6316723,"jasmine/genie.py",13040,301," rng, _rng_noise = jax.random.split(rng)\n noise_level_111 = noise_level.reshape(1, 1, 1)\n noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\n noise_bucket_idx_B11 = jnp.floor(\n (noise_level_B11 * self.noise_buckets) / self.max_noise_level",python,selection_command +3125,6316723,"jasmine/genie.py",13040,333," rng, _rng_noise = jax.random.split(rng)\n noise_level_111 = noise_level.reshape(1, 1, 1)\n noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\n noise_bucket_idx_B11 = jnp.floor(\n (noise_level_B11 * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)",python,selection_command +3126,6316809,"jasmine/genie.py",13040,406," rng, _rng_noise = jax.random.split(rng)\n noise_level_111 = noise_level.reshape(1, 1, 1)\n noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\n noise_bucket_idx_B11 = jnp.floor(\n (noise_level_B11 * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(",python,selection_command +3127,6316809,"jasmine/genie.py",13040,443," rng, _rng_noise = jax.random.split(rng)\n noise_level_111 = noise_level.reshape(1, 1, 1)\n noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\n noise_bucket_idx_B11 = jnp.floor(\n (noise_level_B11 * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(\n noise_bucket_idx_B11",python,selection_command +3128,6316849,"jasmine/genie.py",13040,457," rng, _rng_noise = jax.random.split(rng)\n noise_level_111 = noise_level.reshape(1, 1, 1)\n noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\n noise_bucket_idx_B11 = jnp.floor(\n (noise_level_B11 * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(\n noise_bucket_idx_B11\n )",python,selection_command +3129,6316930,"jasmine/genie.py",13040,541," rng, _rng_noise = jax.random.split(rng)\n noise_level_111 = noise_level.reshape(1, 1, 1)\n noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\n noise_bucket_idx_B11 = jnp.floor(\n (noise_level_B11 * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(\n noise_bucket_idx_B11\n )\n noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))",python,selection_command +3130,6317124,"jasmine/genie.py",13040,542," rng, _rng_noise = jax.random.split(rng)\n noise_level_111 = noise_level.reshape(1, 1, 1)\n noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\n noise_bucket_idx_B11 = jnp.floor(\n (noise_level_B11 * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(\n noise_bucket_idx_B11\n )\n noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\n",python,selection_command +3131,6317276,"jasmine/genie.py",13040,590," rng, _rng_noise = jax.random.split(rng)\n noise_level_111 = noise_level.reshape(1, 1, 1)\n noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\n noise_bucket_idx_B11 = jnp.floor(\n (noise_level_B11 * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(\n noise_bucket_idx_B11\n )\n noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\n\n vid_embed_BSNp2M = jnp.concatenate(",python,selection_command +3132,6317436,"jasmine/genie.py",13040,671," rng, _rng_noise = jax.random.split(rng)\n noise_level_111 = noise_level.reshape(1, 1, 1)\n noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\n noise_bucket_idx_B11 = jnp.floor(\n (noise_level_B11 * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(\n noise_bucket_idx_B11\n )\n noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\n\n vid_embed_BSNp2M = jnp.concatenate(\n [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2",python,selection_command +3133,6317612,"jasmine/genie.py",13040,685," rng, _rng_noise = jax.random.split(rng)\n noise_level_111 = noise_level.reshape(1, 1, 1)\n noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\n noise_bucket_idx_B11 = jnp.floor(\n (noise_level_B11 * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(\n noise_bucket_idx_B11\n )\n noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\n\n vid_embed_BSNp2M = jnp.concatenate(\n [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\n )",python,selection_command +3134,6318085,"jasmine/genie.py",13040,0,"",python,selection_command +3135,6328631,"jasmine/genie.py",22242,0,"",python,selection_mouse 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",python,content +3152,6333684,"jasmine/genie.py",22009,0,"",python,selection_keyboard +3153,6333968,"jasmine/genie.py",22009,0,"T",python,content +3154,6333969,"jasmine/genie.py",22010,0,"",python,selection_keyboard +3155,6334152,"jasmine/genie.py",22010,0,"O",python,content +3156,6334153,"jasmine/genie.py",22011,0,"",python,selection_keyboard +3157,6334191,"jasmine/genie.py",22011,0,"D",python,content +3158,6334193,"jasmine/genie.py",22012,0,"",python,selection_keyboard +3159,6334304,"jasmine/genie.py",22012,0,"O",python,content +3160,6334305,"jasmine/genie.py",22013,0,"",python,selection_keyboard +3161,6334399,"jasmine/genie.py",22013,0," ",python,content +3162,6334399,"jasmine/genie.py",22014,0,"",python,selection_keyboard +3163,6334533,"jasmine/genie.py",22014,0,"m",python,content +3164,6334534,"jasmine/genie.py",22015,0,"",python,selection_keyboard +3165,6334722,"jasmine/genie.py",22015,0,"i",python,content +3166,6334723,"jasmine/genie.py",22016,0,"",python,selection_keyboard +3167,6334782,"jasmine/genie.py",22016,0,"h",python,content +3168,6334783,"jasmine/genie.py",22017,0,"",python,selection_keyboard +3169,6334866,"jasmine/genie.py",22017,0,"i",python,content +3170,6334867,"jasmine/genie.py",22018,0,"",python,selection_keyboard +3171,6334965,"jasmine/genie.py",22018,0,"r",python,content +3172,6334966,"jasmine/genie.py",22019,0,"",python,selection_keyboard +3173,6335135,"jasmine/genie.py",22019,0,"\n ",python,content +3174,6336180,"jasmine/genie.py",22020,12,"",python,content +3175,6336350,"jasmine/genie.py",22020,0,"\n rng, _rng_noise = jax.random.split(rng)\n noise_level_111 = noise_level.reshape(1, 1, 1)\n noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\n noise_bucket_idx_B11 = jnp.floor(\n (noise_level_B11 * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(\n noise_bucket_idx_B11\n )\n noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\n\n vid_embed_BSNp2M = jnp.concatenate(\n [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\n )",python,content +3176,6336362,"jasmine/genie.py",22033,0,"",python,selection_command +3177,6337426,"jasmine/genie.py",22020,0,"",python,selection_command +3178,6341426,"jasmine/genie.py",22401,0,"",python,selection_mouse +3179,6342545,"jasmine/genie.py",22401,7,"",python,content +3180,6342955,"jasmine/genie.py",22401,0,"c",python,content +3181,6342956,"jasmine/genie.py",22402,0,"",python,selection_keyboard +3182,6343132,"jasmine/genie.py",22402,0,"a",python,content +3183,6343133,"jasmine/genie.py",22403,0,"",python,selection_keyboard +3184,6343262,"jasmine/genie.py",22403,0,"u",python,content +3185,6343263,"jasmine/genie.py",22404,0,"",python,selection_keyboard +3186,6343535,"jasmine/genie.py",22404,0,"s",python,content +3187,6343537,"jasmine/genie.py",22405,0,"",python,selection_keyboard +3188,6343633,"jasmine/genie.py",22405,0,"a",python,content 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typing import Tuple, Callable, List\n\nfrom flax import nnx\nimport jax\nimport jax.numpy as jnp\nimport einops\n\n\ndef _get_spatiotemporal_positional_encoding(d_model: int, max_len: int = 5000):\n """"""\n Creates a function that applies separate sinusoidal positional encodings to the temporal and spatial dimensions.\n """"""\n pe = jnp.zeros((max_len, d_model))\n position = jnp.arange(0, max_len, dtype=jnp.float32)[:, None]\n div_term = jnp.exp(jnp.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))\n pe = pe.at[:, 0::2].set(jnp.sin(position * div_term))\n pe = pe.at[:, 1::2].set(jnp.cos(position * div_term))\n\n def _encode(x: jax.Array) -> jax.Array:\n """"""\n Args:\n x: The input tensor of shape (Batch, Time, Space, Dimension).\n\n Returns:\n The input tensor with positional encodings added.\n """"""\n assert x.ndim == 4, f""Input must be 4-dimensional, but got shape {x.shape}""\n\n num_timesteps = x.shape[1]\n num_spatial_patches = x.shape[2]\n\n # Temporal positional encoding: (1, T, 1, D)\n temporal_pe = pe[None, :num_timesteps, None, :]\n x = x + temporal_pe\n\n # Spatial positional encoding: (1, 1, S, D)\n spatial_pe = pe[None, None, :num_spatial_patches, :]\n x = x + spatial_pe\n\n return x\n\n return _encode\n\n\nclass STBlock(nnx.Module):\n def __init__(\n self,\n dim: int,\n ffn_dim: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n rngs: nnx.Rngs,\n sow_weights: bool,\n sow_activations: bool,\n ):\n self.dim = dim\n self.ffn_dim = ffn_dim\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.sow_weights = sow_weights\n self.sow_activations = sow_activations\n\n self.spatial_norm = nnx.LayerNorm(\n num_features=self.dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.spatial_attention = nnx.MultiHeadAttention(\n num_heads=self.num_heads,\n in_features=self.dim,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n attention_fn=_create_flash_attention_fn(\n self.use_flash_attention, is_causal=False\n ),\n rngs=rngs,\n decode=False,\n )\n\n self.temporal_norm = nnx.LayerNorm(\n num_features=self.dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.temporal_attention = nnx.MultiHeadAttention(\n num_heads=self.num_heads,\n in_features=self.dim,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n attention_fn=_create_flash_attention_fn(\n self.use_flash_attention, is_causal=True\n ),\n rngs=rngs,\n decode=False,\n )\n\n self.ffn_norm = nnx.LayerNorm(\n num_features=self.dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.ffn_dense1 = nnx.Linear(\n in_features=self.dim,\n out_features=self.ffn_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.ffn_dense2 = nnx.Linear(\n in_features=self.ffn_dim,\n out_features=self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n @nnx.remat\n def __call__(self, x_BTNM: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z_BTNM = self.spatial_norm(x_BTNM)\n z_BTNM = self.spatial_attention(z_BTNM, sow_weights=self.sow_weights)\n x_BTNM = x_BTNM + z_BTNM\n\n # --- Temporal attention ---\n x_BNTM = x_BTNM.swapaxes(1, 2)\n z_BNTM = self.temporal_norm(x_BNTM)\n z_BNTM = self.temporal_attention(z_BNTM, sow_weights=self.sow_weights)\n x_BNTM = x_BNTM + z_BNTM\n x_BTNM = x_BNTM.swapaxes(1, 2)\n\n # --- Feedforward ---\n z_BTNM = self.ffn_norm(x_BTNM)\n z_BTND = self.ffn_dense1(z_BTNM)\n z_BTND = jax.nn.gelu(z_BTND)\n z_BTNM = self.ffn_dense2(z_BTND)\n x_BTNM = x_BTNM + z_BTNM\n if self.sow_activations:\n self.sow(nnx.Intermediate, ""activations"", x_BTNM)\n return x_BTNM\n\n\nclass STTransformer(nnx.Module):\n """"""\n Dimension keys:\n B: batch size\n T: number of frames\n N: number of patches per frame\n I: number of input features\n M: model dimension\n D: FFN dimension\n V: vocabulary size\n """"""\n\n def __init__(\n self,\n input_dim: int,\n model_dim: int,\n ffn_dim: int,\n out_dim: int,\n num_blocks: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n rngs: nnx.Rngs,\n sow_weights: bool = False,\n sow_activations: bool = False,\n sow_logits: bool = False,\n max_len: int = 5000,\n ):\n self.input_dim = input_dim\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.out_dim = out_dim\n self.num_blocks = num_blocks\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.sow_logits = sow_logits\n self.sow_weights = sow_weights\n self.sow_activations = sow_activations\n\n self.input_norm1 = nnx.LayerNorm(\n num_features=self.input_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.input_dense = nnx.Linear(\n in_features=self.input_dim,\n out_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.input_norm2 = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n\n self.pos_enc = _get_spatiotemporal_positional_encoding(\n self.model_dim, max_len=max_len\n )\n\n self.blocks = []\n for _ in range(self.num_blocks):\n self.blocks.append(\n STBlock(\n dim=self.model_dim,\n ffn_dim=self.ffn_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n sow_weights=self.sow_weights,\n sow_activations=self.sow_activations,\n )\n )\n\n self.output_dense = nnx.Linear(\n in_features=self.model_dim,\n out_features=self.out_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n def __call__(self, x_BTNI: jax.Array) -> jax.Array:\n x_BTNI = self.input_norm1(x_BTNI)\n x_BTNM = self.input_dense(x_BTNI)\n x_BTNM = self.input_norm2(x_BTNM)\n x_BTNM = self.pos_enc(x_BTNM)\n for block in self.blocks:\n x_BTNM = block(x_BTNM)\n\n x_BTNV = self.output_dense(x_BTNM)\n if self.sow_logits:\n self.sow(nnx.Intermediate, ""logits"", x_BTNV)\n return x_BTNV\n\n\nclass TransformerBlock(nnx.Module):\n def __init__(\n self,\n model_dim: int,\n ffn_dim: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n decode: bool,\n rngs: nnx.Rngs,\n sow_weights: bool,\n sow_activations: bool,\n ):\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.decode = decode\n self.sow_weights = sow_weights\n self.sow_activations = sow_activations\n\n self.temporal_norm = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.spatial_norm = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.ffn_norm = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.temporal_attention = nnx.MultiHeadAttention(\n num_heads=self.num_heads,\n in_features=self.model_dim,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n attention_fn=_create_flash_attention_fn(\n self.use_flash_attention, is_causal=True\n ),\n rngs=rngs,\n decode=self.decode,\n )\n self.spatial_attention = nnx.MultiHeadAttention(\n num_heads=self.num_heads,\n in_features=self.model_dim,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n attention_fn=_create_flash_attention_fn(\n self.use_flash_attention, is_causal=True\n ),\n rngs=rngs,\n decode=self.decode,\n )\n self.ffn_dense1 = nnx.Linear(\n in_features=self.model_dim,\n out_features=self.ffn_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.ffn_dense2 = nnx.Linear(\n in_features=self.ffn_dim,\n out_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n @nnx.remat\n def __call__(\n self, x_BTNM: jax.Array, pos_index: Tuple[jax.Array, jax.Array] | None = None\n ) -> jax.Array:\n # --- Spatial attention ---\n B, T, N, M = x_BTNM.shape\n z_FNM = einops.rearrange(x_BTNM, ""b t n m -> (b t) n m"")\n z_FNM = self.spatial_norm(z_FNM)\n z_FNM = self.spatial_attention(z_FNM, sow_weights=self.sow_weights)\n z_BTNM = einops.rearrange(z_FNM, ""(b t) n m -> b t n m"", t=T)\n x_BTNM = x_BTNM + z_BTNM\n # --- Temporal attention ---\n z_PTM = einops.rearrange(x_BTNM, ""b t n m -> (b n) t m"")\n z_PTM = self.temporal_norm(z_PTM)\n z_PTM = self.temporal_attention(z_PTM, sow_weights=self.sow_weights)\n z_BTNM = einops.rearrange(z_PTM, ""(b n) t m -> b t n m"", n=N)\n x_BTNM = x_BTNM + z_BTNM\n # --- Feedforward ---\n z_BTNM = self.ffn_norm(x_BTNM)\n z_BTND = self.ffn_dense1(z_BTNM)\n z_BTND = jax.nn.gelu(z_BTND)\n z_BTNM = self.ffn_dense2(z_BTND)\n x_BTNM = x_BTNM + z_BTNM\n if self.sow_activations:\n self.sow(nnx.Intermediate, ""activations"", x_BTNM)\n\n return x_BTNM\n\n\nclass Transformer(nnx.Module):\n """"""\n Dimension keys:\n B: batch size\n T: number of frames\n N: number of patches per frame\n I: number of input features\n M: model dimension\n D: FFN dimension\n V: vocabulary size\n F: number of frames in batch\n P: number of patch positions in batch\n """"""\n\n def __init__(\n self,\n input_dim: int,\n model_dim: int,\n ffn_dim: int,\n out_dim: int,\n num_blocks: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n decode: bool,\n rngs: nnx.Rngs,\n sow_logits: bool = False,\n sow_weights: bool = False,\n sow_activations: bool = False,\n max_len: int = 5000,\n ):\n self.input_dim = input_dim\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.out_dim = out_dim\n self.num_blocks = num_blocks\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.sow_logits = sow_logits\n self.sow_weights = sow_weights\n self.sow_activations = sow_activations\n\n self.input_norm1 = nnx.LayerNorm(\n num_features=self.input_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.input_dense = nnx.Linear(\n in_features=self.input_dim,\n out_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.input_norm2 = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n\n self.pos_enc = _get_spatiotemporal_positional_encoding(\n self.model_dim, max_len=max_len\n )\n\n self.blocks: List[TransformerBlock] = []\n for _ in range(self.num_blocks):\n self.blocks.append(\n TransformerBlock(\n model_dim=self.model_dim,\n ffn_dim=self.ffn_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=decode,\n sow_weights=self.sow_weights,\n sow_activations=self.sow_activations,\n rngs=rngs,\n )\n )\n self.output_dense = nnx.Linear(\n in_features=self.model_dim,\n out_features=self.out_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n def __call__(\n self, x_BTNI: jax.Array, pos_index: Tuple[jax.Array, jax.Array] | None = None\n ) -> jax.Array:\n x_BTNI = self.input_norm1(x_BTNI)\n x_BTNM = self.input_dense(x_BTNI)\n x_BTNM = self.input_norm2(x_BTNM)\n x_BTNM = self.pos_enc(x_BTNM)\n for block in self.blocks:\n x_BTNM = block(x_BTNM, pos_index)\n\n x_BTNV = self.output_dense(x_BTNM)\n if self.sow_logits:\n self.sow(nnx.Intermediate, ""logits"", x_BTNV)\n return x_BTNV\n\n\ndef normalize(x: jax.Array) -> jax.Array:\n return x / (jnp.linalg.norm(x, ord=2, axis=-1, keepdims=True) + 1e-8)\n\n\nclass VectorQuantizer(nnx.Module):\n """"""\n Dimension keys:\n D: B * T * N\n K: number of latents\n L: latent dimension\n """"""\n\n def __init__(\n self,\n latent_dim: int,\n num_latents: int,\n dropout: float,\n dtype: jnp.dtype,\n rngs: nnx.Rngs,\n ):\n self.latent_dim = latent_dim\n self.num_latents = num_latents\n self.dropout = dropout\n self.dtype = dtype\n\n self.codebook = nnx.Param(\n normalize(\n nnx.initializers.normal(stddev=1)(\n rngs.params(), (self.num_latents, self.latent_dim)\n )\n )\n )\n self.drop = nnx.Dropout(self.dropout, rngs=rngs)\n\n def __call__(\n self, x_DL: jax.Array, training: bool\n ) -> Tuple[jax.Array, jax.Array, jax.Array, jax.Array]:\n # --- Compute distances ---\n x_DL = x_DL.astype(self.dtype)\n codebook = self.codebook.value.astype(self.dtype)\n\n x_DL = normalize(x_DL)\n normalized_codebook_KL = normalize(codebook)\n distance_DK = -jnp.matmul(x_DL, normalized_codebook_KL.T)\n if training:\n distance_DK = self.drop(distance_DK)\n\n # --- Get indices and embeddings ---\n indices_D = jnp.argmin(distance_DK, axis=-1)\n z_DL = codebook[indices_D]\n\n # --- Straight through estimator ---\n z_q_DL = x_DL + jax.lax.stop_gradient(z_DL - x_DL)\n return z_q_DL, z_DL, x_DL, indices_D\n\n def get_codes(self, indices_E: jax.Array) -> jax.Array:\n return self.codebook[indices_E]\n\n\ndef _create_flash_attention_fn(use_flash_attention: bool, is_causal: bool) -> Callable:\n """"""\n Create an attention function that uses flash attention if enabled.\n\n flax.nnx.MultiHeadAttention provides tensors with shape (batch..., length, num_heads, head_dim),\n but jax.nn.dot_product_attention expects (batch, length, num_heads, head_dim). We reshape to\n ensure compatibility. cuDNN's flash attention additionally requires a sequence length that\n is a multiple of 4. We pad the sequence length to the nearest multiple of 4 and mask\n accordingly. Note that cuDNN requires the mask to be broadcast before calling the attention\n function due to strict shape checking.\n """"""\n\n def attention_fn(\n query_BTHD, key_BSHD, value_BSHD, bias=None, mask_B111=None, **kwargs\n ):\n implementation = ""cudnn"" if use_flash_attention else None\n\n def _merge_batch_dims(x):\n return einops.rearrange(x, ""... l h k -> (...) l h k"")\n\n def _pad(x, pad_size):\n return jnp.pad(x, ((0, 0), (0, pad_size), (0, 0), (0, 0)))\n\n original_shape = query_BTHD.shape\n T = query_BTHD.shape[-3]\n S = key_BSHD.shape[-3]\n\n # Pad to nearest multiple of 4\n Q = ((T + 3) // 4) * 4\n pad_size_Q = Q - T\n K = ((S + 3) // 4) * 4\n pad_size_K = K - S\n\n query_BQHD = _pad(_merge_batch_dims(query_BTHD), pad_size_Q)\n key_BKHD = _pad(_merge_batch_dims(key_BSHD), pad_size_K)\n value_BKHD = _pad(_merge_batch_dims(value_BSHD), pad_size_K)\n\n attention_mask = jnp.ones((Q, K), dtype=jnp.bool_)\n attention_mask = attention_mask.at[T:, :].set(False)\n attention_mask = attention_mask.at[:, S:].set(False)\n\n mask_11TS = attention_mask[jnp.newaxis, jnp.newaxis, :, :]\n\n bias_4d = (\n jnp.pad(\n _merge_batch_dims(bias),\n ((0, 0), (0, 0), (0, pad_size_Q), (0, pad_size_K)),\n )\n if bias is not None\n else None\n )\n\n # NOTE: jax.nn.dot_product_attention does not support dropout\n output_4d = jax.nn.dot_product_attention(\n query=query_BQHD,\n key=key_BKHD,\n value=value_BKHD,\n bias=bias_4d,\n mask=mask_11TS,\n implementation=implementation,\n is_causal=is_causal,\n )\n return output_4d[..., :T, :, :].reshape(original_shape)\n\n return attention_fn\n",python,tab +3301,6528970,"jasmine/utils/nn.py",15258,0,"",python,selection_mouse +3302,6529081,"jasmine/utils/nn.py",15253,9,"pos_index",python,selection_mouse +3303,6531981,"jasmine/utils/nn.py",15555,0,"",python,selection_mouse +3304,6533565,"jasmine/utils/nn.py",15548,0,"",python,selection_mouse +3305,6533749,"jasmine/utils/nn.py",15502,0,"",python,selection_command 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jasmine]$ ",,terminal_output +3694,11985362,"TERMINAL",0,0,"s",,terminal_output +3695,11985415,"TERMINAL",0,0,"h",,terminal_output +3696,11985477,"TERMINAL",0,0," ",,terminal_output +3697,11985979,"TERMINAL",0,0,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",,terminal_output +3698,11986312,"TERMINAL",0,0,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=1\r\n#SBATCH --time=06:00:00\r\n#SBATCH --partition=accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\r\n#SBATCH --job-name=train_dyn_default_breakout_longer\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\n# source .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/train\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/val\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\n# lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3512576\r\n# tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3512502\r\n\r\nlam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3518963\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3518963\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python jasmine/train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=10 \\r\n --image_width=10 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=120 \\r\n --init_lr=0 \\r\n --max_lr=3e-5 \\r\n --log_image_interval=250 \\r\n --log_checkpoint_interval=250 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=breakout-dyn-noise-lvl-default-$slurm_job_id \\r\n --tags dyn breakout noise-lvl default \\r\n --entity instant-uv \\r\n --project jafar \\r\n --patch_size 4 \\r\n --lam_patch_size 4 \\r\n --warmup_steps 100 \\r\n --wsd_decay_steps 1000 \\r\n --num_steps 5000 \\r\n --data_dir $array_records_dir_train \\r\n --val_data_dir $array_records_dir_val \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --lam_checkpoint $lam_checkpoint \\r\n --val_interval 1000 \\r\n --eval_full_frame \\r\n",,terminal_output 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+3700,11986598,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +3701,11991300,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +3702,11997423,"TERMINAL",0,0,"Counting all components: ['dynamics', 'lam', 'tokenizer']\r\n",,terminal_output +3703,11997768,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +3704,11998576,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.22.0\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20250928_164722-j3t59oxb\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run breakout-dyn-noise-lvl-default-3527669\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/j3t59oxb\r\n",,terminal_output +3705,11998840,"TERMINAL",0,0,"Parameter counts:\r\n{'dynamics': 26561024, 'lam': 16900976, 'tokenizer': 33750256, 'total': 77212256}\r\n",,terminal_output +3706,12000243,"TERMINAL",0,0,"WARNING:absl:Path /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn/interactive/3527669/000250 could not be identified as a temporary checkpoint path using . Got error: Expected AtomicRenameTemporaryPath (/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn/interactive/3527669/000250) to end with "".orbax-checkpoint-tmp"".\r\n",,terminal_output +3707,12001367,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +3708,12017187,"TERMINAL",0,0,"Traceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 790, in \r\n main(args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 620, in main\r\n compiled = train_step.lower(optimizer, first_batch).compile()\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/transforms/compilation.py"", line 476, in lower\r\n lowered = self.jitted_fn.lower(*pure_args, **pure_kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/transforms/compilation.py"", line 126, in __call__\r\n out = self.f(*args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 476, in train_step\r\n (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/graph.py"", line 2045, in update_context_manager_wrapper\r\n return f(*args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/transforms/autodiff.py"", line 163, in grad_wrapper\r\n fn_out = gradded_fn(*pure_args)\r\n ^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/transforms/autodiff.py"", line 88, in __call__\r\n out = self.f(*args)\r\n ^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 474, in loss_fn\r\n return dynamics_loss_fn(model, inputs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 462, in dynamics_loss_fn\r\n outputs = model(inputs)\r\n ^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/genie.py"", line 196, in __call__\r\n dyna_logits_BTNV, dyna_mask = self.dynamics(outputs)\r\n ^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/models/dynamics.py"", line 137, in __call__\r\n if jnp.any(jnp.isnan(vid_embed_BTNM)):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\njax.errors.TracerBoolConversionError: Attempted boolean conversion of traced array with shape bool[].\r\nThe error occurred while tracing the function train_step at /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py:468 for jit. This concrete value was not available in Python because it depends on the values of the arguments optimizer.states[0][2], optimizer.states[0][4], optimizer.states[0][401], optimizer.states[0][402], optimizer.states[0][403], optimizer.states[0][404], optimizer.states[0][405], optimizer.states[0][406], optimizer.states[0][407], optimizer.states[0][408], optimizer.states[0][409], optimizer.states[0][410], optimizer.states[0][411], optimizer.states[0][412], optimizer.states[0][413], optimizer.states[0][414], optimizer.states[0][415], optimizer.states[0][416], optimizer.states[0][417], optimizer.states[0][418], optimizer.states[0][419], optimizer.states[0][420], optimizer.states[0][421], optimizer.states[0][422], optimizer.states[0][423], optimizer.states[0][424], optimizer.states[0][425], optimizer.states[0][426], optimizer.states[0][427], optimizer.states[0][428], optimizer.states[0][429], optimizer.states[0][430], optimizer.states[0][431], optimizer.states[0][432], optimizer.states[0][433], optimizer.states[0][434], optimizer.states[0][435], optimizer.states[0][436], optimizer.states[0][437], optimizer.states[0][438], optimizer.states[0][439], optimizer.states[0][440], optimizer.states[0][441], optimizer.states[0][442], optimizer.states[0][443], optimizer.states[0][444], optimizer.states[0][445], optimizer.states[0][446], optimizer.states[0][447], optimizer.states[0][448], optimizer.states[0][449], optimizer.states[0][450], optimizer.states[0][451], optimizer.states[0][452], optimizer.states[0][453], optimizer.states[0][454], optimizer.states[0][455], optimizer.states[0][456], optimizer.states[0][457], optimizer.states[0][458], optimizer.states[0][459], optimizer.states[0][460], optimizer.states[0][461], optimizer.states[0][462], optimizer.states[0][463], optimizer.states[0][464], optimizer.states[0][465], optimizer.states[0][466], optimizer.states[0][467], optimizer.states[0][468], optimizer.states[0][469], optimizer.states[0][470], optimizer.states[0][471], optimizer.states[0][472], optimizer.states[0][473], optimizer.states[0][474], optimizer.states[0][475], optimizer.states[0][476], optimizer.states[0][477], optimizer.states[0][478], optimizer.states[0][479], optimizer.states[0][480], optimizer.states[0][481], optimizer.states[0][482], optimizer.states[0][483], optimizer.states[0][484], optimizer.states[0][485], optimizer.states[0][486], optimizer.states[0][487], optimizer.states[0][488], optimizer.states[0][489], optimizer.states[0][490], optimizer.states[0][491], optimizer.states[0][492], optimizer.states[0][493], optimizer.states[0][494], optimizer.states[0][495], optimizer.states[0][496], optimizer.states[0][497], optimizer.states[0][498], optimizer.states[0][499], optimizer.states[0][500], optimizer.states[0][501], optimizer.states[0][502], optimizer.states[0][503], optimizer.states[0][504], optimizer.states[0][505], optimizer.states[0][506], optimizer.states[0][507], optimizer.states[0][508], optimizer.states[0][509], optimizer.states[0][510], optimizer.states[0][511], optimizer.states[0][512], optimizer.states[0][513], inputs['rng'], and inputs['videos'].\r\nSee https://docs.jax.dev/en/latest/errors.html#jax.errors.TracerBoolConversionError\r\n--------------------\r\nFor simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n",,terminal_output +3709,12018824,"TERMINAL",0,0,"wandb: \r\nwandb: 🚀 View run breakout-dyn-noise-lvl-default-3527669 at: https://wandb.ai/instant-uv/jafar/runs/j3t59oxb\r\nwandb: Find logs at: ../../../../../hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20250928_164722-j3t59oxb/logs\r\n",,terminal_output +3710,12018948,"TERMINAL",0,0,"W0928 16:47:43.645570 3694452 pjrt_client.cc:1469] WatchJobStateAsync failed for task goo.gle/debugonly job_name: ""jax_worker"": CANCELLED: CANCELLED\r\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/WatchJobState:\r\n:UNKNOWN:Error received from peer {grpc_status:1, grpc_message:""CANCELLED""} [type.googleapis.com/tensorflow.DerivedStatus='']\r\n",,terminal_output +3711,12019556,"TERMINAL",0,0,"/usr/lib64/python3.12/multiprocessing/resource_tracker.py:254: UserWarning: resource_tracker: There appear to be 20 leaked shared_memory objects to clean up at shutdown\r\n warnings.warn('resource_tracker: There appear to be %d '\r\n",,terminal_output +3712,12019928,"TERMINAL",0,0,"srun: error: hkn0702: task 0: Exited with exit code 1\r\n]0;tum_cte0515@hkn0702:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0702 jasmine]$ ",,terminal_output +3713,12044394,"TERMINAL",0,0,"salloc: Job 3527669 has exceeded its time limit and its allocation has been revoked.\nslurmstepd: error: *** STEP 3527669.interactive ON hkn0702 CANCELLED AT 2025-09-28T16:48:09 DUE TO TIME LIMIT ***\r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\n",,terminal_output +3714,12073960,"TERMINAL",0,0,"srun: error: hkn0702: task 0: Killed\r\n]0;tum_cte0515@hkn1993:~/Projects/jasmine",,terminal_output +3715,12076100,"TERMINAL",0,0,"salloc --time=03:00:00 --partition=accelerated --nodes=1 --gres=gpu:1 --cpus-per-task=8",,terminal_command +3716,12076150,"TERMINAL",0,0,"]633;Csalloc: Pending job allocation 3527864\r\nsalloc: job 3527864 queued and waiting for resources\r\n",,terminal_output +3717,12077807,"TERMINAL",0,0,"^Csalloc: Job allocation 3527864 has been revoked.\r\nsalloc: Job aborted due to signal\r\n]0;tum_cte0515@hkn1993:~/Projects/jasmine",,terminal_output +3718,12078942,"TERMINAL",0,0,"queu",,terminal_command +3719,12079000,"TERMINAL",0,0,"]633;Cbash: queu: command not found...\r\n",,terminal_output +3720,12079710,"TERMINAL",0,0,"^C\r\n]0;tum_cte0515@hkn1993:~/Projects/jasmine",,terminal_output +3721,12080590,"TERMINAL",0,0,"queue",,terminal_command +3722,12080662,"TERMINAL",0,0,"]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Sun Sep 28 16:48:45 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3527669 accelerat interact tum_cte0 CG 3:00:27\t 1 hkn0702",,terminal_output +3723,12081704,"TERMINAL",0,0,"6",,terminal_output +3724,12082030,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jasmine",,terminal_output +3725,12083567,"TERMINAL",0,0,"idling",,terminal_command +3726,12083630,"TERMINAL",0,0,"]633;C[?1049h(B[?7hEvery 1.0s: sinfo_t_idlehkn1993.localdomain: Sun Sep 28 16:48:48 2025Partition dev_cpuonly: 11 nodes idle\rPartition cpuonly: 25 nodes idle\rPartition dev_accelerated:\t 2 nodes idle\rPartition accelerated:\t 6 nodes idle\rPartition dev_accelerated-h100 :\t 1 nodes idle\rPartition accelerated-h100:\t 5 nodes idle\rPartition large:\t 6 nodes idle\rPartition accelerated-h200:\t 2 nodes idle",,terminal_output +3727,12084669,"TERMINAL",0,0,"95",,terminal_output +3728,12085122,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jasmine",,terminal_output +3729,12092599,"TERMINAL",0,0,"salloc --time=01:00:00 --partition=dev_accelerated --nodes=1 --gres=gpu:1 --cpus-per-task=8",,terminal_command +3730,12092654,"TERMINAL",0,0,"]633;Csalloc: Granted job allocation 3527866\r\n",,terminal_output +3731,12092761,"TERMINAL",0,0,"salloc: Waiting for resource configuration\r\n",,terminal_output +3732,12111891,"TERMINAL",0,0,"s",,terminal_output +3733,12112013,"TERMINAL",0,0,"o",,terminal_output +3734,12112091,"TERMINAL",0,0,"ur",,terminal_output +3735,12112335,"TERMINAL",0,0,"c",,terminal_output +3736,12112487,"TERMINAL",0,0,"e",,terminal_output +3737,12112610,"TERMINAL",0,0," .",,terminal_output +3738,12112820,"TERMINAL",0,0,"v",,terminal_output +3739,12112986,"TERMINAL",0,0,"e",,terminal_output +3740,12113210,"TERMINAL",0,0,"n",,terminal_output +3741,12113463,"TERMINAL",0,0,"\t",,terminal_output +3742,12113830,"TERMINAL",0,0,"b",,terminal_output +3743,12114009,"TERMINAL",0,0,"in",,terminal_output +3744,12114223,"TERMINAL",0,0,"\t",,terminal_output +3745,12114595,"TERMINAL",0,0,"a",,terminal_output +3746,12114754,"TERMINAL",0,0,"c",,terminal_output +3747,12114981,"TERMINAL",0,0,"t",,terminal_output +3748,12115350,"TERMINAL",0,0,"\t",,terminal_output +3749,12115564,"TERMINAL",0,0,"\t",,terminal_output +3750,12116214,"TERMINAL",0,0,"",,terminal_output +3751,12116591,"TERMINAL",0,0,"\r\n",,terminal_output +3752,12119819,"TERMINAL",0,0,"salloc: Nodes hkn0402 are ready for job\r\n",,terminal_output +3753,12119965,"TERMINAL",0,0,"source .ven\tbin\tact\t\r\n",,terminal_output +3754,12120701,"TERMINAL",0,0,"]0;tum_cte0515@hkn0402:~/Projects/jasmine[?2004h[tum_cte0515@hkn0402 jasmine]$ source .venv/bin/activate\r\n[?2004l\r]0;tum_cte0515@hkn0402:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0402 jasmine]$ ",,terminal_output +3755,12147335,"jasmine/train_dynamics.py",0,0,"",python,tab +3756,12149106,"jasmine/models/dynamics.py",0,0,"",python,tab +3757,12150584,"jasmine/models/dynamics.py",5035,0,"",python,selection_mouse +3758,12151367,"jasmine/models/dynamics.py",5091,0,"",python,selection_mouse +3759,12151450,"jasmine/models/dynamics.py",5090,0,"",python,selection_command +3760,12152216,"jasmine/models/dynamics.py",5050,68," jax.debug.print(""NaNs in noise_level_embed after noise"")",python,selection_command +3761,12152459,"jasmine/models/dynamics.py",4994,124," if jnp.any(jnp.isnan(noise_level_embed_BT1M)): \n jax.debug.print(""NaNs in noise_level_embed after noise"")",python,selection_command +3762,12152610,"jasmine/models/dynamics.py",4933,185," jax.debug.print(""NaNs in vid_embed after noise"")\n if jnp.any(jnp.isnan(noise_level_embed_BT1M)): \n jax.debug.print(""NaNs in noise_level_embed after noise"")",python,selection_command +3763,12152761,"jasmine/models/dynamics.py",4885,233," if jnp.any(jnp.isnan(vid_embed_BTNM)): \n jax.debug.print(""NaNs in vid_embed after noise"")\n if jnp.any(jnp.isnan(noise_level_embed_BT1M)): \n jax.debug.print(""NaNs in noise_level_embed after noise"")",python,selection_command +3764,12153122,"jasmine/models/dynamics.py",4893,0,"",python,selection_command +3765,12153829,"jasmine/models/dynamics.py",5062,0,"#",python,content +3766,12153830,"jasmine/models/dynamics.py",5002,0,"#",python,content +3767,12153830,"jasmine/models/dynamics.py",4945,0,"#",python,content +3768,12153830,"jasmine/models/dynamics.py",4893,0,"#",python,content +3769,12153831,"jasmine/models/dynamics.py",4894,0,"",python,selection_keyboard +3770,12153906,"jasmine/models/dynamics.py",5066,0," ",python,content +3771,12153906,"jasmine/models/dynamics.py",5005,0," ",python,content +3772,12153906,"jasmine/models/dynamics.py",4947,0," ",python,content +3773,12153906,"jasmine/models/dynamics.py",4894,0," ",python,content +3774,12153907,"jasmine/models/dynamics.py",4895,0,"",python,selection_keyboard +3775,12154185,"jasmine/models/dynamics.py",4894,0,"",python,selection_command +3776,12164698,"TERMINAL",0,0,"source .venv/bin/activate",,terminal_output +3777,12165592,"TERMINAL",0,0,"alloc --time=01:00:00 --partition=accelerated --nodes=1 --gres=gpu:1 --cpus-per-task=8",,terminal_output +3778,12165906,"TERMINAL",0,0,"\ridling",,terminal_output +3779,12166403,"TERMINAL",0,0,"queue",,terminal_output +3780,12167189,"TERMINAL",0,0,"sbatch_dir slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/z-loss",,terminal_output +3781,12169008,"TERMINAL",0,0,"\r",,terminal_output +3782,12169657,"TERMINAL",0,0,"",,terminal_output +3783,12171344,"TERMINAL",0,0,"sh",,terminal_output +3784,12171482,"TERMINAL",0,0," ",,terminal_output +3785,12171726,"TERMINAL",0,0,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",,terminal_output +3786,12172550,"TERMINAL",0,0,"\rslurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=1\r\n#SBATCH --time=06:00:00\r\n#SBATCH --partition=accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\r\n#SBATCH --job-name=train_dyn_default_breakout_longer\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\n# source .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/train\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/val\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\n# lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3512576\r\n# tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3512502\r\n\r\nlam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3518963\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3518963\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python jasmine/train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=10 \\r\n --image_width=10 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=120 \\r\n --init_lr=0 \\r\n --max_lr=3e-5 \\r\n --log_image_interval=250 \\r\n --log_checkpoint_interval=250 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=breakout-dyn-noise-lvl-default-$slurm_job_id \\r\n --tags dyn breakout noise-lvl default \\r\n --entity instant-uv \\r\n --project jafar \\r\n --patch_size 4 \\r\n --lam_patch_size 4 \\r\n --warmup_steps 100 \\r\n --wsd_decay_steps 1000 \\r\n --num_steps 5000 \\r\n --data_dir $array_records_dir_train \\r\n --val_data_dir $array_records_dir_val \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --lam_checkpoint $lam_checkpoint \\r\n --val_interval 1000 \\r\n --eval_full_frame \\r\n",,terminal_output +3787,12172756,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=217802\r\nSLURM_JOB_GPUS=0\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\r\nSLURMD_NODENAME=hkn0402\r\nSLURM_JOB_START_TIME=1759070937\r\nSLURM_STEP_NODELIST=hkn0402\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1759074537\r\nSLURM_PMI2_SRUN_PORT=36803\r\nSLURM_CPUS_ON_NODE=8\r\nSLURM_JOB_CPUS_PER_NODE=8\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=dev_accelerated\r\nSLURM_TRES_PER_TASK=cpu=8\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=1\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3527866\r\nSLURM_PTY_PORT=42519\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.201\r\nSLURM_PTY_WIN_ROW=36\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0402\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.201\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=139\r\nSLURM_NODELIST=hkn0402\r\nSLURM_SRUN_COMM_PORT=45011\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1993.localdomain\r\nSLURM_JOB_ID=3527866\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0402\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_STEP_LAUNCHER_PORT=45011\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0402\r\n",,terminal_output +3788,12172866,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +3789,12185704,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +3790,12191827,"TERMINAL",0,0,"Counting all components: ['dynamics', 'lam', 'tokenizer']\r\n",,terminal_output +3791,12192212,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +3792,12193373,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.22.0\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20250928_165036-64wqq56h\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run breakout-dyn-noise-lvl-default-3527866\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/64wqq56h\r\n",,terminal_output +3793,12193678,"TERMINAL",0,0,"Parameter counts:\r\n{'dynamics': 26561024, 'lam': 16900976, 'tokenizer': 33750256, 'total': 77212256}\r\n",,terminal_output +3794,12198285,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +3795,12203765,"jasmine/train_dynamics.py",0,0,"",python,tab +3796,12206265,"jasmine/genie.py",0,0,"",python,tab +3797,12209488,"jasmine/models/dynamics.py",0,0,"",python,tab +3798,12213842,"jasmine/models/dynamics.py",4332,0,"",python,selection_mouse +3799,12213992,"jasmine/models/dynamics.py",4331,10,"mask_limit",python,selection_mouse +3800,12225317,"jasmine/models/dynamics.py",2577,0,"",python,selection_mouse +3801,12225320,"jasmine/models/dynamics.py",2576,0,"",python,selection_command +3802,12225962,"jasmine/models/dynamics.py",2537,0,"",python,selection_mouse +3803,12238080,"jasmine/models/dynamics.py",0,0,"",python,tab +3804,12280204,"TERMINAL",0,0,"Total memory size: 2.8 GB, Output size: 0.9 GB, Temp size: 1.9 GB, Argument size: 0.9 GB, Host temp size: 0.0 GB.\r\n",,terminal_output +3805,12280316,"TERMINAL",0,0,"FLOPs: 5.902e+10, Bytes: 5.203e+10 (48.5 GB), Intensity: 1.1 FLOPs/byte\r\nStarting training from step 0...\r\n",,terminal_output +3806,12284505,"TERMINAL",0,0,"Entering jdb:\r\n",,terminal_output +3807,12406857,"TERMINAL",0,0,"l",,terminal_output +3808,12407278,"TERMINAL",0,0,"\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/models/dynamics.py(110)\r\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\r\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\r\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\r\n jax.debug.breakpoint()\r\n \r\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\r\n-> jax.debug.breakpoint()\r\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M\r\n \r\n",,terminal_output +3809,12412403,"jasmine/models/dynamics.py",0,0,"",python,tab +3810,12432800,"TERMINAL",0,0,"c",,terminal_output +3811,12433067,"TERMINAL",0,0,"\r\n",,terminal_output +3812,12434548,"TERMINAL",0,0,"(jdb) Entering jdb:\r\n",,terminal_output +3813,12437000,"TERMINAL",0,0,"l",,terminal_output +3814,12437231,"TERMINAL",0,0,"\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/models/dynamics.py(107)\r\n jax.debug.breakpoint()\r\n \r\n # safe sqrt: clip argument to >= 0\r\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\r\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\r\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\r\n-> jax.debug.breakpoint()\r\n \r\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\r\n jax.debug.breakpoint()\r\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M\r\n \r\n",,terminal_output +3815,12456999,"jasmine/models/dynamics.py",0,0,"",python,tab +3816,12459967,"jasmine/models/dynamics.py",2457,0,"",python,selection_mouse +3817,12460105,"jasmine/models/dynamics.py",2452,13,"noise_level_B",python,selection_mouse +3818,12465278,"TERMINAL",0,0,"s",,terminal_output +3819,12465475,"TERMINAL",0,0,"e",,terminal_output +3820,12465536,"TERMINAL",0,0,"l",,terminal_output +3821,12465784,"TERMINAL",0,0,"f",,terminal_output +3822,12466214,"TERMINAL",0,0," ",,terminal_output +3823,12466904,"TERMINAL",0,0,"f",,terminal_output +3824,12466967,"TERMINAL",0,0,".",,terminal_output +3825,12467260,"TERMINAL",0,0,"n",,terminal_output +3826,12467443,"TERMINAL",0,0,"o",,terminal_output +3827,12467520,"TERMINAL",0,0,"is",,terminal_output +3828,12467765,"TERMINAL",0,0,"e",,terminal_output +3829,12467910,"TERMINAL",0,0,"_",,terminal_output +3830,12468174,"TERMINAL",0,0,"b",,terminal_output +3831,12468326,"TERMINAL",0,0,"u",,terminal_output +3832,12468389,"TERMINAL",0,0,"c",,terminal_output +3833,12468534,"TERMINAL",0,0,"k",,terminal_output +3834,12468595,"TERMINAL",0,0,"e",,terminal_output +3835,12468743,"TERMINAL",0,0,"t",,terminal_output +3836,12468805,"TERMINAL",0,0,"s",,terminal_output +3837,12468958,"TERMINAL",0,0,"\r\n(jdb) 10\r\n",,terminal_output +3838,12470799,"jasmine/models/dynamics.py",0,0,"",python,tab +3839,12475530,"TERMINAL",0,0,"s",,terminal_output +3840,12475753,"TERMINAL",0,0,"el",,terminal_output +3841,12476003,"TERMINAL",0,0,".",,terminal_output +3842,12476324,"TERMINAL",0,0," ",,terminal_output +3843,12476387,"TERMINAL",0,0,"d",,terminal_output +3844,12476554,"TERMINAL",0,0,".",,terminal_output +3845,12477088,"TERMINAL",0,0," ",,terminal_output +3846,12477204,"TERMINAL",0,0," ",,terminal_output +3847,12477320,"TERMINAL",0,0,"f",,terminal_output +3848,12477427,"TERMINAL",0,0,".",,terminal_output +3849,12478071,"TERMINAL",0,0,"m",,terminal_output +3850,12478188,"TERMINAL",0,0,"a",,terminal_output +3851,12478420,"TERMINAL",0,0,"x",,terminal_output +3852,12478622,"TERMINAL",0,0,"_",,terminal_output +3853,12478927,"TERMINAL",0,0,"n",,terminal_output +3854,12479087,"TERMINAL",0,0,"o",,terminal_output +3855,12479148,"TERMINAL",0,0,"i",,terminal_output +3856,12479279,"TERMINAL",0,0,"s",,terminal_output +3857,12479770,"TERMINAL",0,0,"e",,terminal_output +3858,12480080,"TERMINAL",0,0,"_",,terminal_output +3859,12480409,"TERMINAL",0,0,"l",,terminal_output +3860,12480515,"TERMINAL",0,0,"e",,terminal_output +3861,12480666,"TERMINAL",0,0,"v",,terminal_output +3862,12480786,"TERMINAL",0,0,"e",,terminal_output +3863,12480890,"TERMINAL",0,0,"l",,terminal_output +3864,12481093,"TERMINAL",0,0,"\r\n(jdb) 0.7\r\n",,terminal_output +3865,12482920,"jasmine/models/dynamics.py",0,0,"",python,tab +3866,12530650,"jasmine/models/dynamics.py",2608,0,"",python,selection_mouse +3867,12530650,"jasmine/models/dynamics.py",2607,0,"",python,selection_command +3868,12536975,"jasmine/models/dynamics.py",0,0,"",python,tab +3869,12539065,"jasmine/models/dynamics.py",2462,0,"",python,selection_mouse +3870,12539196,"jasmine/models/dynamics.py",2452,13,"noise_level_B",python,selection_mouse +3871,12543144,"TERMINAL",0,0,"noise_level_B",,terminal_output +3872,12546054,"TERMINAL",0,0,"[",,terminal_output +3873,12546268,"TERMINAL",0,0,"0",,terminal_output +3874,12546513,"TERMINAL",0,0,"]",,terminal_output +3875,12546844,"TERMINAL",0,0,"\r\n",,terminal_output +3876,12546905,"TERMINAL",0,0,"(jdb) Array(0.45255098, dtype=float32)\r\n",,terminal_output +3877,12548931,"TERMINAL",0,0,"^[[A",,terminal_output +3878,12550790,"TERMINAL",0,0," ",,terminal_output +3879,12550996,"TERMINAL",0,0," ",,terminal_output +3880,12551141,"TERMINAL",0,0,"  ",,terminal_output +3881,12552180,"TERMINAL",0,0,"noise_level_B",,terminal_output +3882,12552866,"TERMINAL",0,0,"[",,terminal_output +3883,12553022,"TERMINAL",0,0,"1",,terminal_output +3884,12553231,"TERMINAL",0,0,"}",,terminal_output +3885,12553594,"TERMINAL",0,0,"\r\n(jdb) *** SyntaxError: closing parenthesis '}' does not match opening parenthesis '['\r\n",,terminal_output +3886,12555231,"TERMINAL",0,0,"noise_level_B",,terminal_output +3887,12555664,"TERMINAL",0,0,"\r\n(jdb) Array([0.45255098, 0.6441636 , 0.66346925, 0.04603618, 0.06802309,\r\n 0.69920456, 0.23514807, 0.65358114, 0.49310675, 0.6987329 ,\r\n 0.29514727, 0.00263699, 0.40009692, 0.6722459 , 0.54544824,\r\n 0.69171315, 0.09180859, 0.634662 , 0.59807974, 0.660335 ,\r\n 0.43653592, 0.20926596, 0.04501296, 0.4408583 , 0.46784565,\r\n 0.2517042 , 0.23913348, 0.12439712, 0.56089723, 0.66213465,\r\n 0.30742902, 0.33678833, 0.4094986 , 0.08725767, 0.40686068,\r\n 0.38637325, 0.54127145, 0.37343186, 0.6557393 , 0.20142 ,\r\n 0.55386937, 0.5326104 , 0.22468646, 0.34933808, 0.45658964,\r\n 0.53491926, 0.42469105, 0.31144363, 0.685411 , 0.4874776 ,\r\n 0.6796607 , 0.15456446, 0.63261634, 0.17223041, 0.09819267,\r\n 0.09345566, 0.20636612, 0.56945485, 0.33953947, 0.1999843 ,\r\n 0.32493833, 0.6805619 , 0.34506497, 0.68235236, 0.1110582 ,\r\n 0.5945545 , 0.14432307, 0.60123146, 0.00452096, 0.47235835,\r\n 0.20066756, 0.642303 , 0.2481875 , 0.56008446, 0.34156114,\r\n 0.648798 , 0.32805106, 0.37387052, 0.20767422, 0.07023842,\r\n 0.61710835, 0.52920985, 0.0995904 , 0.09819601, 0.31743416,\r\n 0.2991825 , 0.3547224 , 0.14432673, 0.31014135, 0.08719108,\r\n 0.1480295 , 0.4119163 , 0.4997636 , 0.20711906, 0.658858 ,\r\n 0.3466018 , 0.15562956, 0.5944861 , 0.4517534 , 0.64904135,\r\n 0.60707915, 0.49129245, 0.23407486, 0.5858251 , 0.24955001,\r\n 0.30913532, 0.10704726, 0.1676466 , 0.36603883, 0.538971 ,\r\n 0.4430276 , 0.1924243 , 0.5491045 , 0.25620812, 0.33868474,\r\n 0.20711271, 0.5150012 , 0.0739684 , 0.21971789, 0.3666449 ], dtype=float32)\r\n",,terminal_output +3888,12559864,"TERMINAL",0,0,"l",,terminal_output +3889,12560022,"TERMINAL",0,0,"\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/models/dynamics.py(107)\r\n jax.debug.breakpoint()\r\n \r\n # safe sqrt: clip argument to >= 0\r\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\r\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\r\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\r\n-> jax.debug.breakpoint()\r\n \r\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\r\n jax.debug.breakpoint()\r\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M\r\n \r\n",,terminal_output +3890,12566924,"TERMINAL",0,0,"sqrt_noise",,terminal_output +3891,12567541,"TERMINAL",0,0,"\r\n(jdb) Array([[[[0.67271906]]],\r\n\r\n\r\n [[[0.80259806]]],\r\n\r\n\r\n [[[0.8145362 ]]],\r\n\r\n\r\n [[[0.21456043]]],\r\n\r\n\r\n [[[0.26081234]]],\r\n\r\n\r\n [[[0.8361845 ]]],\r\n\r\n\r\n [[[0.48492068]]],\r\n\r\n\r\n [[[0.80844367]]],\r\n\r\n\r\n [[[0.7022156 ]]],\r\n\r\n\r\n [[[0.83590245]]],\r\n\r\n\r\n [[[0.5432746 ]]],\r\n\r\n\r\n [[[0.05135166]]],\r\n\r\n\r\n [[[0.6325321 ]]],\r\n\r\n\r\n [[[0.81990606]]],\r\n\r\n\r\n [[[0.7385447 ]]],\r\n\r\n\r\n [[[0.83169293]]],\r\n\r\n\r\n [[[0.30299932]]],\r\n\r\n\r\n [[[0.7966567 ]]],\r\n\r\n\r\n [[[0.77335614]]],\r\n\r\n\r\n [[[0.81261 ]]],\r\n\r\n\r\n [[[0.66070867]]],\r\n\r\n\r\n [[[0.45745596]]],\r\n\r\n\r\n [[[0.21216258]]],\r\n\r\n\r\n [[[0.6639716 ]]],\r\n\r\n\r\n [[[0.68399245]]],\r\n\r\n\r\n [[[0.5017013 ]]],\r\n\r\n\r\n [[[0.48901275]]],\r\n\r\n\r\n [[[0.35269976]]],\r\n\r\n\r\n [[[0.74893075]]],\r\n\r\n\r\n [[[0.81371653]]],\r\n\r\n\r\n [[[0.5544628 ]]],\r\n\r\n\r\n [[[0.58033466]]],\r\n\r\n\r\n [[[0.6399208 ]]],\r\n\r\n\r\n [[[0.2953941 ]]],\r\n\r\n\r\n [[[0.6378563 ]]],\r\n\r\n\r\n [[[0.6215893 ]]],\r\n\r\n\r\n [[[0.7357115 ]]],\r\n\r\n\r\n [[[0.6110907 ]]],\r\n\r\n\r\n [[[0.8097773 ]]],\r\n\r\n\r\n [[[0.4487984 ]]],\r\n\r\n\r\n [[[0.744224 ]]],\r\n\r\n\r\n [[[0.7298016 ]]],\r\n\r\n\r\n [[[0.47401103]]],\r\n\r\n\r\n [[[0.5910483 ]]],\r\n\r\n\r\n [[[0.67571414]]],\r\n\r\n\r\n [[[0.7313818 ]]],\r\n\r\n\r\n [[[0.6516832 ]]],\r\n\r\n\r\n [[[0.5580714 ]]],\r\n\r\n\r\n [[[0.8278955 ]]],\r\n\r\n\r\n [[[0.69819593]]],\r\n\r\n\r\n [[[0.8244154 ]]],\r\n\r\n\r\n [[[0.39314684]]],\r\n\r\n\r\n [[[0.79537183]]],\r\n\r\n\r\n [[[0.41500652]]],\r\n\r\n\r\n [[[0.31335708]]],\r\n\r\n\r\n [[[0.3057052 ]]],\r\n\r\n\r\n [[[0.45427537]]],\r\n\r\n\r\n [[[0.75462234]]],\r\n\r\n\r\n [[[0.58270013]]],\r\n\r\n\r\n [[[0.44719604]]],\r\n\r\n\r\n [[[0.5700336 ]]],\r\n\r\n\r\n [[[0.8249617 ]]],\r\n\r\n\r\n [[[0.5874223 ]]],\r\n\r\n\r\n [[[0.8260462 ]]],\r\n\r\n\r\n [[[0.33325395]]],\r\n\r\n\r\n [[[0.7710736 ]]],\r\n\r\n\r\n [[[0.37989876]]],\r\n\r\n\r\n [[[0.77539116]]],\r\n\r\n\r\n [[[0.06723811]]],\r\n\r\n\r\n [[[0.6872833 ]]],\r\n\r\n\r\n [[[0.44795933]]],\r\n\r\n\r\n [[[0.80143803]]],\r\n\r\n\r\n [[[0.4981842 ]]],\r\n\r\n\r\n [[[0.74838793]]],\r\n\r\n\r\n [[[0.5844323 ]]],\r\n\r\n\r\n [[[0.80547994]]],\r\n\r\n\r\n [[[0.5727574 ]]],\r\n\r\n\r\n [[[0.61144954]]],\r\n\r\n\r\n [[[0.45571285]]],\r\n\r\n\r\n [[[0.26502532]]],\r\n\r\n\r\n [[[0.78556246]]],\r\n\r\n\r\n [[[0.72746813]]],\r\n\r\n\r\n [[[0.31557947]]],\r\n\r\n\r\n [[[0.31336242]]],\r\n\r\n\r\n [[[0.56341296]]],\r\n\r\n\r\n [[[0.5469758 ]]],\r\n\r\n\r\n [[[0.59558576]]],\r\n\r\n\r\n [[[0.37990358]]],\r\n\r\n\r\n [[[0.55690336]]],\r\n\r\n\r\n [[[0.29528135]]],\r\n\r\n\r\n [[[0.38474604]]],\r\n\r\n\r\n [[[0.641807 ]]],\r\n\r\n\r\n [[[0.70693964]]],\r\n\r\n\r\n [[[0.45510334]]],\r\n\r\n\r\n [[[0.81170064]]],\r\n\r\n\r\n [[[0.58872896]]],\r\n\r\n\r\n [[[0.39449912]]],\r\n\r\n\r\n [[[0.77102923]]],\r\n\r\n\r\n [[[0.672126 ]]],\r\n\r\n\r\n [[[0.80563104]]],\r\n\r\n\r\n [[[0.7791528 ]]],\r\n\r\n\r\n [[[0.70092255]]],\r\n\r\n\r\n [[[0.48381284]]],\r\n\r\n\r\n [[[0.7653921 ]]],\r\n\r\n\r\n [[[0.4995498 ]]],\r\n\r\n\r\n [[[0.55599934]]],\r\n\r\n\r\n [[[0.32718077]]],\r\n\r\n\r\n [[[0.4094467 ]]],\r\n\r\n\r\n [[[0.6050114 ]]],\r\n\r\n\r\n [[[0.7341464 ]]],\r\n\r\n\r\n [[[0.66560316]]],\r\n\r\n\r\n [[[0.43866193]]],\r\n\r\n\r\n [[[0.74101585]]],\r\n\r\n\r\n [[[0.50617003]]],\r\n\r\n\r\n [[[0.5819663 ]]],\r\n\r\n\r\n [[[0.45509636]]],\r\n\r\n\r\n [[[0.7176358 ]]],\r\n\r\n\r\n [[[0.27197132]]],\r\n\r\n\r\n [[[0.46874073]]],\r\n\r\n\r\n [[[0.6055121 ]]]], dtype=float32)\r\n",,terminal_output +3892,12597573,"TERMINAL",0,0,"sqrt_one_minus",,terminal_output +3893,12598040,"TERMINAL",0,0,"\r\n(jdb) Array([[[[0.73989797]]],\r\n\r\n\r\n [[[0.59652025]]],\r\n\r\n\r\n [[[0.5801127 ]]],\r\n\r\n\r\n [[[0.9767107 ]]],\r\n\r\n\r\n [[[0.9653895 ]]],\r\n\r\n\r\n [[[0.5484482 ]]],\r\n\r\n\r\n [[[0.87455815]]],\r\n\r\n\r\n [[[0.5885736 ]]],\r\n\r\n\r\n [[[0.71196437]]],\r\n\r\n\r\n [[[0.548878 ]]],\r\n\r\n\r\n [[[0.839555 ]]],\r\n\r\n\r\n [[[0.99868065]]],\r\n\r\n\r\n [[[0.7745341 ]]],\r\n\r\n\r\n [[[0.5724981 ]]],\r\n\r\n\r\n [[[0.6742045 ]]],\r\n\r\n\r\n [[[0.55523586]]],\r\n\r\n\r\n [[[0.9529907 ]]],\r\n\r\n\r\n [[[0.604432 ]]],\r\n\r\n\r\n [[[0.6339718 ]]],\r\n\r\n\r\n [[[0.58280784]]],\r\n\r\n\r\n [[[0.7506424 ]]],\r\n\r\n\r\n [[[0.8892323 ]]],\r\n\r\n\r\n [[[0.97723436]]],\r\n\r\n\r\n [[[0.7477578 ]]],\r\n\r\n\r\n [[[0.7294891 ]]],\r\n\r\n\r\n [[[0.8650409 ]]],\r\n\r\n\r\n [[[0.8722766 ]]],\r\n\r\n\r\n [[[0.93573654]]],\r\n\r\n\r\n [[[0.6626483 ]]],\r\n\r\n\r\n [[[0.5812619 ]]],\r\n\r\n\r\n [[[0.8322085 ]]],\r\n\r\n\r\n [[[0.8143781 ]]],\r\n\r\n\r\n [[[0.7684409 ]]],\r\n\r\n\r\n [[[0.9553755 ]]],\r\n\r\n\r\n [[[0.7701554 ]]],\r\n\r\n\r\n [[[0.78334326]]],\r\n\r\n\r\n [[[0.677295 ]]],\r\n\r\n\r\n [[[0.79156053]]],\r\n\r\n\r\n [[[0.58673733]]],\r\n\r\n\r\n [[[0.893633 ]]],\r\n\r\n\r\n [[[0.6679301 ]]],\r\n\r\n\r\n [[[0.68365896]]],\r\n\r\n\r\n [[[0.8805189 ]]],\r\n\r\n\r\n [[[0.80663615]]],\r\n\r\n\r\n [[[0.7371637 ]]],\r\n\r\n\r\n [[[0.6819683 ]]],\r\n\r\n\r\n [[[0.7584912 ]]],\r\n\r\n\r\n [[[0.829793 ]]],\r\n\r\n\r\n [[[0.56088233]]],\r\n\r\n\r\n [[[0.7159067 ]]],\r\n\r\n\r\n [[[0.56598526]]],\r\n\r\n\r\n [[[0.9194757 ]]],\r\n\r\n\r\n [[[0.6061218 ]]],\r\n\r\n\r\n [[[0.9098185 ]]],\r\n\r\n\r\n [[[0.9496353 ]]],\r\n\r\n\r\n [[[0.9521262 ]]],\r\n\r\n\r\n [[[0.89086133]]],\r\n\r\n\r\n [[[0.6561594 ]]],\r\n\r\n\r\n [[[0.8126872 ]]],\r\n\r\n\r\n [[[0.89443594]]],\r\n\r\n\r\n [[[0.82162136]]],\r\n\r\n\r\n [[[0.5651885 ]]],\r\n\r\n\r\n [[[0.8092806 ]]],\r\n\r\n\r\n [[[0.5636024 ]]],\r\n\r\n\r\n [[[0.9428371 ]]],\r\n\r\n\r\n [[[0.63674605]]],\r\n\r\n\r\n [[[0.9250281 ]]],\r\n\r\n\r\n [[[0.63148123]]],\r\n\r\n\r\n [[[0.99773693]]],\r\n\r\n\r\n [[[0.72638947]]],\r\n\r\n\r\n [[[0.89405394]]],\r\n\r\n\r\n [[[0.5980778 ]]],\r\n\r\n\r\n [[[0.8670712 ]]],\r\n\r\n\r\n [[[0.6632613 ]]],\r\n\r\n\r\n [[[0.81144243]]],\r\n\r\n\r\n [[[0.592623 ]]],\r\n\r\n\r\n [[[0.8197249 ]]],\r\n\r\n\r\n [[[0.7912834 ]]],\r\n\r\n\r\n [[[0.8901268 ]]],\r\n\r\n\r\n [[[0.96424145]]],\r\n\r\n\r\n [[[0.6187824 ]]],\r\n\r\n\r\n [[[0.6861415 ]]],\r\n\r\n\r\n [[[0.94889915]]],\r\n\r\n\r\n [[[0.9496336 ]]],\r\n\r\n\r\n [[[0.8261754 ]]],\r\n\r\n\r\n [[[0.8371484 ]]],\r\n\r\n\r\n [[[0.80329174]]],\r\n\r\n\r\n [[[0.92502606]]],\r\n\r\n\r\n [[[0.8305773 ]]],\r\n\r\n\r\n [[[0.9554103 ]]],\r\n\r\n\r\n [[[0.92302245]]],\r\n\r\n\r\n [[[0.76686615]]],\r\n\r\n\r\n [[[0.7072739 ]]],\r\n\r\n\r\n [[[0.8904386 ]]],\r\n\r\n\r\n [[[0.5840736 ]]],\r\n\r\n\r\n [[[0.8083305 ]]],\r\n\r\n\r\n [[[0.9188963 ]]],\r\n\r\n\r\n [[[0.63679975]]],\r\n\r\n\r\n [[[0.74043673]]],\r\n\r\n\r\n [[[0.59241766]]],\r\n\r\n\r\n [[[0.626834 ]]],\r\n\r\n\r\n [[[0.71323735]]],\r\n\r\n\r\n [[[0.8751715 ]]],\r\n\r\n\r\n [[[0.6435642 ]]],\r\n\r\n\r\n [[[0.86628515]]],\r\n\r\n\r\n [[[0.83118266]]],\r\n\r\n\r\n [[[0.9449617 ]]],\r\n\r\n\r\n [[[0.912334 ]]],\r\n\r\n\r\n [[[0.7962168 ]]],\r\n\r\n\r\n [[[0.67899114]]],\r\n\r\n\r\n [[[0.7463058 ]]],\r\n\r\n\r\n [[[0.89865214]]],\r\n\r\n\r\n [[[0.6714875 ]]],\r\n\r\n\r\n [[[0.8624337 ]]],\r\n\r\n\r\n [[[0.81321293]]],\r\n\r\n\r\n [[[0.8904422 ]]],\r\n\r\n\r\n [[[0.6964186 ]]],\r\n\r\n\r\n [[[0.96230537]]],\r\n\r\n\r\n [[[0.88333577]]],\r\n\r\n\r\n [[[0.7958361 ]]]], dtype=float32)\r\n",,terminal_output +3894,12618984,"TERMINAL",0,0,"l",,terminal_output +3895,12619191,"TERMINAL",0,0,"\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/models/dynamics.py(107)\r\n jax.debug.breakpoint()\r\n \r\n # safe sqrt: clip argument to >= 0\r\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\r\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\r\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\r\n-> jax.debug.breakpoint()\r\n \r\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\r\n jax.debug.breakpoint()\r\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M\r\n \r\n",,terminal_output +3896,12624951,"jasmine/train_dynamics.py",0,0,"",python,tab +3897,12625973,"jasmine/models/dynamics.py",0,0,"",python,tab +3898,12630715,"jasmine/models/dynamics.py",2625,0,"",python,selection_mouse +3899,12630859,"jasmine/models/dynamics.py",2617,10,"noise_BTNM",python,selection_mouse +3900,12642002,"jasmine/models/dynamics.py",3156,0,"",python,selection_mouse +3901,12642159,"jasmine/models/dynamics.py",3153,4,"self",python,selection_mouse +3902,12643017,"jasmine/models/dynamics.py",3160,0,"",python,selection_mouse +3903,12643166,"jasmine/models/dynamics.py",3158,17,"noise_level_embed",python,selection_mouse +3904,12653059,"jasmine/models/dynamics.py",3182,0,"",python,selection_mouse +3905,12653223,"jasmine/models/dynamics.py",3176,20,"noise_bucket_idx_B11",python,selection_mouse +3906,12657373,"jasmine/models/dynamics.py",3154,0,"",python,selection_mouse +3907,12657506,"jasmine/models/dynamics.py",3153,4,"self",python,selection_mouse +3908,12657721,"jasmine/models/dynamics.py",3153,5,"self.",python,selection_mouse +3909,12657738,"jasmine/models/dynamics.py",3153,22,"self.noise_level_embed",python,selection_mouse +3910,12661260,"TERMINAL",0,0,"self.noise_level_embed",,terminal_output +3911,12662046,"TERMINAL",0,0,"(",,terminal_output +3912,12662422,"TERMINAL",0,0,"0",,terminal_output +3913,12663036,"TERMINAL",0,0,")",,terminal_output +3914,12663526,"TERMINAL",0,0,"\r\n(jdb) *** AttributeError: 'int' object has no attribute 'dtype'\r\n",,terminal_output +3915,12666853,"TERMINAL",0,0,"o",,terminal_output +3916,12668550,"TERMINAL",0,0," ",,terminal_output +3917,12669041,"TERMINAL",0,0,"z",,terminal_output +3918,12669467,"TERMINAL",0,0,"e",,terminal_output +3919,12669796,"TERMINAL",0,0,"r",,terminal_output +3920,12670404,"TERMINAL",0,0,"o",,terminal_output +3921,12670525,"TERMINAL",0,0," ",,terminal_output +3922,12670718,"TERMINAL",0,0,"=",,terminal_output +3923,12670836,"TERMINAL",0,0," ",,terminal_output +3924,12673139,"TERMINAL",0,0,"j",,terminal_output +3925,12673244,"TERMINAL",0,0,"a",,terminal_output +3926,12673417,"TERMINAL",0,0,"x",,terminal_output +3927,12673600,"TERMINAL",0,0,"-",,terminal_output +3928,12674209,"TERMINAL",0,0," ",,terminal_output +3929,12674446,"TERMINAL",0,0,".",,terminal_output +3930,12677213,"TERMINAL",0,0,"bash",,terminal_focus +3931,12677719,"TERMINAL",0,0,"srun",,terminal_focus +3932,12678149,"jasmine/train_dynamics.py",0,0,"",python,tab +3933,12679765,"jasmine/genie.py",0,0,"",python,tab +3934,12681021,"jasmine/models/dynamics.py",0,0,"",python,tab +3935,12682777,"jasmine/models/dynamics.py",3259,0,"",python,selection_mouse +3936,12682954,"jasmine/models/dynamics.py",3240,22,"noise_level_embed_B11M",python,selection_mouse +3937,12685231,"jasmine/models/dynamics.py",2635,0,"",python,selection_mouse +3938,12686343,"jasmine/models/dynamics.py",2600,0,"",python,selection_mouse +3939,12690057,"TERMINAL",0,0,"A",,terminal_output +3940,12690306,"TERMINAL",0,0,"r",,terminal_output +3941,12690454,"TERMINAL",0,0,"r",,terminal_output +3942,12690660,"TERMINAL",0,0,"a",,terminal_output +3943,12690876,"TERMINAL",0,0,"y",,terminal_output +3944,12691300,"TERMINAL",0,0,"(",,terminal_output +3945,12692371,"TERMINAL",0,0,"0",,terminal_output +3946,12693514,"TERMINAL",0,0,")",,terminal_output +3947,12694089,"TERMINAL",0,0,"\r\n(jdb) *** SyntaxError: invalid syntax\r\n",,terminal_output +3948,12699578,"jasmine/train_dynamics.py",0,0,"",python,tab +3949,12703499,"jasmine/models/dynamics.py",0,0,"",python,tab +3950,12710533,"TERMINAL",0,0,"n",,terminal_output +3951,12710714,"TERMINAL",0,0,"o",,terminal_output +3952,12710781,"TERMINAL",0,0,"i",,terminal_output +3953,12710884,"TERMINAL",0,0,"s",,terminal_output +3954,12711027,"TERMINAL",0,0,"e",,terminal_output +3955,12711240,"TERMINAL",0,0,"_",,terminal_output +3956,12711493,"TERMINAL",0,0,"l",,terminal_output +3957,12711556,"TERMINAL",0,0,"e",,terminal_output +3958,12711710,"TERMINAL",0,0,"v",,terminal_output +3959,12711869,"TERMINAL",0,0,"e",,terminal_output +3960,12711932,"TERMINAL",0,0,"l",,terminal_output +3961,12712169,"TERMINAL",0,0,"_",,terminal_output +3962,12712409,"TERMINAL",0,0,"e",,terminal_output +3963,12712471,"TERMINAL",0,0,"m",,terminal_output +3964,12712708,"TERMINAL",0,0,"b",,terminal_output +3965,12712771,"TERMINAL",0,0,"e",,terminal_output +3966,12712893,"TERMINAL",0,0,"d",,terminal_output +3967,12713094,"TERMINAL",0,0,"\t",,terminal_output +3968,12713882,"TERMINAL",0,0,"",,terminal_output +3969,12715070,"TERMINAL",0,0,"_",,terminal_output +3970,12715439,"TERMINAL",0,0,"B",,terminal_output +3971,12716616,"TERMINAL",0,0,"1",,terminal_output +3972,12716766,"TERMINAL",0,0,"1",,terminal_output +3973,12717600,"TERMINAL",0,0,"M",,terminal_output +3974,12717945,"TERMINAL",0,0,"\r\n(jdb) Array([[[[ 4.4392552e-02, -2.9844549e-02, -3.1102074e-02, ...,\r\n -1.2073218e-02, 4.3277056e-03, -6.1354764e-02]]],\r\n\r\n\r\n [[[ 1.1847667e-01, -1.0830977e-02, -1.6250851e-02, ...,\r\n -2.4750691e-02, -2.7230620e-02, -8.5215392e-03]]],\r\n\r\n\r\n [[[ 1.1847667e-01, -1.0830977e-02, -1.6250851e-02, ...,\r\n -2.4750691e-02, -2.7230620e-02, -8.5215392e-03]]],\r\n\r\n\r\n ...,\r\n\r\n\r\n [[[-5.4778680e-02, 4.7858335e-02, 1.9920882e-02, ...,\r\n 5.6935117e-02, -1.5326574e-02, 2.6951581e-02]]],\r\n\r\n\r\n [[[ 1.6207712e-02, -4.6082556e-02, 3.1736858e-05, ...,\r\n 9.7126283e-02, 2.2778701e-02, 4.0227242e-02]]],\r\n\r\n\r\n [[[ 3.6209282e-02, -5.9933860e-02, 2.8886722e-02, ...,\r\n -1.1514775e-02, -2.0673620e-02, 2.2546995e-02]]]], dtype=float32)\r\n",,terminal_output +3975,12725948,"jasmine/models/dynamics.py",0,0,"",python,tab +3976,12764262,"jasmine/models/dynamics.py",3052,0,"",python,selection_mouse +3977,12764263,"jasmine/models/dynamics.py",3051,0,"",python,selection_command +3978,12814446,"jasmine/models/dynamics.py",0,0,"",python,tab +3979,12816343,"jasmine/models/dynamics.py",3169,0,"",python,selection_mouse +3980,12816513,"jasmine/models/dynamics.py",3158,17,"noise_level_embed",python,selection_mouse +3981,12823453,"jasmine/models/dynamics.py",4421,0,"",python,selection_command +3982,12827338,"jasmine/models/dynamics.py",3222,0,"",python,selection_mouse +3983,12827537,"jasmine/models/dynamics.py",3206,22,"noise_level_embed_BT1M",python,selection_mouse +3984,12831764,"jasmine/models/dynamics.py",4809,0,"",python,selection_mouse +3985,12831943,"jasmine/models/dynamics.py",4808,22,"noise_level_embed_BT1M",python,selection_mouse +3986,12834175,"jasmine/models/dynamics.py",5127,0,"",python,selection_mouse +3987,12835731,"jasmine/models/dynamics.py",5127,0,"k",python,content +3988,12835733,"jasmine/models/dynamics.py",5128,0,"",python,selection_keyboard +3989,12836295,"jasmine/models/dynamics.py",5127,1,"",python,content +3990,12836580,"jasmine/models/dynamics.py",5056,0,"",python,selection_command +3991,12836946,"jasmine/models/dynamics.py",4998,129,"",python,content +3992,12837102,"jasmine/models/dynamics.py",4935,0,"",python,selection_command +3993,12837470,"jasmine/models/dynamics.py",4885,113,"",python,content +3994,12840069,"jasmine/models/dynamics.py",5193,0,"",python,selection_mouse +3995,12840226,"jasmine/models/dynamics.py",5182,22,"noise_level_embed_BT1M",python,selection_mouse +3996,12889786,"jasmine/models/dynamics.py",2905,0,"",python,selection_mouse +3997,12889925,"jasmine/models/dynamics.py",2890,18,"noise_bucket_idx_B",python,selection_mouse +3998,12893742,"TERMINAL",0,0,"noise_bucket_idx_B",,terminal_output +3999,12894038,"TERMINAL",0,0,"\r\n(jdb) Array([6, 9, 9, 0, 0, 9, 3, 9, 7, 9, 4, 0, 5, 9, 7, 9, 1, 9, 8, 9, 6, 2,\r\n 0, 6, 6, 3, 3, 1, 8, 9, 4, 4, 5, 1, 5, 5, 7, 5, 9, 2, 7, 7, 3, 4,\r\n 6, 7, 6, 4, 9, 6, 9, 2, 9, 2, 1, 1, 2, 8, 4, 2, 4, 9, 4, 9, 1, 8,\r\n 2, 8, 0, 6, 2, 9, 3, 8, 4, 9, 4, 5, 2, 1, 8, 7, 1, 1, 4, 4, 5, 2,\r\n 4, 1, 2, 5, 7, 2, 9, 4, 2, 8, 6, 9, 8, 7, 3, 8, 3, 4, 1, 2, 5, 7,\r\n 6, 2, 7, 3, 4, 2, 7, 1, 3, 5], dtype=int32)\r\n",,terminal_output +4000,12906415,"jasmine/train_dynamics.py",0,0,"",python,tab +4001,12908002,"TERMINAL",0,0,"bash",,terminal_focus +4002,12908661,"TERMINAL",0,0,"srun",,terminal_focus +4003,12910061,"jasmine/train_dynamics.py",0,0,"",python,tab +4004,12911724,"jasmine/genie.py",0,0,"",python,tab +4005,12917770,"jasmine/models/dynamics.py",0,0,"",python,tab +4006,12944000,"jasmine/train_dynamics.py",0,0,"",python,tab +4007,12963349,"jasmine/train_dynamics.py",22407,0,"",python,selection_mouse +4008,12963515,"jasmine/train_dynamics.py",22405,3,"rng",python,selection_mouse +4009,13056201,"jasmine/train_dynamics.py",15973,0,"",python,selection_mouse +4010,13056877,"jasmine/train_dynamics.py",15899,0,"",python,selection_mouse +4011,13056890,"jasmine/train_dynamics.py",15898,0,"",python,selection_command +4012,13092691,"jasmine/models/dynamics.py",0,0,"",python,tab +4013,13110859,"jasmine/models/dynamics.py",3052,0,"",python,selection_mouse +4014,13110861,"jasmine/models/dynamics.py",3051,0,"",python,selection_command +4015,13111621,"jasmine/models/dynamics.py",3021,0,"",python,selection_mouse +4016,13111624,"jasmine/models/dynamics.py",3020,0,"",python,selection_command +4017,13112807,"jasmine/models/dynamics.py",3052,0,"",python,selection_mouse +4018,13112808,"jasmine/models/dynamics.py",3051,0,"",python,selection_command +4019,13127737,"jasmine/models/dynamics.py",3634,0,"",python,selection_mouse +4020,13127738,"jasmine/models/dynamics.py",3633,0,"",python,selection_command +4021,13128356,"jasmine/models/dynamics.py",3684,0,"",python,selection_mouse +4022,13129053,"jasmine/models/dynamics.py",3668,0,"",python,selection_mouse +4023,13129167,"jasmine/models/dynamics.py",3644,30,"noise_augmented_vid_embed_BTNM",python,selection_mouse +4024,13132062,"jasmine/models/dynamics.py",3516,0,"",python,selection_command +4025,13140011,"TERMINAL",0,0,"l",,terminal_output +4026,13140207,"TERMINAL",0,0,"\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/models/dynamics.py(107)\r\n jax.debug.breakpoint()\r\n \r\n # safe sqrt: clip argument to >= 0\r\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\r\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\r\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\r\n-> jax.debug.breakpoint()\r\n \r\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\r\n jax.debug.breakpoint()\r\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M\r\n \r\n",,terminal_output +4027,13143637,"TERMINAL",0,0,"s",,terminal_output +4028,13143864,"TERMINAL",0,0,"q",,terminal_output +4029,13144123,"TERMINAL",0,0,"r",,terminal_output +4030,13144256,"TERMINAL",0,0,"t",,terminal_output +4031,13144517,"TERMINAL",0,0,"_",,terminal_output +4032,13145252,"TERMINAL",0,0,"m",,terminal_output +4033,13145443,"TERMINAL",0,0,"i",,terminal_output +4034,13145506,"TERMINAL",0,0,"n",,terminal_output +4035,13146039,"TERMINAL",0,0,"u",,terminal_output +4036,13146774,"TERMINAL",0,0," ",,terminal_output +4037,13146933,"TERMINAL",0,0," ",,terminal_output +4038,13147066,"TERMINAL",0,0," ",,terminal_output +4039,13147187,"TERMINAL",0,0," ",,terminal_output +4040,13147492,"TERMINAL",0,0,"o",,terminal_output +4041,13147644,"TERMINAL",0,0,"n",,terminal_output +4042,13147780,"TERMINAL",0,0,"e",,terminal_output +4043,13147973,"TERMINAL",0,0,"_",,terminal_output +4044,13148237,"TERMINAL",0,0,"m",,terminal_output +4045,13148411,"TERMINAL",0,0,"i",,terminal_output +4046,13148472,"TERMINAL",0,0,"n",,terminal_output +4047,13148706,"TERMINAL",0,0,"u",,terminal_output +4048,13148846,"TERMINAL",0,0,"s",,terminal_output +4049,13149011,"TERMINAL",0,0,"\r\n(jdb) Array([[[[0.73989797]]],\r\n\r\n\r\n [[[0.59652025]]],\r\n\r\n\r\n [[[0.5801127 ]]],\r\n\r\n\r\n [[[0.9767107 ]]],\r\n\r\n\r\n [[[0.9653895 ]]],\r\n\r\n\r\n [[[0.5484482 ]]],\r\n\r\n\r\n [[[0.87455815]]],\r\n\r\n\r\n [[[0.5885736 ]]],\r\n\r\n\r\n [[[0.71196437]]],\r\n\r\n\r\n [[[0.548878 ]]],\r\n\r\n\r\n [[[0.839555 ]]],\r\n\r\n\r\n [[[0.99868065]]],\r\n\r\n\r\n [[[0.7745341 ]]],\r\n\r\n\r\n [[[0.5724981 ]]],\r\n\r\n\r\n [[[0.6742045 ]]],\r\n\r\n\r\n [[[0.55523586]]],\r\n\r\n\r\n [[[0.9529907 ]]],\r\n\r\n\r\n [[[0.604432 ]]],\r\n\r\n\r\n [[[0.6339718 ]]],\r\n\r\n\r\n [[[0.58280784]]],\r\n\r\n\r\n [[[0.7506424 ]]],\r\n\r\n\r\n [[[0.8892323 ]]],\r\n\r\n\r\n [[[0.97723436]]],\r\n\r\n\r\n [[[0.7477578 ]]],\r\n\r\n\r\n [[[0.7294891 ]]],\r\n\r\n\r\n [[[0.8650409 ]]],\r\n\r\n\r\n [[[0.8722766 ]]],\r\n\r\n\r\n [[[0.93573654]]],\r\n\r\n\r\n [[[0.6626483 ]]],\r\n\r\n\r\n [[[0.5812619 ]]],\r\n\r\n\r\n [[[0.8322085 ]]],\r\n\r\n\r\n [[[0.8143781 ]]],\r\n\r\n\r\n [[[0.7684409 ]]],\r\n\r\n\r\n [[[0.9553755 ]]],\r\n\r\n\r\n [[[0.7701554 ]]],\r\n\r\n\r\n [[[0.78334326]]],\r\n\r\n\r\n [[[0.677295 ]]],\r\n\r\n\r\n [[[0.79156053]]],\r\n\r\n\r\n [[[0.58673733]]],\r\n\r\n\r\n [[[0.893633 ]]],\r\n\r\n\r\n [[[0.6679301 ]]],\r\n\r\n\r\n [[[0.68365896]]],\r\n\r\n\r\n [[[0.8805189 ]]],\r\n\r\n\r\n [[[0.80663615]]],\r\n\r\n\r\n [[[0.7371637 ]]],\r\n\r\n\r\n [[[0.6819683 ]]],\r\n\r\n\r\n [[[0.7584912 ]]],\r\n\r\n\r\n [[[0.829793 ]]],\r\n\r\n\r\n [[[0.56088233]]],\r\n\r\n\r\n [[[0.7159067 ]]],\r\n\r\n\r\n [[[0.56598526]]],\r\n\r\n\r\n [[[0.9194757 ]]],\r\n\r\n\r\n [[[0.6061218 ]]],\r\n\r\n\r\n [[[0.9098185 ]]],\r\n\r\n\r\n [[[0.9496353 ]]],\r\n\r\n\r\n [[[0.9521262 ]]],\r\n\r\n\r\n [[[0.89086133]]],\r\n\r\n\r\n [[[0.6561594 ]]],\r\n\r\n\r\n [[[0.8126872 ]]],\r\n\r\n\r\n [[[0.89443594]]],\r\n\r\n\r\n [[[0.82162136]]],\r\n\r\n\r\n [[[0.5651885 ]]],\r\n\r\n\r\n [[[0.8092806 ]]],\r\n\r\n\r\n [[[0.5636024 ]]],\r\n\r\n\r\n [[[0.9428371 ]]],\r\n\r\n\r\n [[[0.63674605]]],\r\n\r\n\r\n [[[0.9250281 ]]],\r\n\r\n\r\n [[[0.63148123]]],\r\n\r\n\r\n [[[0.99773693]]],\r\n\r\n\r\n [[[0.72638947]]],\r\n\r\n\r\n [[[0.89405394]]],\r\n\r\n\r\n [[[0.5980778 ]]],\r\n\r\n\r\n [[[0.8670712 ]]],\r\n\r\n\r\n [[[0.6632613 ]]],\r\n\r\n\r\n [[[0.81144243]]],\r\n\r\n\r\n [[[0.592623 ]]],\r\n\r\n\r\n [[[0.8197249 ]]],\r\n\r\n\r\n [[[0.7912834 ]]],\r\n\r\n\r\n [[[0.8901268 ]]],\r\n\r\n\r\n [[[0.96424145]]],\r\n\r\n\r\n [[[0.6187824 ]]],\r\n\r\n\r\n [[[0.6861415 ]]],\r\n\r\n\r\n [[[0.94889915]]],\r\n\r\n\r\n [[[0.9496336 ]]],\r\n\r\n\r\n [[[0.8261754 ]]],\r\n\r\n\r\n [[[0.8371484 ]]],\r\n\r\n\r\n [[[0.80329174]]],\r\n\r\n\r\n [[[0.92502606]]],\r\n\r\n\r\n [[[0.8305773 ]]],\r\n\r\n\r\n [[[0.9554103 ]]],\r\n\r\n\r\n [[[0.92302245]]],\r\n\r\n\r\n [[[0.76686615]]],\r\n\r\n\r\n [[[0.7072739 ]]],\r\n\r\n\r\n [[[0.8904386 ]]],\r\n\r\n\r\n [[[0.5840736 ]]],\r\n\r\n\r\n [[[0.8083305 ]]],\r\n\r\n\r\n [[[0.9188963 ]]],\r\n\r\n\r\n [[[0.63679975]]],\r\n\r\n\r\n [[[0.74043673]]],\r\n\r\n\r\n [[[0.59241766]]],\r\n\r\n\r\n [[[0.626834 ]]],\r\n\r\n\r\n [[[0.71323735]]],\r\n\r\n\r\n [[[0.8751715 ]]],\r\n\r\n\r\n [[[0.6435642 ]]],\r\n\r\n\r\n [[[0.86628515]]],\r\n\r\n\r\n [[[0.83118266]]],\r\n\r\n\r\n [[[0.9449617 ]]],\r\n\r\n\r\n [[[0.912334 ]]],\r\n\r\n\r\n [[[0.7962168 ]]],\r\n\r\n\r\n [[[0.67899114]]],\r\n\r\n\r\n [[[0.7463058 ]]],\r\n\r\n\r\n [[[0.89865214]]],\r\n\r\n\r\n [[[0.6714875 ]]],\r\n\r\n\r\n [[[0.8624337 ]]],\r\n\r\n\r\n [[[0.81321293]]],\r\n\r\n\r\n [[[0.8904422 ]]],\r\n\r\n\r\n [[[0.6964186 ]]],\r\n\r\n\r\n [[[0.96230537]]],\r\n\r\n\r\n [[[0.88333577]]],\r\n\r\n\r\n [[[0.7958361 ]]]], dtype=float32)\r\n",,terminal_output +4050,13155452,"TERMINAL",0,0,"l",,terminal_output +4051,13155560,"TERMINAL",0,0,"\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/models/dynamics.py(107)\r\n jax.debug.breakpoint()\r\n \r\n # safe sqrt: clip argument to >= 0\r\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\r\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\r\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\r\n-> jax.debug.breakpoint()\r\n \r\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\r\n jax.debug.breakpoint()\r\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M\r\n \r\n",,terminal_output +4052,13161360,"TERMINAL",0,0,"noise_level_B111",,terminal_output +4053,13162500,"TERMINAL",0,0,"\r\n(jdb) Array([[[[0.45255098]]],\r\n\r\n\r\n [[[0.6441636 ]]],\r\n\r\n\r\n [[[0.66346925]]],\r\n\r\n\r\n [[[0.04603618]]],\r\n\r\n\r\n [[[0.06802309]]],\r\n\r\n\r\n [[[0.69920456]]],\r\n\r\n\r\n [[[0.23514807]]],\r\n\r\n\r\n [[[0.65358114]]],\r\n\r\n\r\n [[[0.49310675]]],\r\n\r\n\r\n [[[0.6987329 ]]],\r\n\r\n\r\n [[[0.29514727]]],\r\n\r\n\r\n [[[0.00263699]]],\r\n\r\n\r\n [[[0.40009692]]],\r\n\r\n\r\n [[[0.6722459 ]]],\r\n\r\n\r\n [[[0.54544824]]],\r\n\r\n\r\n [[[0.69171315]]],\r\n\r\n\r\n [[[0.09180859]]],\r\n\r\n\r\n [[[0.634662 ]]],\r\n\r\n\r\n [[[0.59807974]]],\r\n\r\n\r\n [[[0.660335 ]]],\r\n\r\n\r\n [[[0.43653592]]],\r\n\r\n\r\n [[[0.20926596]]],\r\n\r\n\r\n [[[0.04501296]]],\r\n\r\n\r\n [[[0.4408583 ]]],\r\n\r\n\r\n [[[0.46784565]]],\r\n\r\n\r\n [[[0.2517042 ]]],\r\n\r\n\r\n [[[0.23913348]]],\r\n\r\n\r\n [[[0.12439712]]],\r\n\r\n\r\n [[[0.56089723]]],\r\n\r\n\r\n [[[0.66213465]]],\r\n\r\n\r\n [[[0.30742902]]],\r\n\r\n\r\n [[[0.33678833]]],\r\n\r\n\r\n [[[0.4094986 ]]],\r\n\r\n\r\n [[[0.08725767]]],\r\n\r\n\r\n [[[0.40686068]]],\r\n\r\n\r\n [[[0.38637325]]],\r\n\r\n\r\n [[[0.54127145]]],\r\n\r\n\r\n [[[0.37343186]]],\r\n\r\n\r\n [[[0.6557393 ]]],\r\n\r\n\r\n [[[0.20142 ]]],\r\n\r\n\r\n [[[0.55386937]]],\r\n\r\n\r\n [[[0.5326104 ]]],\r\n\r\n\r\n [[[0.22468646]]],\r\n\r\n\r\n [[[0.34933808]]],\r\n\r\n\r\n [[[0.45658964]]],\r\n\r\n\r\n [[[0.53491926]]],\r\n\r\n\r\n [[[0.42469105]]],\r\n\r\n\r\n [[[0.31144363]]],\r\n\r\n\r\n [[[0.685411 ]]],\r\n\r\n\r\n [[[0.4874776 ]]],\r\n\r\n\r\n [[[0.6796607 ]]],\r\n\r\n\r\n [[[0.15456446]]],\r\n\r\n\r\n [[[0.63261634]]],\r\n\r\n\r\n [[[0.17223041]]],\r\n\r\n\r\n [[[0.09819267]]],\r\n\r\n\r\n [[[0.09345566]]],\r\n\r\n\r\n [[[0.20636612]]],\r\n\r\n\r\n [[[0.56945485]]],\r\n\r\n\r\n [[[0.33953947]]],\r\n\r\n\r\n [[[0.1999843 ]]],\r\n\r\n\r\n [[[0.32493833]]],\r\n\r\n\r\n [[[0.6805619 ]]],\r\n\r\n\r\n [[[0.34506497]]],\r\n\r\n\r\n [[[0.68235236]]],\r\n\r\n\r\n [[[0.1110582 ]]],\r\n\r\n\r\n [[[0.5945545 ]]],\r\n\r\n\r\n [[[0.14432307]]],\r\n\r\n\r\n [[[0.60123146]]],\r\n\r\n\r\n [[[0.00452096]]],\r\n\r\n\r\n [[[0.47235835]]],\r\n\r\n\r\n [[[0.20066756]]],\r\n\r\n\r\n [[[0.642303 ]]],\r\n\r\n\r\n [[[0.2481875 ]]],\r\n\r\n\r\n [[[0.56008446]]],\r\n\r\n\r\n [[[0.34156114]]],\r\n\r\n\r\n [[[0.648798 ]]],\r\n\r\n\r\n [[[0.32805106]]],\r\n\r\n\r\n [[[0.37387052]]],\r\n\r\n\r\n [[[0.20767422]]],\r\n\r\n\r\n [[[0.07023842]]],\r\n\r\n\r\n [[[0.61710835]]],\r\n\r\n\r\n [[[0.52920985]]],\r\n\r\n\r\n [[[0.0995904 ]]],\r\n\r\n\r\n [[[0.09819601]]],\r\n\r\n\r\n [[[0.31743416]]],\r\n\r\n\r\n [[[0.2991825 ]]],\r\n\r\n\r\n [[[0.3547224 ]]],\r\n\r\n\r\n [[[0.14432673]]],\r\n\r\n\r\n [[[0.31014135]]],\r\n\r\n\r\n [[[0.08719108]]],\r\n\r\n\r\n [[[0.1480295 ]]],\r\n\r\n\r\n [[[0.4119163 ]]],\r\n\r\n\r\n [[[0.4997636 ]]],\r\n\r\n\r\n [[[0.20711906]]],\r\n\r\n\r\n [[[0.658858 ]]],\r\n\r\n\r\n [[[0.3466018 ]]],\r\n\r\n\r\n [[[0.15562956]]],\r\n\r\n\r\n [[[0.5944861 ]]],\r\n\r\n\r\n [[[0.4517534 ]]],\r\n\r\n\r\n [[[0.64904135]]],\r\n\r\n\r\n [[[0.60707915]]],\r\n\r\n\r\n [[[0.49129245]]],\r\n\r\n\r\n [[[0.23407486]]],\r\n\r\n\r\n [[[0.5858251 ]]],\r\n\r\n\r\n [[[0.24955001]]],\r\n\r\n\r\n [[[0.30913532]]],\r\n\r\n\r\n [[[0.10704726]]],\r\n\r\n\r\n [[[0.1676466 ]]],\r\n\r\n\r\n [[[0.36603883]]],\r\n\r\n\r\n [[[0.538971 ]]],\r\n\r\n\r\n [[[0.4430276 ]]],\r\n\r\n\r\n [[[0.1924243 ]]],\r\n\r\n\r\n [[[0.5491045 ]]],\r\n\r\n\r\n [[[0.25620812]]],\r\n\r\n\r\n [[[0.33868474]]],\r\n\r\n\r\n [[[0.20711271]]],\r\n\r\n\r\n [[[0.5150012 ]]],\r\n\r\n\r\n [[[0.0739684 ]]],\r\n\r\n\r\n [[[0.21971789]]],\r\n\r\n\r\n [[[0.3666449 ]]]], dtype=float32)\r\n",,terminal_output +4054,13257722,"jasmine/train_dynamics.py",0,0,"",python,tab +4055,13259153,"jasmine/genie.py",0,0,"",python,tab +4056,13259811,"jasmine/models/dynamics.py",0,0,"",python,tab +4057,13263153,"jasmine/models/dynamics.py",3835,0,"",python,selection_mouse +4058,13263168,"jasmine/models/dynamics.py",3834,0,"",python,selection_command +4059,13263289,"jasmine/models/dynamics.py",3834,2,"M\n",python,selection_mouse +4060,13263289,"jasmine/models/dynamics.py",3835,1,"\n",python,selection_command +4061,13263352,"jasmine/models/dynamics.py",3765,70,"\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4062,13263352,"jasmine/models/dynamics.py",3370,465,"\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4063,13263364,"jasmine/models/dynamics.py",3120,715," noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n jax.debug.breakpoint()\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4064,13263438,"jasmine/models/dynamics.py",2994,841," ).astype(jnp.int32)\n jax.debug.breakpoint()\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n jax.debug.breakpoint()\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4065,13263438,"jasmine/models/dynamics.py",2922,913," (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n jax.debug.breakpoint()\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n jax.debug.breakpoint()\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4066,13263439,"jasmine/models/dynamics.py",2882,953," noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n jax.debug.breakpoint()\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n jax.debug.breakpoint()\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4067,13263479,"jasmine/models/dynamics.py",2795,1040," # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n jax.debug.breakpoint()\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n jax.debug.breakpoint()\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4068,13263545,"jasmine/models/dynamics.py",2680,1155," jax.debug.breakpoint()\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n jax.debug.breakpoint()\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n jax.debug.breakpoint()\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4069,13263562,"jasmine/models/dynamics.py",2609,1226," noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n jax.debug.breakpoint()\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n jax.debug.breakpoint()\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n jax.debug.breakpoint()\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4070,13263623,"jasmine/models/dynamics.py",2578,1257," jax.debug.breakpoint()\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n jax.debug.breakpoint()\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n jax.debug.breakpoint()\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n jax.debug.breakpoint()\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4071,13263623,"jasmine/models/dynamics.py",2568,1267," )\n jax.debug.breakpoint()\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n jax.debug.breakpoint()\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n jax.debug.breakpoint()\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n jax.debug.breakpoint()\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4072,13263646,"jasmine/models/dynamics.py",2488,1347," _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n jax.debug.breakpoint()\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n jax.debug.breakpoint()\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n jax.debug.breakpoint()\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n jax.debug.breakpoint()\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4073,13263710,"jasmine/models/dynamics.py",2377,1458," rng, _rng_noise_lvl, _rng_noise = jax.random.split(rng, 3)\n noise_level_B = jax.random.uniform(\n _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n jax.debug.breakpoint()\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n jax.debug.breakpoint()\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n jax.debug.breakpoint()\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n jax.debug.breakpoint()\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4074,13263775,"jasmine/models/dynamics.py",2444,1391," noise_level_B = jax.random.uniform(\n _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n jax.debug.breakpoint()\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n jax.debug.breakpoint()\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n jax.debug.breakpoint()\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n jax.debug.breakpoint()\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4075,13263787,"jasmine/models/dynamics.py",2578,1257," jax.debug.breakpoint()\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n jax.debug.breakpoint()\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n jax.debug.breakpoint()\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n jax.debug.breakpoint()\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4076,13263802,"jasmine/models/dynamics.py",3024,811," jax.debug.breakpoint()\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n jax.debug.breakpoint()\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4077,13263822,"jasmine/models/dynamics.py",3282,553," noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n jax.debug.breakpoint()\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4078,13263842,"jasmine/models/dynamics.py",3370,465,"\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4079,13263853,"jasmine/models/dynamics.py",3422,413,"one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4080,13263869,"jasmine/models/dynamics.py",3492,343,"sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4081,13263891,"jasmine/models/dynamics.py",3544,291,"qrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4082,13263902,"jasmine/models/dynamics.py",3614,221,"x.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4083,13263978,"jasmine/models/dynamics.py",3615,220,".debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4084,13264032,"jasmine/models/dynamics.py",3546,289,"t_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4085,13264033,"jasmine/models/dynamics.py",3495,340,"t_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4086,13264033,"jasmine/models/dynamics.py",3382,453,"afe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4087,13264048,"jasmine/models/dynamics.py",3370,465,"\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4088,13264069,"jasmine/models/dynamics.py",2885,950," noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n jax.debug.breakpoint()\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n jax.debug.breakpoint()\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4089,13264090,"jasmine/models/dynamics.py",2609,1226," noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n jax.debug.breakpoint()\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n jax.debug.breakpoint()\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n jax.debug.breakpoint()\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4090,13264102,"jasmine/models/dynamics.py",2488,1347," _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n jax.debug.breakpoint()\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n jax.debug.breakpoint()\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n jax.debug.breakpoint()\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n jax.debug.breakpoint()\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4091,13264121,"jasmine/models/dynamics.py",2377,1458," rng, _rng_noise_lvl, _rng_noise = jax.random.split(rng, 3)\n noise_level_B = jax.random.uniform(\n _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n jax.debug.breakpoint()\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n jax.debug.breakpoint()\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n jax.debug.breakpoint()\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n jax.debug.breakpoint()\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4092,13264133,"jasmine/models/dynamics.py",2335,1500," B, T, N, M = vid_embed_BTNM.shape\n rng, _rng_noise_lvl, _rng_noise = jax.random.split(rng, 3)\n noise_level_B = jax.random.uniform(\n _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n jax.debug.breakpoint()\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n jax.debug.breakpoint()\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n jax.debug.breakpoint()\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n jax.debug.breakpoint()\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4093,13264360,"jasmine/models/dynamics.py",2304,1531," jax.debug.breakpoint()\n B, T, N, M = vid_embed_BTNM.shape\n rng, _rng_noise_lvl, _rng_noise = jax.random.split(rng, 3)\n noise_level_B = jax.random.uniform(\n _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n jax.debug.breakpoint()\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n jax.debug.breakpoint()\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n jax.debug.breakpoint()\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n jax.debug.breakpoint()\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4094,13264444,"jasmine/models/dynamics.py",2242,1593," def _apply_noise_augmentation(self, vid_embed_BTNM, rng):\n jax.debug.breakpoint()\n B, T, N, M = vid_embed_BTNM.shape\n rng, _rng_noise_lvl, _rng_noise = jax.random.split(rng, 3)\n noise_level_B = jax.random.uniform(\n _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\n )\n jax.debug.breakpoint()\n noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\n jax.debug.breakpoint()\n # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\n # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\n noise_bucket_idx_B = jnp.floor(\n (noise_level_B * self.noise_buckets) / self.max_noise_level\n ).astype(jnp.int32)\n jax.debug.breakpoint()\n noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\n noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\n noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\n noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\n jax.debug.breakpoint()\n\n # safe sqrt: clip argument to >= 0\n one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\n sqrt_one_minus = jnp.sqrt(one_minus_noise)\n sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\n jax.debug.breakpoint()\n\n noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\n jax.debug.breakpoint()\n return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M",python,selection_mouse +4095,13349333,"jasmine/models/dynamics.py",5215,0,"",python,selection_mouse +4096,13392609,"jasmine/models/dynamics.py",4138,0,"",python,selection_mouse +4097,13392612,"jasmine/models/dynamics.py",4137,0,"",python,selection_command +4098,13393348,"jasmine/models/dynamics.py",3973,0,"",python,selection_mouse +4099,13393349,"jasmine/models/dynamics.py",3972,0,"",python,selection_command +4100,13436426,"jasmine/models/dynamics.py",4119,0,"",python,selection_mouse +4101,13438830,"jasmine/models/dynamics.py",3765,0,"",python,selection_mouse +4102,13438841,"jasmine/models/dynamics.py",3764,0,"",python,selection_command +4103,13444107,"jasmine/models/dynamics.py",3668,0,"",python,selection_mouse +4104,13444461,"jasmine/models/dynamics.py",3765,0,"",python,selection_mouse +4105,13444467,"jasmine/models/dynamics.py",3764,0,"",python,selection_command +4106,13444958,"jasmine/models/dynamics.py",3765,0,"\n ",python,content +4107,13445445,"jasmine/models/dynamics.py",3774,0," assert not jnp.isnan(self.max_noise_level)\n assert self.max_noise_level > 0\n assert not jnp.isnan(noise_level_B).any()\n assert not jnp.isnan(vid_embed_BTNM).any()",python,content +4108,13446538,"jasmine/models/dynamics.py",3778,0,"",python,selection_mouse +4109,13446884,"jasmine/models/dynamics.py",3774,4,"",python,content +4110,13447297,"jasmine/models/dynamics.py",3774,0,"^",python,content +4111,13447298,"jasmine/models/dynamics.py",3775,0,"",python,selection_keyboard +4112,13447838,"jasmine/models/dynamics.py",3774,1,"",python,content +4113,13447839,"jasmine/models/dynamics.py",3774,0,"",python,selection_keyboard +4114,13448245,"jasmine/models/dynamics.py",3773,0,"",python,selection_command +4115,13448422,"jasmine/models/dynamics.py",3824,0,"",python,selection_command +4116,13448942,"jasmine/models/dynamics.py",3817,35," assert self.max_noise_level > 0",python,selection_command +4117,13449138,"jasmine/models/dynamics.py",3817,81," assert self.max_noise_level > 0\n assert not jnp.isnan(noise_level_B).any()",python,selection_command +4118,13449269,"jasmine/models/dynamics.py",3817,128," assert self.max_noise_level > 0\n assert not jnp.isnan(noise_level_B).any()\n assert not jnp.isnan(vid_embed_BTNM).any()",python,selection_command +4119,13449655,"jasmine/models/dynamics.py",3821,0,"",python,selection_command +4120,13449800,"jasmine/models/dynamics.py",3903,0," ",python,content +4121,13449801,"jasmine/models/dynamics.py",3857,0," ",python,content +4122,13449801,"jasmine/models/dynamics.py",3821,0," ",python,content +4123,13450280,"jasmine/models/dynamics.py",3824,0,"",python,selection_command +4124,13451054,"jasmine/models/dynamics.py",3855,0,"",python,selection_mouse +4125,13451692,"jasmine/models/dynamics.py",3815,0,"",python,selection_mouse +4126,13452205,"jasmine/models/dynamics.py",3816,0,"",python,selection_command +4127,13452431,"jasmine/models/dynamics.py",3816,0,",",python,content +4128,13452432,"jasmine/models/dynamics.py",3817,0,"",python,selection_keyboard +4129,13452526,"jasmine/models/dynamics.py",3817,0," ",python,content 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SpawnProcess-5:\r\nProcess SpawnProcess-4:\r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\nKeyboardInterrupt\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\nTraceback (most recent call last):\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\nKeyboardInterrupt\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\nslurmstepd: error: *** STEP 3527866.0 ON hkn0402 CANCELLED AT 2025-09-28T17:13:25 ***\r\n",,terminal_output +4497,13561751,"TERMINAL",0,0,"(jdb) ",,terminal_output +4498,13561862,"TERMINAL",0,0,"]0;tum_cte0515@hkn0402:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0402 jasmine]$ ",,terminal_output +4499,13561926,"TERMINAL",0,0,"^C[?2004l\r[?2004h[?2004l\r\r\n]0;tum_cte0515@hkn0402:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0402 jasmine]$ ",,terminal_output +4500,13564298,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",,terminal_output +4501,13567342,"jasmine/train_dynamics.py",0,0,"",python,tab +4502,13567814,"jasmine/train_dynamics.py",16147,0,"",python,selection_mouse +4503,13568968,"jasmine/train_dynamics.py",15917,0,"",python,selection_mouse +4504,13569454,"jasmine/train_dynamics.py",15888,0,"",python,selection_mouse +4505,13570061,"jasmine/train_dynamics.py",15873,0,"",python,selection_command 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+4531,13582708,"TERMINAL",0,0,"\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=1\r\n#SBATCH --time=06:00:00\r\n#SBATCH --partition=accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\r\n#SBATCH --job-name=train_dyn_default_breakout_longer\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\n# source .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/train\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/val\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\n# lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3512576\r\n# tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3512502\r\n\r\nlam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3518963\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3518963\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python jasmine/train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=10 \\r\n --image_width=10 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=120 \\r\n --init_lr=0 \\r\n --max_lr=3e-5 \\r\n --log_image_interval=250 \\r\n --log_checkpoint_interval=250 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=breakout-dyn-noise-lvl-default-$slurm_job_id \\r\n --tags dyn breakout noise-lvl default \\r\n --entity instant-uv \\r\n --project jafar \\r\n --patch_size 4 \\r\n --lam_patch_size 4 \\r\n --warmup_steps 100 \\r\n --wsd_decay_steps 1000 \\r\n --num_steps 5000 \\r\n --data_dir $array_records_dir_train \\r\n --val_data_dir $array_records_dir_val \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --lam_checkpoint $lam_checkpoint \\r\n --val_interval 1000 \\r\n --eval_full_frame \\r\n",,terminal_output +4532,13582861,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=217802\r\nSLURM_JOB_GPUS=0\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\r\nSLURMD_NODENAME=hkn0402\r\nSLURM_JOB_START_TIME=1759070937\r\nSLURM_STEP_NODELIST=hkn0402\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1759074537\r\nSLURM_PMI2_SRUN_PORT=36803\r\nSLURM_CPUS_ON_NODE=8\r\nSLURM_JOB_CPUS_PER_NODE=8\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=dev_accelerated\r\nSLURM_TRES_PER_TASK=cpu=8\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=1\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3527866\r\nSLURM_PTY_PORT=42519\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.201\r\nSLURM_PTY_WIN_ROW=36\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0402\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.201\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=139\r\nSLURM_NODELIST=hkn0402\r\nSLURM_SRUN_COMM_PORT=45011\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1993.localdomain\r\nSLURM_JOB_ID=3527866\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0402\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_STEP_LAUNCHER_PORT=45011\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0402\r\n",,terminal_output +4533,13583039,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +4534,13585745,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +4535,13591857,"TERMINAL",0,0,"Counting all components: ['dynamics', 'lam', 'tokenizer']\r\n",,terminal_output +4536,13592174,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +4537,13592980,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.22.0\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20250928_171356-6r8e2qaj\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run breakout-dyn-noise-lvl-default-3527866\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/6r8e2qaj\r\n",,terminal_output +4538,13593117,"TERMINAL",0,0,"Parameter counts:\r\n{'dynamics': 26561024, 'lam': 16900976, 'tokenizer': 33750256, 'total': 77212256}\r\n",,terminal_output +4539,13595965,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +4540,13607932,"TERMINAL",0,0,"Traceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 790, in \r\n main(args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 620, in main\r\n compiled = train_step.lower(optimizer, first_batch).compile()\r\n ^^^^^^^^^^^^^^^^\r\nAttributeError: 'function' object has no attribute 'lower'\r\n",,terminal_output +4541,13609266,"TERMINAL",0,0,"wandb: \r\nwandb: 🚀 View run breakout-dyn-noise-lvl-default-3527866 at: https://wandb.ai/instant-uv/jafar/runs/6r8e2qaj\r\nwandb: Find logs at: ../../../../../hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20250928_171356-6r8e2qaj/logs\r\n",,terminal_output +4542,13609370,"TERMINAL",0,0,"W0928 17:14:14.034988 226316 pjrt_client.cc:1469] WatchJobStateAsync failed for task goo.gle/debugonly job_name: ""jax_worker"": CANCELLED: CANCELLED\r\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/WatchJobState:\r\n:UNKNOWN:Error received from peer {grpc_message:""CANCELLED"", grpc_status:1} [type.googleapis.com/tensorflow.DerivedStatus='']\r\n",,terminal_output +4543,13609942,"TERMINAL",0,0,"/usr/lib64/python3.12/multiprocessing/resource_tracker.py:254: UserWarning: resource_tracker: There appear to be 4 leaked shared_memory objects to clean up at shutdown\r\n warnings.warn('resource_tracker: There appear to be %d '\r\n",,terminal_output +4544,13610292,"TERMINAL",0,0,"srun: error: hkn0402: task 0: Exited with exit code 1\r\n]0;tum_cte0515@hkn0402:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0402 jasmine]$ ",,terminal_output +4545,13619659,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"import os\n\n\nos.environ.setdefault(""XLA_PYTHON_CLIENT_MEM_FRACTION"", ""0.98"")\n\nfrom dataclasses import dataclass, field\nimport itertools\nfrom typing import cast, Optional\n\nimport einops\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\nimport flax.nnx as nnx\n\nfrom genie import Genie, restore_genie_components\nfrom utils.dataloader import get_dataloader\nfrom utils.train_utils import (\n get_lr_schedule,\n count_parameters_by_component,\n print_mem_stats,\n print_compiled_memory_stats,\n print_compiled_cost_analysis,\n)\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n batch_size: int = 36\n init_lr: float = 0.0\n max_lr: float = 3e-5\n decay_end: float = 0.0\n wsd_decay_steps: int = (\n 10000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n )\n warmup_steps: int = 5000\n lr_schedule: str = ""wsd"" # supported options: wsd, cos\n # Tokenizer\n tokenizer_dim: int = 512\n tokenizer_ffn_dim: int = 2048\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 4\n tokenizer_num_heads: int = 8\n tokenizer_checkpoint: str = """"\n # LAM\n lam_dim: int = 512\n lam_ffn_dim: int = 2048\n latent_action_dim: int = 32\n num_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 4\n lam_num_heads: int = 8\n lam_checkpoint: str = """"\n # Dynamics\n dyna_type: str = ""maskgit"" # supported options: maskgit, causal\n dyna_dim: int = 512\n dyna_ffn_dim: int = 2048\n dyna_num_blocks: int = 6\n dyna_num_heads: int = 8\n max_noise_level: float = 0.7\n noise_buckets: int = 10\n dropout: float = 0.0\n mask_limit: float = 0.5\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\n use_flash_attention: bool = True\n use_gt_actions: bool = False\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n name: str = ""train_dynamics""\n tags: list[str] = field(default_factory=lambda: [""dynamics""])\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 25000\n log_checkpoint_keep_period: int = 20000\n log_gradients: bool = False\n val_data_dir: str = """"\n val_interval: int = 20_000\n val_steps: int = 50\n eval_full_frame: bool = False\n val_maskgit_steps: int = 25\n val_temperature: float = 1\n val_sample_argmax: bool = False\n wandb_id: str = """"\n\n\ndef build_model(args: Args, rng: jax.Array) -> tuple[Genie, jax.Array]:\n rng, _rng = jax.random.split(rng)\n rngs = nnx.Rngs(_rng)\n genie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n tokenizer_ffn_dim=args.tokenizer_ffn_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n lam_ffn_dim=args.lam_ffn_dim,\n latent_action_dim=args.latent_action_dim,\n num_actions=args.num_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n lam_co_train=not args.lam_checkpoint,\n use_gt_actions=args.use_gt_actions,\n # Dynamics\n dyna_type=args.dyna_type,\n dyna_dim=args.dyna_dim,\n dyna_ffn_dim=args.dyna_ffn_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n dropout=args.dropout,\n max_noise_level=args.max_noise_level,\n noise_buckets=args.noise_buckets,\n mask_limit=args.mask_limit,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n decode=False,\n rngs=rngs,\n )\n if args.use_gt_actions:\n assert (\n not args.lam_checkpoint\n ), ""Cannot use LAM when using ground-truth actions.""\n else:\n assert genie.lam is not None\n del genie.lam.decoder\n return genie, rng\n\n\ndef build_optimizer(genie: Genie, args: Args) -> nnx.ModelAndOptimizer:\n lr_schedule = get_lr_schedule(\n args.lr_schedule,\n args.init_lr,\n args.max_lr,\n args.decay_end,\n args.num_steps,\n args.warmup_steps,\n args.wsd_decay_steps,\n )\n tx = optax.adamw(\n learning_rate=lr_schedule,\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.param_dtype, # moments in full precision\n )\n optimizer = nnx.ModelAndOptimizer(genie, tx)\n return optimizer\n\n\ndef build_mesh_and_sharding(\n num_devices: int,\n) -> tuple[Mesh, NamedSharding, NamedSharding, NamedSharding]:\n device_mesh_arr = create_device_mesh((num_devices,))\n mesh = Mesh(devices=device_mesh_arr, axis_names=(""data"",))\n replicated_sharding = NamedSharding(mesh, PartitionSpec())\n videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n actions_sharding = NamedSharding(mesh, PartitionSpec(""data"", None))\n return mesh, replicated_sharding, videos_sharding, actions_sharding\n\n\ndef shard_optimizer_states(\n optimizer: nnx.ModelAndOptimizer, replicated_sharding: NamedSharding\n) -> None:\n model_state = nnx.state(optimizer.model)\n model_sharded_state = jax.lax.with_sharding_constraint(\n model_state, replicated_sharding\n )\n nnx.update(optimizer.model, model_sharded_state)\n optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)\n optimizer_sharded_state = jax.lax.with_sharding_constraint(\n optimizer_state, replicated_sharding\n )\n nnx.update(optimizer, optimizer_sharded_state)\n\n\ndef build_dataloader(args: Args, data_dir: str) -> grain.DataLoaderIterator:\n image_shape = (args.image_height, args.image_width, args.image_channels)\n array_record_files = [\n os.path.join(data_dir, x)\n for x in os.listdir(data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n return grain_iterator\n\n\ndef build_checkpoint_manager(args: Args) -> Optional[ocp.CheckpointManager]:\n if args.restore_ckpt or args.save_ckpt:\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n if args.val_data_dir:\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(\n ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler\n ),\n )\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(\n ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler\n ),\n )\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n return checkpoint_manager\n else:\n return None\n\n\ndef restore_or_initialize_components(\n args: Args,\n checkpoint_manager: Optional[ocp.CheckpointManager],\n optimizer: nnx.ModelAndOptimizer,\n train_iterator: grain.DataLoaderIterator,\n rng: jax.Array,\n replicated_sharding: NamedSharding,\n val_iterator: Optional[grain.DataLoaderIterator],\n restore_step: Optional[int] = None,\n) -> tuple[\n int,\n nnx.ModelAndOptimizer,\n grain.DataLoaderIterator,\n grain.DataLoaderIterator,\n jax.Array,\n]:\n step = 0\n if checkpoint_manager and restore_step is None:\n restore_step = checkpoint_manager.latest_step()\n if args.restore_ckpt:\n assert checkpoint_manager is not None\n abstract_optimizer = nnx.eval_shape(lambda: optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n if val_iterator:\n restore_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointRestore(train_iterator), # type: ignore\n val_dataloader_state=grain.checkpoint.CheckpointRestore(val_iterator), # type: ignore\n )\n else:\n restore_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointRestore(train_iterator), # type: ignore\n )\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(), args=restore_args\n )\n restored_optimizer_state = restored[""model_state""]\n nnx.update(optimizer, restored_optimizer_state)\n train_iterator = restored[""train_dataloader_state""]\n if val_iterator:\n val_iterator = restored[""val_dataloader_state""]\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored dataloader and model state from step {step}"")\n else:\n # Restore from pre-trained tokenizer (and LAM)\n rng, _rng = jax.random.split(rng)\n optimizer = restore_genie_components(optimizer, replicated_sharding, _rng, args)\n return step, optimizer, train_iterator, val_iterator, rng\n\n\ndef _calculate_step_metrics(\n outputs: dict[str, jax.Array],\n gt: jax.Array,\n num_actions: int,\n num_patch_latents: int,\n) -> tuple[jax.Array, dict]:\n mask = outputs[""mask""]\n outputs[""token_logits""] = outputs[""token_logits""].astype(jnp.float32)\n ce_loss = optax.softmax_cross_entropy_with_integer_labels(\n outputs[""token_logits""], outputs[""video_tokens""]\n )\n ce_loss = (mask * ce_loss).sum() / mask.sum()\n acc = outputs[""token_logits""].argmax(-1) == outputs[""video_tokens""]\n acc = (mask * acc).sum() / mask.sum()\n select_probs = jax.nn.softmax(outputs[""token_logits""])\n gt_val = gt.clip(0, 1).reshape(-1, *gt.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt_val, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt_val, recon)).mean()\n _, index_counts_tokenizer = jnp.unique_counts(\n jnp.ravel(outputs[""video_tokens""]),\n size=num_patch_latents,\n fill_value=0,\n )\n codebook_usage_tokenizer = (index_counts_tokenizer != 0).mean()\n metrics = dict(\n cross_entropy_loss=ce_loss,\n masked_token_accuracy=acc,\n select_logit=outputs[""token_logits""].max(-1).mean(),\n select_p=select_probs.max(-1).mean(),\n entropy=jax.scipy.special.entr(select_probs).sum(-1).mean(),\n psnr=psnr,\n ssim=ssim,\n codebook_usage_tokenizer=codebook_usage_tokenizer,\n )\n if ""lam_indices"" in outputs.keys():\n _, index_counts_lam = jnp.unique_counts(\n jnp.ravel(outputs[""lam_indices""]),\n size=num_actions,\n fill_value=0,\n )\n codebook_usage_lam = (index_counts_lam != 0).mean()\n metrics[""codebook_usage_lam""] = codebook_usage_lam\n return ce_loss, metrics\n\n\ndef main(args: Args) -> None:\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 rng = jax.random.key(args.seed)\n\n # --- Initialize model ---\n genie, rng = build_model(args, rng)\n _, params, _ = nnx.split(genie, nnx.Param, ...)\n param_counts = count_parameters_by_component(params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\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 if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n optimizer = build_optimizer(genie, args)\n del genie\n\n # FIXME: switch to create_hybrid_device_mesh for runs spanning multiple nodes\n _, replicated_sharding, videos_sharding, actions_sharding = build_mesh_and_sharding(\n num_devices\n )\n\n shard_optimizer_states(optimizer, replicated_sharding)\n\n # --- Initialize checkpoint manager ---\n checkpoint_manager = build_checkpoint_manager(args)\n\n # --- Create DataLoaderIterator from dataloader ---\n train_iterator = build_dataloader(args, args.data_dir)\n val_iterator = None\n if args.val_data_dir:\n val_iterator = build_dataloader(args, args.val_data_dir)\n\n # --- Restore checkpoint ---\n step, optimizer, train_iterator, val_iterator, rng = (\n restore_or_initialize_components(\n args,\n checkpoint_manager,\n optimizer,\n train_iterator,\n rng,\n replicated_sharding,\n val_iterator,\n )\n )\n\n # --- Define loss and train step (close over args) ---\n def dynamics_loss_fn(\n model: Genie,\n inputs: dict,\n ) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs)\n ce_loss, metrics = _calculate_step_metrics(\n outputs, gt, args.num_actions, args.num_patch_latents\n )\n return ce_loss, (outputs[""recon""], metrics)\n\n # @nnx.jit(donate_argnums=0)\n def train_step(\n optimizer: nnx.ModelAndOptimizer, inputs: dict\n ) -> tuple[jax.Array, jax.Array, dict]:\n def loss_fn(model: Genie) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n model.train()\n return dynamics_loss_fn(model, inputs)\n\n (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(\n optimizer.model\n )\n optimizer.update(grads)\n if args.log_gradients:\n metrics[""gradients_std/""] = jax.tree.map(\n lambda x: x.std(), grads[""params""][""dynamics""]\n )\n return loss, recon, metrics\n\n # @nnx.jit\n def val_step(genie: Genie, inputs: dict) -> dict:\n """"""Evaluate model and compute metrics""""""\n genie.eval()\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n (loss, (recon, metrics)) = dynamics_loss_fn(genie, inputs)\n val_output = {""loss"": loss, ""recon"": recon, ""metrics"": metrics}\n\n # --- Evaluate full frame prediction (sampling) ---\n if args.eval_full_frame:\n inputs[""videos""] = gt.astype(args.dtype)\n tokenizer_outputs = genie.tokenizer.vq_encode(\n inputs[""videos""], training=False\n )\n tokens_full_frame = tokenizer_outputs[""indices""]\n lam_indices_E = None\n if not args.use_gt_actions:\n lam_indices_E = genie.vq_encode(inputs, training=False)\n inputs[""latent_actions""] = lam_indices_E\n inputs[""videos""] = inputs[""videos""][\n :, :-1\n ] # remove last frame for generation\n recon_full_frame, logits_full_frame = genie.sample(\n batch=inputs,\n seq_len=args.seq_len,\n noise_level=0.,\n temperature=args.val_temperature,\n sample_argmax=args.val_sample_argmax,\n maskgit_steps=args.val_maskgit_steps,\n )\n # Calculate metrics for the last frame only\n step_outputs = {\n ""recon"": recon_full_frame[:, -1],\n ""token_logits"": logits_full_frame[:, -1],\n ""video_tokens"": tokens_full_frame[:, -1],\n ""mask"": jnp.ones_like(tokens_full_frame[:, -1]),\n }\n if lam_indices_E is not None:\n lam_indices_B = lam_indices_E.reshape((-1, args.seq_len - 1))[:, -1]\n step_outputs[""lam_indices""] = lam_indices_B\n\n loss_full_frame, metrics_full_frame = _calculate_step_metrics(\n step_outputs, gt[:, -1], args.num_actions, args.num_patch_latents\n )\n val_output.update(\n {\n ""loss_full_frame"": loss_full_frame,\n ""recon_full_frame"": recon_full_frame,\n ""metrics_full_frame"": metrics_full_frame,\n }\n )\n return val_output\n\n def calculate_validation_metrics(val_dataloader, genie, rng):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n loss_full_frame_per_step = []\n metrics_full_frame_per_step = []\n batch = None\n recon = None\n recon_full_frame = None\n for batch in val_dataloader:\n rng, _rng_mask = jax.random.split(rng, 2)\n batch[""rng""] = _rng_mask\n val_outputs = val_step(genie, batch)\n loss_per_step.append(val_outputs[""loss""])\n metrics_per_step.append(val_outputs[""metrics""])\n recon = val_outputs[""recon""]\n if args.eval_full_frame:\n loss_full_frame_per_step.append(val_outputs[""loss_full_frame""])\n metrics_full_frame_per_step.append(val_outputs[""metrics_full_frame""])\n recon_full_frame = val_outputs[""recon_full_frame""]\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(\n f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}""\n )\n\n val_metrics = {\n f""val_{key}"": np.mean([float(m[key]) for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n val_metrics[""val_loss""] = np.mean(loss_per_step)\n if args.eval_full_frame:\n val_metrics_full_frame = {\n f""val_full_frame_{key}"": np.mean(\n [float(m[key]) for m in metrics_full_frame_per_step]\n )\n for key in metrics_full_frame_per_step[0].keys()\n }\n val_metrics.update(val_metrics_full_frame)\n val_metrics[""val_full_frame_loss""] = np.mean(loss_full_frame_per_step)\n return val_metrics, batch, recon, recon_full_frame\n\n # --- TRAIN LOOP ---\n dataloader_train = (\n {\n ""videos"": jax.make_array_from_process_local_data(\n videos_sharding, local_data=elem[""videos""]\n ),\n ""actions"": (\n jax.make_array_from_process_local_data(\n actions_sharding, elem[""actions""]\n )\n if args.use_gt_actions\n else None\n ),\n }\n for elem in train_iterator\n )\n dataloader_val = None\n if val_iterator:\n dataloader_val = (\n {\n ""videos"": jax.make_array_from_process_local_data(\n videos_sharding, elem[""videos""]\n ),\n ""actions"": (\n jax.make_array_from_process_local_data(\n actions_sharding, elem[""actions""]\n )\n if args.use_gt_actions\n else None\n ),\n }\n for elem in val_iterator\n )\n if jax.process_index() == 0:\n first_batch = next(dataloader_train)\n first_batch[""rng""] = rng # type: ignore\n compiled = train_step.lower(optimizer, first_batch).compile()\n print_compiled_memory_stats(compiled.memory_analysis())\n print_compiled_cost_analysis(compiled.cost_analysis())\n # Do not skip the first batch during training\n dataloader_train = itertools.chain([first_batch], dataloader_train)\n print(f""Starting training from step {step}..."")\n first_step = step\n while step < args.num_steps:\n for batch in dataloader_train:\n # --- Train step ---\n rng, _rng_mask = jax.random.split(rng, 2)\n batch[""rng""] = _rng_mask\n loss, recon, metrics = train_step(optimizer, batch)\n if step == first_step:\n print_mem_stats(""After params initialized"")\n step += 1\n\n # --- Validation loss ---\n val_results = {}\n if dataloader_val and step % args.val_interval == 0:\n rng, _rng_mask_val = jax.random.split(rng, 2)\n print(""Calculating validation metrics..."")\n val_metrics, val_gt_batch, val_recon, val_recon_full_frame = (\n calculate_validation_metrics(\n dataloader_val, optimizer.model, _rng_mask_val\n )\n )\n print(f""Step {step}, validation loss: {val_metrics['val_loss']}"")\n val_results = {\n ""metrics"": val_metrics,\n ""gt_batch"": val_gt_batch,\n ""recon"": val_recon,\n ""full_frame"": val_recon_full_frame,\n }\n\n # --- Logging ---\n if args.log:\n if step % args.log_interval == 0 and jax.process_index() == 0:\n log_dict = {""loss"": loss, ""step"": step, **metrics}\n if val_results:\n log_dict.update(val_results[""metrics""])\n wandb.log(log_dict)\n if step % args.log_image_interval == 0:\n gt_seq = batch[""videos""][0].astype(jnp.float32) / 255.0\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 if val_results:\n val_results[""gt_seq_val""] = (\n val_results[""gt_batch""][""videos""][0].astype(jnp.float32)\n / 255.0\n )\n val_results[""recon_seq_val""] = val_results[""recon""][0].clip(\n 0, 1\n )\n val_comparison_seq = jnp.concatenate(\n (val_results[""gt_seq_val""], val_results[""recon_seq_val""]),\n axis=1,\n )\n val_results[""val_comparison_seq""] = einops.rearrange(\n val_comparison_seq * 255, ""t h w c -> h (t w) c""\n )\n if args.eval_full_frame:\n val_results[""full_frame_seq_val""] = val_results[\n ""full_frame""\n ][0].clip(0, 1)\n val_results[""val_full_frame_comparison_seq""] = (\n jnp.concatenate(\n (\n val_results[""gt_seq_val""],\n val_results[""full_frame_seq_val""],\n ),\n axis=1,\n )\n )\n val_results[""val_full_frame_comparison_seq""] = (\n einops.rearrange(\n val_results[""val_full_frame_comparison_seq""] * 255,\n ""t h w c -> h (t w) c"",\n )\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[args.seq_len - 1])),\n recon=wandb.Image(np.asarray(recon_seq[args.seq_len - 1])),\n true_vs_recon=wandb.Image(\n np.asarray(comparison_seq.astype(np.uint8))\n ),\n )\n if val_results:\n log_images.update(\n dict(\n val_image=wandb.Image(\n np.asarray(\n val_results[""gt_seq_val""][args.seq_len - 1]\n )\n ),\n val_recon=wandb.Image(\n np.asarray(\n val_results[""recon_seq_val""][\n args.seq_len - 1\n ]\n )\n ),\n val_true_vs_recon=wandb.Image(\n np.asarray(\n val_results[""val_comparison_seq""].astype(\n np.uint8\n )\n )\n ),\n )\n )\n if args.eval_full_frame:\n log_images.update(\n dict(\n val_full_frame=wandb.Image(\n np.asarray(\n val_results[""full_frame_seq_val""][\n args.seq_len - 1\n ]\n )\n ),\n val_true_vs_full_frame=wandb.Image(\n np.asarray(\n val_results[\n ""val_full_frame_comparison_seq""\n ].astype(np.uint8)\n )\n ),\n )\n )\n wandb.log(log_images)\n # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n assert checkpoint_manager is not None\n optimizer_state = nnx.state(optimizer)\n if val_iterator:\n ckpt_manager_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n train_iterator # type: ignore\n ),\n val_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n val_iterator # type: ignore\n ),\n )\n else:\n ckpt_manager_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n train_iterator # type: ignore\n ),\n )\n checkpoint_manager.save(step, args=ckpt_manager_args)\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n if checkpoint_manager:\n checkpoint_manager.close()\n\n\nif __name__ == ""__main__"":\n args = tyro.cli(Args)\n main(args)\n",python,tab +4546,13619660,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",21891,0,"",python,selection_command +4547,13619791,"TERMINAL",0,0,"\r(jasmine) [tum_cte0515@hkn0402 jasmine]$ ",,terminal_output +4548,13621911,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"",python,tab +4549,13623225,"TERMINAL",0,0,"\r(jasmine) [tum_cte0515@hkn0402 jasmine]$ ",,terminal_output +4550,13623721,"jasmine/train_dynamics.py",0,0,"",python,tab +4551,13624367,"jasmine/train_dynamics.py",16397,0,"",python,selection_mouse +4552,13626729,"jasmine/train_dynamics.py",21921,0,"",python,selection_command +4553,13635029,"jasmine/train_dynamics.py",21891,69," compiled = train_step.lower(optimizer, first_batch).compile()",python,selection_command +4554,13635260,"jasmine/train_dynamics.py",21891,133," compiled = train_step.lower(optimizer, first_batch).compile()\n print_compiled_memory_stats(compiled.memory_analysis())",python,selection_command +4555,13635378,"jasmine/train_dynamics.py",21891,196," compiled = train_step.lower(optimizer, first_batch).compile()\n print_compiled_memory_stats(compiled.memory_analysis())\n print_compiled_cost_analysis(compiled.cost_analysis())",python,selection_command +4556,13635637,"jasmine/train_dynamics.py",21899,0,"",python,selection_command +4557,13636191,"jasmine/train_dynamics.py",22033,0,"#",python,content +4558,13636191,"jasmine/train_dynamics.py",21969,0,"#",python,content +4559,13636191,"jasmine/train_dynamics.py",21899,0,"#",python,content +4560,13636192,"jasmine/train_dynamics.py",21900,0,"",python,selection_keyboard +4561,13636282,"jasmine/train_dynamics.py",22036,0," ",python,content +4562,13636282,"jasmine/train_dynamics.py",21971,0," ",python,content +4563,13636282,"jasmine/train_dynamics.py",21900,0," ",python,content +4564,13636283,"jasmine/train_dynamics.py",21901,0,"",python,selection_keyboard +4565,13636631,"jasmine/train_dynamics.py",21900,0,"",python,selection_command +4566,13646052,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",,terminal_output +4567,13646279,"TERMINAL",0,0,"\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=1\r\n#SBATCH --time=06:00:00\r\n#SBATCH --partition=accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\r\n#SBATCH --job-name=train_dyn_default_breakout_longer\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\n# source .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/train\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/val\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\n# lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3512576\r\n# tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3512502\r\n\r\nlam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3518963\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3518963\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python jasmine/train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=10 \\r\n --image_width=10 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=120 \\r\n --init_lr=0 \\r\n --max_lr=3e-5 \\r\n --log_image_interval=250 \\r\n --log_checkpoint_interval=250 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=breakout-dyn-noise-lvl-default-$slurm_job_id \\r\n --tags dyn breakout noise-lvl default \\r\n --entity instant-uv \\r\n --project jafar \\r\n --patch_size 4 \\r\n --lam_patch_size 4 \\r\n --warmup_steps 100 \\r\n --wsd_decay_steps 1000 \\r\n --num_steps 5000 \\r\n --data_dir $array_records_dir_train \\r\n --val_data_dir $array_records_dir_val \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --lam_checkpoint $lam_checkpoint \\r\n --val_interval 1000 \\r\n --eval_full_frame \\r\n",,terminal_output +4568,13646410,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=217802\r\nSLURM_JOB_GPUS=0\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\r\nSLURMD_NODENAME=hkn0402\r\nSLURM_JOB_START_TIME=1759070937\r\nSLURM_STEP_NODELIST=hkn0402\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1759074537\r\nSLURM_PMI2_SRUN_PORT=36803\r\nSLURM_CPUS_ON_NODE=8\r\nSLURM_JOB_CPUS_PER_NODE=8\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=dev_accelerated\r\nSLURM_TRES_PER_TASK=cpu=8\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=1\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3527866\r\nSLURM_PTY_PORT=42519\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.201\r\nSLURM_PTY_WIN_ROW=36\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0402\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.201\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=139\r\nSLURM_NODELIST=hkn0402\r\nSLURM_SRUN_COMM_PORT=45011\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1993.localdomain\r\nSLURM_JOB_ID=3527866\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0402\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_STEP_LAUNCHER_PORT=45011\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0402\r\n",,terminal_output +4569,13646514,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +4570,13649201,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +4571,13655139,"TERMINAL",0,0,"Counting all components: ['dynamics', 'lam', 'tokenizer']\r\n",,terminal_output +4572,13655446,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +4573,13656215,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.22.0\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20250928_171500-r4rqbg5n\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run breakout-dyn-noise-lvl-default-3527866\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/r4rqbg5n\r\n",,terminal_output +4574,13656325,"TERMINAL",0,0,"Parameter counts:\r\n{'dynamics': 26561024, 'lam': 16900976, 'tokenizer': 33750256, 'total': 77212256}\r\n",,terminal_output +4575,13658725,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +4576,13670626,"TERMINAL",0,0,"Starting training from step 0...\r\n",,terminal_output +4577,13748427,"TERMINAL",0,0,"\r\nMemstats: After params initialized:\r\n\tUsing (GB) 1.11 / 38.7 (2.868217%) on cuda:0\r\n",,terminal_output +4578,13774104,"TERMINAL",0,0,"bash",,terminal_focus +4579,13774960,"TERMINAL",0,0,"srun",,terminal_focus +4580,13788696,"jasmine/train_dynamics.py",0,0,"",python,tab +4581,13796440,"jasmine/models/dynamics.py",0,0,"",python,tab +4582,13797325,"jasmine/models/dynamics.py",3959,0,"",python,selection_mouse +4583,13797852,"jasmine/models/dynamics.py",3837,0,"",python,selection_mouse +4584,13800091,"jasmine/models/dynamics.py",3888,0,"",python,selection_mouse +4585,13801520,"jasmine/models/dynamics.py",3866,0,"",python,selection_mouse +4586,13802624,"jasmine/models/dynamics.py",3803,84," assert not jnp.isnan(vid_embed_BTNM).any(), ""[DEBUG] vid_embed_BTNM has NaN""",python,selection_command +4587,13802835,"jasmine/models/dynamics.py",3720,167," assert not jnp.isnan(noise_level_B).any(), ""[DEBUG] noise_level_B has NaN""\n assert not jnp.isnan(vid_embed_BTNM).any(), ""[DEBUG] vid_embed_BTNM has NaN""",python,selection_command +4588,13802961,"jasmine/models/dynamics.py",3635,252," assert self.max_noise_level > 0, ""[DEBUG] Max noise level is smaller than 0""\n assert not jnp.isnan(noise_level_B).any(), ""[DEBUG] noise_level_B has NaN""\n assert not jnp.isnan(vid_embed_BTNM).any(), ""[DEBUG] vid_embed_BTNM has NaN""",python,selection_command +4589,13803108,"jasmine/models/dynamics.py",3550,337," assert not jnp.isnan(self.max_noise_level), ""[DEBUG] Max noise level is NaN""\n assert self.max_noise_level > 0, ""[DEBUG] Max noise level is smaller than 0""\n assert not jnp.isnan(noise_level_B).any(), ""[DEBUG] noise_level_B has NaN""\n assert not jnp.isnan(vid_embed_BTNM).any(), ""[DEBUG] vid_embed_BTNM has NaN""",python,selection_command +4590,13804124,"jasmine/models/dynamics.py",3558,0,"",python,selection_command +4591,13821752,"jasmine/models/dynamics.py",3550,0,"",python,selection_command +4592,13823982,"jasmine/models/dynamics.py",3550,0," assert not jnp.isnan(self.max_noise_level), f""[DEBUG] Max noise level is NaN, value: {self.max_noise_level}""\n",python,content +4593,13824271,"jasmine/models/dynamics.py",3667,0," assert self.max_noise_level > 0, f""[DEBUG] Max noise level is smaller than 0, value: {self.max_noise_level}""\n",python,content +4594,13824600,"jasmine/models/dynamics.py",3784,0," assert not jnp.isnan(noise_level_B).any(), f""[DEBUG] noise_level_B has NaN, values: {noise_level_B}""\n",python,content +4595,13824925,"jasmine/models/dynamics.py",3893,0," assert not jnp.isnan(vid_embed_BTNM).any(), f""[DEBUG] vid_embed_BTNM has NaN, values: {vid_embed_BTNM}""\n",python,content +4596,13824928,"jasmine/models/dynamics.py",4005,338,"",python,content +4597,13894040,"TERMINAL",0,0,"^Csrun: interrupt (one more within 1 sec to abort)\r\nsrun: StepId=3527866.2 task 0: running\r\n",,terminal_output +4598,13894449,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3527866.2\r\nsrun: forcing job termination\r\nProcess SpawnProcess-4:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-6:\r\nProcess SpawnProcess-1:\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nTraceback (most recent call last):\r\nKeyboardInterrupt\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nTraceback (most recent call last):\r\nKeyboardInterrupt\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\nTraceback (most recent call last):\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\nslurmstepd: error: *** STEP 3527866.2 ON hkn0402 CANCELLED AT 2025-09-28T17:18:59 ***\r\n",,terminal_output +4599,13894593,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3527866.2\r\nsrun: job abort in progress\r\n",,terminal_output +4600,13894747,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3527866.2\r\n",,terminal_output +4601,13894827,"TERMINAL",0,0,"]0;tum_cte0515@hkn0402:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0402 jasmine]$ ",,terminal_output +4602,13933381,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",,terminal_output +4603,13935162,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",0,0,"",shellscript,tab +4604,13935324,"TERMINAL",0,0,"\r(jasmine) [tum_cte0515@hkn0402 jasmine]$ sh slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",,terminal_output +4605,13936797,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",84,0,"",shellscript,selection_mouse +4606,13937871,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",83,1,"",shellscript,content +4607,13938001,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",82,1,"",shellscript,content +4608,13938848,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",82,0,"4",shellscript,content +4609,13938849,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",83,0,"",shellscript,selection_keyboard +4610,13938906,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",83,0,"9",shellscript,content +4611,13938907,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",84,0,"",shellscript,selection_keyboard +4612,13939210,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",83,1,"",shellscript,content +4613,13939362,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",83,0,"8",shellscript,content +4614,13939363,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",84,0,"",shellscript,selection_keyboard +4615,13941614,"TERMINAL",0,0,"\r\n\r\r\n\r\r\n\r",,terminal_output +4616,13941762,"TERMINAL",0,0,"",,terminal_output +4617,13942505,"TERMINAL",0,0,"s \r",,terminal_output +4618,13942723,"TERMINAL",0,0,"y",,terminal_output +4619,13942773,"TERMINAL",0,0,"n",,terminal_output +4620,13942993,"TERMINAL",0,0,"c",,terminal_output +4621,13943454,"TERMINAL",0,0,"-",,terminal_output +4622,13943812,"TERMINAL",0,0,"r",,terminal_output +4623,13943901,"TERMINAL",0,0,"u",,terminal_output +4624,13944135,"TERMINAL",0,0,"n",,terminal_output +4625,13944321,"TERMINAL",0,0,"n",,terminal_output +4626,13944677,"TERMINAL",0,0,"e",,terminal_output +4627,13944783,"TERMINAL",0,0,"r",,terminal_output +4628,13944935,"TERMINAL",0,0,"-",,terminal_output +4629,13945099,"TERMINAL",0,0,"2",,terminal_output +4630,13945396,"TERMINAL",0,0,"\r\n[?2004l\rsending incremental file list\r\n",,terminal_output +4631,13950785,"TERMINAL",0,0,"jasmine/\r\njasmine/genie.py\r\njasmine/sample.py\r\njasmine/train_dynamics.py\r\njasmine/models/dynamics.py\r\nslurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh\r\nslurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_gt_actions.sh\r\nslurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_gt_actions_50k.sh\r\nslurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_gt_actions_smaller_lr_50k.sh\r\nslurm/jobs/mihir/horeka/breakout/noise_schedule_runs/z-loss/\r\nslurm/jobs/mihir/horeka/breakout/noise_schedule_runs/z-loss/train_dyn_single_gpu.sh\r\nslurm/jobs/mihir/horeka/breakout/noise_schedule_runs/z-loss/train_dyn_single_gpu_50k.sh\r\nslurm/jobs/mihir/horeka/breakout/noise_schedule_runs/z-loss/train_dyn_single_gpu_gt_actions.sh\r\nslurm/jobs/mihir/horeka/breakout/noise_schedule_runs/z-loss/train_dyn_single_gpu_gt_actions_50k.sh\r\nslurm/jobs/mihir/horeka/breakout/noise_schedule_runs/z-loss/train_dyn_single_gpu_gt_actions_smaller_lr_50k.sh\r\nslurm/jobs/mihir/horeka/breakout/noise_schedule_runs/z-loss/train_dyn_single_gpu_smaller_lr_50k.sh\r\n",,terminal_output +4632,13951082,"TERMINAL",0,0,"\r\nsent 135,267 bytes received 495 bytes 24,684.00 bytes/sec\r\ntotal size is 28,031,762 speedup is 206.48\r\n]0;tum_cte0515@hkn0402:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0402 jasmine]$ ",,terminal_output +4633,13952578,"TERMINAL",0,0,"r \r",,terminal_output +4634,13952734,"TERMINAL",0,0,"u",,terminal_output +4635,13952798,"TERMINAL",0,0,"n",,terminal_output +4636,13952935,"TERMINAL",0,0,"n",,terminal_output +4637,13953024,"TERMINAL",0,0,"e",,terminal_output +4638,13953083,"TERMINAL",0,0,"r",,terminal_output +4639,13953212,"TERMINAL",0,0,"-",,terminal_output +4640,13953274,"TERMINAL",0,0,"2",,terminal_output +4641,13953602,"TERMINAL",0,0,"\r\n[?2004l\r]0;tum_cte0515@hkn0402:~/Projects/jasmine_jobs_2[?2004h(jasmine) [tum_cte0515@hkn0402 jasmine_jobs_2]$ ",,terminal_output +4642,13954338,"TERMINAL",0,0,"s",,terminal_output +4643,13954417,"TERMINAL",0,0,"b",,terminal_output +4644,13954522,"TERMINAL",0,0,"a",,terminal_output +4645,13954580,"TERMINAL",0,0,"t",,terminal_output +4646,13954710,"TERMINAL",0,0,"c",,terminal_output +4647,13954762,"TERMINAL",0,0,"h",,terminal_output +4648,13954878,"TERMINAL",0,0," ",,terminal_output +4649,13955295,"TERMINAL",0,0,"[DEBUG]",,terminal_output +4650,13956064,"TERMINAL",0,0,"",,terminal_output +4651,13957285,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",0,0,"",shellscript,tab +4652,13963815,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",0,0,"",shellscript,tab +4653,13963816,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",1960,0,"",shellscript,selection_mouse +4654,13964363,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",1967,0,"",shellscript,selection_mouse +4655,13965065,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",1970,0,"",shellscript,selection_mouse +4656,13965712,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",1968,0,"",shellscript,selection_mouse +4657,13966461,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",1969,0,"",shellscript,selection_command +4658,13966757,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",1962,7,"",shellscript,content +4659,13967889,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",1962,0,"default",shellscript,content +4660,13973999,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu copy.sh",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\n#SBATCH --job-name=train_dyn_default_breakout_longer\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\n# source .venv/bin/activate\n\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/train\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/val\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\n# lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3512576\n# tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3512502\n\nlam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3518963\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3518963\n\nenv | grep SLURM\n\nexport PYTHONUNBUFFERED=1\n\nsrun python jasmine/train_dynamics.py \\n --save_ckpt \\n --image_height=10 \\n --image_width=10 \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=120 \\n --init_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=250 \\n --log_checkpoint_interval=250 \\n --dyna_type=maskgit \\n --log \\n --name=breakout-dyn-noise-lvl-default-$slurm_job_id \\n --tags dyn breakout noise-lvl default \\n --entity instant-uv \\n --project jafar \\n --patch_size 4 \\n --lam_patch_size 4 \\n --warmup_steps 100 \\n --wsd_decay_steps 1000 \\n --num_steps 5000 \\n --data_dir $array_records_dir_train \\n --val_data_dir $array_records_dir_val \\n --tokenizer_checkpoint $tokenizer_checkpoint \\n --lam_checkpoint $lam_checkpoint \\n --val_interval 1000 \\n --eval_full_frame \\n",shellscript,tab +4661,13982061,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu-nan-debug.sh",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\n#SBATCH --job-name=train_dyn_default_breakout_longer\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\n# source .venv/bin/activate\n\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/train\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/val\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\n# lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3512576\n# tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3512502\n\nlam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3518963\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3518963\n\nenv | grep SLURM\n\nexport PYTHONUNBUFFERED=1\n\nsrun python jasmine/train_dynamics.py \\n --save_ckpt \\n --image_height=10 \\n --image_width=10 \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=120 \\n --init_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=250 \\n --log_checkpoint_interval=250 \\n --dyna_type=maskgit \\n --log \\n --name=breakout-dyn-noise-lvl-default-$slurm_job_id \\n --tags dyn breakout noise-lvl default \\n --entity instant-uv \\n --project jafar \\n --patch_size 4 \\n --lam_patch_size 4 \\n --warmup_steps 100 \\n --wsd_decay_steps 1000 \\n --num_steps 5000 \\n --data_dir $array_records_dir_train \\n --val_data_dir $array_records_dir_val \\n --tokenizer_checkpoint $tokenizer_checkpoint \\n --lam_checkpoint $lam_checkpoint \\n --val_interval 1000 \\n --eval_full_frame \\n",shellscript,tab +4662,13984492,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu-nan-debug.sh",2427,0,"",shellscript,selection_mouse 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+4889,14151891,"TERMINAL",0,0,"g",,terminal_output +4890,14151963,"TERMINAL",0,0,"i",,terminal_output +4891,14152031,"TERMINAL",0,0,"t",,terminal_output +4892,14152134,"TERMINAL",0,0," ",,terminal_output +4893,14152287,"TERMINAL",0,0,"s",,terminal_output +4894,14152412,"TERMINAL",0,0,"t",,terminal_output +4895,14152746,"TERMINAL",0,0,"",,terminal_output +4896,14152811,"TERMINAL",0,0,"",,terminal_output +4897,14153288,"TERMINAL",0,0,"d",,terminal_output +4898,14153358,"TERMINAL",0,0,"i",,terminal_output +4899,14153466,"TERMINAL",0,0,"f",,terminal_output +4900,14153627,"TERMINAL",0,0,"f\r\n[?2004l\r[?1h=\r",,terminal_output +4901,14153761,"TERMINAL",0,0,"diff --git a/jasmine/genie.py b/jasmine/genie.py\r\nindex 061cebd..54da37e 100644\r\n--- a/jasmine/genie.py\r\n+++ b/jasmine/genie.py\r\n@@ -207,16 +207,20 @@ class Genie(nnx.Module):\r\n self,\r\n batch: Dict[str, jax.Array],\r\n seq_len: int,\r\n+ noise_level: float = 0.,\r\n temperature: float = 1,\r\n sample_argmax: bool = False,\r\n maskgit_steps: int = 25,\r\n ) -> tuple[jax.Array, jax.Array]:\r\n+ assert (\r\n+ noise_level < self.max_noise_level\r\n+ ), ""Noise level must me smaller than max_noise_level.""\r\n if self.dyna_type == ""maskgit"":\r\n return self.sample_maskgit(\r\n- batch, seq_len, 0.0, maskgit_steps, temperature, sample_argmax\r\n+ batch, seq_len, noise_level, maskgit_steps, temperature, sample_argmax\r\n )\r\n elif self.dyna_type == ""causal"":\r\n- return self.sample_causal(batch, seq_len, temperature, sample_argmax)\r\n+ return self.sample_causal(batch, seq_len, noise_level, temperature, sample_ argmax)\r\n else:\r\n raise ValueError(f""Dynamics model type unknown: {self.dyna_type}"")\r\n \r\n@@ -255,9 +259,6 @@ class Genie(nnx.Module):\r\n P: S * N\r\n """"""\r\n assert isinstance(self.dynamics, DynamicsMaskGIT)\r\n- assert (\r\n- noise_level < self.max_noise_level\r\n- ), ""Noise level must me smaller than max_noise_level.""\r\n:",,terminal_output +4902,14156120,"TERMINAL",0,0,"\r # --- Encode videos and actions ---\r\n:",,terminal_output +4903,14160459,"TERMINAL",0,0,"\r videos_BTHWC = batch[""videos""]\r\n:",,terminal_output +4904,14160652,"TERMINAL",0,0,"\r tokenizer_out = self.tokenizer.vq_encode(videos_BTHWC, training=False)\r\n:",,terminal_output +4905,14160892,"TERMINAL",0,0,"\r@@ -328,19 +329,18 @@ class Genie(nnx.Module):\r\n:",,terminal_output +4906,14162056,"TERMINAL",0,0,"\r act_embed_BS1M = jnp.reshape(\r\n:",,terminal_output +4907,14162642,"TERMINAL",0,0,"\r act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1])\r\n:",,terminal_output +4908,14163159,"TERMINAL",0,0,"\r )\r\n:\r- # TODO mihir\r\n:\r \r\n:",,terminal_output +4909,14163217,"TERMINAL",0,0,"\r+ # TODO mihir\r\n:",,terminal_output +4910,14163388,"TERMINAL",0,0,"\r rng, _rng_noise = jax.random.split(rng)\r\n:",,terminal_output +4911,14163766,"TERMINAL",0,0,"\r noise_level_111 = noise_level.reshape(1, 1, 1)\r\n:",,terminal_output +4912,14164006,"TERMINAL",0,0,"\r noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\r\n:",,terminal_output +4913,14165092,"TERMINAL",0,0,"\r noise_bucket_idx_B11 = jnp.floor(\r\n:\r- (noise_level_B11 / self.max_noise_level) * self.noise_buckets\r\n:\r+ (noise_level_B11 * self.noise_buckets) / self.max_noise_level\r\n:\r ).astype(jnp.int32)\r\n:\r noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(\r\n:\r noise_bucket_idx_B11\r\n:\r )\r\n:\r noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\r\n:\r- vid_embed_BSNM += jnp.expand_dims(noise_level_B11, -1)\r\n:\r \r\n:\r vid_embed_BSNp2M = jnp.concatenate(\r\n:\r [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\r\n:\r@@ -441,6 +441,7 @@ class Genie(nnx.Module):\r\n:\r self,\r\n:\r batch: Dict[str, jax.Array],\r\n:\r seq_len: int,\r\n:\r+ noise_level: float,\r\n:\r temperature: float = 1,\r\n:\r sample_argmax: bool = False,\r\n:\r ) -> tuple[jax.Array, jax.Array]:\r\n:\r@@ -528,12 +529,28 @@ class Genie(nnx.Module):\r\n:\r act_embed_BS1M = jnp.reshape(\r\n:",,terminal_output +4914,14165152,"TERMINAL",0,0,"\r act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1])\r\n:",,terminal_output +4915,14166233,"TERMINAL",0,0,"\r )\r\n:",,terminal_output +4916,14167280,"TERMINAL",0,0,"\r- vid_embed_BSNp1M = jnp.concatenate([act_embed_BS1M, vid_embed_BSNM], axis=2 :\r)\r\n:\r- final_logits_BTNp1V = (\r\n:\r- dynamics_causal.transformer(vid_embed_BSNp1M, (step_t, step_n))\r\n:\r+\r\n:\r+ # TODO mihir\r\n:\r+\r\n:\r+ rng, _rng_noise = jax.random.split(rng)\r\n:\r+ noise_level_111 = noise_level.reshape(1, 1, 1)\r\n:\r+ noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\r\n:\r+ noise_bucket_idx_B11 = jnp.floor(\r\n:\r+ (noise_level_B11 * self.noise_buckets) / self.max_noise_level\r\n:\r+ ).astype(jnp.int32)\r\n:\r+ noise_level_embed_B11M = dynamics_causal.noise_level_embed(\r\n:\r+ noise_bucket_idx_B11\r\n:\r+ )\r\n:\r+ noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\r\n:\r+\r\n:\r+ vid_embed_BSNp2M = jnp.concatenate(\r\n:\r+ [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\r\n:",,terminal_output +4917,14167332,"TERMINAL",0,0,"\r+ )\r\n:",,terminal_output +4918,14167391,"TERMINAL",0,0,"\r+ final_logits_BTNp2V = (\r\n:",,terminal_output +4919,14167787,"TERMINAL",0,0,"\r+ dynamics_causal.transformer(vid_embed_BSNp2M, (step_t, step_n))\r\n:",,terminal_output +4920,14168264,"TERMINAL",0,0,"\r / temperature\r\n:",,terminal_output +4921,14169223,"TERMINAL",0,0,"\r )\r\n:\r- final_logits_BV = final_logits_BTNp1V[:, step_t, step_n, :]\r\n:\r+ final_logits_BV = final_logits_BTNp2V[:, step_t, step_n + 1, :]\r\n:\r \r\n:\r # --- Sample new tokens for final frame ---\r\n:\r if sample_argmax:\r\n:\rdiff --git a/jasmine/models/dynamics.py b/jasmine/models/dynamics.py\r\n:\rindex 5ce4dcd..0a69649 100644\r\n:\r--- a/jasmine/models/dynamics.py\r\n:\r+++ b/jasmine/models/dynamics.py\r\n:\r@@ -78,6 +78,33 @@ class DynamicsMaskGIT(nnx.Module):\r\n:\r self.noise_buckets, self.model_dim, rngs=rngs\r\n:\r )\r\n:\r \r\n:\r+ def _apply_noise_augmentation(self, vid_embed_BTNM, rng):\r\n:\r+ B, T, N, M = vid_embed_BTNM.shape\r\n:\r+ rng, _rng_noise_lvl, _rng_noise = jax.random.split(rng, 3)\r\n:\r+ eps = 5e-3\r\n:\r+ noise_level_B = jax.random.uniform(\r\n:\r+ _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level - eps\r\n:\r+ )\r\n:\r+ noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\r\n:\r+ # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\r\n:\r+ # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\r\n:\r+ noise_bucket_idx_B = jnp.floor(\r\n:\r+ (noise_level_B * self.noise_buckets) / self.max_noise_level\r\n:\r+ ).astype(jnp.int32)\r\n:\r+ noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\r\n:\r+ noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\r\n:\r+ noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\r\n:\r+ noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\r\n:\r+\r\n:",,terminal_output +4922,14170176,"TERMINAL",0,0,"\r+ # safe sqrt: clip argument to >= 0\r\n:",,terminal_output +4923,14170707,"TERMINAL",0,0,"\r+ one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\r\n:\r+ sqrt_one_minus = jnp.sqrt(one_minus_noise)\r\n:\r+ sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\r\n:",,terminal_output +4924,14170768,"TERMINAL",0,0,"\r+\r\n:",,terminal_output +4925,14170945,"TERMINAL",0,0,"\r+ noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * :",,terminal_output +4926,14171543,"TERMINAL",0,0,"\r noise_BTNM\r\n:\r+\r\n:\r+ return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M\r\n:\r+\r\n:",,terminal_output +4927,14171605,"TERMINAL",0,0,"\r def __call__(\r\n:",,terminal_output +4928,14176941,"TERMINAL",0,0,"\r self,\r\n:",,terminal_output +4929,14178208,"TERMINAL",0,0,"\r batch: Dict[str, jax.Array],\r\n:\r@@ -87,7 +114,7 @@ class DynamicsMaskGIT(nnx.Module):\r\n:\r latent_actions_BTm11L = batch[""latent_actions""]\r\n:\r vid_embed_BTNM = self.patch_embed(video_tokens_BTN)\r\n:\r \r\n:\r- B, T, N, M = vid_embed_BTNM.shape\r\n:\r+ B = vid_embed_BTNM.shape[0]\r\n:\r rng, _rng_prob, *_rngs_mask = jax.random.split(batch[""mask_rng""], B + 2)\r\n:\r mask_prob = jax.random.uniform(_rng_prob, shape=(B,), minval=self.mask_limit)\r\n:\r per_sample_shape = vid_embed_BTNM.shape[1:-1]\r\n:\r@@ -100,28 +127,9 @@ class DynamicsMaskGIT(nnx.Module):\r\n:\r jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM\r\n:\r )\r\n:\r \r\n:\r- # --- Sample noise ---\r\n:\r- rng, _rng_noise_lvl, _rng_noise = jax.random.split(rng, 3)\r\n:\r- noise_level_B = jax.random.uniform(\r\n:\r- _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\r\n:\r- )\r\n:\r- noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\r\n:\r- # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\r\n:\r- # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\r\n:\r- noise_bucket_idx_B = jnp.floor(\r\n:\r- (noise_level_B * self.noise_buckets) / self.max_noise_level\r\n:\r- ).astype(jnp.int32)\r\n:\r- noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\r\n:\r- noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\r\n:",,terminal_output +4930,14178266,"TERMINAL",0,0,"\r- noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\r\n:",,terminal_output +4931,14179052,"TERMINAL",0,0,"\r- noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\r\n:",,terminal_output +4932,14179658,"TERMINAL",0,0,"\r-\r\n:\r- # safe sqrt: clip argument to >= 0\r\n:\r- one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\r\n:\r- sqrt_one_minus = jnp.sqrt(one_minus_noise)\r\n:\r- sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\r\n:",,terminal_output +4933,14180299,"TERMINAL",0,0,"\r+ # --- Apply noise augmentation ---\r\n:",,terminal_output +4934,14180959,"TERMINAL",0,0,"\r+ vid_embed_BTNM, noise_level_embed_BT1M = self._apply_noise_augmentation(vid_emb :\red_BTNM, rng)\r\n:\r \r\n:\r- vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * noise_BTNM\r\n:\r # --- Predict transition ---\r\n:\r act_embed_BTm11M = self.action_up(latent_actions_BTm11L)\r\n:\r padded_act_embed_BT1M = jnp.pad(\r\n:\r@@ -147,6 +155,8 @@ class DynamicsCausal(nnx.Module):\r\n:",,terminal_output +4935,14181021,"TERMINAL",0,0,"\r num_blocks: int,\r\n:",,terminal_output +4936,14181904,"TERMINAL",0,0,"\r num_heads: int,\r\n:",,terminal_output +4937,14182369,"TERMINAL",0,0,"\r dropout: float,\r\n:",,terminal_output +4938,14183057,"TERMINAL",0,0,"\r+ max_noise_level: float,\r\n:\r+ noise_buckets: int,\r\n:\r param_dtype: jnp.dtype,\r\n:\r dtype: jnp.dtype,\r\n:\r use_flash_attention: bool,\r\n:\r@@ -160,6 +170,8 @@ class DynamicsCausal(nnx.Module):\r\n:\r self.num_blocks = num_blocks\r\n:\r self.num_heads = num_heads\r\n:\r self.dropout = dropout\r\n:\r+ self.max_noise_level = max_noise_level\r\n:\r+ self.noise_buckets = noise_buckets\r\n:\r self.param_dtype = param_dtype\r\n:\r self.dtype = dtype\r\n:\r self.use_flash_attention = use_flash_attention\r\n:\r@@ -187,6 +199,34 @@ class DynamicsCausal(nnx.Module):\r\n:\r dtype=self.dtype,\r\n:\r rngs=rngs,\r\n:\r )\r\n:\r+ self.noise_level_embed = nnx.Embed(\r\n:\r+ self.noise_buckets, self.model_dim, rngs=rngs\r\n:",,terminal_output +4939,14185301,"TERMINAL",0,0,"\r+ )\r\n:",,terminal_output +4940,14186535,"TERMINAL",0,0,"\r+\r\n:\r+ def _apply_noise_augmentation(self, vid_embed_BTNM, rng):\r\n:\r+ B, T, N, M = vid_embed_BTNM.shape\r\n:\r+ rng, _rng_noise_lvl, _rng_noise = jax.random.split(rng, 3)\r\n:\r+ noise_level_B = jax.random.uniform(\r\n:\r+ _rng_noise_lvl, shape=(B,), minval=0.0, maxval=self.max_noise_level\r\n:\r+ )\r\n:\r+ noise_BTNM = jax.random.normal(_rng_noise, shape=(B, T, N, M))\r\n:\r+ # We calculate `(noise_level * noise_buckets) / max_noise_level` instead of\r\n:\r+ # `(noise_level_B / max_noise_level) * noise_buckets` for numerical stability.\r\n:\r+ noise_bucket_idx_B = jnp.floor(\r\n:\r+ (noise_level_B * self.noise_buckets) / self.max_noise_level\r\n:\r+ ).astype(jnp.int32)\r\n:\r+ noise_bucket_idx_B11 = noise_bucket_idx_B.reshape(B, 1, 1)\r\n:\r+ noise_level_embed_B11M = self.noise_level_embed(noise_bucket_idx_B11)\r\n:\r+ noise_level_embed_BT1M = jnp.tile(noise_level_embed_B11M, (1, T, 1, 1))\r\n:\r+ noise_level_B111 = noise_level_B.reshape(B, 1, 1, 1)\r\n:\r+\r\n:\r+ # safe sqrt: clip argument to >= 0\r\n:\r+ one_minus_noise = jnp.clip(1.0 - noise_level_B111, a_min=0.0)\r\n:\r+ sqrt_one_minus = jnp.sqrt(one_minus_noise)\r\n:\r+ sqrt_noise = jnp.sqrt(jnp.clip(noise_level_B111, a_min=0.0))\r\n:\r+\r\n:\r+ noise_augmented_vid_embed_BTNM = sqrt_one_minus * vid_embed_BTNM + sqrt_noise * :\r noise_BTNM\r\n:\r+ return noise_augmented_vid_embed_BTNM, noise_level_embed_BT1M\r\n:",,terminal_output +4941,14186591,"TERMINAL",0,0,"\r \r\n:",,terminal_output +4942,14188128,"TERMINAL",0,0,"\r def __call__(\r\n:",,terminal_output +4943,14188683,"TERMINAL",0,0,"\r self,\r\n:\r@@ -199,9 +239,10 @@ class DynamicsCausal(nnx.Module):\r\n:\r padded_act_embed_BT1M = jnp.pad(\r\n:",,terminal_output +4944,14189422,"TERMINAL",0,0,"\r act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))\r\n:\r )\r\n:\r- vid_embed_BTNp1M = jnp.concatenate(\r\n:\r- [padded_act_embed_BT1M, vid_embed_BTNM], axis=2\r\n:\r+ vid_embed_BTNM, noise_level_embed_BT1M = self._apply_noise_augmentation(video_t :\rokens_BTN, batch[""rng""])\r\n:\r+ vid_embed_BTNp2M = jnp.concatenate(\r\n:\r+ [padded_act_embed_BT1M, noise_level_embed_BT1M, vid_embed_BTNM], axis=2\r\n:\r )\r\n:\r- logits_BTNp1V = self.transformer(vid_embed_BTNp1M)\r\n:\r- logits_BTNV = logits_BTNp1V[:, :, :-1]\r\n:\r+ logits_BTNp2V = self.transformer(vid_embed_BTNp2M)\r\n:\r+ logits_BTNV = logits_BTNp2V[:, :, 1:-1]\r\n:\r return logits_BTNV, jnp.ones_like(video_tokens_BTN)\r\n:\rdiff --git a/jasmine/train_dynamics.py b/jasmine/train_dynamics.py\r\n:\rindex 2734952..20568ff 100644\r\n:\r--- a/jasmine/train_dynamics.py\r\n:\r+++ b/jasmine/train_dynamics.py\r\n:\r@@ -506,11 +506,12 @@ def main(args: Args) -> None:\r\n:\r :, :-1\r\n:\r ] # remove last frame for generation\r\n:\r recon_full_frame, logits_full_frame = genie.sample(\r\n:\r- inputs,\r\n:\r- args.seq_len,\r\n:",,terminal_output +4945,14197232,"TERMINAL",0,0,"\r- args.val_temperature,\r\n:",,terminal_output +4946,14198428,"TERMINAL",0,0,"\r- args.val_sample_argmax,\r\n:\r- args.val_maskgit_steps,\r\n:\r+ batch=inputs,\r\n:\r+ seq_len=args.seq_len,\r\n:\r+ noise_level=0.,\r\n:\r+ temperature=args.val_temperature,\r\n:\r+ sample_argmax=args.val_sample_argmax,\r\n:\r+ maskgit_steps=args.val_maskgit_steps,\r\n:\r )\r\n:\r # Calculate metrics for the last frame only\r\n:\r step_outputs = {\r\n:\r@@ -616,9 +617,9 @@ def main(args: Args) -> None:\r\n:\r if jax.process_index() == 0:\r\n:\r first_batch = next(dataloader_train)\r\n:\r first_batch[""rng""] = rng # type: ignore\r\n:\r- compiled = train_step.lower(optimizer, first_batch).compile()\r\n:\r- print_compiled_memory_stats(compiled.memory_analysis())\r\n:\r- print_compiled_cost_analysis(compiled.cost_analysis())\r\n:\r+ # compiled = train_step.lower(optimizer, first_batch).compile()\r\n:\r+ # print_compiled_memory_stats(compiled.memory_analysis())\r\n:\r+ # print_compiled_cost_analysis(compiled.cost_analysis())\r\n:\r # Do not skip the first batch during training\r\n:\r dataloader_train = itertools.chain([first_batch], dataloader_train)\r\n:\r print(f""Starting training from step {step}..."")\r\n:\r\r(END)",,terminal_output +4947,14198487,"TERMINAL",0,0,"\r\r(END)",,terminal_output +4948,14204809,"TERMINAL",0,0,"\r\r(END)",,terminal_output +4949,14205416,"TERMINAL",0,0,"\r\r(END)\r\r(END)\r\r(END)\r\r(END)\r\r(END)",,terminal_output +4950,14205473,"TERMINAL",0,0,"\r\r(END)",,terminal_output +4951,14205666,"TERMINAL",0,0,"\r\r(END)",,terminal_output +4952,14205778,"TERMINAL",0,0,"\r\r(END)",,terminal_output +4953,14205950,"TERMINAL",0,0,"\r\r(END)",,terminal_output +4954,14212620,"jasmine/train_dynamics.py",0,0,"",python,tab +4955,14212621,"jasmine/train_dynamics.py",21760,512," if jax.process_index() == 0:\n first_batch = next(dataloader_train)\n first_batch[""rng""] = rng # type: ignore\n # compiled = train_step.lower(optimizer, first_batch).compile()\n # print_compiled_memory_stats(compiled.memory_analysis())\n # print_compiled_cost_analysis(compiled.cost_analysis())\n # Do not skip the first batch during training\n dataloader_train = itertools.chain([first_batch], dataloader_train)\n print(f""Starting training from step {step}..."")\n",python,selection_command +4956,14213513,"jasmine/train_dynamics.py",22291,0,"",python,selection_mouse +4957,14214125,"jasmine/train_dynamics.py",22111,0,"",python,selection_mouse +4958,14214762,"jasmine/train_dynamics.py",22090,53," # Do not skip the first batch during training",python,selection_command +4959,14214945,"jasmine/train_dynamics.py",22025,118," # print_compiled_cost_analysis(compiled.cost_analysis())\n # Do not skip the first batch during training",python,selection_command +4960,14215090,"jasmine/train_dynamics.py",21959,184," # print_compiled_memory_stats(compiled.memory_analysis())\n # print_compiled_cost_analysis(compiled.cost_analysis())\n # Do not skip the first batch during training",python,selection_command +4961,14215215,"jasmine/train_dynamics.py",21887,256," # compiled = train_step.lower(optimizer, first_batch).compile()\n # print_compiled_memory_stats(compiled.memory_analysis())\n # print_compiled_cost_analysis(compiled.cost_analysis())\n # Do not skip the first batch during training",python,selection_command +4962,14215364,"jasmine/train_dynamics.py",21838,305," first_batch[""rng""] = rng # type: ignore\n # compiled = train_step.lower(optimizer, first_batch).compile()\n # print_compiled_memory_stats(compiled.memory_analysis())\n # print_compiled_cost_analysis(compiled.cost_analysis())\n # Do not skip the first batch during training",python,selection_command +4963,14215694,"jasmine/train_dynamics.py",21887,256," # compiled = train_step.lower(optimizer, first_batch).compile()\n # print_compiled_memory_stats(compiled.memory_analysis())\n # print_compiled_cost_analysis(compiled.cost_analysis())\n # Do not skip the first batch during training",python,selection_command +4964,14215909,"jasmine/train_dynamics.py",21895,0,"",python,selection_command +4965,14216357,"jasmine/train_dynamics.py",22098,1,"",python,content +4966,14216357,"jasmine/train_dynamics.py",22033,1,"",python,content +4967,14216357,"jasmine/train_dynamics.py",21967,1,"",python,content +4968,14216357,"jasmine/train_dynamics.py",21895,1,"",python,content +4969,14216508,"jasmine/train_dynamics.py",22095,1,"",python,content +4970,14216509,"jasmine/train_dynamics.py",22031,1,"",python,content +4971,14216509,"jasmine/train_dynamics.py",21966,1,"",python,content +4972,14216509,"jasmine/train_dynamics.py",21895,1,"",python,content +4973,14216693,"jasmine/train_dynamics.py",21894,0,"",python,selection_command +4974,14218693,"jasmine/train_dynamics.py",22091,0," #",python,content +4975,14218694,"jasmine/train_dynamics.py",22029,0,"# ",python,content +4976,14218705,"jasmine/train_dynamics.py",21964,0," #",python,content +4977,14218706,"jasmine/train_dynamics.py",21895,0,"# ",python,content +4978,14218717,"jasmine/train_dynamics.py",21895,0,"",python,selection_command +4979,14219877,"jasmine/train_dynamics.py",21887,71," # compiled = train_step.lower(optimizer, first_batch).compile()",python,selection_command +4980,14220194,"jasmine/train_dynamics.py",21887,137," # compiled = train_step.lower(optimizer, first_batch).compile()\n # print_compiled_memory_stats(compiled.memory_analysis())",python,selection_command +4981,14220341,"jasmine/train_dynamics.py",21887,202," # compiled = train_step.lower(optimizer, first_batch).compile()\n # print_compiled_memory_stats(compiled.memory_analysis())\n # print_compiled_cost_analysis(compiled.cost_analysis())",python,selection_command +4982,14220777,"jasmine/train_dynamics.py",21895,0,"",python,selection_command +4983,14221227,"jasmine/train_dynamics.py",22033,1,"",python,content +4984,14221228,"jasmine/train_dynamics.py",21967,1,"",python,content +4985,14221228,"jasmine/train_dynamics.py",21895,1,"",python,content +4986,14221424,"jasmine/train_dynamics.py",22031,1,"",python,content +4987,14221424,"jasmine/train_dynamics.py",21966,1,"",python,content +4988,14221424,"jasmine/train_dynamics.py",21895,1,"",python,content +4989,14221594,"jasmine/train_dynamics.py",21894,0,"",python,selection_command +4990,14223989,"jasmine/train_dynamics.py",22105,0,"",python,selection_mouse +4991,14228701,"TERMINAL",0,0,"\r[?1l>]0;tum_cte0515@hkn0402:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0402 jasmine]$ ",,terminal_output +4992,14229531,"TERMINAL",0,0,"r",,terminal_output +4993,14229594,"TERMINAL",0,0,"u",,terminal_output +4994,14229705,"TERMINAL",0,0,"n",,terminal_output +4995,14229820,"TERMINAL",0,0,"n",,terminal_output +4996,14229884,"TERMINAL",0,0,"e",,terminal_output +4997,14229988,"TERMINAL",0,0,"r",,terminal_output +4998,14230251,"TERMINAL",0,0,"\r\n[?2004l\r]0;tum_cte0515@hkn0402:~/Projects/jasmine_jobs[?2004h(jasmine) [tum_cte0515@hkn0402 jasmine_jobs]$ ",,terminal_output +4999,14236074,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu-nan-debug.sh",0,0,"",shellscript,tab +5000,14236181,"TERMINAL",0,0,"\r(jasmine) [tum_cte0515@hkn0402 jasmine_jobs]$ ",,terminal_output +5001,14245056,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu-nan-debug.sh",2188,0,"",shellscript,selection_mouse +5002,14245660,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu-nan-debug.sh",2192,0,"",shellscript,selection_mouse +5003,14250037,"TERMINAL",0,0,"\r(jasmine) [tum_cte0515@hkn0402 jasmine_jobs]$ ",,terminal_output +5004,14250947,"TERMINAL",0,0,"ru",,terminal_output +5005,14251065,"TERMINAL",0,0,"n",,terminal_output +5006,14251186,"TERMINAL",0,0,"n",,terminal_output +5007,14251246,"TERMINAL",0,0,"e",,terminal_output +5008,14251317,"TERMINAL",0,0,"r",,terminal_output +5009,14251478,"TERMINAL",0,0,"-",,terminal_output +5010,14251538,"TERMINAL",0,0,"2",,terminal_output +5011,14251882,"TERMINAL",0,0,"\r\n[?2004l\r]0;tum_cte0515@hkn0402:~/Projects/jasmine_jobs_2[?2004h(jasmine) [tum_cte0515@hkn0402 jasmine_jobs_2]$ ",,terminal_output +5012,14252763,"TERMINAL",0,0,"v",,terminal_output +5013,14252828,"TERMINAL",0,0,"i",,terminal_output +5014,14252943,"TERMINAL",0,0,"m",,terminal_output +5015,14253006,"TERMINAL",0,0," ",,terminal_output +5016,14254760,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu.sh",0,0,"",shellscript,tab +5017,14254853,"TERMINAL",0,0,"\r(jasmine) [tum_cte0515@hkn0402 jasmine_jobs_2]$ v\r\n\rim ",,terminal_output +5018,14256247,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_gt_actions_smaller_lr_50k.sh",0,0,"",shellscript,tab +5019,14257403,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_gt_actions.sh",0,0,"",shellscript,tab +5020,14259348,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_50k.sh",0,0,"",shellscript,tab +5021,14263400,"TERMINAL",0,0,"slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_50k.sh",,terminal_output +5022,14263652,"TERMINAL",0,0,"\rslurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_50k.sh\r\n[?2004l\r[?1049h[>4;2m[?1h=[?2004h[?1004h[?12h[?12l[?25lc]10;?]11;?#!/usr/bin/env bash#SBATCH --nodes=1#SBATCH --ntasks-per-node=1\r\n#SBATCH --time=06:00:00\r\n#SBATCH --partition=accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scraatch/tum_ind3695-jafa_ws_shared/logs/logs_mmihir/breakout/dyn/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scrattch/tum_ind3695-jafa_ws_shared/logs/logs_miihir/breakout/dyn/%x_%j.log\r\n#SBATCH --job-name=train_dyn_default_breakkout_longer\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min beforee timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\n# source .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspaace/scratch/tum_ind3695-jafa_ws_shared/dataa_breakout/breakout_episodes_perfect/train\r\narray_records_dir_val=/hkfs/work/workspacee/scratch/tum_ind3695-jafa_ws_shared/data_bbreakout/breakout_episodes_perfect/val\r\n\r\njob_name=$SLURM_JOB_NAME1,1Top[?25h",,terminal_output +5023,14263707,"TERMINAL",0,0,"P+q436f\P+q6b75\P+q6b64\P+q6b72\P+q6b6c\P+q2332\P+q2334\P+q2569\P+q2a37\P+q6b31\[?12$p[?25l/3333/3333 [?25h[?25l/f6f6/e3e3 [?25h",,terminal_output +5024,14265206,"TERMINAL",0,0,"[?25l#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=1\r\n#SBATCH --time=06:00:00\r\n#SBATCH --partition=accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihirr/breakout/dyn/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir//breakout/dyn/%x_%j.log\r\n#SBATCH --job-name=train_dyn_default_breakout_longer\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\n# source .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breeakout/breakout_episodes_perfect/train\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakkout/breakout_episodes_perfect/val\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakkout/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR1,1Top[?25h",,terminal_output +5025,14266143,"TERMINAL",0,0,"[?25lj 2,0-1[?25h",,terminal_output +5026,14267840,"TERMINAL",0,0,"[?25lj 3,1 [?25h[?25lj 4[?25h[?25lj 5[?25h[?25lj 6[?25h[?25lj 7[?25h[?25lj 8[?25h[?25lj 9[?25h[?25lj 10,1[?25h[?25lj 1[?25h[?25lj 2[?25h[?25lj 3[?25h[?25lj 4,0-1[?25h[?25lj 5,1 [?25h[?25lj 6[?25h[?25lj 7,0-1[?25h[?25lj 8,1 [?25h[?25lj 9[?25h[?25lj 20[?25h[?25lj 1,0-1[?25h[?25lj 2,1 [?25h[?25lj 3[?25h[?25lj 4,0-1[?25h[?25lj 5,1 [?25h[?25lj 6[?25h[?25lj # lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breeakout/lam/interactive/351257627,0-15%[?25h[?25lj # tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoinnts/breakout/tokenizer/interactive/351250228,110%[?25h[?25lj 9[?25h[?25lj \r\n30,0-113%[?25h[?25lj lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakkout/lam/interactive/351896331,118%[?25h[?25lj tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpointss/breakout/tokenizer/interactive/351896332,123%[?25h[?25lj 3,0-1[?25h[?25lj env | grep SLURM34,127%[?25h[?25lj srun python jasmine/train_dynamics.py \\r\n --save_ckpt \35,132%[?25h[?25lj \r\n--image_height=10 \36,0-135%[?25h[?25lj \r\n--image_width=10 \37,137%[?25h[?25lj \r\n--ckpt_dir $CHECKPOINT_DIR \38,0-140%[?25h[?25lj \r\n--batch_size=120 \39,143%[?25h[?25lj \r\n--init_lr=0 \40,145%[?25h[?25lj \r\n--max_lr=3e-5 \41,148%[?25h[?25lj \r\n--log_image_interval=1000 \42,151%[?25h[?25lj \r\n--log_checkpoint_interval=1000 \43,154%[?25h[?25lj \r\n--dyna_type=maskgit \44,156%[?25h",,terminal_output +5027,14268084,"TERMINAL",0,0,"[?25lj --log \\r\n --name=breakout-dyn-50ksteps-3e-5-noise-lvl-$slurm_job_id \45,161%[?25h",,terminal_output +5028,14268300,"TERMINAL",0,0,"[?25lj 6[?25h",,terminal_output +5029,14268885,"TERMINAL",0,0,"[?25l--tags dyn breakout 50ksteps 3e-5 noise-lvl \\r\n --entity instant-uv \47,165%[?25h[?25lj 8[?25h[?25lj \r\n--project jafar \49,168%[?25h[?25lj \r\n--patch_size 4 \50,171%[?25h[?25lj \r\n--lam_patch_size 4 \51,174%[?25h",,terminal_output +5030,14269038,"TERMINAL",0,0,"[?25lj \r\n--warmup_steps 1000 \52,177%[?25h",,terminal_output +5031,14269203,"TERMINAL",0,0,"[?25lj --wsd_decay_steps 10000 \\r\n --num_steps 50000 \53,182%[?25h",,terminal_output +5032,14269345,"TERMINAL",0,0,"[?25lj 4[?25h",,terminal_output +5033,14269515,"TERMINAL",0,0,"[?25lj \r\n--data_dir $array_records_dir_train \55,185%[?25h",,terminal_output +5034,14269621,"TERMINAL",0,0,"[?25lj \r\n--val_data_dir $array_records_dir_val \56,188%[?25h",,terminal_output +5035,14269814,"TERMINAL",0,0,"[?25lk 5[?25h",,terminal_output +5036,14270005,"TERMINAL",0,0,"[?25lk 4[?25h",,terminal_output +5037,14270150,"TERMINAL",0,0,"[?25lk 3[?25h",,terminal_output +5038,14270290,"TERMINAL",0,0,"[?25lk 2[?25h",,terminal_output +5039,14270437,"TERMINAL",0,0,"[?25lk 1[?25h",,terminal_output +5040,14270768,"TERMINAL",0,0,"[?25ll 2[?25h",,terminal_output +5041,14271307,"TERMINAL",0,0,"[?25ll 3[?25h[?25ll 4[?25h[?25ll 5[?25h",,terminal_output +5042,14271677,"TERMINAL",0,0,"[?25ll 6[?25h[?25ll 7[?25h[?25ll 8[?25h[?25ll 9[?25h[?25ll 10[?25h[?25ll 1[?25h[?25ll 2[?25h[?25ll 3[?25h[?25ll 4[?25h[?25ll 5[?25h[?25ll 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slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_50k.sh",,terminal_output +5097,14290133,"TERMINAL",0,0,"",,terminal_output +5098,14291040,"TERMINAL",0,0,"\r",,terminal_output +5099,14291443,"TERMINAL",0,0,"",,terminal_output +5100,14291879,"TERMINAL",0,0," slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_50k.sh",,terminal_output +5101,14292038,"TERMINAL",0,0," slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_50k.sh",,terminal_output +5102,14292175,"TERMINAL",0,0," slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_50k.sh",,terminal_output +5103,14292375,"TERMINAL",0,0,"s slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_50k.sh",,terminal_output +5104,14292524,"TERMINAL",0,0,"b slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_50k.sh",,terminal_output 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+5124,14300669,"TERMINAL",0,0,"\r\n\r\r\n[?2004l\r[?1049h[>4;2m[?1h=[?2004h[?1004h[?12h[?12l[?25lc]10;?]11;?lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakkout/lam/interactive/3518963tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpointss/breakout/tokenizer/interactive/3518963\r\n\r\nenv | grep SLURM\r\n\r\nsrun python jasmine/train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=10 \\r\n --image_width=10 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=120 \\r\n --init_lr=0 \\r\n --max_lr=3e-5 \\r\n --log_image_interval=1000 \\r\n --log_checkpoint_interval=1000 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=breakout-dyn-50ksteps-3e-5-nan-debug-$slurm_job_id \\r\n --tags dyn breakout 50ksteps 3e-5 nan-debug \\r\n --entity instant-uv \\r\n --project jafar \\r\n --patch_size 4 \\r\n --lam_patch_size 4 \\r\n --warmup_steps 1000 \\r\n --wsd_decay_steps 10000 \\r\n --num_steps 50000 \\r\n --data_dir $array_records_dir_train \\r\n --val_data_dir $array_records_dir_val \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --lam_checkpoint $lam_checkpoint \\r\n --val_interval 1000 \\r\n --eval_full_frame \52,47Bot[?25hP+q436f\P+q6b75\P+q6b64\P+q6b72\P+q6b6c\P+q2332\P+q2334\P+q2569\P+q2a37\P+q6b31\[?12$p[?25l/3333/3333 [?25h[?25l/f6f6/e3e3 [?25h",,terminal_output +5125,14301272,"TERMINAL",0,0,"[?25lk 1[?25h",,terminal_output +5126,14302525,"TERMINAL",0,0,"[?25lk 0,11[?25h[?25lk 49,25[?25h[?25lk 8,36[?25h[?25lk 7,31[?25h[?25lk 6,19[?25h[?25lk 5,17[?25h[?25lk 4,22[?25h[?25lk 3,3[?25h[?25lk 2,2[?25h[?25lk 1,23[?25h[?25lk 0,17[?25h[?25lk 39,39[?25h[?25lk 8,0-1[?25h[?25lk 7,16 [?25h[?25lk 6,0-1[?25h[?25lk # tokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoinnts/breakout/tokenizer/interactive/351250235,4793%[?25h[?25lk # lam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breeakout/lam/interactive/351257634,4788%[?25h[?25lk 33,0-185%[?25h[?25lk CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakkout/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR32,4777%[?25h[?25lk 31,4774%[?25h[?25lk slurm_job_id=$SLURM_JOB_ID30,0-171%[?25h[?25lk job_name=$SLURM_JOB_NAME29,2468%[?25h[?25lk array_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakkout/breakout_episodes_perfect/val28,4761%[?25h[?25lk 7,0-1[?25h[?25lk array_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breeakout/breakout_episodes_perfect/train26,2656%[?25h[?25lk 5,24[?25h[?25lk 24,0-154%[?25h",,terminal_output +5127,14302994,"TERMINAL",0,0,"[?25lk module unload devel/cuda/12.4\r\n# source .venv/bin/activate23,4748%[?25h[?25lk module unload mpi/openmpi/5.022,4743%[?25h[?25lk cat $021,0-140%[?25h[?25lk # Log the sbatch script20,2737%[?25h[?25lk 19,2935%[?25h[?25lk #SBATCH --signal=b:usr1@300 # 5 min before timeout18,2932%[?25h[?25lk #SBATCH --requeue17,0-129%[?25h[?25lk #SBATCH --job-name=train_dyn_default_breakout_longer16,627%[?25h[?25lk #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir//breakout/dyn/%x_%j.log15,2323%[?25h[?25lk 4,0-1[?25h[?25lk #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihirr/breakout/dyn/%x_%j.log@@@ 13,4720%[?25h[?25lk 2,1[?25h[?25lk #SBATCH --gres=gpu:111,4718%[?25h[?25lk #SBATCH --partition=accelerated\r\n#SBATCH --cpus-per-task=510,4713%[?25h[?25lk #SBATCH --ntasks-per-node=1\r\n#SBATCH --time=06:00:00@@@ 9,478%[?25h[?25lk #SBATCH --nodes=18,205%[?25h",,terminal_output +5128,14303382,"TERMINAL",0,0,"[?25lj 9,47[?25h",,terminal_output +5129,14303794,"TERMINAL",0,0,"[?25lk 8,20[?25h",,terminal_output +5130,14304386,"TERMINAL",0,0,"[?25lk @@@ 7,252%[?25h",,terminal_output 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slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_50k.shh slurm/jobs/mihir/horeka/breakout/noise_schedule_runs/mixed_prec/train_dyn_single_gpu_50k.sh",,terminal_output +5165,14316125,"TERMINAL",0,0,"\r\n\r\r\n[?2004l\rSubmitted batch job 3527904\r\n]0;tum_cte0515@hkn0402:~/Projects/jasmine_jobs_2[?2004h(jasmine) [tum_cte0515@hkn0402 jasmine_jobs_2]$ ",,terminal_output +5166,14318692,"TERMINAL",0,0,"qu",,terminal_output +5167,14318806,"TERMINAL",0,0,"e",,terminal_output +5168,14318859,"TERMINAL",0,0,"u",,terminal_output +5169,14319456,"TERMINAL",0,0,"e",,terminal_output +5170,14319622,"TERMINAL",0,0,"\r\n[?2004l\r[?1049h(B[?7hEvery 1.0s: squeue --mehkn0402.localdomain: Sun Sep 28 17:26:04 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3527904 accelerat train_dy tum_cte0 PD\t0:00\t 1 (Priority)3527891 accelerat train_dy tum_cte0 R\t3:26\t 1 hkn07303527866 dev_accel interact tum_cte0 R37:07\t 1 hkn0402",,terminal_output 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+5215,14350433,"TERMINAL",0,0," ",,terminal_output +5216,14350674,"TERMINAL",0,0,"d",,terminal_output +5217,14350788,"TERMINAL",0,0,"i",,terminal_output +5218,14350913,"TERMINAL",0,0,"f",,terminal_output +5219,14351057,"TERMINAL",0,0,"f",,terminal_output +5220,14351123,"TERMINAL",0,0,"\r\n[?2004l\r[?1h=\rdiff --git a/jasmine/genie.py b/jasmine/genie.py\r\nindex 061cebd..54da37e 100644\r\n--- a/jasmine/genie.py\r\n+++ b/jasmine/genie.py\r\n@@ -207,16 +207,20 @@ class Genie(nnx.Module):\r\n self,\r\n batch: Dict[str, jax.Array],\r\n seq_len: int,\r\n+ noise_level: float = 0.,\r\n temperature: float = 1,\r\n sample_argmax: bool = False,\r\n maskgit_steps: int = 25,\r\n ) -> tuple[jax.Array, jax.Array]:\r\n+ assert (\r\n+ noise_level < self.max_noise_level\r\n+ ), ""Noise level must me smaller than max_noise_level.""\r\n if self.dyna_type == ""maskgit"":\r\n return self.sample_maskgit(\r\n- batch, seq_len, 0.0, maskgit_steps, temperature, sample_argmax\r\n+ batch, seq_len, noise_level, maskgit_steps, temperature, sample_argmax\r\n )\r\n elif self.dyna_type == ""causal"":\r\n- return self.sample_causal(batch, seq_len, temperature, sample_argmax)\r\n+ return self.sample_causal(batch, seq_len, noise_level, temperature, sample_ argmax)\r\n else:\r\n raise ValueError(f""Dynamics model type unknown: {self.dyna_type}"")\r\n \r\n@@ -255,9 +259,6 @@ class Genie(nnx.Module):\r\n P: S * N\r\n """"""\r\n assert isinstance(self.dynamics, DynamicsMaskGIT)\r\n- assert (\r\n- noise_level < self.max_noise_level\r\n- ), ""Noise level must me smaller than max_noise_level.""\r\n:",,terminal_output +5221,14352347,"TERMINAL",0,0,"\r # --- Encode videos and actions ---\r\n:",,terminal_output +5222,14353088,"TERMINAL",0,0,"\r videos_BTHWC = batch[""videos""]\r\n:\r tokenizer_out = self.tokenizer.vq_encode(videos_BTHWC, training=False)\r\n:\r@@ -328,19 +329,18 @@ class Genie(nnx.Module):\r\n:\r act_embed_BS1M = jnp.reshape(\r\n:\r act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1])\r\n:\r )\r\n:\r- # TODO mihir\r\n:\r \r\n:\r+ # TODO mihir\r\n:",,terminal_output +5223,14353728,"TERMINAL",0,0,"\r rng, _rng_noise = jax.random.split(rng)\r\n:\r noise_level_111 = noise_level.reshape(1, 1, 1)\r\n:\r noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\r\n:\r noise_bucket_idx_B11 = jnp.floor(\r\n:\r- (noise_level_B11 / self.max_noise_level) * self.noise_buckets\r\n:\r+ (noise_level_B11 * self.noise_buckets) / self.max_noise_level\r\n:\r ).astype(jnp.int32)\r\n:\r noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(\r\n:\r noise_bucket_idx_B11\r\n:\r )\r\n:\r noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\r\n:\r- vid_embed_BSNM += jnp.expand_dims(noise_level_B11, -1)\r\n:\r \r\n:\r vid_embed_BSNp2M = jnp.concatenate(\r\n:\r [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\r\n:\r@@ -441,6 +441,7 @@ class Genie(nnx.Module):\r\n:\r self,\r\n:\r batch: Dict[str, jax.Array],\r\n:\r seq_len: int,\r\n:\r+ noise_level: float,\r\n:\r temperature: float = 1,\r\n:",,terminal_output +5224,14354491,"TERMINAL",0,0,"\r sample_argmax: bool = False,\r\n:",,terminal_output +5225,14355138,"TERMINAL",0,0,"\r ) -> tuple[jax.Array, jax.Array]:\r\n:\r@@ -528,12 +529,28 @@ class Genie(nnx.Module):\r\n:\r act_embed_BS1M = jnp.reshape(\r\n:\r act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1])\r\n:\r )\r\n:\r- vid_embed_BSNp1M = jnp.concatenate([act_embed_BS1M, vid_embed_BSNM], axis=2 :\r)\r\n:",,terminal_output +5226,14355195,"TERMINAL",0,0,"\r- final_logits_BTNp1V = (\r\n:",,terminal_output +5227,14355351,"TERMINAL",0,0,"\r- dynamics_causal.transformer(vid_embed_BSNp1M, (step_t, step_n))\r\n:",,terminal_output +5228,14356222,"TERMINAL",0,0,"\r+\r\n:\r+ # TODO mihir\r\n:\r+\r\n:\r+ rng, _rng_noise = jax.random.split(rng)\r\n:\r+ noise_level_111 = noise_level.reshape(1, 1, 1)\r\n:\r+ noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\r\n:\r+ noise_bucket_idx_B11 = jnp.floor(\r\n:\r+ (noise_level_B11 * self.noise_buckets) / self.max_noise_level\r\n:\r+ ).astype(jnp.int32)\r\n:\r+ noise_level_embed_B11M = dynamics_causal.noise_level_embed(\r\n:\r+ noise_bucket_idx_B11\r\n:\r+ )\r\n:\r+ noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\r\n:",,terminal_output +5229,14358062,"TERMINAL",0,0,"\r+\r\n:",,terminal_output +5230,14358890,"TERMINAL",0,0,"\r+ vid_embed_BSNp2M = jnp.concatenate(\r\n:\r+ [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\r\n:\r+ )\r\n:\r+ final_logits_BTNp2V = (\r\n:\r+ dynamics_causal.transformer(vid_embed_BSNp2M, (step_t, step_n))\r\n:\r / temperature\r\n:\r )\r\n:\r- final_logits_BV = final_logits_BTNp1V[:, step_t, step_n, :]\r\n:\r+ final_logits_BV = final_logits_BTNp2V[:, step_t, step_n + 1, :]\r\n:\r \r\n:\r # --- Sample new tokens for final frame ---\r\n:\r if sample_argmax:\r\n:\rdiff --git a/jasmine/models/dynamics.py b/jasmine/models/dynamics.py\r\n:\rindex 5ce4dcd..0a69649 100644\r\n:",,terminal_output +5231,14358946,"TERMINAL",0,0,"\r--- a/jasmine/models/dynamics.py\r\n:",,terminal_output +5232,14359271,"TERMINAL",0,0,"\r+++ b/jasmine/models/dynamics.py\r\n:",,terminal_output +5233,14360880,"TERMINAL",0,0,"\r@@ -78,6 +78,33 @@ class DynamicsMaskGIT(nnx.Module):\r\n:",,terminal_output +5234,14361306,"TERMINAL",0,0,"\rM )\r\n\r:",,terminal_output +5235,14362448,"TERMINAL",0,0,"\rM act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1])\r\n\r:\rM act_embed_BS1M = jnp.reshape(\r\n\r:\rM@@ -528,12 +529,28 @@ class Genie(nnx.Module):\r\n\r:\rM ) -> tuple[jax.Array, jax.Array]:\r\n\r:\rM sample_argmax: bool = False,\r\n\r:\rM temperature: float = 1,\r\n\r:\rM+ noise_level: float,\r\n\r:\rM seq_len: int,\r\n\r:\rM batch: Dict[str, jax.Array],\r\n\r:\rM self,\r\n\r:\rM@@ -441,6 +441,7 @@ class Genie(nnx.Module):\r\n\r:\rM [act_embed_BS1M, noise_level_embed_BS1M, vid_embed_BSNM], axis=2\r\n\r:\rM vid_embed_BSNp2M = jnp.concatenate(\r\n\r:\rM \r\n\r:\rM- vid_embed_BSNM += jnp.expand_dims(noise_level_B11, -1)\r\n\r:\rM noise_level_embed_BS1M = jnp.tile(noise_level_embed_B11M, (1, S, 1, 1))\r\n\r:\rM )\r\n\r:\rM noise_bucket_idx_B11\r\n\r:\rM noise_level_embed_B11M = dynamics_maskgit.noise_level_embed(\r\n\r:\rM ).astype(jnp.int32)\r\n\r:\rM+ (noise_level_B11 * self.noise_buckets) / self.max_noise_level\r\n\r:\rM- (noise_level_B11 / self.max_noise_level) * self.noise_buckets\r\n\r:\rM noise_bucket_idx_B11 = jnp.floor(\r\n\r:",,terminal_output +5236,14362722,"TERMINAL",0,0,"\rM noise_level_B11 = jnp.tile(noise_level_111, (B, 1, 1))\r\n\r:\rM noise_level_111 = noise_level.reshape(1, 1, 1)\r\n\r:\rM rng, _rng_noise = jax.random.split(rng)\r\n\r:\rM+ # TODO mihir\r\n\r:\rM \r\n\r:\rM- # TODO mihir\r\n\r:\rM )\r\n\r:\rM act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1])\r\n\r:\rM act_embed_BS1M = jnp.reshape(\r\n\r:\rM@@ -328,19 +329,18 @@ class Genie(nnx.Module):\r\n\r:",,terminal_output +5237,14362785,"TERMINAL",0,0,"\rM tokenizer_out = self.tokenizer.vq_encode(videos_BTHWC, training=False)\r\n\r:",,terminal_output +5238,14363033,"TERMINAL",0,0,"\rM videos_BTHWC = batch[""videos""]\r\n\r:",,terminal_output +5239,14363672,"TERMINAL",0,0,"\rM # --- Encode videos and actions ---\r\n\r:\rM- ), ""Noise level must me smaller than max_noise_level.""\r\n\r:\rM- noise_level < self.max_noise_level\r\n\r:\rM- assert (\r\n\r:\rM assert isinstance(self.dynamics, DynamicsMaskGIT)\r\n\r:\rM """"""\r\n\r:\rM P: S * N\r\n\r:",,terminal_output +5240,14367394,"TERMINAL",0,0,"\r+ noise_level: float,\r\n:",,terminal_output +5241,14367936,"TERMINAL",0,0,"\r[?1l>]0;tum_cte0515@hkn0402:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0402 jasmine]$ ",,terminal_output +5242,14410644,"TERMINAL",0,0,"q",,terminal_output +5243,14410757,"TERMINAL",0,0,"u",,terminal_output +5244,14410817,"TERMINAL",0,0,"e",,terminal_output +5245,14410881,"TERMINAL",0,0,"u",,terminal_output +5246,14410985,"TERMINAL",0,0,"e",,terminal_output 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_maskgit_causal.sh\r\nslurm/jobs/alfred/berlin/minecraft/minecraft_sampling/sample_causal.sbatch\r\nslurm/jobs/alfred/berlin/minecraft/minecraft_sampling/sample_dynamics-maskgit-8-node-darkness-filter-3423250.sbatch\r\nslurm/jobs/alfred/helmholtz_cluster/\r\nslurm/jobs/alfred/helmholtz_cluster/jafar_og_reproduction/\r\nslurm/jobs/alfred/helmholtz_cluster/jafar_og_reproduction/generate_dataset.sbatch\r\nslurm/jobs/alfred/helmholtz_cluster/jafar_og_reproduction/generate_dataset_10m.sbatch\r\nslurm/jobs/alfred/helmholtz_cluster/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch\r\nslurm/jobs/alfred/helmholtz_cluster/jafar_og_reproduction/og_coinrun_lam_reproduction.sbatch\r\nslurm/jobs/alfred/helmholtz_cluster/jafar_og_reproduction/og_coinrun_tokenizer_repoduction.sbatch\r\nslurm/jobs/alfred/horeka/\r\nslurm/jobs/alfred/horeka/atari_dynamics/\r\nslurm/jobs/alfred/horeka/atari_dynamics/atari_dyn_maskgit_causal.sh\r\n",,terminal_output 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Is it installed?\r\nWARNING:2025-09-28 17:42:26,913:jax._src.xla_bridge:864: An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\r\nArray(1., dtype=float32)\r\n>>> ",,terminal_output +6086,15311031,"TERMINAL",0,0,"jax.numpy.asarray(0.69999999).astype(jax.numpy.float32)/0.7",,terminal_output +6087,15311834,"TERMINAL",0,0,"",,terminal_output +6088,15311971,"TERMINAL",0,0,"",,terminal_output +6089,15312167,"TERMINAL",0,0,"",,terminal_output +6090,15312323,"TERMINAL",0,0,"",,terminal_output +6091,15312437,"TERMINAL",0,0,"",,terminal_output +6092,15312640,"TERMINAL",0,0,"",,terminal_output +6093,15312881,"TERMINAL",0,0,"",,terminal_output +6094,15313280,"TERMINAL",0,0,"",,terminal_output +6095,15313775,"TERMINAL",0,0,").astype(jax.numpy.float32)/0.7",,terminal_output +6096,15313934,"TERMINAL",0,0,"\r\nArray(0.99999994, dtype=float32)\r\n>>> ",,terminal_output +6097,15320177,"TERMINAL",0,0,"jax.numpy.asarray(0.6999999).astype(jax.numpy.float32)/0.7",,terminal_output +6098,15321257,"TERMINAL",0,0,"",,terminal_output +6099,15322005,"TERMINAL",0,0,"",,terminal_output +6100,15322714,"TERMINAL",0,0,"jax.numpy.asarray(0.6999999).astype(jax.numpy.float32)/0.7\r",,terminal_output +6101,15324747,"TERMINAL",0,0,"ajax.numpy.asarray(0.6999999).astype(jax.numpy.float32)/0.7\r",,terminal_output +6102,15325024,"TERMINAL",0,0,"xjax.numpy.asarray(0.6999999).astype(jax.numpy.float32)/0.7\r",,terminal_output +6103,15325260,"TERMINAL",0,0,"-jax.numpy.asarray(0.6999999).astype(jax.numpy.float32)/0.7\r",,terminal_output +6104,15325849,"TERMINAL",0,0,"jax.numpy.asarray(0.6999999).astype(jax.numpy.float32)/0.7\r",,terminal_output +6105,15326100,"TERMINAL",0,0,".jax.numpy.asarray(0.6999999).astype(jax.numpy.float32)/0.7\r",,terminal_output +6106,15326363,"TERMINAL",0,0,"njax.numpy.asarray(0.6999999).astype(jax.numpy.float32)/0.7\r",,terminal_output +6107,15326504,"TERMINAL",0,0,"ujax.numpy.asarray(0.6999999).astype(jax.numpy.float32)/0.7\r",,terminal_output +6108,15326690,"TERMINAL",0,0,"mjax.numpy.asarray(0.6999999).astype(jax.numpy.float32)/0.7\r",,terminal_output +6109,15326860,"TERMINAL",0,0,"pjax.numpy.asarray(0.6999999).astype(jax.numpy.float32)/0.7\r",,terminal_output +6110,15326979,"TERMINAL",0,0,"yjax.numpy.asarray(0.6999999).astype(jax.numpy.float32)/0.7\r",,terminal_output +6111,15327095,"TERMINAL",0,0,".jax.numpy.asarray(0.6999999).astype(jax.numpy.float32)/0.7\r",,terminal_output +6112,15327554,"TERMINAL",0,0,"fljax.numpy.asarray(0.6999999).astype(jax.numpy.float32)/0.7\r",,terminal_output +6113,15327723,"TERMINAL",0,0,"ojax.numpy.asarray(0.6999999).astype(jax.numpy.float32)/0.7\r",,terminal_output +6114,15327854,"TERMINAL",0,0,"ojax.numpy.asarray(0.6999999).astype(jax.numpy.float32)/0.7\r",,terminal_output +6115,15327915,"TERMINAL",0,0,"rjax.numpy.asarray(0.6999999).astype(jax.numpy.float32)/0.7\r",,terminal_output +6116,15328450,"TERMINAL",0,0,"(jax.numpy.asarray(0.6999999).astype(jax.numpy.float32)/0.7\r",,terminal_output +6117,15328924,"TERMINAL",0,0,"",,terminal_output +6118,15329658,"TERMINAL",0,0,"",,terminal_output +6119,15330419,"TERMINAL",0,0,")",,terminal_output +6120,15330654,"TERMINAL",0,0,"\r\nArray(0., dtype=float32)\r\n>>> ",,terminal_output +6121,15405208,"TERMINAL",0,0,"\r\n",,terminal_output +6122,15405291,"TERMINAL",0,0,"]0;tum_cte0515@hkn0402:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0402 jasmine]$ ",,terminal_output +6123,15703369,"TERMINAL",0,0,"salloc: Job 3527866 has exceeded its time limit and its allocation has been revoked.\nslurmstepd: error: *** STEP 3527866.interactive ON hkn0402 CANCELLED AT 2025-09-28T17:49:08 DUE TO TIME LIMIT ***\r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\n",,terminal_output +6124,15733026,"TERMINAL",0,0,"srun: error: hkn0402: task 0: Killed\r\n]0;tum_cte0515@hkn1993:~/Projects/jasmine",,terminal_output +6125,16211852,"jasmine/train_dynamics.py",0,0,"",python,tab +6126,16212535,"jasmine/models/dynamics.py",0,0,"",python,tab +6127,16214166,"jasmine/models/dynamics.py",3566,0,"",python,selection_mouse +6128,16214465,"jasmine/models/dynamics.py",3558,13,"noise_level_B",python,selection_mouse +6129,16219443,"jasmine/models/dynamics.py",4036,0,"",python,selection_mouse +6130,16219446,"jasmine/models/dynamics.py",4035,0,"",python,selection_command +6131,16222304,"jasmine/models/dynamics.py",4030,0,"",python,selection_mouse +6132,16223266,"jasmine/models/dynamics.py",4036,0,"",python,selection_mouse +6133,16223267,"jasmine/models/dynamics.py",4035,0,"",python,selection_command +6134,16223391,"jasmine/models/dynamics.py",4035,1,")",python,selection_mouse +6135,16223392,"jasmine/models/dynamics.py",4036,0,"",python,selection_command +6136,16223456,"jasmine/models/dynamics.py",4031,5,"BTNM)",python,selection_mouse +6137,16223472,"jasmine/models/dynamics.py",3958,78,"{}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6138,16223472,"jasmine/models/dynamics.py",3953,83,"ise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6139,16223494,"jasmine/models/dynamics.py",3949,87,"t_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6140,16223545,"jasmine/models/dynamics.py",3948,88,"rt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6141,16223546,"jasmine/models/dynamics.py",3884,152,"sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6142,16223547,"jasmine/models/dynamics.py",3882,154,"(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6143,16223559,"jasmine/models/dynamics.py",3880,156,"nt(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6144,16223576,"jasmine/models/dynamics.py",3879,157,"int(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6145,16223633,"jasmine/models/dynamics.py",3814,222,"rint(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6146,16223633,"jasmine/models/dynamics.py",3813,223,"print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6147,16223634,"jasmine/models/dynamics.py",3811,225,"g.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6148,16223655,"jasmine/models/dynamics.py",3731,305,"bug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6149,16223674,"jasmine/models/dynamics.py",3730,306,"ebug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6150,16223733,"jasmine/models/dynamics.py",3729,307,"debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6151,16223734,"jasmine/models/dynamics.py",3658,378,".debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6152,16223734,"jasmine/models/dynamics.py",3656,380,"ax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6153,16223755,"jasmine/models/dynamics.py",3655,381,"jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6154,16223813,"jasmine/models/dynamics.py",3654,382," jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6155,16223874,"jasmine/models/dynamics.py",3653,383," jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6156,16223894,"jasmine/models/dynamics.py",3599,437," jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6157,16223907,"jasmine/models/dynamics.py",3598,438," jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6158,16223924,"jasmine/models/dynamics.py",3597,439," jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6159,16223982,"jasmine/models/dynamics.py",3596,440," jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6160,16223988,"jasmine/models/dynamics.py",3535,501," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6161,16224025,"jasmine/models/dynamics.py",3534,502," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6162,16225165,"jasmine/models/dynamics.py",3534,0,"",python,selection_mouse +6163,16225166,"jasmine/models/dynamics.py",3533,8," ",python,selection_mouse +6164,16225400,"jasmine/models/dynamics.py",3533,62," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n ",python,selection_mouse +6165,16225430,"jasmine/models/dynamics.py",3533,63," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n ",python,selection_mouse +6166,16225492,"jasmine/models/dynamics.py",3533,64," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n ",python,selection_mouse +6167,16225493,"jasmine/models/dynamics.py",3533,65," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n ",python,selection_mouse +6168,16225502,"jasmine/models/dynamics.py",3533,66," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n ",python,selection_mouse +6169,16225517,"jasmine/models/dynamics.py",3533,121," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n ",python,selection_mouse +6170,16225535,"jasmine/models/dynamics.py",3533,122," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n ",python,selection_mouse +6171,16225586,"jasmine/models/dynamics.py",3533,125," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax",python,selection_mouse +6172,16225586,"jasmine/models/dynamics.py",3533,131," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug",python,selection_mouse +6173,16225601,"jasmine/models/dynamics.py",3533,201," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug",python,selection_mouse +6174,16225623,"jasmine/models/dynamics.py",3533,202," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.",python,selection_mouse +6175,16225676,"jasmine/models/dynamics.py",3533,207," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print",python,selection_mouse +6176,16225676,"jasmine/models/dynamics.py",3533,208," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(",python,selection_mouse +6177,16225676,"jasmine/models/dynamics.py",3533,231," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M",python,selection_mouse +6178,16225685,"jasmine/models/dynamics.py",3533,262," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n",python,selection_mouse +6179,16225700,"jasmine/models/dynamics.py",3533,302," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise",python,selection_mouse +6180,16225783,"jasmine/models/dynamics.py",3533,366," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus:",python,selection_mouse +6181,16225803,"jasmine/models/dynamics.py",3533,368," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {",python,selection_mouse +6182,16225833,"jasmine/models/dynamics.py",3533,369," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}",python,selection_mouse +6183,16225886,"jasmine/models/dynamics.py",3533,370," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}""",python,selection_mouse +6184,16225887,"jasmine/models/dynamics.py",3533,371," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"",",python,selection_mouse +6185,16225887,"jasmine/models/dynamics.py",3533,372," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"", ",python,selection_mouse +6186,16225946,"jasmine/models/dynamics.py",3533,386," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"", sqrt_one_minus",python,selection_mouse +6187,16226216,"jasmine/models/dynamics.py",3533,440," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise",python,selection_mouse +6188,16226247,"jasmine/models/dynamics.py",3533,441," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)",python,selection_mouse +6189,16226734,"jasmine/models/dynamics.py",3533,503," jax.debug.print(""noise_level_B: {}"", noise_level_B)\n jax.debug.print(""noise_BTNM: {}"", noise_BTNM)\n jax.debug.print(""noise_bucket_idx_B: {}"", noise_bucket_idx_B)\n jax.debug.print(""noise_level_embed_B11M: {}"", noise_level_embed_B11M)\n jax.debug.print(""one_minus_noise: {}"", one_minus_noise)\n jax.debug.print(""sqrt_one_minus: {}"", sqrt_one_minus)\n jax.debug.print(""sqrt_noise: {}"", sqrt_noise)\n jax.debug.print(""vid_embed_BTNM: {}"", vid_embed_BTNM)",python,selection_mouse +6190,16295655,"jasmine/models/dynamics.py",4272,0,"",python,selection_mouse +6191,16296247,"jasmine/models/dynamics.py",3907,0,"",python,selection_mouse +6192,16296733,"jasmine/models/dynamics.py",4023,0,"",python,selection_mouse diff --git a/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-04d29a91-f2bd-4b62-89da-678008b090861755160613254-2025_08_14-10.37.33.283/source.csv b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-04d29a91-f2bd-4b62-89da-678008b090861755160613254-2025_08_14-10.37.33.283/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..111db95a0c01a6b88ebeffe20e6433f7d44ebce5 --- /dev/null +++ b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-04d29a91-f2bd-4b62-89da-678008b090861755160613254-2025_08_14-10.37.33.283/source.csv @@ -0,0 +1,10125 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +1,4,"slurm/jobs/mihir/horeka/causal_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/causal/dynamics-cotraining/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/causal/dynamics-cotraining/%x_%j.log\n#SBATCH --job-name=train_dynamics_causal_8_node\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/holiday/causal/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\n# tokenizer with the new structure supporting larger ffn_dim\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401\n\nenv | grep SLURM\n\n# export PYTHONUNBUFFERED=1\n\nsrun python train_dynamics.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=160 \\n --init_lr=0 \\n --dyna_type=causal \\n --num_latent_actions=100 \\n --darkness_threshold=50 \\n --max_lr=8e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-causal-8-node-darkness-filter-$slurm_job_id \\n --tags dynamics causal 8-node post-launch-main darkness-filter \\n --entity instant-uv \\n --project jafar \\n --dyna_dim=1024 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=16 \\n --dyna_ffn_dim=4096 \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n",shellscript,tab +2,723,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"10:37:33 AM [info] Activating crowd-code\n10:37:33 AM [info] Recording started\n10:37:33 AM [info] Initializing git provider using file system watchers...\n10:37:33 AM [info] Git repository found\n10:37:33 AM [info] Git provider initialized successfully\n10:37:33 AM [info] Initial git state: [object Object]\n",Log,tab +3,1451,"slurm/jobs/mihir/horeka/causal_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",0,0,"",shellscript,tab +4,2714,"TERMINAL",0,0,"queue",,terminal_command +5,2778,"TERMINAL",0,0,"]633;E;2025-08-14 10:37:35 queue;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Thu Aug 14 10:37:35 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3415713 accelerat train_dy tum_cte0 PD\t0:00\t 1 (Priority)3412401 accelerat train_to tum_cte0 R 1-16:37:34\t 8 hkn[0412,0416,0425,0433,0718,0732,0803,0807]3418832 accelerat train_dy tum_cte0 R 20:48:39\t 8 hkn[0407,0415,0420,0422-0424,0729,0735]",,terminal_output +6,3845,"TERMINAL",0,0,"6641",,terminal_output +7,4886,"TERMINAL",0,0,"872",,terminal_output +8,5937,"TERMINAL",0,0,"983",,terminal_output +9,6973,"TERMINAL",0,0,"4094",,terminal_output +10,7378,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Thu Aug 14 10:37:40 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3415713 accelerat train_dy tum_cte0 PD\t0:00\t 1 (Priority)3412401 accelerat train_to tum_cte0 R 1-16:37:39\t 8 hkn[0412,0416,0425,0433,0718,0732,0803,0807]3418832 accelerat train_dy tum_cte0 R 20:48:44\t 8 hkn[0407,0415,0420,0422-0424,0729,0735]",,terminal_output +11,8474,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Thu Aug 14 10:37:41 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3415713 accelerat train_dy tum_cte0 PD\t0:00\t 1 (Priority)3412401 accelerat train_to tum_cte0 R 1-16:37:40\t 8 hkn[0412,0416,0425,0433,0718,0732,0803,0807]3418832 accelerat train_dy tum_cte0 R 20:48:45\t 8 hkn[0407,0415,0420,0422-0424,0729,0735]",,terminal_output +12,8602,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Thu Aug 14 10:37:41 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3415713 accelerat train_dy tum_cte0 PD\t0:00\t 1 (Priority)3412401 accelerat train_to tum_cte0 R 1-16:37:40\t 8 hkn[0412,0416,0425,0433,0718,0732,0803,0807]3418832 accelerat train_dy tum_cte0 R 20:48:45\t 8 hkn[0407,0415,0420,0422-0424,0729,0735]",,terminal_output +13,9315,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +14,10786,"TERMINAL",0,0,"logs",,terminal_command +15,12854,"TERMINAL",0,0,"ls",,terminal_command +16,12896,"TERMINAL",0,0,"]633;E;2025-08-14 10:37:46 ls;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;C",,terminal_output +17,13251,"TERMINAL",0,0,"atari train_lam_action_space_scaling_50_3329804.log\r\nbig_run train_lam_action_space_scaling_50_3331286.log\r\nbig-runs train_lam_action_space_scaling_6_3318549.log\r\ncausal train_lam_action_space_scaling_6_3320178.log\r\ncoinrun train_lam_action_space_scaling_6_3321528.log\r\nmaskgit train_lam_action_space_scaling_6_3329790.log\r\nmaskgit-maskprob-fix train_lam_action_space_scaling_6_3329805.log\r\npreprocess train_lam_action_space_scaling_6_3331287.log\r\ntrain_dyn_causal_180M_3372931.log train_lam_action_space_scaling_8_3318550.log\r\ntrain_dyn_causal_180M_3372963.log train_lam_action_space_scaling_8_3329791.log\r\ntrain_dyn_causal_180M_3372969.log train_lam_action_space_scaling_8_3329806.log\r\ntrain_dyn_causal_180M_3373107.log train_lam_action_space_scaling_8_3331288.log\r\ntrain_dyn_causal_255M_3372932.log train_lam_minecraft_overfit_sample_3309655.log\r\ntrain_dyn_causal_255M_3372970.log train_lam_model_size_scaling_38M_3317098.log\r\ntrain_dyn_causal_255M_3373108.log train_lam_model_size_scaling_38M_3317115.log\r\ntrain_dyn_causal_356M_3372934.log train_lam_model_size_scaling_38M_3317231.log\r\ntrain_dyn_causal_356M_3372971.log train_tokenizer_batch_size_scaling_16_node_3321526.log\r\ntrain_dyn_causal_356M_3373109.log train_tokenizer_batch_size_scaling_1_node_3318551.log\r\ntrain_dyn_causal_500M_3372936.log train_tokenizer_batch_size_scaling_2_node_3318552.log\r\ntrain_dyn_causal_500M_3372972.log train_tokenizer_batch_size_scaling_2_node_3330806.log\r\ntrain_dyn_causal_500M_3373110.log train_tokenizer_batch_size_scaling_2_node_3330848.log\r\ntrain_dyn_new_arch-bugfixed-spatial-shift_3359343.log train_tokenizer_batch_size_scaling_2_node_3331282.log\r\ntrain_dyn_new_arch-bugfixed-temporal-shift_3359349.log train_tokenizer_batch_size_scaling_4_node_3318553.log\r\ntrain_dyn_yolorun_3333026.log train_tokenizer_batch_size_scaling_4_node_3320175.log\r\ntrain_dyn_yolorun_3333448.log train_tokenizer_batch_size_scaling_4_node_3321524.log\r\ntrain_dyn_yolorun_3335345.log train_tokenizer_batch_size_scaling_8_node_3320176.log\r\ntrain_dyn_yolorun_3335362.log train_tokenizer_batch_size_scaling_8_node_3321525.log\r\ntrain_dyn_yolorun_3348592.log train_tokenizer_minecraft_overfit_sample_3309656.log\r\ntrain_dyn_yolorun_new_arch_3351743.log train_tokenizer_model_size_scaling_127M_3317233.log\r\ntrain_dyn_yolorun_new_arch_3352103.log train_tokenizer_model_size_scaling_127M_3318554.log\r\ntrain_dyn_yolorun_new_arch_3352115.log train_tokenizer_model_size_scaling_140M_3313562.log\r\ntrain_dyn_yolorun_new_arch_3358457.log train_tokenizer_model_size_scaling_140M_3316019.log\r\ntrain_lam_action_space_scaling_10_3320179.log train_tokenizer_model_size_scaling_200M_3313563.log\r\ntrain_lam_action_space_scaling_10_3321529.log train_tokenizer_model_size_scaling_200M_3316020.log\r\ntrain_lam_action_space_scaling_10_3329786.log train_tokenizer_model_size_scaling_227M_3317234.log\r\ntrain_lam_action_space_scaling_10_3329801.log train_tokenizer_model_size_scaling_227M_3318555.log\r\ntrain_lam_action_space_scaling_10_3331283.log train_tokenizer_model_size_scaling_227M_3320173.log\r\ntrain_lam_action_space_scaling_12_3318546.log train_tokenizer_model_size_scaling_227M_3321523.log\r\ntrain_lam_action_space_scaling_12_3320177.log train_tokenizer_model_size_scaling_37M_3313565.log\r\ntrain_lam_action_space_scaling_12_3321527.log train_tokenizer_model_size_scaling_37M_3316022.log\r\ntrain_lam_action_space_scaling_12_3329787.log train_tokenizer_model_size_scaling_37M_3317232.log\r\ntrain_lam_action_space_scaling_12_3329802.log train_tokenizer_model_size_scaling_37M_3317239.log\r\ntrain_lam_action_space_scaling_12_3331284.log train_tokenizer_model_size_scaling_37M_3318556.log\r\ntrain_lam_action_space_scaling_20_3318547.log train_tokenizer_model_size_scaling_74M_3318557.log\r\ntrain_lam_action_space_scaling_20_3329788.log train_tokenizer_model_size_scaling_74M_3320174.log\r\ntrain_lam_action_space_scaling_20_3329803.log train_tokenizer_model_size_scaling_74M_3321522.log\r\ntrain_lam_action_space_scaling_20_3331285.log train_tokenizer_model_size_scaling_80M_3313564.log\r\ntrain_lam_action_space_scaling_50_3320180.log train_tokenizer_model_size_scaling_80M_3316026.log\r\ntrain_lam_action_space_scaling_50_3329789.log yoloruns\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output +18,23366,"TERMINAL",0,0,"cd causal/",,terminal_command +19,23742,"TERMINAL",0,0,"ls",,terminal_command +20,30822,"TERMINAL",0,0,"cd dynamics-cotraining/",,terminal_command +21,31118,"TERMINAL",0,0,"ls",,terminal_command +22,31166,"TERMINAL",0,0,"]633;E;2025-08-14 10:38:04 ls;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;C",,terminal_output +23,31264,"TERMINAL",0,0,"train_dynamics_causal_2_node_3373407.log train_dynamics_causal_8_node_3388140.log train_dynamics_causal_8_node_3412397.log\r\ntrain_dynamics_causal_2_node_3373407.log_bak train_dynamics_causal_8_node_3389928.log train_dynamics_causal_8_node_3412399.log\r\ntrain_dynamics_causal_2_node_3388135.log train_dynamics_causal_8_node_3390458.log train_dynamics_causal_8_node_3414710.log\r\ntrain_dynamics_causal_2_node_3388147.log train_dynamics_causal_8_node_3393060.log train_dynamics_causal_8_node_3415128.log\r\ntrain_dynamics_causal_2_node_3389801.log train_dynamics_causal_8_node_3393061.log train_dynamics_causal_8_node_3415137.log\r\ntrain_dynamics_causal_2_node_3393065.log train_dynamics_causal_8_node_3393066.log train_dynamics_causal_8_node_3417223.log\r\ntrain_dynamics_causal_2_node_dev_3416774.log train_dynamics_causal_8_node_3412343.log train_dynamics_causal_8_node_3417224.log\r\ntrain_dynamics_causal_2_node_dev_3416819.log train_dynamics_causal_8_node_3412349.log train_dynamics_causal_8_node_3418831.log\r\ntrain_dynamics_causal_8_node_3373408.log train_dynamics_causal_8_node_3412356.log train_dynamics_causal_8_node_3418832.log\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/causal/dynamics-cotraining]633;D;0",,terminal_output +24,38596,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/causal/dynamics-cotraining/train_dynamics_causal_8_node_3418831.log",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/causal/dynamics-cotraining/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/causal/dynamics-cotraining/%x_%j.log\n#SBATCH --job-name=train_dynamics_causal_8_node\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/holiday/causal/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\n# tokenizer with the new structure supporting larger ffn_dim\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401\n\nenv | grep SLURM\n\n# export PYTHONUNBUFFERED=1\n\nsrun python train_dynamics.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=160 \\n --num_latent_actions=100 \\n --init_lr=0 \\n --dyna_type=causal \\n --max_lr=8e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-causal-8-node-$slurm_job_id \\n --tags dynamics causal 8-node post-launch-main \\n --entity instant-uv \\n --project jafar \\n --dyna_dim=1024 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=16 \\n --dyna_ffn_dim=4096 \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n/var/spool/slurmd/job3418831/slurm_script: line 42: .venv/bin/activate: No such file or directory\nSLURM_JOB_USER=tum_cte0515\nSLURM_TASKS_PER_NODE=4(x8)\nSLURM_JOB_UID=999226\nSLURM_TASK_PID=1596326\nSLURM_JOB_GPUS=0,1,2,3\nSLURM_LOCALID=0\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs\nSLURMD_NODENAME=hkn0415\nSLURM_JOB_START_TIME=1755085369\nSLURM_CLUSTER_NAME=hk\nSLURM_JOB_END_TIME=1755258169\nSLURM_CPUS_ON_NODE=24\nSLURM_JOB_CPUS_PER_NODE=24(x8)\nSLURM_GPUS_ON_NODE=4\nSLURM_GTIDS=0\nSLURM_JOB_PARTITION=accelerated\nSLURM_TRES_PER_TASK=cpu=5\nSLURM_OOM_KILL_STEP=0\nSLURM_JOB_NUM_NODES=8\nSLURM_JOBID=3418831\nSLURM_JOB_QOS=normal\nSLURM_PROCID=0\nSLURM_CPUS_PER_TASK=5\nSLURM_NTASKS=32\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e12.hkn0415\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\nSLURM_SCRIPT_CONTEXT=prolog_task\nSLURM_NODELIST=hkn[0415,0423-0424,0531,0605,0729,0735,0814]\nSLURM_JOB_ACCOUNT=hk-project-p0023960\nSLURM_PRIO_PROCESS=0\nSLURM_NPROCS=32\nSLURM_NNODES=8\nSLURM_SUBMIT_HOST=hkn1990.localdomain\nSLURM_JOB_ID=3418831\nSLURM_NODEID=0\nSLURM_CONF=/etc/slurm/slurm.conf\nSLURM_JOB_NAME=train_dynamics_causal_8_node\nSLURM_NTASKS_PER_NODE=4\nSLURM_JOB_GID=502226\nSLURM_JOB_NODELIST=hkn[0415,0423-0424,0531,0605,0729,0735,0814]\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\n2025-08-13 13:43:44.591197: E external/xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\n2025-08-13 13:43:44.591342: E external/xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\n2025-08-13 13:43:44.591430: E external/xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\n2025-08-13 13:43:44.591432: E external/xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n num_devices = jax.device_count()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 907, in device_count\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n num_devices = jax.device_count()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 907, in device_count\n return int(get_backend(backend).device_count())\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 876, in get_backend\n return _get_backend_uncached(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 855, in _get_backend_uncached\n num_devices = jax.device_count()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 907, in device_count\n num_devices = jax.device_count()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 907, in device_count\n bs = backends()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 758, in backends\n return int(get_backend(backend).device_count())\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 876, in get_backend\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': FAILED_PRECONDITION: No visible GPU devices. (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\n return int(get_backend(backend).device_count())\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 876, in get_backend\n return int(get_backend(backend).device_count())\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 876, in get_backend\n return _get_backend_uncached(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 855, in _get_backend_uncached\n return _get_backend_uncached(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 855, in _get_backend_uncached\n bs = backends()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 758, in backends\n return _get_backend_uncached(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 855, in _get_backend_uncached\n bs = backends()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 758, in backends\n bs = backends()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 758, in backends\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': FAILED_PRECONDITION: No visible GPU devices. (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': FAILED_PRECONDITION: No visible GPU devices. (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': FAILED_PRECONDITION: No visible GPU devices. (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: Getting local topologies failed: Error 1: GetKeyValue() timed out with key: cuda:local_topology/cuda/12 and duration: 2m\n\nError 2: GetKeyValue() timed out with key: cuda:local_topology/cuda/13 and duration: 2m\n\nError 3: GetKeyValue() timed out with key: cuda:local_topology/cuda/14 and duration: 2m\n\nError 4: GetKeyValue() timed out with key: cuda:local_topology/cuda/15 and duration: 2m\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n num_devices = jax.device_count()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 907, in device_count\n return int(get_backend(backend).device_count())\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 876, in get_backend\n return _get_backend_uncached(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 855, in _get_backend_uncached\n bs = backends()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 758, in backends\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': INTERNAL: Getting local topologies failed: Error 1: GetKeyValue() timed out with key: cuda:local_topology/cuda/12 and duration: 2m\n\nError 2: GetKeyValue() timed out with key: cuda:local_topology/cuda/13 and duration: 2m\n\nError 3: GetKeyValue() timed out with key: cuda:local_topology/cuda/14 and duration: 2m\n\nError 4: GetKeyValue() timed out with key: cuda:local_topology/cuda/15 and duration: 2m (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\n2025-08-13 13:48:45.162543: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service.cc:1798] Shutdown barrier in coordination service has failed:\nDEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263']\nThis suggests that the workers are out of sync. Either at least one worker (a) crashed early due to program error or scheduler events (e.g. preemption, eviction), (b) was too fast in its execution, or (c) too slow / hanging. Check the logs (both the program and scheduler events) for an earlier error to identify the root cause.\n2025-08-13 13:48:45.162669: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service.cc:1839] Use error polling to propagate the following error to all tasks: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 13:48:45.162928: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 13:48:45.162977: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.163142: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 13:48:45.163205: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.163118: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.163466: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.163359: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263']\n\n2025-08-13 13:48:45.163442: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.163466: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 13:48:45.163406: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.163597: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.163655: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.163817: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263']\n\n2025-08-13 13:48:45.163509: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.163397: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.163605: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 13:48:45.163511: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.163582: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n2025-08-13 13:48:45.163608: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.163815: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263']\n\n2025-08-13 13:48:45.164136: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263']\n\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 13:48:45.163687: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263']\n\n2025-08-13 13:48:45.164140: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263']\n\n2025-08-13 13:48:45.164197: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 13:48:45.163644: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 13:48:45.163729: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 13:48:45.164303: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 13:48:45.164300: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 13:48:45.163148: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.175424: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 13:48:45.175375: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 13:48:45.175765: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 13:48:45.178589: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 13:48:45.175833: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.175902: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.179447: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 13:48:45.179456: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 13:48:45.176050: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.179966: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::13736296003622477263::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:14. Some timed out task names:\n/job:jax_worker/replica:0/task:18\n/job:jax_worker/replica:0/task:6\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::13736296003622477263']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 13:48:45.352090: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.352127: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n:{""created"":""@1755085725.351949699"",""description"":""Error received from peer ipv4:10.0.1.47:64207"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\n2025-08-13 13:48:45.352201: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.352180: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.352202: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n:{""created"":""@1755085725.352087203"",""description"":""Error received from peer ipv4:10.0.1.47:64207"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\n2025-08-13 13:48:45.352237: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.352271: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n:{""created"":""@1755085725.351927525"",""description"":""Error received from peer ipv4:10.0.1.47:64207"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\n2025-08-13 13:48:45.352243: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.352276: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n2025-08-13 13:48:45.352055: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.352078: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n:{""created"":""@1755085725.352132883"",""description"":""Error received from peer ipv4:10.0.1.47:64207"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\n:{""created"":""@1755085725.351952405"",""description"":""Error received from peer ipv4:10.0.1.47:64207"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\n2025-08-13 13:48:45.352214: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.352283: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n2025-08-13 13:48:45.352298: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n:{""created"":""@1755085725.351994661"",""description"":""Error received from peer ipv4:10.0.1.47:64207"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\n2025-08-13 13:48:45.352195: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.352228: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n:{""created"":""@1755085725.351969875"",""description"":""Error received from peer ipv4:10.0.1.47:64207"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\n:{""created"":""@1755085725.352052510"",""description"":""Error received from peer ipv4:10.0.1.47:64207"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\n2025-08-13 13:48:45.352284: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.352350: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n:{""created"":""@1755085725.352023698"",""description"":""Error received from peer ipv4:10.0.1.47:64207"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\n2025-08-13 13:48:45.352343: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.352406: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n:{""created"":""@1755085725.352099318"",""description"":""Error received from peer ipv4:10.0.1.47:64207"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\n2025-08-13 13:48:45.352353: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 13:48:45.352426: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n:{""created"":""@1755085725.352099423"",""description"":""Error received from peer ipv4:10.0.1.47:64207"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\nsrun: error: hkn0531: tasks 12-15: Aborted (core dumped)\nsrun: error: hkn0424: tasks 9,11: Aborted (core dumped)\nsrun: error: hkn0415: tasks 0,2: Aborted (core dumped)\nsrun: error: hkn0735: task 25: Aborted (core dumped)\nsrun: error: hkn0729: tasks 21,23: Aborted (core dumped)\nsrun: error: hkn0605: tasks 18-19: Aborted (core dumped)\nsrun: error: hkn0423: tasks 4,6: Aborted (core dumped)\nsrun: error: hkn0814: tasks 30-31: Aborted (core dumped)\nsrun: error: hkn0415: task 1: Aborted (core dumped)\nsrun: error: hkn0729: task 20: Aborted (core dumped)\nsrun: error: hkn0605: task 16: Aborted (core dumped)\nsrun: error: hkn0423: task 5: Aborted (core dumped)\nsrun: error: hkn0814: task 28: Aborted (core dumped)\nsrun: error: hkn0735: tasks 24,27: Aborted (core dumped)\nsrun: error: hkn0424: task 10: Aborted (core dumped)\nsrun: error: hkn0415: task 3: Aborted (core dumped)\nsrun: error: hkn0729: task 22: Aborted (core dumped)\nsrun: error: hkn0605: task 17: Aborted (core dumped)\nsrun: error: hkn0423: task 7: Aborted (core dumped)\nsrun: error: hkn0814: task 29: Aborted (core dumped)\nsrun: error: hkn0424: task 8: Aborted (core dumped)\nsrun: error: hkn0735: task 26: Aborted (core dumped)\n\n============================= JOB FEEDBACK =============================\n\nJob ID: 3418831\nCluster: hk\nUser/Group: tum_cte0515/hk-project-p0023960\nAccount: hk-project-p0023960\nState: FAILED (exit code 134)\nPartition: accelerated\nNodes: 8\nCores per node: 24\nNodelist: hkn[0415,0423-0424,0531,0605,0729,0735,0814]\nCPU Utilized: 00:02:35\nCPU Efficiency: 0.23% of 19:02:24 core-walltime\nJob Wall-clock time: 00:05:57\nStarttime: Wed Aug 13 13:42:49 2025\nEndtime: Wed Aug 13 13:48:46 2025\nMemory Utilized: 11.96 GB (estimated maximum)\nMemory Efficiency: 0.00% of 0.00 MB (0.00 MB/node)\nEnergy Consumed: 4294164 Joule / 1192.82333333333 Watthours\nAverage node power draw: 12028.4705882353 Watt\n",log,tab +25,39365,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/causal/dynamics-cotraining/train_dynamics_causal_8_node_3418831.log",746,0,"",log,selection_mouse +26,39384,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/causal/dynamics-cotraining/train_dynamics_causal_8_node_3418831.log",745,0,"",log,selection_command +27,39869,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/causal/dynamics-cotraining/train_dynamics_causal_8_node_3418831.log",61841,0,"",log,selection_command +28,82828,"TERMINAL",0,0,"cd ..",,terminal_command +29,82833,"TERMINAL",0,0,"]633;E;2025-08-14 10:38:56 cd ..;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;C]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/causal]633;D;0",,terminal_output +30,83954,"TERMINAL",0,0,"cd ..",,terminal_command +31,84396,"TERMINAL",0,0,"l",,terminal_command +32,84418,"TERMINAL",0,0,"]633;E;2025-08-14 10:38:57 l;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;Cbash: l: command not found...\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;127",,terminal_output +33,85599,"TERMINAL",0,0,"ls",,terminal_command +34,85637,"TERMINAL",0,0,"]633;E;2025-08-14 10:38:58 ls;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;C",,terminal_output +35,85704,"TERMINAL",0,0,"atari train_lam_action_space_scaling_50_3329804.log\r\nbig_run train_lam_action_space_scaling_50_3331286.log\r\nbig-runs train_lam_action_space_scaling_6_3318549.log\r\ncausal train_lam_action_space_scaling_6_3320178.log\r\ncoinrun train_lam_action_space_scaling_6_3321528.log\r\nmaskgit train_lam_action_space_scaling_6_3329790.log\r\nmaskgit-maskprob-fix train_lam_action_space_scaling_6_3329805.log\r\npreprocess train_lam_action_space_scaling_6_3331287.log\r\ntrain_dyn_causal_180M_3372931.log train_lam_action_space_scaling_8_3318550.log\r\ntrain_dyn_causal_180M_3372963.log train_lam_action_space_scaling_8_3329791.log\r\ntrain_dyn_causal_180M_3372969.log train_lam_action_space_scaling_8_3329806.log\r\ntrain_dyn_causal_180M_3373107.log train_lam_action_space_scaling_8_3331288.log\r\ntrain_dyn_causal_255M_3372932.log train_lam_minecraft_overfit_sample_3309655.log\r\ntrain_dyn_causal_255M_3372970.log train_lam_model_size_scaling_38M_3317098.log\r\ntrain_dyn_causal_255M_3373108.log train_lam_model_size_scaling_38M_3317115.log\r\ntrain_dyn_causal_356M_3372934.log train_lam_model_size_scaling_38M_3317231.log\r\ntrain_dyn_causal_356M_3372971.log train_tokenizer_batch_size_scaling_16_node_3321526.log\r\ntrain_dyn_causal_356M_3373109.log train_tokenizer_batch_size_scaling_1_node_3318551.log\r\ntrain_dyn_causal_500M_3372936.log train_tokenizer_batch_size_scaling_2_node_3318552.log\r\ntrain_dyn_causal_500M_3372972.log train_tokenizer_batch_size_scaling_2_node_3330806.log\r\ntrain_dyn_causal_500M_3373110.log train_tokenizer_batch_size_scaling_2_node_3330848.log\r\ntrain_dyn_new_arch-bugfixed-spatial-shift_3359343.log train_tokenizer_batch_size_scaling_2_node_3331282.log\r\ntrain_dyn_new_arch-bugfixed-temporal-shift_3359349.log train_tokenizer_batch_size_scaling_4_node_3318553.log\r\ntrain_dyn_yolorun_3333026.log train_tokenizer_batch_size_scaling_4_node_3320175.log\r\ntrain_dyn_yolorun_3333448.log train_tokenizer_batch_size_scaling_4_node_3321524.log\r\ntrain_dyn_yolorun_3335345.log train_tokenizer_batch_size_scaling_8_node_3320176.log\r\ntrain_dyn_yolorun_3335362.log train_tokenizer_batch_size_scaling_8_node_3321525.log\r\ntrain_dyn_yolorun_3348592.log train_tokenizer_minecraft_overfit_sample_3309656.log\r\ntrain_dyn_yolorun_new_arch_3351743.log train_tokenizer_model_size_scaling_127M_3317233.log\r\ntrain_dyn_yolorun_new_arch_3352103.log train_tokenizer_model_size_scaling_127M_3318554.log\r\ntrain_dyn_yolorun_new_arch_3352115.log train_tokenizer_model_size_scaling_140M_3313562.log\r\ntrain_dyn_yolorun_new_arch_3358457.log train_tokenizer_model_size_scaling_140M_3316019.log\r\ntrain_lam_action_space_scaling_10_3320179.log train_tokenizer_model_size_scaling_200M_3313563.log\r\ntrain_lam_action_space_scaling_10_3321529.log train_tokenizer_model_size_scaling_200M_3316020.log\r\ntrain_lam_action_space_scaling_10_3329786.log train_tokenizer_model_size_scaling_227M_3317234.log\r\ntrain_lam_action_space_scaling_10_3329801.log train_tokenizer_model_size_scaling_227M_3318555.log\r\ntrain_lam_action_space_scaling_10_3331283.log train_tokenizer_model_size_scaling_227M_3320173.log\r\ntrain_lam_action_space_scaling_12_3318546.log train_tokenizer_model_size_scaling_227M_3321523.log\r\ntrain_lam_action_space_scaling_12_3320177.log train_tokenizer_model_size_scaling_37M_3313565.log\r\ntrain_lam_action_space_scaling_12_3321527.log train_tokenizer_model_size_scaling_37M_3316022.log\r\ntrain_lam_action_space_scaling_12_3329787.log train_tokenizer_model_size_scaling_37M_3317232.log\r\ntrain_lam_action_space_scaling_12_3329802.log train_tokenizer_model_size_scaling_37M_3317239.log\r\ntrain_lam_action_space_scaling_12_3331284.log train_tokenizer_model_size_scaling_37M_3318556.log\r\ntrain_lam_action_space_scaling_20_3318547.log train_tokenizer_model_size_scaling_74M_3318557.log\r\ntrain_lam_action_space_scaling_20_3329788.log train_tokenizer_model_size_scaling_74M_3320174.log\r\ntrain_lam_action_space_scaling_20_3329803.log train_tokenizer_model_size_scaling_74M_3321522.log\r\ntrain_lam_action_space_scaling_20_3331285.log train_tokenizer_model_size_scaling_80M_3313564.log\r\ntrain_lam_action_space_scaling_50_3320180.log train_tokenizer_model_size_scaling_80M_3316026.log\r\ntrain_lam_action_space_scaling_50_3329789.log yoloruns\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output +36,87243,"TERMINAL",0,0,"cd maskgit",,terminal_command +37,87564,"TERMINAL",0,0,"ls",,terminal_command +38,87571,"TERMINAL",0,0,"]633;E;2025-08-14 10:39:00 ls;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;Cdynamics-cotraining\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit]633;D;0",,terminal_output +39,89811,"TERMINAL",0,0,"cd dynamics-cotraining/",,terminal_command +40,90204,"TERMINAL",0,0,"ls",,terminal_command +41,90234,"TERMINAL",0,0,"]633;E;2025-08-14 10:39:03 ls;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;Ctrain_dynamics_maskgit_8_node_3412350.log train_dynamics_maskgit_8_node_3417225.log train_dynamics_maskgit_8_node_3418833.log\r\ntrain_dynamics_maskgit_8_node_3412354.log train_dynamics_maskgit_8_node_3417226.log train_dynamics_maskgit_8_node_3418834.log\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining]633;D;0",,terminal_output +42,94109,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418833.log",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/%x_%j.log\n#SBATCH --job-name=train_dynamics_maskgit_8_node\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/holiday/maskgit/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=160 \\n --init_lr=0 \\n --dyna_type=maskgit \\n --num_latent_actions=100 \\n --max_lr=8e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-maskgit-8-node-$slurm_job_id \\n --tags dynamics maskgit 8-node post-launch-main \\n --entity instant-uv \\n --project jafar \\n --dyna_dim=1024 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=16 \\n --dyna_ffn_dim=4096 \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n/var/spool/slurmd/job3418833/slurm_script: line 42: .venv/bin/activate: No such file or directory\nSLURM_JOB_USER=tum_cte0515\nSLURM_TASKS_PER_NODE=4(x8)\nSLURM_JOB_UID=999226\nSLURM_TASK_PID=2787129\nSLURM_JOB_GPUS=0,1,2,3\nSLURM_LOCALID=0\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs\nSLURMD_NODENAME=hkn0531\nSLURM_JOB_START_TIME=1755086164\nSLURM_CLUSTER_NAME=hk\nSLURM_JOB_END_TIME=1755258964\nSLURM_CPUS_ON_NODE=24\nSLURM_JOB_CPUS_PER_NODE=24(x8)\nSLURM_GPUS_ON_NODE=4\nSLURM_GTIDS=0\nSLURM_JOB_PARTITION=accelerated\nSLURM_TRES_PER_TASK=cpu=5\nSLURM_OOM_KILL_STEP=0\nSLURM_JOB_NUM_NODES=8\nSLURM_JOBID=3418833\nSLURM_JOB_QOS=normal\nSLURM_PROCID=0\nSLURM_CPUS_PER_TASK=5\nSLURM_NTASKS=32\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0531\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\nSLURM_SCRIPT_CONTEXT=prolog_task\nSLURM_NODELIST=hkn[0531,0605,0608,0621,0728,0812,0814,0821]\nSLURM_JOB_ACCOUNT=hk-project-p0023960\nSLURM_PRIO_PROCESS=0\nSLURM_NPROCS=32\nSLURM_NNODES=8\nSLURM_SUBMIT_HOST=hkn1990.localdomain\nSLURM_JOB_ID=3418833\nSLURM_NODEID=0\nSLURM_CONF=/etc/slurm/slurm.conf\nSLURM_JOB_NAME=train_dynamics_maskgit_8_node\nSLURM_NTASKS_PER_NODE=4\nSLURM_JOB_GID=502226\nSLURM_JOB_NODELIST=hkn[0531,0605,0608,0621,0728,0812,0814,0821]\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\n2025-08-13 13:56:59.039557: E external/xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\n2025-08-13 13:56:59.039649: E external/xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\n2025-08-13 13:56:59.039599: E external/xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\n2025-08-13 13:56:59.039730: E external/xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n num_devices = jax.device_count()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 907, in device_count\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n num_devices = jax.device_count()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 907, in device_count\n num_devices = jax.device_count()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 907, in device_count\n return int(get_backend(backend).device_count())\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 876, in get_backend\n num_devices = jax.device_count()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 907, in device_count\n return int(get_backend(backend).device_count())\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 876, in get_backend\n return int(get_backend(backend).device_count())\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 876, in get_backend\n return _get_backend_uncached(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 855, in _get_backend_uncached\n return int(get_backend(backend).device_count())\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 876, in get_backend\n return _get_backend_uncached(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 855, in _get_backend_uncached\n return _get_backend_uncached(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 855, in _get_backend_uncached\n bs = backends()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 758, in backends\n bs = backends()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 758, in backends\n bs = backends()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 758, in backends\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': FAILED_PRECONDITION: No visible GPU devices. (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': FAILED_PRECONDITION: No visible GPU devices. (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\n return _get_backend_uncached(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 855, in _get_backend_uncached\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': FAILED_PRECONDITION: No visible GPU devices. (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\n bs = backends()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 758, in backends\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': FAILED_PRECONDITION: No visible GPU devices. (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\n2025-08-13 14:02:00.026193: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service.cc:1798] Shutdown barrier in coordination service has failed:\nDEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\nThis suggests that the workers are out of sync. Either at least one worker (a) crashed early due to program error or scheduler events (e.g. preemption, eviction), (b) was too fast in its execution, or (c) too slow / hanging. Check the logs (both the program and scheduler events) for an earlier error to identify the root cause.\n2025-08-13 14:02:00.026235: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service.cc:1839] Use error polling to propagate the following error to all tasks: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:02:00.026303: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.026375: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.026488: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:02:00.026484: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:02:00.026766: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.026781: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.026779: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.026952: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.026920: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n2025-08-13 14:02:00.026781: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.026833: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.026908: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:02:00.026745: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.027030: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.026932: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:02:00.026956: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:02:00.026986: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.026997: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\n2025-08-13 14:02:00.027172: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:02:00.027213: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.027389: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:02:00.027415: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\n2025-08-13 14:02:00.027093: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.027004: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.027102: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.027012: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.027091: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.027193: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\n2025-08-13 14:02:00.026992: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:02:00.027045: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.027109: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\n2025-08-13 14:02:00.027062: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:02:00.027017: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.027112: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\n2025-08-13 14:02:00.027032: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:02:00.027690: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\n2025-08-13 14:02:00.027421: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\n2025-08-13 14:02:00.027240: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.027532: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:02:00.027612: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:02:00.027657: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\n2025-08-13 14:02:00.027624: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:02:00.027483: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:02:00.027561: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:02:00.027595: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\n2025-08-13 14:02:00.027623: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:02:00.027612: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:02:00.027563: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:02:00.064663: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:02:00.064634: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:02:00.064853: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::1026922694718633181::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:28\n/job:jax_worker/replica:0/task:9\n [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::1026922694718633181'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:02:00.106695: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.106720: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n2025-08-13 14:02:00.106625: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.106647: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n:{""created"":""@1755086520.106534982"",""description"":""Error received from peer ipv4:10.0.1.99:64209"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\n2025-08-13 14:02:00.106864: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.106889: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n:{""created"":""@1755086520.106772094"",""description"":""Error received from peer ipv4:10.0.1.99:64209"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\n:{""created"":""@1755086520.106600545"",""description"":""Error received from peer ipv4:10.0.1.99:64209"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\n2025-08-13 14:02:00.106886: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.106913: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n2025-08-13 14:02:00.107058: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.107082: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n:{""created"":""@1755086520.106784864"",""description"":""Error received from peer ipv4:10.0.1.99:64209"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\n:{""created"":""@1755086520.106957136"",""description"":""Error received from peer ipv4:10.0.1.99:64209"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\n2025-08-13 14:02:00.107117: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:02:00.107178: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n:{""created"":""@1755086520.106878160"",""description"":""Error received from peer ipv4:10.0.1.99:64209"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\nsrun: error: hkn0531: tasks 0-3: Aborted (core dumped)\nsrun: error: hkn0605: tasks 4,6: Aborted (core dumped)\nsrun: error: hkn0728: tasks 18-19: Aborted (core dumped)\nsrun: error: hkn0608: tasks 8-9: Aborted (core dumped)\nsrun: error: hkn0621: tasks 12,14: Aborted (core dumped)\nsrun: error: hkn0814: tasks 25-26: Aborted (core dumped)\nsrun: error: hkn0812: tasks 21,23: Aborted (core dumped)\nsrun: error: hkn0821: tasks 28,31: Aborted (core dumped)\nsrun: error: hkn0605: task 7: Aborted (core dumped)\nsrun: error: hkn0728: task 16: Aborted (core dumped)\nsrun: error: hkn0621: task 15: Aborted (core dumped)\nsrun: error: hkn0608: task 11: Aborted (core dumped)\nsrun: error: hkn0812: task 20: Aborted (core dumped)\nsrun: error: hkn0814: task 27: Aborted (core dumped)\nsrun: error: hkn0821: task 30: Aborted (core dumped)\nsrun: error: hkn0605: task 5: Aborted (core dumped)\nsrun: error: hkn0728: task 17: Aborted (core dumped)\nsrun: error: hkn0608: task 10: Aborted (core dumped)\nsrun: error: hkn0621: task 13: Aborted (core dumped)\nsrun: error: hkn0812: task 22: Aborted (core dumped)\nsrun: error: hkn0814: task 24: Aborted (core dumped)\nsrun: error: hkn0821: task 29: Aborted (core dumped)\n\n============================= JOB FEEDBACK =============================\n\nJob ID: 3418833\nCluster: hk\nUser/Group: tum_cte0515/hk-project-p0023960\nAccount: hk-project-p0023960\nState: FAILED (exit code 134)\nPartition: accelerated\nNodes: 8\nCores per node: 24\nNodelist: hkn[0531,0605,0608,0621,0728,0812,0814,0821]\nCPU Utilized: 00:02:20\nCPU Efficiency: 0.20% of 19:02:24 core-walltime\nJob Wall-clock time: 00:05:57\nStarttime: Wed Aug 13 13:56:04 2025\nEndtime: Wed Aug 13 14:02:01 2025\nMemory Utilized: 11.92 GB (estimated maximum)\nMemory Efficiency: 0.00% of 0.00 MB (0.00 MB/node)\nEnergy Consumed: 2591063 Joule / 719.739722222222 Watthours\nAverage node power draw: 7257.87955182073 Watt\n",log,tab +43,102885,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/%x_%j.log\n#SBATCH --job-name=train_dynamics_maskgit_8_node\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/holiday/maskgit/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=160 \\n --init_lr=0 \\n --darkness_threshold=50 \\n --dyna_type=maskgit \\n --num_latent_actions=100 \\n --max_lr=8e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-maskgit-8-node-darkness-filter-$slurm_job_id \\n --tags dynamics maskgit 8-node post-launch-main darkness-filter \\n --entity instant-uv \\n --project jafar \\n --dyna_dim=1024 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=16 \\n --dyna_ffn_dim=4096 \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n/var/spool/slurmd/job3418834/slurm_script: line 42: .venv/bin/activate: No such file or directory\nSLURM_JOB_USER=tum_cte0515\nSLURM_TASKS_PER_NODE=4(x8)\nSLURM_JOB_UID=999226\nSLURM_TASK_PID=2792684\nSLURM_JOB_GPUS=0,1,2,3\nSLURM_LOCALID=0\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs\nSLURMD_NODENAME=hkn0531\nSLURM_JOB_START_TIME=1755086531\nSLURM_CLUSTER_NAME=hk\nSLURM_JOB_END_TIME=1755259331\nSLURM_CPUS_ON_NODE=24\nSLURM_JOB_CPUS_PER_NODE=24(x8)\nSLURM_GPUS_ON_NODE=4\nSLURM_GTIDS=0\nSLURM_JOB_PARTITION=accelerated\nSLURM_TRES_PER_TASK=cpu=5\nSLURM_OOM_KILL_STEP=0\nSLURM_JOB_NUM_NODES=8\nSLURM_JOBID=3418834\nSLURM_JOB_QOS=normal\nSLURM_PROCID=0\nSLURM_CPUS_PER_TASK=5\nSLURM_NTASKS=32\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0531\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\nSLURM_SCRIPT_CONTEXT=prolog_task\nSLURM_NODELIST=hkn[0531,0605,0608,0621,0728,0812,0814,0817]\nSLURM_JOB_ACCOUNT=hk-project-p0023960\nSLURM_PRIO_PROCESS=0\nSLURM_NPROCS=32\nSLURM_NNODES=8\nSLURM_SUBMIT_HOST=hkn1990.localdomain\nSLURM_JOB_ID=3418834\nSLURM_NODEID=0\nSLURM_CONF=/etc/slurm/slurm.conf\nSLURM_JOB_NAME=train_dynamics_maskgit_8_node\nSLURM_NTASKS_PER_NODE=4\nSLURM_JOB_GID=502226\nSLURM_JOB_NODELIST=hkn[0531,0605,0608,0621,0728,0812,0814,0817]\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\n2025-08-13 14:03:12.754648: E external/xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n2025-08-13 14:03:12.754945: E external/xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\n2025-08-13 14:03:12.754961: E external/xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n2025-08-13 14:03:12.755393: E external/xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n num_devices = jax.device_count()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 907, in device_count\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n num_devices = jax.device_count()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 907, in device_count\n return int(get_backend(backend).device_count())\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 876, in get_backend\n num_devices = jax.device_count()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 907, in device_count\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _get_backend_uncached(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 855, in _get_backend_uncached\n num_devices = jax.device_count()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 907, in device_count\n return int(get_backend(backend).device_count())\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 876, in get_backend\n return int(get_backend(backend).device_count())\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 876, in get_backend\n bs = backends()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 758, in backends\n return int(get_backend(backend).device_count())\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 876, in get_backend\n return _get_backend_uncached(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 855, in _get_backend_uncached\n return _get_backend_uncached(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 855, in _get_backend_uncached\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': FAILED_PRECONDITION: No visible GPU devices. (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\n return _get_backend_uncached(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 855, in _get_backend_uncached\n bs = backends()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 758, in backends\n bs = backends()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 758, in backends\n bs = backends()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 758, in backends\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': FAILED_PRECONDITION: No visible GPU devices. (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': FAILED_PRECONDITION: No visible GPU devices. (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': FAILED_PRECONDITION: No visible GPU devices. (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\n2025-08-13 14:08:13.373538: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service.cc:1798] Shutdown barrier in coordination service has failed:\nDEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\nThis suggests that the workers are out of sync. Either at least one worker (a) crashed early due to program error or scheduler events (e.g. preemption, eviction), (b) was too fast in its execution, or (c) too slow / hanging. Check the logs (both the program and scheduler events) for an earlier error to identify the root cause.\n2025-08-13 14:08:13.373564: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service.cc:1839] Use error polling to propagate the following error to all tasks: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:08:13.373830: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.373806: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.373972: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:08:13.374005: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:08:13.373979: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:08:13.374003: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:08:13.374236: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.374055: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.374175: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.374101: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.374282: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\n2025-08-13 14:08:13.374175: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.374096: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.374298: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n2025-08-13 14:08:13.374440: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:08:13.374446: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.374299: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:08:13.374317: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.374276: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.374215: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.374160: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.374372: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.374510: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.374639: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.374520: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.374599: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.374375: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.374697: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\n2025-08-13 14:08:13.374871: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\n2025-08-13 14:08:13.374620: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.374414: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:08:13.374600: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.374669: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.374823: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\n2025-08-13 14:08:13.374758: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:08:13.374535: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.374784: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:08:13.374805: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\n2025-08-13 14:08:13.374945: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\n2025-08-13 14:08:13.374790: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:08:13.374890: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:08:13.374801: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:08:13.374866: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:08:13.374963: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\n2025-08-13 14:08:13.374985: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:08:13.374968: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\n2025-08-13 14:08:13.375000: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:08:13.374968: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:08:13.374969: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:08:13.375006: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:08:13.375018: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:08:13.375020: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::7350480505452186648::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:2. Some timed out task names:\n/job:jax_worker/replica:0/task:29\n/job:jax_worker/replica:0/task:17\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::7350480505452186648']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-13 14:08:13.453060: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.453091: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n2025-08-13 14:08:13.453144: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.453164: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n:{""created"":""@1755086893.452946699"",""description"":""Error received from peer ipv4:10.0.1.99:64210"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\n:{""created"":""@1755086893.453054591"",""description"":""Error received from peer ipv4:10.0.1.99:64210"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\n2025-08-13 14:08:13.453150: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.453220: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n2025-08-13 14:08:13.453124: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.453152: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n:{""created"":""@1755086893.452915579"",""description"":""Error received from peer ipv4:10.0.1.99:64210"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\n:{""created"":""@1755086893.453028863"",""description"":""Error received from peer ipv4:10.0.1.99:64210"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\n2025-08-13 14:08:13.453301: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-13 14:08:13.453359: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n:{""created"":""@1755086893.453059547"",""description"":""Error received from peer ipv4:10.0.1.99:64210"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\nsrun: error: hkn0531: tasks 0-3: Aborted (core dumped)\nsrun: error: hkn0814: tasks 25,27: Aborted (core dumped)\nsrun: error: hkn0728: tasks 16,18: Aborted (core dumped)\nsrun: error: hkn0605: tasks 4-5: Aborted (core dumped)\nsrun: error: hkn0608: tasks 8,10: Aborted (core dumped)\nsrun: error: hkn0817: tasks 29-30: Aborted (core dumped)\nsrun: error: hkn0812: tasks 20,22: Aborted (core dumped)\nsrun: error: hkn0621: tasks 13,15: Aborted (core dumped)\nsrun: error: hkn0728: task 19: Aborted (core dumped)\nsrun: error: hkn0814: task 24: Aborted (core dumped)\nsrun: error: hkn0605: task 7: Aborted (core dumped)\nsrun: error: hkn0608: task 11: Aborted (core dumped)\nsrun: error: hkn0817: task 31: Aborted (core dumped)\nsrun: error: hkn0621: task 14: Aborted (core dumped)\nsrun: error: hkn0812: task 23: Aborted (core dumped)\nsrun: error: hkn0814: task 26: Aborted (core dumped)\nsrun: error: hkn0728: task 17: Aborted (core dumped)\nsrun: error: hkn0608: task 9: Aborted (core dumped)\nsrun: error: hkn0605: task 6: Aborted (core dumped)\nsrun: error: hkn0817: task 28: Aborted (core dumped)\nsrun: error: hkn0812: task 21: Aborted (core dumped)\nsrun: error: hkn0621: task 12: Aborted (core dumped)\n\n============================= JOB FEEDBACK =============================\n\nJob ID: 3418834\nCluster: hk\nUser/Group: tum_cte0515/hk-project-p0023960\nAccount: hk-project-p0023960\nState: FAILED (exit code 134)\nPartition: accelerated\nNodes: 8\nCores per node: 24\nNodelist: hkn[0531,0605,0608,0621,0728,0812,0814,0817]\nCPU Utilized: 00:02:03\nCPU Efficiency: 0.18% of 19:21:36 core-walltime\nJob Wall-clock time: 00:06:03\nStarttime: Wed Aug 13 14:02:11 2025\nEndtime: Wed Aug 13 14:08:14 2025\nMemory Utilized: 11.91 GB (estimated maximum)\nMemory Efficiency: 0.00% of 0.00 MB (0.00 MB/node)\nEnergy Consumed: 1494485 Joule / 415.134722222222 Watthours\nAverage node power draw: 4117.03856749311 Watt\n",log,tab +44,325138,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8721,0,"",log,selection_mouse +45,325277,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8718,15,"XlaRuntimeError",log,selection_mouse 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""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices",log,selection_mouse +101,331356,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8477,0,"",log,selection_mouse +102,331357,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,9,"Traceback",log,selection_mouse +103,331513,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,35,"Traceback (most recent call last):\n",log,selection_mouse +104,331687,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,156,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n",log,selection_mouse +105,331712,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,231,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\n",log,selection_mouse +106,331793,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,305,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +107,331961,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,306,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n\n",log,selection_mouse +108,332188,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,305,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +109,332809,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8777,0,"",log,selection_mouse +110,332810,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8771,7,"devices",log,selection_mouse +111,333032,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8767,11,"GPU devices",log,selection_mouse +112,333033,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8759,19,"visible GPU devices",log,selection_mouse +113,333033,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8735,43,"FAILED_PRECONDITION: No visible GPU devices",log,selection_mouse +114,333034,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8664,114,"plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices",log,selection_mouse +115,333049,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8647,131,"get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices",log,selection_mouse +116,333125,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8646,132,".get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices",log,selection_mouse +117,333160,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8642,136,"_xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices",log,selection_mouse +118,333238,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8641,137," _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices",log,selection_mouse +119,333266,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8519,259,"hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices",log,selection_mouse +120,333267,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8517,261,"""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices",log,selection_mouse +121,333286,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8516,262," ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices",log,selection_mouse +122,333287,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8512,266,"File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices",log,selection_mouse 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+125,333473,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,303,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices",log,selection_mouse +126,334476,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8477,0,"",log,selection_mouse +127,334477,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,9,"Traceback",log,selection_mouse 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+132,334803,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,72,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project",log,selection_mouse +133,334803,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,200,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name",log,selection_mouse +134,334837,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,201,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name,",log,selection_mouse +135,334875,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,209,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options",log,selection_mouse +136,334909,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,281,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: ",log,selection_mouse +137,334942,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,283,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No",log,selection_mouse +138,334945,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,284,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No ",log,selection_mouse +139,334980,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,291,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible",log,selection_mouse +140,335057,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,305,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +141,335233,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,303,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices",log,selection_mouse +142,336020,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8777,0,"",log,selection_mouse +143,336021,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8771,7,"devices",log,selection_mouse +144,336222,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8767,11,"GPU devices",log,selection_mouse +145,336261,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8759,19,"visible GPU devices",log,selection_mouse +146,336296,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8756,22,"No visible GPU devices",log,selection_mouse +147,336297,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8754,24,": No visible GPU devices",log,selection_mouse +148,336332,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8735,43,"FAILED_PRECONDITION: No visible GPU devices",log,selection_mouse +149,336332,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8675,103,", options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices",log,selection_mouse +150,336368,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8664,114,"plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices",log,selection_mouse 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\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices",log,selection_mouse +154,336513,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8528,250,"/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices",log,selection_mouse +155,336546,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8524,254,"home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices",log,selection_mouse +156,336583,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8523,255,"/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices",log,selection_mouse +157,336584,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8486,292,"most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices",log,selection_mouse 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+167,337519,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,72,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project",log,selection_mouse +168,337555,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,200,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name",log,selection_mouse +169,337591,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,209,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options",log,selection_mouse +170,337627,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,211,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, ",log,selection_mouse +171,337668,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,229,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client",log,selection_mouse +172,337750,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,295,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU",log,selection_mouse +173,337766,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,296,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU ",log,selection_mouse 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+176,338242,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8779,0,"",log,selection_mouse +177,338275,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8778,0,"",log,selection_command +178,338420,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8779,0,"",log,selection_mouse +179,338425,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8778,0,"",log,selection_command +180,338588,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8778,2,".\n",log,selection_mouse +181,338604,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8779,1,"\n",log,selection_command +182,338658,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8759,20,"visible GPU devices.",log,selection_mouse +183,338692,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8756,23,"No visible GPU devices.",log,selection_mouse +184,338693,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8754,25,": No visible GPU devices.",log,selection_mouse 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+190,338867,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8524,255,"home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.",log,selection_mouse +191,338902,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8523,256,"/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.",log,selection_mouse 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+196,339201,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,304,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.",log,selection_mouse +197,339835,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8477,0,"",log,selection_mouse +198,339835,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,9,"Traceback",log,selection_mouse 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""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No ",log,selection_mouse +205,340209,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,291,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible",log,selection_mouse +206,340290,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,305,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +207,340829,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8780,0,"",log,selection_mouse +208,341222,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8735,45,"FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +209,341255,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8734,46," FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +210,341255,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8718,62,"XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +211,341347,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8646,134,".get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +212,341375,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8642,138,"_xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +213,341454,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8641,139," _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +214,341455,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8635,145,"return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +215,341467,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8518,262,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +216,341478,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8517,263,"""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +217,341507,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8516,264," ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +218,341541,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8512,268,"File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +219,341851,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,305,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +220,342271,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8478,0,"",log,selection_mouse +221,342271,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,9,"Traceback",log,selection_mouse +222,342415,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,35,"Traceback (most recent call last):\n",log,selection_mouse +223,342574,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,156,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n",log,selection_mouse +224,342608,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,231,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\n",log,selection_mouse +225,342683,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,305,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +226,342764,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,306,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n\n",log,selection_mouse +227,343180,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8780,0,"",log,selection_mouse +228,343504,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8735,45,"FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +229,343554,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8647,133,"get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +230,343555,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8490,290," recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +231,343555,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8474,306,"\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +232,343581,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8406,374,"During handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +233,343814,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8474,306,"\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse +234,343874,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",8475,305,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_dynamics.py"", line 158, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n",log,selection_mouse 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accelerat train_dy tum_cte0 PD\t0:00\t 1 (Priority)3412401 accelerat train_to tum_cte0 R 1-16:54:53\t 8 hkn[0412,0416,0425,0433,0718,0732,0803,0807]3418832 accelerat train_dy tum_cte0 R 21:05:58\t 8 hkn[0407,0415,0420,0422-0424,0729,0735]",,terminal_output +257,1041913,"TERMINAL",0,0,"549",,terminal_output +258,1042989,"TERMINAL",0,0,"656:00",,terminal_output +259,1044028,"TERMINAL",0,0,"761",,terminal_output +260,1045079,"TERMINAL",0,0,"872",,terminal_output +261,1046133,"TERMINAL",0,0,"983",,terminal_output +262,1047236,"TERMINAL",0,0,"5:0094",,terminal_output +263,1048269,"TERMINAL",0,0,"15:005",,terminal_output +264,1049388,"TERMINAL",0,0,"216",,terminal_output +265,1050337,"TERMINAL",0,0,"327",,terminal_output +266,1051536,"TERMINAL",0,0,"438",,terminal_output +267,1052433,"TERMINAL",0,0,"549",,terminal_output +268,1053590,"TERMINAL",0,0,"6510",,terminal_output +269,1054610,"TERMINAL",0,0,"761",,terminal_output +270,1055636,"TERMINAL",0,0,"872",,terminal_output 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+306,1094045,"TERMINAL",0,0,"]633;E;2025-08-14 10:55:47 ls;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;C",,terminal_output +307,1094173,"TERMINAL",0,0,"data frames LICENSE overfit_dir.zip requirements.txt tests wandb\r\ndata_atari generate_dataset.py log.log __pycache__ sample.py train_dynamics.py\r\ndebug genie.py logs README.md samples train_lam.py\r\nframe-knoms.png gifs models read_tf_record.py scripts_cremers train_tokenizer.py\r\nframe.png input_pipeline overfit_dir requirements-franz.txt slurm utils\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +308,1096844,"TERMINAL",0,0,"cd slurm/",,terminal_command +309,1097099,"TERMINAL",0,0,"ls",,terminal_command +310,1097143,"TERMINAL",0,0,"]633;E;2025-08-14 10:55:50 ls;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;C",,terminal_output +311,1097237,"TERMINAL",0,0,"common dev jobs README.md templates utils\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar/slurm]633;D;0",,terminal_output +312,1103680,"TERMINAL",0,0,"git status",,terminal_command +313,1103721,"TERMINAL",0,0,"]633;E;2025-08-14 10:55:56 git statu;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;Cgit: 'statu' is not a git command. See 'git --help'.\r\n\r\nThe most similar commands are\r\n\tstatus\r\n\tstage\r\n\tstash\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar/slurm]633;D;1",,terminal_output +314,1108123,"TERMINAL",0,0,"git status",,terminal_command +315,1108164,"TERMINAL",0,0,"]633;E;2025-08-14 10:56:01 git status;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;C",,terminal_output +316,1109072,"TERMINAL",0,0,"On branch main\r\nYour branch is up to date with 'origin/main'.\r\n\r\nChanges not staged for commit:\r\n (use ""git add ..."" to update what will be committed)\r\n (use ""git restore ..."" to discard changes in working directory)\r\n\tmodified: jobs/mihir/horeka/causal_big_runs/train_dynamics_8_nodes.sbatch\r\n\tmodified: jobs/mihir/horeka/causal_big_runs/train_dynamics_8_nodes_filter_dark.sbatch\r\n\tmodified: jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes.sbatch\r\n\tmodified: jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes_filter_dark.sbatch\r\n\r\nUntracked files:\r\n (use ""git add ..."" to include in what will be committed)\r\n\tutils/alfred/sqrt_lr_scaling.py\r\n\r\nno changes added to commit (use ""git add"" and/or ""git commit -a"")\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar/slurm]633;D;0",,terminal_output +317,1137989,"TERMINAL",0,0,"git pull",,terminal_command +318,1137998,"TERMINAL",0,0,"]633;E;2025-08-14 10:56:31 git pull;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;C",,terminal_output +319,1139744,"TERMINAL",0,0,"remote: Enumerating objects: 53, done.\r\nremote: Counting objects: 1% (1/53)\rremote: Counting objects: 3% (2/53)\rremote: Counting objects: 5% (3/53)\rremote: Counting objects: 7% (4/53)\rremote: Counting objects: 9% (5/53)\rremote: Counting objects: 11% (6/53)\rremote: Counting objects: 13% (7/53)\rremote: Counting objects: 15% (8/53)\rremote: Counting objects: 16% (9/53)\rremote: Counting objects: 18% (10/53)\rremote: Counting objects: 20% (11/53)\rremote: Counting objects: 22% (12/53)\rremote: Counting objects: 24% (13/53)\rremote: Counting objects: 26% (14/53)\rremote: Counting objects: 28% (15/53)\rremote: Counting objects: 30% (16/53)\rremote: Counting objects: 32% (17/53)\rremote: Counting objects: 33% (18/53)\rremote: Counting objects: 35% (19/53)\rremote: Counting objects: 37% (20/53)\rremote: Counting objects: 39% (21/53)\rremote: Counting objects: 41% (22/53)\rremote: Counting objects: 43% (23/53)\rremote: Counting objects: 45% (24/53)\rremote: Counting objects: 47% (25/53)\rremote: Counting objects: 49% (26/53)\rremote: Counting objects: 50% (27/53)\rremote: Counting objects: 52% (28/53)\rremote: Counting objects: 54% (29/53)\rremote: Counting objects: 56% (30/53)\rremote: Counting objects: 58% (31/53)\rremote: Counting objects: 60% (32/53)\rremote: Counting objects: 62% (33/53)\rremote: Counting objects: 64% (34/53)\rremote: Counting objects: 66% (35/53)\rremote: Counting objects: 67% (36/53)\rremote: Counting objects: 69% (37/53)\rremote: Counting objects: 71% (38/53)\rremote: Counting objects: 73% (39/53)\rremote: Counting objects: 75% (40/53)\rremote: Counting objects: 77% (41/53)\rremote: Counting objects: 79% (42/53)\rremote: Counting objects: 81% (43/53)\rremote: Counting objects: 83% (44/53)\rremote: Counting objects: 84% (45/53)\rremote: Counting objects: 86% (46/53)\rremote: Counting objects: 88% (47/53)\rremote: Counting objects: 90% (48/53)\r",,terminal_output +320,1139842,"TERMINAL",0,0,"remote: Counting objects: 92% (49/53)\rremote: Counting objects: 94% (50/53)\rremote: Counting objects: 96% (51/53)\rremote: Counting objects: 98% (52/53)\rremote: Counting objects: 100% (53/53)\rremote: Counting objects: 100% (53/53), done.\r\nremote: Compressing objects: 3% (1/30)\rremote: Compressing objects: 6% (2/30)\rremote: Compressing objects: 10% (3/30)\rremote: Compressing objects: 13% (4/30)\rremote: Compressing objects: 16% (5/30)\rremote: Compressing objects: 20% (6/30)\rremote: Compressing objects: 23% (7/30)\rremote: Compressing objects: 26% (8/30)\rremote: Compressing objects: 30% (9/30)\rremote: Compressing objects: 33% (10/30)\rremote: Compressing objects: 36% (11/30)\rremote: Compressing objects: 40% (12/30)\rremote: Compressing objects: 43% (13/30)\rremote: Compressing objects: 46% (14/30)\rremote: Compressing objects: 50% (15/30)\rremote: Compressing objects: 53% (16/30)\rremote: Compressing objects: 56% (17/30)\rremote: Compressing objects: 60% (18/30)\rremote: Compressing objects: 63% (19/30)\rremote: Compressing objects: 66% (20/30)\rremote: Compressing objects: 70% (21/30)\rremote: Compressing objects: 73% (22/30)\rremote: Compressing objects: 76% (23/30)\rremote: Compressing objects: 80% (24/30)\rremote: Compressing objects: 83% (25/30)\rremote: Compressing objects: 86% (26/30)\rremote: Compressing objects: 90% (27/30)\rremote: Compressing objects: 93% (28/30)\rremote: Compressing objects: 96% (29/30)\rremote: Compressing objects: 100% (30/30)\rremote: Compressing objects: 100% (30/30), done.\r\nremote: Total 42 (delta 15), reused 35 (delta 10), pack-reused 0 (from 0)\r\n",,terminal_output +321,1139948,"TERMINAL",0,0,"Unpacking objects: 2% (1/42)\rUnpacking objects: 4% (2/42)\r",,terminal_output +322,1140038,"TERMINAL",0,0,"Unpacking objects: 7% (3/42)\rUnpacking objects: 9% (4/42)\rUnpacking objects: 11% (5/42)\rUnpacking objects: 14% (6/42)\rUnpacking objects: 16% (7/42)\r",,terminal_output +323,1140358,"TERMINAL",0,0,"Unpacking objects: 19% (8/42)\rUnpacking objects: 21% (9/42)\rUnpacking objects: 23% (10/42)\rUnpacking objects: 26% (11/42)\rUnpacking objects: 28% (12/42)\rUnpacking objects: 30% (13/42)\rUnpacking objects: 33% (14/42)\rUnpacking objects: 35% (15/42)\rUnpacking objects: 38% (16/42)\rUnpacking objects: 40% (17/42)\rUnpacking objects: 42% (18/42)\rUnpacking objects: 45% (19/42)\rUnpacking objects: 47% (20/42)\rUnpacking objects: 50% (21/42)\rUnpacking objects: 52% (22/42)\rUnpacking objects: 54% (23/42)\rUnpacking objects: 57% (24/42)\rUnpacking objects: 59% (25/42)\rUnpacking objects: 61% (26/42)\rUnpacking objects: 64% (27/42)\rUnpacking objects: 66% (28/42)\rUnpacking objects: 69% (29/42), 5.36 KiB | 10.00 KiB/s\rUnpacking objects: 71% (30/42), 5.36 KiB | 10.00 KiB/s\rUnpacking objects: 73% (31/42), 5.36 KiB | 10.00 KiB/s\r",,terminal_output +324,1140456,"TERMINAL",0,0,"Unpacking objects: 76% (32/42), 5.36 KiB | 10.00 KiB/s\rUnpacking objects: 78% (33/42), 5.36 KiB | 10.00 KiB/s\rUnpacking objects: 80% (34/42), 5.36 KiB | 10.00 KiB/s\rUnpacking objects: 83% (35/42), 5.36 KiB | 10.00 KiB/s\rUnpacking objects: 85% (36/42), 5.36 KiB | 10.00 KiB/s\rUnpacking objects: 88% (37/42), 5.36 KiB | 10.00 KiB/s\rUnpacking objects: 90% (38/42), 5.36 KiB | 10.00 KiB/s\r",,terminal_output +325,1140586,"TERMINAL",0,0,"Unpacking objects: 92% (39/42), 5.36 KiB | 10.00 KiB/s\rUnpacking objects: 95% (40/42), 5.36 KiB | 10.00 KiB/s\rUnpacking objects: 97% (41/42), 5.36 KiB | 10.00 KiB/s\rUnpacking objects: 100% (42/42), 5.36 KiB | 10.00 KiB/s\rUnpacking objects: 100% (42/42), 6.88 KiB | 10.00 KiB/s, done.\r\n",,terminal_output +326,1141139,"TERMINAL",0,0,"From github.com:p-doom/slurm\r\n 5ba7db4..d92a560 main -> origin/main\r\n",,terminal_output +327,1141301,"TERMINAL",0,0,"Updating 5ba7db4..d92a560\r\n",,terminal_output +328,1141477,"TERMINAL",0,0,"Fast-forward\r\n",,terminal_output +329,1141522,"TERMINAL",0,0," dev/alfred/horeka/input_pipeline_ws/actions/download_actions.sbatch | 18 +++++++\r\n dev/alfred/horeka/input_pipeline_ws/actions/download_actions_all.sh | 17 +++++++\r\n dev/alfred/horeka/jobs_cur/atari/sample_causal.sbatch | 35 +++++++++++++\r\n dev/alfred/horeka/jobs_cur/atari/sample_maskgit.sbatch | 35 +++++++++++++\r\n dev/alfred/horeka/jobs_cur/atari/train_dynamics_causal.sbatch | 83 ++++++++++++++++++++++++++++++\r\n dev/alfred/horeka/jobs_cur/atari/train_dynamics_maskgit.sbatch | 80 +++++++++++++++++++++++++++++\r\n dev/alfred/horeka/jobs_cur/atari/train_tokenizer_lr_1e-4.sbatch | 74 +++++++++++++++++++++++++++\r\n .../dyn_gt_actions_ablation_prepend/dyn_gt_actions_ablation_single_gpu copy.sbatch | 114 ++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/atari_dynamics/atari_dyn_maskgit_causal.sh | 13 +++++\r\n jobs/alfred/berlin/atari_dynamics/atari_dynamics.sbatch | 80 +++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/{ => downlaod}/download_actions.sbatch | 0\r\n jobs/alfred/berlin/{ => downlaod}/download_actions.sh | 0\r\n jobs/alfred/horeka/atari_dynamics/atari_dyn_maskgit_causal.sh | 13 +++++\r\n jobs/alfred/horeka/atari_dynamics/atari_dynamics.sbatch | 83 ++++++++++++++++++++++++++++++\r\n utils/alfred/scp_scripts/copy_to_berlin.sh | 7 +++\r\n 15 files changed, 652 insertions(+)\r\n create mode 100644 dev/alfred/horeka/input_pipeline_ws/actions/download_actions.sbatch\r\n create mode 100644 dev/alfred/horeka/input_pipeline_ws/actions/download_actions_all.sh\r\n create mode 100644 dev/alfred/horeka/jobs_cur/atari/sample_causal.sbatch\r\n create mode 100644 dev/alfred/horeka/jobs_cur/atari/sample_maskgit.sbatch\r\n create mode 100644 dev/alfred/horeka/jobs_cur/atari/train_dynamics_causal.sbatch\r\n create mode 100644 dev/alfred/horeka/jobs_cur/atari/train_dynamics_maskgit.sbatch\r\n create mode 100644 dev/alfred/horeka/jobs_cur/atari/train_tokenizer_lr_1e-4.sbatch\r\n create mode 100644 dev/alfred/horeka/jobs_cur/dyn_gt_actions_ablation_prepend/dyn_gt_actions_ablation_single_gpu copy.sbatch\r\n create mode 100644 jobs/alfred/berlin/atari_dynamics/atari_dyn_maskgit_causal.sh\r\n create mode 100644 jobs/alfred/berlin/atari_dynamics/atari_dynamics.sbatch\r\n rename jobs/alfred/berlin/{ => downlaod}/download_actions.sbatch (100%)\r\n rename jobs/alfred/berlin/{ => downlaod}/download_actions.sh (100%)\r\n create mode 100644 jobs/alfred/horeka/atari_dynamics/atari_dyn_maskgit_causal.sh\r\n create mode 100644 jobs/alfred/horeka/atari_dynamics/atari_dynamics.sbatch\r\n create mode 100644 utils/alfred/scp_scripts/copy_to_berlin.sh\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar/slurm]633;D;0",,terminal_output +330,1231382,"TERMINAL",0,0,"git commit -am ""update scripts""",,terminal_command +331,1231437,"TERMINAL",0,0,"]633;E;2025-08-14 10:58:04 git commit -am ""update scripts"";7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;C",,terminal_output +332,1231678,"TERMINAL",0,0,"[main d700975] update scripts\r\n 4 files changed, 4 insertions(+), 4 deletions(-)\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar/slurm]633;D;0",,terminal_output +333,1233815,"TERMINAL",0,0,"git push",,terminal_command +334,1233888,"TERMINAL",0,0,"]633;E;2025-08-14 10:58:06 git push;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;C",,terminal_output +335,1235090,"TERMINAL",0,0,"Enumerating objects: 19, done.\r\nCounting objects: 5% (1/19)\rCounting objects: 10% (2/19)\rCounting objects: 15% (3/19)\rCounting objects: 21% (4/19)\rCounting objects: 26% (5/19)\rCounting objects: 31% (6/19)\rCounting objects: 36% (7/19)\rCounting objects: 42% (8/19)\rCounting objects: 47% (9/19)\rCounting objects: 52% (10/19)\rCounting objects: 57% (11/19)\rCounting objects: 63% (12/19)\rCounting objects: 68% (13/19)\rCounting objects: 73% (14/19)\rCounting objects: 78% (15/19)\rCounting objects: 84% (16/19)\rCounting objects: 89% (17/19)\rCounting objects: 94% (18/19)\rCounting objects: 100% (19/19)\rCounting objects: 100% (19/19), done.\r\nDelta compression using up to 152 threads\r\nCompressing objects: 9% (1/11)\rCompressing objects: 18% (2/11)\rCompressing objects: 27% (3/11)\rCompressing objects: 36% (4/11)\rCompressing objects: 45% (5/11)\rCompressing objects: 54% (6/11)\rCompressing objects: 63% (7/11)\rCompressing objects: 72% (8/11)\rCompressing objects: 81% (9/11)\rCompressing objects: 90% (10/11)\rCompressing objects: 100% (11/11)\rCompressing objects: 100% (11/11), done.\r\nWriting objects: 9% (1/11)\rWriting objects: 18% (2/11)\rWriting objects: 27% (3/11)\rWriting objects: 36% (4/11)\rWriting objects: 45% (5/11)\rWriting objects: 54% (6/11)\rWriting objects: 63% (7/11)\rWriting objects: 72% (8/11)\rWriting objects: 81% (9/11)\rWriting objects: 90% (10/11)\rWriting objects: 100% (11/11)\rWriting objects: 100% (11/11), 938 bytes | 469.00 KiB/s, done.\r\nTotal 11 (delta 8), reused 0 (delta 0), pack-reused 0\r\n",,terminal_output +336,1235220,"TERMINAL",0,0,"remote: Resolving deltas: 0% (0/8)\rremote: Resolving deltas: 12% (1/8)\rremote: Resolving deltas: 25% (2/8)\rremote: Resolving deltas: 37% (3/8)\rremote: Resolving deltas: 50% (4/8)\rremote: Resolving deltas: 62% (5/8)\rremote: Resolving deltas: 75% (6/8)\rremote: Resolving deltas: 87% (7/8)\rremote: Resolving deltas: 100% (8/8)\rremote: Resolving deltas: 100% (8/8), completed with 6 local objects.\r\n",,terminal_output +337,1235369,"TERMINAL",0,0,"To github.com:p-doom/slurm.git\r\n d92a560..d700975 main -> main\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar/slurm]633;D;0",,terminal_output +338,1239124,"slurm/jobs/mihir/horeka/causal_big_runs/train_dynamics_8_nodes_dev.sh",0,0,"# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/holiday/causal/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\n# tokenizer with the new structure supporting larger ffn_dim\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3416521\n\nenv | grep SLURM\n\nexport PYTHONUNBUFFERED=1\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=24 \\n --init_lr=0 \\n --darkness_threshold=50 \\n --max_lr=8e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-causal-test-$slurm_job_id \\n --tags dynamics causal \\n --entity instant-uv \\n --project jafar \\n --dyna_dim=1024 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=16 \\n --dyna_ffn_dim=4096 \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir",shellscript,tab +339,1240177,"slurm/jobs/mihir/horeka/causal_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",0,0,"",shellscript,tab +340,3809848,"slurm/jobs/mihir/horeka/causal_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",2095,0,"",shellscript,selection_mouse +341,3809884,"slurm/jobs/mihir/horeka/causal_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",2094,0,"",shellscript,selection_command +342,3815250,"slurm/jobs/mihir/horeka/atari/train_tokenizer_lr_1e-4.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/atari/tokenizer/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/atari/tokenizer/%x_%j.log\n#SBATCH --job-name=train_tokenizer_1e-4\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_atari/array_records\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --save_ckpt \\n --image_height=84 \\n --image_width=64 \\n $restore_ckpt_flag \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=atari-tokenizer-1e-4-$slurm_job_id \\n --tags tokenizer atari 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n\n",shellscript,tab +343,3816390,"slurm/jobs/mihir/horeka/atari/train_tokenizer_lr_1e-4.sbatch",1720,0,"",shellscript,selection_mouse +344,3816965,"slurm/jobs/mihir/horeka/atari/train_tokenizer_lr_1e-4.sbatch",1793,0,"",shellscript,selection_mouse +345,3816972,"slurm/jobs/mihir/horeka/atari/train_tokenizer_lr_1e-4.sbatch",1792,0,"",shellscript,selection_command +346,3837141,"slurm/jobs/mihir/horeka/atari/train_tokenizer_lr_1e-4.sbatch",1751,0,"",shellscript,selection_mouse +347,3838491,"slurm/jobs/mihir/horeka/atari/train_tokenizer_lr_1e-4.sbatch",1751,1,"",shellscript,content +348,3838614,"slurm/jobs/mihir/horeka/atari/train_tokenizer_lr_1e-4.sbatch",1751,1,"",shellscript,content +349,3839909,"slurm/jobs/mihir/horeka/atari/train_tokenizer_lr_1e-4.sbatch",1751,0,"4",shellscript,content +350,3839910,"slurm/jobs/mihir/horeka/atari/train_tokenizer_lr_1e-4.sbatch",1752,0,"",shellscript,selection_keyboard +351,3840023,"slurm/jobs/mihir/horeka/atari/train_tokenizer_lr_1e-4.sbatch",1752,0,"8",shellscript,content 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$SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_atari/array_records\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --save_ckpt \\n --image_height=84 \\n --image_width=64 \\n $restore_ckpt_flag \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=48 \\n --init_lr=0 \\n 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+486,4698666,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/%x_%j.log\n#SBATCH --job-name=train_tokenizer_1e-4\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-1e-4-$slurm_job_id \\n --tags tokenizer big-run 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n\n",shellscript,tab +487,4709173,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_8nodes.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/%x_%j.log\n#SBATCH --job-name=train_tokenizer_1e-4\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=256 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-8-nodes-$slurm_job_id \\n --tags tokenizer big-run 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid",shellscript,tab +488,4710536,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_8nodes copy.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/%x_%j.log\n#SBATCH --job-name=train_tokenizer_1e-4\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=256 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-8-nodes-$slurm_job_id \\n --tags tokenizer big-run 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid",shellscript,tab +489,4719300,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/%x_%j.log\n#SBATCH --job-name=train_tokenizer_1e-4\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=256 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-8-nodes-$slurm_job_id \\n --tags tokenizer big-run 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid",shellscript,tab +490,4720488,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",38,0,"",shellscript,selection_mouse +491,4720490,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",37,0,"",shellscript,selection_command +492,4720781,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",38,0,"",shellscript,selection_command +493,4720965,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",37,1,"",shellscript,content +494,4721284,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",37,0,"4",shellscript,content +495,4721285,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",38,0,"",shellscript,selection_keyboard +496,4721766,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",37,0,"",shellscript,selection_command +497,4726498,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",1734,0,"",shellscript,selection_mouse +498,4727626,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",1733,1,"",shellscript,content +499,4727759,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",1732,1,"",shellscript,content +500,4728106,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",1731,1,"",shellscript,content +501,4733650,"TERMINAL",0,0,"python",,terminal_command +502,4733715,"TERMINAL",0,0,"]633;E;2025-08-14 11:56:26 python;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;CPython 3.9.18 (main, Jun 27 2025, 00:00:00) \r\n[GCC 11.4.1 20231218 (Red Hat 11.4.1-4)] on linux\r\nType ""help"", ""copyright"", ""credits"" or ""license"" for more information.\r\n",,terminal_output +503,4733784,"TERMINAL",0,0,">>> ",,terminal_output +504,4752362,"TERMINAL",0,0,"^?",,terminal_output +505,4752800,"TERMINAL",0,0,"[?25l?[?25h",,terminal_output +506,4752929,"TERMINAL",0,0,"",,terminal_output +507,4753486,"TERMINAL",0,0,"384\r\n384\r\n>>> ",,terminal_output +508,4754869,"TERMINAL",0,0,"384\r\n384\r\n>>> ",,terminal_output +509,4756704,"TERMINAL",0,0,"3",,terminal_output +510,4757793,"TERMINAL",0,0,"[?25l8[?25h",,terminal_output +511,4757896,"TERMINAL",0,0,"[?25l4[?25h",,terminal_output +512,4758585,"TERMINAL",0,0,"[?25l/[?25h",,terminal_output +513,4758884,"TERMINAL",0,0,"[?25l2[?25h",,terminal_output +514,4758955,"TERMINAL",0,0,"\r\n192.0\r\n>>> ",,terminal_output +515,4761038,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",1730,0,"",shellscript,selection_mouse +516,4762033,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",1731,0,"",shellscript,selection_mouse +517,4762633,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",1731,0,"1",shellscript,content +518,4762634,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",1732,0,"",shellscript,selection_keyboard +519,4762769,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",1732,0,"9",shellscript,content +520,4762770,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",1733,0,"",shellscript,selection_keyboard +521,4762883,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",1733,0,"2",shellscript,content +522,4762884,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",1734,0,"",shellscript,selection_keyboard +523,4766449,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_8nodes.sbatch",0,0,"",shellscript,tab +524,4770566,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_8nodes.sbatch",1734,0,"",shellscript,selection_command +525,4771184,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_8nodes.sbatch",1733,1,"",shellscript,content +526,4771283,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_8nodes.sbatch",1732,1,"",shellscript,content +527,4771440,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_8nodes.sbatch",1731,1,"",shellscript,content +528,4772194,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_8nodes.sbatch",1731,0,"3",shellscript,content +529,4772195,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_8nodes.sbatch",1732,0,"",shellscript,selection_keyboard +530,4772998,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_8nodes.sbatch",1732,0,"8",shellscript,content +531,4772998,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_8nodes.sbatch",1733,0,"",shellscript,selection_keyboard +532,4773137,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_8nodes.sbatch",1733,0,"4",shellscript,content +533,4773138,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_8nodes.sbatch",1734,0,"",shellscript,selection_keyboard +534,4814230,"TERMINAL",0,0,"1",,terminal_output +535,4814501,"TERMINAL",0,0,"[?25l9[?25h",,terminal_output +536,4814611,"TERMINAL",0,0,"[?25l2[?25h",,terminal_output +537,4815272,"TERMINAL",0,0,"[?25l/[?25h[?25l2[?25h",,terminal_output +538,4815516,"TERMINAL",0,0,"\r\n96.0\r\n>>> ",,terminal_output +539,4821781,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",0,0,"",shellscript,tab +540,4824111,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes copy.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=4\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/%x_%j.log\n#SBATCH --job-name=train_tokenizer_1e-4\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=192 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-8-nodes-$slurm_job_id \\n --tags tokenizer big-run 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid",shellscript,tab +541,4830145,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=4\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/%x_%j.log\n#SBATCH --job-name=train_tokenizer_1e-4\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=192 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-8-nodes-$slurm_job_id \\n --tags tokenizer big-run 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid",shellscript,tab +542,4831142,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes.sbatch",38,0,"",shellscript,selection_mouse +543,4831143,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes.sbatch",37,0,"",shellscript,selection_command +544,4831834,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes.sbatch",38,0,"",shellscript,selection_command +545,4832023,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes.sbatch",37,1,"",shellscript,content +546,4832142,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes.sbatch",37,0,"2",shellscript,content +547,4832143,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes.sbatch",38,0,"",shellscript,selection_keyboard 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copy.sbatch\r\nslurm/jobs/alfred/\r\nslurm/jobs/alfred/berlin/\r\nslurm/jobs/alfred/berlin/atari_dynamics/\r\nslurm/jobs/alfred/berlin/atari_dynamics/atari_dyn_maskgit_causal.sh\r\nslurm/jobs/alfred/berlin/atari_dynamics/atari_dynamics.sbatch\r\nslurm/jobs/alfred/berlin/downlaod/\r\nslurm/jobs/alfred/berlin/downlaod/download_actions.sbatch\r\nslurm/jobs/alfred/berlin/downlaod/download_actions.sh\r\nslurm/jobs/alfred/horeka/\r\nslurm/jobs/alfred/horeka/atari_dynamics/\r\nslurm/jobs/alfred/horeka/atari_dynamics/atari_dyn_maskgit_causal.sh\r\nslurm/jobs/alfred/horeka/atari_dynamics/atari_dynamics.sbatch\r\nslurm/jobs/mihir/horeka/atari/\r\nslurm/jobs/mihir/horeka/atari/train_tokenizer_lr_3e-5.sbatch\r\nslurm/jobs/mihir/horeka/lr_tuning/tokenizer/\r\nslurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes.sbatch\r\nslurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch\r\nslurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_8nodes.sbatch\r\nslurm/utils/alfred/\r\nslurm/utils/alfred/scp_scripts/\r\nslurm/utils/alfred/scp_scripts/copy_to_berlin.sh\r\n",,terminal_output 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scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_atari/array_records\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/atari/maskgit/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\n# @mihir TODO\ntokenizer_ckpt_dir=TODO\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --image_height=84 \\n --image_width=64 \\n --dyna_type=maskgit \\n --init_lr=0 \\n --max_lr=1e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=atari-dynamics-maskgit-2-node-$slurm_job_id \\n --tags atari dynamics maskgit 2-node \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n",shellscript,tab +1160,5662631,"slurm/jobs/mihir/horeka/coinrun/train_dynamics_maskgit.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/maskgit/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/maskgit/%x_%j.log\n#SBATCH --job-name=train_dynamics_maskgit_1_node\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/array_records\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/coinrun/maskgit/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3414046""\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=48 \\n --image_height=64 \\n --image_width=64 \\n --dyna_type=maskgit \\n --init_lr=0 \\n --max_lr=1e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=coinrun-dynamics-maskgit-1-node-$slurm_job_id \\n --tags coinrun dynamics maskgit 1-node \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n",shellscript,tab +1161,5686753,"TERMINAL",0,0,"sbatch slurm/jobs/mihir/horeka/coinrun/train_dynamics_maskgit.sbatch",,terminal_command +1162,5686784,"TERMINAL",0,0,"]633;E;2025-08-14 12:12:19 sbatch slurm/jobs/mihir/horeka/coinrun/train_dynamics_maskgit.sbatch;101ae38c-fbbc-44ac-a671-e512ad144ba4]633;C",,terminal_output +1163,5686849,"TERMINAL",0,0,"Submitted batch job 3422007\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar_jobs]633;D;0",,terminal_output +1164,5688066,"TERMINAL",0,0,"queue",,terminal_command +1165,5688157,"TERMINAL",0,0,"]633;E;2025-08-14 12:12:21 queue;101ae38c-fbbc-44ac-a671-e512ad144ba4]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Thu Aug 14 12:12:21 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3415713 accelerat train_dy tum_cte0 PD\t0:00\t 1 (Priority)3422007 accelerat train_dy tum_cte0 PD\t0:00\t 1 (Priority)3412401 accelerat train_to tum_cte0 R 1-18:12:20\t 8 hkn[0412,0416,0425,0433,0718,0732,0803,0807]3418832 accelerat train_dy tum_cte0 R 22:23:25\t 8 hkn[0407,0415,0420,0422-0424,0729,0735]3421982 accelerat train_to tum_cte0 R11:11\t 4 hkn[0724,0726,0728,0731]3421973 accelerat train_to tum_cte0 R11:20\t 2 hkn[0722-0723]3421964 accelerat train_to tum_cte0 R11:35\t 1 hkn0720",,terminal_output +1166,5689195,"TERMINAL",0,0,"216216",,terminal_output +1167,5690234,"TERMINAL",0,0,"327327",,terminal_output +1168,5691285,"TERMINAL",0,0,"438438",,terminal_output +1169,5692360,"TERMINAL",0,0,"549549",,terminal_output +1170,5693471,"TERMINAL",0,0,"65306540",,terminal_output +1171,5694496,"TERMINAL",0,0,"761761",,terminal_output +1172,5695422,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar_jobs]633;D;0",,terminal_output +1173,5696901,"TERMINAL",0,0,"fqueue",,terminal_command +1174,5696968,"TERMINAL",0,0,"]633;E;2025-08-14 12:12:30 fqueue;101ae38c-fbbc-44ac-a671-e512ad144ba4]633;C[?1049h(B[?7hEvery 1.0s: squeue -o ""%.10i %.16P %.30j %.8u %.8T %.10M %.9l %.6D %R""hkn1993.localdomain: Thu Aug 14 12:12:30 2025JOBIDPARTITIONNAME USER STATE\t TIME TIME_LIMI NODES NODELIST(REASON)3415713\taccelerated train_dynamics_maskgit_1_node tum_cte0 PENDING\t 0:00 2-00:00:00\t1 (Priority)3422007\taccelerated train_dynamics_maskgit_1_node tum_cte0 PENDING\t 0:00 2-00:00:00\t1 (Priority)3412401\tacceleratedtrain_tokenizer_1e-4 tum_cte0 RUNNING 1-18:12:29 2-00:00:00\t8 hkn[0412,0416,0425,0433,0718,0732,0803,0807]3418832\taccelerated train_dynamics_causal_8_node tum_cte0 RUNNING 22:23:34 2-00:00:00\t8 hkn[0407,0415,0420,0422-0424,0729,0735]3421982\tacceleratedtrain_tokenizer_1e-4 tum_cte0 RUNNING\t 11:20 2-00:00:00\t4 hkn[0724,0726,0728,0731]3421973\tacceleratedtrain_tokenizer_1e-4 tum_cte0 RUNNING\t 11:29 2-00:00:00\t2 hkn[0722-0723]3421964\tacceleratedtrain_tokenizer_3e-5 tum_cte0 RUNNING\t 11:44 2-00:00:00\t1 hkn0720",,terminal_output +1175,5697956,"TERMINAL",0,0,"13051305",,terminal_output +1176,5699210,"TERMINAL",0,0,"216216",,terminal_output +1177,5700070,"TERMINAL",0,0,"327327",,terminal_output +1178,5701081,"TERMINAL",0,0,"438438",,terminal_output +1179,5702084,"TERMINAL",0,0,"549549",,terminal_output +1180,5703134,"TERMINAL",0,0,"65406550",,terminal_output +1181,5704152,"TERMINAL",0,0,"761761",,terminal_output +1182,5705188,"TERMINAL",0,0,"872872",,terminal_output +1183,5706277,"TERMINAL",0,0,"983983",,terminal_output +1184,5707260,"TERMINAL",0,0,"40943094",,terminal_output +1185,5708319,"TERMINAL",0,0,"14051405",,terminal_output +1186,5708862,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar_jobs]633;D;0",,terminal_output +1187,5710829,"TERMINAL",0,0,"scancel 3422007",,terminal_command +1188,5710866,"TERMINAL",0,0,"]633;E;2025-08-14 12:12:43 scancel 3422007;101ae38c-fbbc-44ac-a671-e512ad144ba4]633;C]0;tum_cte0515@hkn1993:~/Projects/jafar_jobs]633;D;0]633;P;Cwd=/home/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs",,terminal_output +1189,5714913,"TERMINAL",0,0,"fqueue",,terminal_command +1190,5714974,"TERMINAL",0,0,"]633;E;2025-08-14 12:12:48 fqueue;101ae38c-fbbc-44ac-a671-e512ad144ba4]633;C[?1049h(B[?7hEvery 1.0s: squeue -o ""%.10i %.16P %.30j %.8u %.8T %.10M %.9l %.6D %R""hkn1993.localdomain: Thu Aug 14 12:12:48 2025JOBIDPARTITIONNAME USER STATE\t TIME TIME_LIMI NODES NODELIST(REASON)3415713\taccelerated train_dynamics_maskgit_1_node tum_cte0 PENDING\t 0:00 2-00:00:00\t1 (Priority)3412401\tacceleratedtrain_tokenizer_1e-4 tum_cte0 RUNNING 1-18:12:47 2-00:00:00\t8 hkn[0412,0416,0425,0433,0718,0732,0803,0807]3418832\taccelerated train_dynamics_causal_8_node tum_cte0 RUNNING 22:23:52 2-00:00:00\t8 hkn[0407,0415,0420,0422-0424,0729,0735]3421982\tacceleratedtrain_tokenizer_1e-4 tum_cte0 RUNNING\t 11:38 2-00:00:00\t4 hkn[0724,0726,0728,0731]3421973\tacceleratedtrain_tokenizer_1e-4 tum_cte0 RUNNING\t 11:47 2-00:00:00\t2 hkn[0722-0723]3421964\tacceleratedtrain_tokenizer_3e-5 tum_cte0 RUNNING\t 12:02 2-00:00:00\t1 hkn0720",,terminal_output +1191,5715768,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar_jobs]633;D;0",,terminal_output +1192,5719545,"slurm/jobs/mihir/horeka/coinrun/train_dynamics_maskgit.sbatch",0,0,"",shellscript,tab +1193,5722165,"slurm/jobs/mihir/horeka/coinrun/train_dynamics_maskgit.sbatch",483,0,"",shellscript,selection_mouse +1194,5722168,"slurm/jobs/mihir/horeka/coinrun/train_dynamics_maskgit.sbatch",482,0,"",shellscript,selection_command +1195,5722649,"slurm/jobs/mihir/horeka/coinrun/train_dynamics_maskgit.sbatch",444,0,"",shellscript,selection_mouse +1196,5723823,"slurm/jobs/mihir/horeka/atari/train_tokenizer_lr_3e-5.sbatch",0,0,"",shellscript,tab +1197,5726517,"slurm/jobs/mihir/horeka/coinrun/train_dynamics_maskgit.sbatch",0,0,"",shellscript,tab +1198,5727473,"slurm/jobs/mihir/horeka/coinrun/train_dynamics_maskgit.sbatch",465,0,"\n#SBATCH --reservation=llmtum",shellscript,content +1199,5727496,"slurm/jobs/mihir/horeka/coinrun/train_dynamics_maskgit.sbatch",466,0,"",shellscript,selection_command +1200,5730352,"TERMINAL",0,0,"sync-runner",,terminal_command +1201,5730436,"TERMINAL",0,0,"]633;E;2025-08-14 12:13:03 sync-runner;101ae38c-fbbc-44ac-a671-e512ad144ba4]633;Csending incremental file list\r\n",,terminal_output +1202,5732208,"TERMINAL",0,0,"slurm/jobs/mihir/horeka/coinrun/train_dynamics_maskgit.sbatch\r\n",,terminal_output +1203,5732283,"TERMINAL",0,0,"\r\nsent 25,843 bytes received 184 bytes 10,410.80 bytes/sec\r\ntotal size is 128,148,759 speedup is 4,923.69\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar_jobs]633;D;0",,terminal_output +1204,5736356,"TERMINAL",0,0,"sbatch slurm/jobs/mihir/horeka/coinrun/train_dynamics_maskgit.sbatch",,terminal_command +1205,5736418,"TERMINAL",0,0,"]633;E;2025-08-14 12:13:09 sbatch slurm/jobs/mihir/horeka/coinrun/train_dynamics_maskgit.sbatch;101ae38c-fbbc-44ac-a671-e512ad144ba4]633;CSubmitted batch job 3422008\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar_jobs]633;D;0",,terminal_output +1206,5737347,"TERMINAL",0,0,"queue",,terminal_command +1207,5737445,"TERMINAL",0,0,"]633;E;2025-08-14 12:13:10 queue;101ae38c-fbbc-44ac-a671-e512ad144ba4]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Thu Aug 14 12:13:10 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3415713 accelerat train_dy tum_cte0 PD\t0:00\t 1 (Priority)3412401 accelerat train_to tum_cte0 R 1-18:13:09\t 8 hkn[0412,0416,0425,0433,0718,0732,0803,0807]3418832 accelerat train_dy tum_cte0 R 22:24:14\t 8 hkn[0407,0415,0420,0422-0424,0729,0735]3422008 accelerat train_dy tum_cte0 R\t0:00\t 1 hkn07133421982 accelerat train_to tum_cte0 R12:00\t 4 hkn[0724,0726,0728,0731]3421973 accelerat train_to tum_cte0 R12:09\t 2 hkn[0722-0723]3421964 accelerat train_to tum_cte0 R12:24\t 1 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causal/",,terminal_command +2528,12819480,"TERMINAL",0,0,"ls",,terminal_command +2529,12820910,"TERMINAL",0,0,"cd dynamics-cotraining/",,terminal_command +2530,12822029,"TERMINAL",0,0,"ls",,terminal_command +2531,12823981,"TERMINAL",0,0,"tail -f train_dynamics_causal_8_node_3422130.log",,terminal_command +2532,12824053,"TERMINAL",0,0,"]633;E;2025-08-14 14:11:17 tail -f train_dynamics_causal_8_node_3422130.log ;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;CFiltering out sequence with average brightness 48.25839369270832, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 16.81418723394097, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 4.37693323784722, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 17.93325634809028, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.28341355642361, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 23.290651599826386, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 10.685171124565974, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 7.973923466145835, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.96465803776041, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 17.555535260416665, which is below the darkness threshold 50.0.\r\n",,terminal_output +2533,12825090,"TERMINAL",0,0,"Filtering out sequence with average brightness 6.248491104166666, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.026395559027776, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 14.32088695746528, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 13.287892904513892, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.699837139322916, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.35339623003471, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 28.515516414062496, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 13.779079039062502, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 31.77856176866319, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 15.801213352430556, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.445178874565972, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.22831657595486, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.642893111111114, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.82428636067709, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 23.826017976128472, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.369552199218745, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 27.205715572048604, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.80163310763888, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 19.21067757769097, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 29.756402393229166, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.90283489192707, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 11.064562754774306, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.96316022178819, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.351987908854174, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.47436232682293, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.904636282986104, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.30252202300348, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.50610748003473, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.83450543229167, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 3, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 43.50629163671874, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.25673835112847, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.17340608333334, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 34.061884940104164, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.26984409418403, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 12.782408529513887, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.534414733072918, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.83258071657986, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 10.201719534722223, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.418512315538194, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.291210371961807, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 28.396078554253478, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 19.273443664062498, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.447791945746527, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.783089046440985, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 14.316311935329859, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.161475852430556, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.72070090451389, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.54771968793403, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.68763006163195, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 15.59348041970486, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.49130341753471, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 12.410399775607639, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.73322909157986, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.389401227864575, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.299453903211806, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.42551810199654, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 17.461762073350695, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 15.428984342881945, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 8.41517641623264, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.258012031250004, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.84490517274306, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.395675162326405, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.939019926215266, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.32156702560764, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.05500592751736, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.14982335807292, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.28745239453125, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 28.03377534982639, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 1, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 38.291162500868055, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.500775280381944, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.11585116927084, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.341027131944436, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 16.53352068663194, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 19.105931896267364, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.851931579861116, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 15.783686277777777, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 14, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 3.430726105468751, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.391618353732646, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.483258674479174, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.90867199392361, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.89368974956597, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 23.308401263454858, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.57481587109373, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 21.550750368923605, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.28874605946181, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 11.313530924479169, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 14.177338001302086, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.77951588411458, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.298603133680555, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 4.929624919704861, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 34.7758957578125, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.7887266111111, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.57599053993056, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 12.004942078125001, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 14.866432825086806, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.99970993012153, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.484809298611108, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.990326822048612, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.76507261979167, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.50315575217014, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.640106145833336, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 13.475888818576388, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.887308973524306, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 5.163022049045138, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.16183431814236, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.22922659505208, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 15.61054293098958, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 27.001348415798603, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.836997713975695, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.84720079123263, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 17.436243806857643, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.55236843446179, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 15.661866783420137, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 2, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 5.745865147135416, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.16920592013887, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 11.739338965711806, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.64539661024304, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.69868717621527, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.61229421744791, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.408982079427076, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.878024863715275, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 4.087599994357639, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.782459053385416, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.26742317404514, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 17.066657614149303, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 21.102210330295144, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.68000962022569, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.108358296006934, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.01184064149306, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 10.07952100173611, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.23764219401041, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 29.273121290364582, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.67403715755209, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.159634221788195, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 10.859693625434026, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.267163098958335, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.97735686935763, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.05856661848958, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 23.21590727300348, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.729370394965265, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 11.069526227864582, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.73526423394098, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 12.858907507378472, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 13.11182256857639, which is below the darkness threshold 50.0.\r\n",,terminal_output +2534,12826139,"TERMINAL",0,0,"Filtering out sequence with average brightness 41.464187955295145, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.02931689626736, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.360279158420138, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.369152598090274, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 28.589123648003472, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.655651488281247, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 14.331612240885416, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 9.972827269097222, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 28.879556708767364, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 28.65433171397569, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 4.830970013888888, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.30589482986111, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 10.284072398871528, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.68896297743055, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.66199507335069, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 12.532190937934027, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.90413924479166, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.854636536892354, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.51112091102431, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.90547663975693, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 31.335604068576384, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 21.77122214453125, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 23.825450489149308, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.98555696961805, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 21.725477375000004, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.201561266927065, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.48539023394097, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 31.880347332031246, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 12.520327518229164, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.54553545833332, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.441437454427074, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 11.25730537890625, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.692825124565964, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 10.114622904947916, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.15385722309028, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.720363145833325, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 2, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 42.261345737413194, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 29.205406408854167, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.28746638802084, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 12.706706666232641, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.66873425911458, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 27.760344266059025, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.609183381076406, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 28.39644596310764, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.231705520399295, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.815763887586805, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 20.838116223958338, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.20830425130209, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 31.612449674045138, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.64177461154513, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 2.6250118437500003, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 28.652351098090275, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 10.236154825520833, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 14.732674315972222, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 20.76571675434028, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 19.09408901215278, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 29.613179714843746, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 4.137212364583333, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.480846973524304, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 11.922143979166663, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.552360985677087, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 17.33964979774306, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.98366849435763, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 1, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 42.19896424131946, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.28097957465277, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.31633569487847, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.227408264756946, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.91574588802084, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.008100246527775, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 20.670641799479167, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.22008243446181, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 10.730366986979167, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 27.35534505859375, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.85925859765625, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.549803424913193, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.906939588541654, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.471726490885413, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.98253778689236, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.01438754513889, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 6.185368413628472, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.60145767447917, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.11110933550347, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 6.471034640190972, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.89805972309027, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.59533146440973, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 19.08339677517361, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.241696391059023, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.71200667708333, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.3321095030382, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.191030071180556, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 14.631816058159721, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 15.175148952256944, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.51353421961806, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 29.14789116493056, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.62533254817709, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 6.170116545572916, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.80586637456596, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.00045060069444, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.97285501996528, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.87322573263887, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.405553090711805, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.41258210937499, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 16.1705804375, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 13.187679411024307, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 20.418184020399305, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 27.342379329861103, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.958920232638885, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.8499512404514, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.48266377777778, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 29.634783483506947, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.30427697699653, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.932166159288194, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 17.47815596701389, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 19.1499930264757, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 29.42777363107638, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 16.340278833333333, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.62191007595485, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.362136504340278, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 12.656163871527777, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.05662233637153, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.05428888888888, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 9.809410006510415, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 31.894950679687504, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 29.252755410156247, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.19600844618055, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.576112999565964, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.02128690104168, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.638448952256944, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.71198654036458, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 29.075061014322912, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 15.5968681484375, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.306010959201394, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.53528334027777, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.10780612065972, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.81797238715277, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 19.30913177126736, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.3419572313368, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.09825508420139, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.21295064366319, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.09468324956597, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 16.846888147569448, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.994928267361104, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.56066381163193, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.63902894878471, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.60075063628472, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.298270464843746, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.6677207938368, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 20.656851608940972, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.44361700217013, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.01214288064236, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.749617506076376, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.156503000868064, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.700470125434016, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.692577922309034, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.39059139019096, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.944281329427085, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.98361345312499, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.160125339409724, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.88992796657987, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.04279984027778, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.35145857118055, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 17.421225546006944, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.91267774782986, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 4.961705045138888, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 2.8816884709201394, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 20.59353025737847, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.814528867621526, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 28.700266996527777, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.792198299913192, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.6903745577257, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.18161632031251, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.671145737847226, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.81221030989583, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.980041447048606, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 16.09131488541667, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.40287151649305, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 20.277096176215277, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 10.947070907118055, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.08994145182291, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 23.293038946180555, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.391716476128476, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 15.729449763020833, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 29.963518907118054, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.76784844444445, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.53240935329861, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 28.994459208767367, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.574013672743053, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 8.741062166666666, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.988881016059025, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.19184354470487, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.19927317404513, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.343309812934024, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 15.72330868923611, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 1, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 32.185184884114584, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.69413179600695, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.24734658463541, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.79358963845487, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.850761896701385, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.34360246137154, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 15.71899516189236, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.432555463541675, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 16.210264149305555, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 19.53775068663194, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.44783652387153, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.963878419704862, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 20.25145430815973, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.86519064236111, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.735804779513884, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.322472456597225, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.741105429687508, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.57213467838541, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.844601434027794, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.257634926649303, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.814926668836804, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.93532238845486, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.714602090711807, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 9.851494995225694, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.99958494140625, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.6568950546875, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 28.45888567925347, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 15.252253840277776, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.36907496310765, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.463286263020834, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 14, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 34.204493317274306, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.224961368923594, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.96766257769097, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.94426772743055, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 13.308978797309027, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.23581079340278, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 28.14020086979166, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.915178693142366, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 1, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 24.836928993489593, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.46069146050348, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 14.223201267795135, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.69297598567709, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.54188554210069, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.28372880555556, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.88308756901043, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 16.61451967144097, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.35835799392361, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 12.337642720920142, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 1, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 41.409151208333334, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.406037435329864, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 23.80914919357639, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.79415559331597, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 29.30357367578125, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.03802509201388, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.667169706597225, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 20.482073514756944, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.695623681423605, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.18017578038195, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.07815861458333, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.55580262847222, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.946071781684026, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 13.152852060763891, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.03724315451389, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.62049548307292, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 20.550028684027776, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 28.214637049479162, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.13950512500001, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 11.879113871961803, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 10.897983416232641, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 3.1173806132812505, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.17875073828126, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.61300194140625, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.561536807291645, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 7.561142590711805, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.73062853689238, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.12580900260417, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.00336980815972, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 17.213181888020834, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.3500005625, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.97662650998263, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 11.280135458767361, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.642397835069442, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 10.310776618055554, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.83485321180554, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 9.975132686631946, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.12815167361112, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.344765733940974, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.7087069014757, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 1, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 41.07775581423611, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 15, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 47.44254116015625, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 31.810507785156243, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.39119336154514, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.84895600607638, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.23003100781251, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.59613746180556, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.287873690104156, which is below the darkness threshold 50.0.\r\n",,terminal_output +2535,12827134,"TERMINAL",0,0,"Filtering out sequence with average brightness 43.69283903168401, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 15, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 27.917037622395828, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.067997086805555, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.96044309505208, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 7.540668100260416, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 13.785180790364585, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.823761831597224, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.454952747395836, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.185998623263888, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.684065997829855, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.12983470529514, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.855540866753476, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.12479111631945, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 20.028692759548612, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.21180633072916, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.90460269314237, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.90338756944444, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.623259755642366, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.91172327647569, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.921175553819445, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 9.222967984375003, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 8, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 47.231410530381936, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.86746961197917, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.6776365486111, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.367244151909716, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 21.82563311631944, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.699530347656264, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.92555766406249, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 3.3143295755208344, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.81272714800346, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 21.594541773437495, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 20.249960812065975, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.86814599956596, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.002799543402773, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.08420196484374, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.22825359244791, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.950280488281244, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 9.465649786458334, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.34526884722222, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 10.5924828125, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.927245735243055, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 28.33458676822917, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.226825424479166, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.90795716883681, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.409973788194442, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.67603242664931, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.258384671874996, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.77652489019097, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.915616134982635, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.82448556553819, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.082291970052076, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 2, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 19.404355583333334, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.597213169270834, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.246115174913186, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.23935652690973, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.46171998828126, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.299551327690974, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.16669221875, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.90996709722223, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 14.06910895095486, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.54379095703124, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.2939763671875, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.59947224348959, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.5598106610243, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 14.079517940538192, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.87307643619791, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.28747654470487, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.07631629340278, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.949276658420146, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.00432015104167, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 3.5492821276041666, which is below the darkness threshold 50.0.\r\n",,terminal_output +2536,12829188,"TERMINAL",0,0,"Filtering out sequence with average brightness 47.198341880208325, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.40728582291666, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.340056402777776, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.36404093402778, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 19.675579679253467, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.49749498611111, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.786759471788194, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.022464996527773, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.37718409505208, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.494366769097216, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.81318576649305, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 16.960078757378472, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.55674429557292, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.61251941145834, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.87782646744791, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.31903804340277, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.632209563368047, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 12.04666593142361, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.57846514756944, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.715776291666664, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.87392485937499, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.89639959331597, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 17.464378066406248, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 21.613432802517362, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.25213565060765, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.99575514192709, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 7.040606244357639, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.980406286892354, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.94310283420139, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.83854703428819, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 20.880232010416666, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 31.190118578125, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 29.774855567274304, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.21080336241319, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 27.119482376302084, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.237615, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.454526124565973, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 21.63910823090278, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 4.867321037760415, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.77601594097221, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 21.749389063368056, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.85911107161458, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 9.37265406684028, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.13102692534722, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.82766136588541, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.967041765624984, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 6.80206761892361, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.521571290364584, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.396949013888896, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.604577486979146, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.617727667968758, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 14, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 22.07299042404514, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.08642860069443, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.63921443359376, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.564326149739586, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.47013460546875, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.608252359375005, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.525131890190973, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.18121266189236, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 11, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 35.667371157118055, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 10.838729708333332, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.527233295138892, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.40376301996527, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.632272440104167, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 23.950613430555553, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.452043534288194, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 19.918594010850693, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 15, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 35.20020058333334, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.76126238064235, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.2591979578993, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.493715072916665, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.27150613585069, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.15607400607639, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 16.192714589409725, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 11.346160309461803, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.620414561197904, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 34.67117838368055, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.10995779166667, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.57885933680555, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.498023140190966, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 1, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 20.69735179427084, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.25380546614583, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.50233857682292, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 27.52856073697917, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.21707877734375, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.35167131336805, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.06861614713542, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.72666005685764, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.00228797395833, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 14.047586053385412, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.888655702690976, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.10108218185764, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 27.532313350694434, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.33136517621527, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.60887864105902, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.703137481336807, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.59762666102431, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.657530521701396, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.9746550733507, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.555182551649295, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 14, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 13.38810968793403, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 19.121301209635412, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 11.04249641276042, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.596459095920135, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.095723200520833, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.66970105642362, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.87765283940971, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.40955750260417, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.666515397135417, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.01179643532987, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.615333272569444, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.35851682378473, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.90901723828126, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.34328444574651, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 8.337617865017362, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.25026827604167, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.602502460937494, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.86566910677083, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 7.414749640190972, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.963286688802086, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.992752450086805, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 12.578313046875001, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.04283973263889, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 23.162726562065973, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.09226792578125, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.359980235243057, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.770273491753464, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 17.321374582899306, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.02284484548611, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.894497451822915, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 15.778345146267359, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.80967586501737, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.182953991319444, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.925921832899306, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.28472446571182, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 16.625564571614582, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.18354208984375, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.54798894444444, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.527609464409718, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 5.510687426649303, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.84359200390624, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 1, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 32.99687792751736, which is below the darkness threshold 50.0.\r\n",,terminal_output +2537,12830132,"TERMINAL",0,0,"Filtering out sequence with average brightness 20.584517582899295, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.85491881380207, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.615683417968754, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 15.143158724392363, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.94644012369791, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 3.4838853832465277, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.21313230295139, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 21.16577651909722, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.88132032725694, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 34.88540337630208, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.54971527690973, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.577339532118053, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.62320235112846, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.43717969965277, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.99100119140624, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 13.40595642404514, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.085197149739585, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 28.599812453559025, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.23602431423611, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.65727039019096, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 10.744193267361112, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 29.396549269097214, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.37920105251737, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.88340945572917, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 10.44764171267361, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.05193628515625, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 23.49738452126736, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 13.712017695312497, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.51124050911458, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.46037941536458, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.26853993793402, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 28.72962086197916, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 29.70888270095486, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 11.908322227430554, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.766876286024306, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.73980721180554, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 4.247701313368054, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 14, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 22.44674719140625, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 9.785861887586806, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.94737023480904, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.1168075750868, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.21994015494791, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 10.18011365581597, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.2814726766493, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.044114148871515, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.984321318576384, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 16.078478967013886, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.24182104817708, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 12.405390644097226, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 12.648661205295141, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 21.70184042317708, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 27.753682626736115, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.23127126519097, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.96963414583333, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.82039700217013, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.6048155655382, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.00690611241319, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.57101773003472, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.671724417100695, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 16.5533767578125, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.960590548611115, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.34763674435764, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 19.14637792925347, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 19.065293465711804, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 14.546474480034718, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 20.009234914062493, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 23.458230740885416, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.286202267795126, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.899898362413204, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.30979557248264, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 19.96450533159722, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.260386103298607, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.677346032118045, which is below the darkness threshold 50.0.\r\n",,terminal_output +2538,12831144,"TERMINAL",0,0,"Filtering out episode with length 6, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 32.862843753906255, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.6795984874132, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.04458074609376, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.03164207769096, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.19243184244791, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.491503715277766, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.70997716319444, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 34.44411474175347, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.51261280078125, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 29.43365499479167, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.43792941796874, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.06409200911457, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 15.603385859809025, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.681302502604154, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.13043981032986, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 13.41378156206597, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.59997234114583, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.51647772699653, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.322956052517355, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.059334759548605, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.522987754774306, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 5.639856405381943, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 13.359573090711807, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.435467890624995, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.813368881510407, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.21610072699653, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.390818709201387, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 14, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 32.46940284852431, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.49886976779513, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 9.542988711371526, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.401129498263888, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.9952937282986, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.99812473871528, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.78518679947916, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 14.359289539062502, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 11.147173033854164, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 28.586465228732635, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.76773339105903, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 15.705631027777777, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 34.34850776996528, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 31.211462479600687, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 15.417603453993056, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.66003829861111, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.65604112456597, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.00930867404513, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.369178937065975, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.50743714105902, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.594955946180555, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 34.08040523828125, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 23.857080977864584, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 23.230154930121525, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.74555226996527, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.60679850130209, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.19186065842013, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 13.635319138020833, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.494882779079866, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 27.02789135546875, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.289346652777766, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 13.733238383246528, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.65975987413195, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.98584924348958, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 27.991792206597218, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.43363980555555, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 6, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 35.46088256814235, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.901566312499998, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.99567389409722, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.208291830295124, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 11.805345717013887, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.4354558485243, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 11.443111936197916, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.96171985720487, which is below the darkness threshold 50.0.\r\n",,terminal_output +2539,12832160,"TERMINAL",0,0,"Filtering out sequence with average brightness 45.56216610677084, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 23.700271710503472, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.305229101996527, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 3.317470993923611, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 27.59633501736111, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.11482972092013, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.57808961458333, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.96180032118055, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.93626566796875, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.80606769444444, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 34.5031797452257, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.61120937282986, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 31.283901809895834, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 21.080622509982632, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.607679651041664, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.03240911935765, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.853900456597213, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 9.543171285156248, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 27.07596157118056, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.24305379253473, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.75990732942708, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.92470306510416, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 23.737464522569447, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 9.533172161024304, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.28104738237848, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.57187957508681, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 9.28303004513889, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 16.434875995225692, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.62051286458332, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.736149002604165, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 29.433547449218754, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 16.88892702907986, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.29590201128471, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.18326280598959, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.085143552951397, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.49563035243056, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.344287523003466, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 13.285958331163195, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 13.690684492187497, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 1, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 20.1771911922743, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.59776680815973, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.795674034722214, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 21.211715393229163, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 21.258736057291667, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.41129976605903, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.04576054296875, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.01707182118056, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.896484003472228, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.56767216623265, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 16.46249483463542, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.489087004340274, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.13610442578125, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 7.291982520833333, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.769582384982634, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.28725516536459, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.12756687065973, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.228390414496516, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.72119899652776, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.56270264105902, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 8.796899195746526, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.470349585937484, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.16261827821181, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.01894919140624, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.733159609809025, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 9.07459774262153, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.731335756076405, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 20.736553951822913, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 11.892008654513885, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.73564039019097, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.27213774001736, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.067593857204855, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 19.235811047309028, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 12.818970408420137, which is below the darkness threshold 50.0.\r\n",,terminal_output +2540,12833144,"TERMINAL",0,0,"^C\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/causal/dynamics-cotraining]633;D;130",,terminal_output +2541,12834172,"TERMINAL",0,0,"cd ..",,terminal_command +2542,12834521,"TERMINAL",0,0,"ls",,terminal_command +2543,12834555,"TERMINAL",0,0,"]633;E;2025-08-14 14:11:27 l;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;Cbash: l: command not found...\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/causal]633;D;127",,terminal_output +2544,12882919,"TERMINAL",0,0,"tail -f train_dynamics_causal_8_node_3422130.log",,terminal_command +2545,12885341,"TERMINAL",0,0,"cd ..",,terminal_command +2546,12886744,"TERMINAL",0,0,"cd maskgit/",,terminal_command +2547,12887027,"TERMINAL",0,0,"ls",,terminal_command +2548,12887056,"TERMINAL",0,0,"]633;E;2025-08-14 14:12:20 ls;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;Cdynamics-cotraining\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit]633;D;0",,terminal_output +2549,12888271,"TERMINAL",0,0,"cd dynamics-cotraining/",,terminal_command +2550,12891181,"TERMINAL",0,0,"ls",,terminal_command +2551,12891218,"TERMINAL",0,0,"]633;E;2025-08-14 14:12:24 ls;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;Ctrain_dynamics_maskgit_8_node_3422132.log\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining]633;D;0",,terminal_output +2552,12893569,"TERMINAL",0,0,"tail -f train_dynamics_maskgit_8_node_3422132.log",,terminal_command +2553,12893641,"TERMINAL",0,0,"]633;E;2025-08-14 14:12:26 tail -f train_dynamics_maskgit_8_node_3422132.log ;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;CNodelist: hkn[0530-0534,0536,0701,0715]\r\nCPU Utilized: 00:02:54\r\nCPU Efficiency: 0.25% of 19:02:24 core-walltime\r\nJob Wall-clock time: 00:05:57\r\nStarttime: Thu Aug 14 13:24:52 2025\r\nEndtime: Thu Aug 14 13:30:49 2025\r\nMemory Utilized: 12.67 GB (estimated maximum)\r\nMemory Efficiency: 0.00% of 0.00 MB (0.00 MB/node)\r\nEnergy Consumed: 1339227 Joule / 372.0075 Watthours\r\nAverage node power draw: 3751.33613445378 Watt\r\n",,terminal_output +2554,12896067,"TERMINAL",0,0,"^C\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining]633;D;130",,terminal_output +2555,12900284,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/%x_%j.log\n#SBATCH --job-name=train_dynamics_maskgit_8_node\n#SBATCH --requeue\n#SBATCH --reservation=llmtum\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/holiday/maskgit/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=160 \\n --init_lr=0 \\n --darkness_threshold=50 \\n --dyna_type=maskgit \\n --num_latent_actions=6 \\n --max_lr=8e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-maskgit-8-node-darkness-filter-$slurm_job_id \\n --tags dynamics maskgit 8-node post-launch-main darkness-filter \\n --entity instant-uv \\n --project jafar \\n --dyna_dim=1024 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=16 \\n --dyna_ffn_dim=4096 \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\nSLURM_JOB_USER=tum_dbd0378\nSLURM_TASKS_PER_NODE=4(x8)\nSLURM_JOB_UID=996262\nSLURM_TASK_PID=17640\nSLURM_JOB_GPUS=0,1,2,3\nSLURM_LOCALID=0\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar\nSLURMD_NODENAME=hkn0530\nSLURM_JOB_START_TIME=1755170692\nSLURM_CLUSTER_NAME=hk\nSLURM_JOB_END_TIME=1755343492\nSLURM_CPUS_ON_NODE=24\nSLURM_JOB_CPUS_PER_NODE=24(x8)\nSLURM_GPUS_ON_NODE=4\nSLURM_GTIDS=0\nSLURM_JOB_PARTITION=accelerated\nSLURM_TRES_PER_TASK=cpu=5\nSLURM_OOM_KILL_STEP=0\nSLURM_JOB_NUM_NODES=8\nSLURM_JOBID=3422132\nSLURM_JOB_QOS=normal\nSLURM_PROCID=0\nSLURM_CPUS_PER_TASK=5\nSLURM_JOB_RESERVATION=llmtum\nSLURM_NTASKS=32\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0530\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\nSLURM_SCRIPT_CONTEXT=prolog_task\nSLURM_NODELIST=hkn[0530-0534,0536,0701,0715]\nSLURM_JOB_ACCOUNT=hk-project-p0023960\nSLURM_PRIO_PROCESS=0\nSLURM_NPROCS=32\nSLURM_NNODES=8\nSLURM_SUBMIT_HOST=hkn1990.localdomain\nSLURM_JOB_ID=3422132\nSLURM_NODEID=0\nSLURM_CONF=/etc/slurm/slurm.conf\nSLURM_JOB_NAME=train_dynamics_maskgit_8_node\nSLURM_NTASKS_PER_NODE=4\nSLURM_JOB_GID=502226\nSLURM_JOB_NODELIST=hkn[0530-0534,0536,0701,0715]\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\n2025-08-14 13:25:47.802222: W external/xla/xla/service/platform_util.cc:220] unable to create StreamExecutor for CUDA:3: CUDA error: Failed call to cuDeviceGet: CUDA_ERROR_INVALID_DEVICE: invalid device ordinal\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/train_dynamics.py"", line 158, in \n num_devices = jax.device_count()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 907, in device_count\n return int(get_backend(backend).device_count())\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 876, in get_backend\n return _get_backend_uncached(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 855, in _get_backend_uncached\n bs = backends()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 758, in backends\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': INTERNAL: no supported devices found for platform CUDA (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: Getting local topologies failed: Error 1: GetKeyValue() timed out with key: cuda:local_topology/cuda/19 and duration: 2m\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/train_dynamics.py"", line 158, in \n num_devices = jax.device_count()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 907, in device_count\n return int(get_backend(backend).device_count())\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 876, in get_backend\n return _get_backend_uncached(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 855, in _get_backend_uncached\n bs = backends()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 758, in backends\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': INTERNAL: Getting local topologies failed: Error 1: GetKeyValue() timed out with key: cuda:local_topology/cuda/19 and duration: 2m (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: DEADLINE_EXCEEDED: GetKeyValue() timed out with key: cuda:global_topology/cuda and duration: 5m\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/train_dynamics.py"", line 158, in \n num_devices = jax.device_count()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 907, in device_count\n return int(get_backend(backend).device_count())\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 876, in get_backend\n return _get_backend_uncached(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 855, in _get_backend_uncached\n bs = backends()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 758, in backends\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': DEADLINE_EXCEEDED: GetKeyValue() timed out with key: cuda:global_topology/cuda and duration: 5m (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: DEADLINE_EXCEEDED: GetKeyValue() timed out with key: cuda:global_topology/cuda and duration: 5m\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/train_dynamics.py"", line 158, in \n num_devices = jax.device_count()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 907, in device_count\n return int(get_backend(backend).device_count())\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 876, in get_backend\n return _get_backend_uncached(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 855, in _get_backend_uncached\n bs = backends()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 758, in backends\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': DEADLINE_EXCEEDED: GetKeyValue() timed out with key: cuda:global_topology/cuda and duration: 5m (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: DEADLINE_EXCEEDED: GetKeyValue() timed out with key: cuda:global_topology/cuda and duration: 5m\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/train_dynamics.py"", line 158, in \n num_devices = jax.device_count()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 907, in device_count\n return int(get_backend(backend).device_count())\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 876, in get_backend\n return _get_backend_uncached(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 855, in _get_backend_uncached\n bs = backends()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 758, in backends\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': DEADLINE_EXCEEDED: GetKeyValue() timed out with key: cuda:global_topology/cuda and duration: 5m (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\n2025-08-14 13:30:48.404038: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service.cc:1798] Shutdown barrier in coordination service has failed:\nDEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\nThis suggests that the workers are out of sync. Either at least one worker (a) crashed early due to program error or scheduler events (e.g. preemption, eviction), (b) was too fast in its execution, or (c) too slow / hanging. Check the logs (both the program and scheduler events) for an earlier error to identify the root cause.\n2025-08-14 13:30:48.404124: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service.cc:1839] Use error polling to propagate the following error to all tasks: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.404702: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.404606: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.404610: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.404778: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.405016: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.404842: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.404891: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\n2025-08-14 13:30:48.404932: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.405085: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.404913: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.405092: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.404916: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.405030: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.405001: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\n2025-08-14 13:30:48.405055: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.405142: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.405049: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.405214: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.405066: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.405307: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.405334: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\n2025-08-14 13:30:48.405487: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\n2025-08-14 13:30:48.405255: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.405406: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.405332: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\n2025-08-14 13:30:48.405365: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.405622: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.405408: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\n2025-08-14 13:30:48.405451: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.405527: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.405438: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.405537: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.405620: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.405587: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.405776: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.405493: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\n2025-08-14 13:30:48.405770: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.405462: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.405773: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\n2025-08-14 13:30:48.405592: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.405765: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\n2025-08-14 13:30:48.405437: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.405935: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.405994: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.405866: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\n2025-08-14 13:30:48.406066: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.405889: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\n2025-08-14 13:30:48.406077: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.404956: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.404986: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.405212: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.405510: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.421574: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.421570: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.421590: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.421569: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.512644: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.512567: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.512644: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.512749: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-08-14 13:30:48.513476: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.513541: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.513499: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-08-14 13:30:48.513545: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::3040337475261767296::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 5/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:19. Some timed out task names:\n/job:jax_worker/replica:0/task:20\n/job:jax_worker/replica:0/task:30\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::3040337475261767296']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nsrun: error: hkn0534: task 19: Aborted (core dumped)\nsrun: error: hkn0534: tasks 16,18: Aborted (core dumped)\nsrun: error: hkn0533: tasks 12,14: Aborted (core dumped)\nsrun: error: hkn0532: tasks 9,11: Aborted (core dumped)\nsrun: error: hkn0530: tasks 0,2: Aborted (core dumped)\nsrun: error: hkn0531: tasks 4,7: Aborted (core dumped)\nsrun: error: hkn0536: tasks 21-22: Aborted (core dumped)\nsrun: error: hkn0701: tasks 26-27: Aborted (core dumped)\nsrun: error: hkn0715: tasks 30-31: Aborted (core dumped)\nsrun: error: hkn0534: task 17: Aborted (core dumped)\nsrun: error: hkn0533: task 15: Aborted (core dumped)\nsrun: error: hkn0532: task 8: Aborted (core dumped)\nsrun: error: hkn0531: task 6: Aborted (core dumped)\nsrun: error: hkn0530: task 3: Aborted (core dumped)\nsrun: error: hkn0536: task 23: Aborted (core dumped)\nsrun: error: hkn0701: task 25: Aborted (core dumped)\nsrun: error: hkn0715: task 28: Aborted (core dumped)\nsrun: error: hkn0533: task 13: Aborted (core dumped)\nsrun: error: hkn0532: task 10: Aborted (core dumped)\nsrun: error: hkn0530: task 1: Aborted (core dumped)\nsrun: error: hkn0531: task 5: Aborted (core dumped)\nsrun: error: hkn0536: task 20: Aborted (core dumped)\nsrun: error: hkn0701: task 24: Aborted (core dumped)\nsrun: error: hkn0715: task 29: Aborted (core dumped)\n\n============================= JOB FEEDBACK =============================\n\nJob ID: 3422132\nCluster: hk\nUser/Group: tum_dbd0378/hk-project-p0023960\nAccount: hk-project-p0023960\nState: FAILED (exit code 134)\nPartition: accelerated\nNodes: 8\nCores per node: 24\nNodelist: hkn[0530-0534,0536,0701,0715]\nCPU Utilized: 00:02:54\nCPU Efficiency: 0.25% of 19:02:24 core-walltime\nJob Wall-clock time: 00:05:57\nStarttime: Thu Aug 14 13:24:52 2025\nEndtime: Thu Aug 14 13:30:49 2025\nMemory Utilized: 12.67 GB (estimated maximum)\nMemory Efficiency: 0.00% of 0.00 MB (0.00 MB/node)\nEnergy Consumed: 1339227 Joule / 372.0075 Watthours\nAverage node power draw: 3751.33613445378 Watt\n",log,tab +2556,12900733,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",489,0,"",log,selection_mouse +2557,12900767,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",488,0,"",log,selection_command +2558,12904425,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",562,0,"",log,selection_mouse +2559,12905011,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",460,0,"",log,selection_mouse +2560,12946943,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",6017,0,"",log,selection_mouse +2561,12947065,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",6016,12,"RuntimeError",log,selection_mouse +2562,12958605,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",6163,0,"",log,selection_mouse +2563,12958758,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",6161,6,"plugin",log,selection_mouse +2564,12958920,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",6016,209,"RuntimeError: Unable to initialize backend 'cuda': INTERNAL: no supported devices found for platform CUDA (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\n",log,selection_mouse +2565,12960277,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",6080,0,"",log,selection_mouse +2566,12960277,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",6080,9,"supported",log,selection_mouse +2567,12960459,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",6016,209,"RuntimeError: Unable to initialize backend 'cuda': INTERNAL: no supported devices found for platform CUDA (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\n",log,selection_mouse +2568,12968171,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",13857,0,"",log,selection_mouse +2569,12968883,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",13788,0,"",log,selection_mouse +2570,12969018,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",13780,11,"GetKeyValue",log,selection_mouse +2571,12969164,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",13732,125,"jaxlib._jax.XlaRuntimeError: DEADLINE_EXCEEDED: GetKeyValue() timed out with key: cuda:global_topology/cuda and duration: 5m\n",log,selection_mouse +2572,12987028,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4964,0,"",log,selection_mouse +2573,12987173,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4957,8,"INTERNAL",log,selection_mouse +2574,12987366,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4957,9,"INTERNAL:",log,selection_mouse +2575,12987386,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4957,12,"INTERNAL: no",log,selection_mouse +2576,12987403,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4957,22,"INTERNAL: no supported",log,selection_mouse +2577,12987481,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4957,23,"INTERNAL: no supported ",log,selection_mouse +2578,12987482,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4957,30,"INTERNAL: no supported devices",log,selection_mouse +2579,12987526,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4908,57,"distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL",log,selection_mouse +2580,12987561,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4928,37,"jaxlib._jax.XlaRuntimeError: INTERNAL",log,selection_mouse +2581,12987562,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4957,36,"INTERNAL: no supported devices found",log,selection_mouse +2582,12987596,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4957,37,"INTERNAL: no supported devices found ",log,selection_mouse +2583,12987597,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4957,40,"INTERNAL: no supported devices found for",log,selection_mouse +2584,12987630,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4957,41,"INTERNAL: no supported devices found for ",log,selection_mouse +2585,12987665,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4957,49,"INTERNAL: no supported devices found for platform",log,selection_mouse +2586,12987817,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4957,50,"INTERNAL: no supported devices found for platform ",log,selection_mouse +2587,12987855,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4957,54,"INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2588,12988372,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",5011,0,"",log,selection_mouse +2589,12988386,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",5010,0,"",log,selection_command +2590,12988827,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",5011,0,"",log,selection_mouse +2591,12988838,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",5010,0,"",log,selection_command +2592,12988990,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",5010,1,"A",log,selection_mouse +2593,12988996,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",5011,0,"",log,selection_command +2594,12989009,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",5009,2,"DA",log,selection_mouse +2595,12989025,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",5006,5," CUDA",log,selection_mouse +2596,12989048,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4999,12,"latform CUDA",log,selection_mouse +2597,12989060,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4993,18," for platform CUDA",log,selection_mouse +2598,12989083,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4912,99,"ributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2599,12989098,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4749,262,"_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2600,12989116,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4744,267,"0/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2601,12989130,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4695,316,"nt(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2602,12989160,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4544,467,"oject-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2603,12989165,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4540,471,"k-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2604,12989192,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4498,513,"ion.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2605,12989192,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4495,516,"ration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2606,12989229,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4340,671,"e/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2607,12989230,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4338,673,"ome/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2608,12989262,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4298,713," _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2609,12989262,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4297,714,"= _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2610,12989298,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4296,715," = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2611,12989338,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4295,716,"d = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2612,12989338,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4147,864,"hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2613,12989375,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4146,865,"/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2614,12989415,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4145,866,"""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2615,12989455,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4109,902,"ack (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2616,12989456,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4108,903,"back (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2617,12989490,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4107,904,"eback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2618,12989491,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4106,905,"ceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2619,12989492,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4105,906,"aceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2620,12989527,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4104,907,"raceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2621,12989528,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4103,908,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2622,12989658,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",3892,1119,"2025-08-14 13:25:47.802222: W external/xla/xla/service/platform_util.cc:220] unable to create StreamExecutor for CUDA:3: CUDA error: Failed call to cuDeviceGet: CUDA_ERROR_INVALID_DEVICE: invalid device ordinal\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_dbd0378/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA",log,selection_mouse +2623,12998021,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4322,0,"",log,selection_mouse +2624,12998058,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3422132.log",4321,0,"",log,selection_command +2625,14273636,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes.sbatch",0,0,"",shellscript,tab +2626,14274240,"slurm/jobs/mihir/horeka/atari/train_tokenizer_lr_3e-5.sbatch",0,0,"",shellscript,tab +2627,14276107,"slurm/jobs/mihir/horeka/causal_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",0,0,"",shellscript,tab +2628,14276810,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/train_dynamics_maskgit_8_node_3418834.log",0,0,"",log,tab +2629,14278829,"sample.py",0,0,"from dataclasses import dataclass\nimport time\nimport os\nimport optax\n\nimport dm_pix as pix\nimport einops\nimport jax\nimport jax.numpy as jnp\nimport flax.linen as nn\nimport numpy as np\nimport orbax.checkpoint as ocp\nfrom PIL import Image, ImageDraw\nimport tyro\nfrom flax import nnx\n\nfrom genie import Genie\nfrom utils.dataloader import get_dataloader\n\n\n@dataclass\nclass Args:\n # Experiment\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/coinrun_episodes""\n checkpoint: str = """"\n # Sampling\n batch_size: int = 1\n maskgit_steps: int = 25\n temperature: float = 1.0\n sample_argmax: bool = True\n start_frame: int = 0\n # Tokenizer checkpoint\n tokenizer_dim: int = 512\n tokenizer_ffn_dim: int = 2048\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 4\n tokenizer_num_heads: int = 8\n # LAM checkpoint\n lam_dim: int = 512\n lam_ffn_dim: int = 2048\n latent_action_dim: int = 32\n num_latent_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 4\n lam_num_heads: int = 8\n # Dynamics checkpoint\n dyna_type: str = ""maskgit""\n dyna_dim: int = 512\n dyna_ffn_dim: int = 2048\n dyna_num_blocks: int = 6\n dyna_num_heads: int = 8\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\n use_flash_attention: bool = True\n # Additional parameters\n darkness_threshold: float = 0.0\n\n\nargs = tyro.cli(Args)\n\nif __name__ == ""__main__"":\n """"""\n Dimension keys:\n B: batch size\n T: number of input (conditioning) frames\n N: number of patches per frame\n S: sequence length\n H: height\n W: width\n E: B * (S - 1)\n """"""\n jax.distributed.initialize()\n\n rng = jax.random.key(args.seed)\n\n # --- Load Genie checkpoint ---\n rngs = nnx.Rngs(rng)\n genie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n tokenizer_ffn_dim=args.tokenizer_ffn_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n lam_ffn_dim=args.lam_ffn_dim,\n latent_action_dim=args.latent_action_dim,\n num_latent_actions=args.num_latent_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n lam_co_train=False,\n # Dynamics\n dyna_type=args.dyna_type,\n dyna_dim=args.dyna_dim,\n dyna_ffn_dim=args.dyna_ffn_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n # FIXME (f.srambical): implement spatiotemporal KV caching and set decode=True\n decode=False,\n rngs=rngs,\n )\n\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n checkpoint_options = ocp.CheckpointManagerOptions(\n step_format_fixed_length=6,\n )\n checkpoint_manager = ocp.CheckpointManager(\n args.checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n\n dummy_tx = optax.adamw(\n learning_rate=optax.linear_schedule(0.0001, 0.0001, 10000),\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.dtype,\n )\n dummy_optimizer = nnx.Optimizer(genie, dummy_tx)\n\n abstract_optimizer = nnx.eval_shape(lambda: dummy_optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n ),\n )\n restored_optimizer_state = restored[""model_state""]\n nnx.update(dummy_optimizer, restored_optimizer_state)\n\n # --- Define sampling function ---\n def _sampling_fn(model: Genie, batch: dict) -> jax.Array:\n """"""Runs Genie.sample with pre-defined generation hyper-parameters.""""""\n if args.dyna_type == ""maskgit"":\n return model.sample(\n batch,\n args.seq_len,\n args.maskgit_steps,\n args.temperature,\n args.sample_argmax,\n )\n elif args.dyna_type == ""causal"":\n return model.sample_causal(\n batch,\n args.seq_len,\n args.temperature,\n args.sample_argmax,\n )\n else:\n raise ValueError(f""Invalid dynamics type: {args.dyna_type}"")\n\n # --- Define autoregressive sampling loop ---\n def _autoreg_sample(genie, rng, video_batch_BSHWC, action_batch_E):\n input_video_BTHWC = video_batch_BSHWC[:, : args.start_frame]\n rng, _rng = jax.random.split(rng)\n batch = dict(videos=input_video_BTHWC, latent_actions=action_batch_E, rng=_rng)\n generated_vid_BSHWC = _sampling_fn(genie, batch)\n return generated_vid_BSHWC\n\n # --- Get video + latent actions ---\n array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n args.batch_size,\n args.image_height,\n args.image_width,\n args.image_channels,\n # We don't use workers in order to avoid grain shutdown issues (https://github.com/google/grain/issues/398)\n num_workers=0,\n prefetch_buffer_size=1,\n seed=args.seed,\n darkness_threshold=args.darkness_threshold,\n )\n dataloader = iter(dataloader)\n video_batch_BSHWC = next(dataloader)\n gt_video = jnp.asarray(video_batch_BSHWC, dtype=jnp.float32) / 255.0\n video_batch_BSHWC = gt_video.astype(args.dtype)\n # Get latent actions for all videos in the batch\n batch = dict(videos=video_batch_BSHWC)\n action_batch_E = genie.vq_encode(batch, training=False)\n\n # --- Sample + evaluate video ---\n recon_video_BSHWC = _autoreg_sample(genie, rng, video_batch_BSHWC, action_batch_E)\n recon_video_BSHWC = recon_video_BSHWC.astype(jnp.float32)\n gt = gt_video[:, : recon_video_BSHWC.shape[1]].clip(0, 1).reshape(-1, *gt_video.shape[2:])\n recon = recon_video_BSHWC.clip(0, 1).reshape(-1, *recon_video_BSHWC.shape[2:])\n ssim = jnp.asarray(\n pix.ssim(gt[:, args.start_frame:], recon[:, args.start_frame:])\n ).mean()\n print(f""SSIM: {ssim}"")\n\n # --- Construct video ---\n true_videos = (gt_video * 255).astype(np.uint8)\n pred_videos = (recon_video_BSHWC * 255).astype(np.uint8)\n video_comparison = np.zeros((2, *recon_video_BSHWC.shape), dtype=np.uint8)\n video_comparison[0] = true_videos[:, : args.seq_len]\n video_comparison[1] = pred_videos\n frames = einops.rearrange(video_comparison, ""n b t h w c -> t (b h) (n w) c"")\n\n # --- Save video ---\n imgs = [Image.fromarray(img) for img in frames]\n # Write actions on each frame, on each row (i.e., for each video in the batch, on the GT row)\n B, S, _, _, _ = video_batch_BSHWC.shape\n action_batch_BSm11 = jnp.reshape(action_batch_E, (B, S-1, 1))\n for t, img in enumerate(imgs[1:]):\n d = ImageDraw.Draw(img)\n for row in range(action_batch_BSm11.shape[0]):\n action = action_batch_BSm11[row, t, 0]\n y_offset = row * video_batch_BSHWC.shape[2] + 2\n d.text((2, y_offset), f""{action}"", fill=255)\n imgs[0].save(\n f""generation_{time.time()}.gif"",\n save_all=True,\n append_images=imgs[1:],\n duration=250,\n loop=0,\n )\n",python,tab +2630,14279643,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py",0,0,"from dataclasses import dataclass, field\nimport os\nfrom typing import cast\n\nimport einops\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\nimport flax.nnx as nnx\n\nfrom genie import Genie, restore_genie_components\nfrom utils.dataloader import get_dataloader\nfrom utils.lr_utils import get_lr_schedule\nfrom utils.parameter_utils import count_parameters_by_component\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n batch_size: int = 36\n init_lr: float = 0.0\n max_lr: float = 3e-5\n decay_end: float = 0.0\n wsd_decay_steps: int = (\n 10000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n )\n warmup_steps: int = 5000\n lr_schedule: str = ""wsd"" # supported options: wsd, cos\n darkness_threshold: float = 0.0\n # Tokenizer\n tokenizer_dim: int = 512\n tokenizer_ffn_dim: int = 2048\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 4\n tokenizer_num_heads: int = 8\n tokenizer_checkpoint: str = """"\n # LAM\n lam_dim: int = 512\n lam_ffn_dim: int = 2048\n latent_action_dim: int = 32\n num_latent_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 4\n lam_num_heads: int = 8\n lam_checkpoint: str = """"\n # Dynamics\n dyna_type: str = ""maskgit"" # supported options: maskgit, causal\n dyna_dim: int = 512\n dyna_ffn_dim: int = 2048\n dyna_num_blocks: int = 6\n dyna_num_heads: int = 8\n dropout: float = 0.0\n mask_limit: float = 0.5\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\n use_flash_attention: bool = True\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n name: str = ""train_dynamics""\n tags: list[str] = field(default_factory=lambda: [""dynamics""])\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 25000\n log_checkpoint_keep_period: int = 20000\n log_gradients: bool = False\n wandb_id: str = """"\n\n\nargs = tyro.cli(Args)\n\n\ndef dynamics_loss_fn(\n model: Genie, inputs: dict\n) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n """"""Compute masked dynamics loss""""""\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n model.train()\n outputs = model(inputs, training=True)\n mask = outputs[""mask""]\n outputs[""token_logits""] = outputs[""token_logits""].astype(jnp.float32)\n ce_loss = optax.softmax_cross_entropy_with_integer_labels(\n outputs[""token_logits""], outputs[""video_tokens""]\n )\n ce_loss = (mask * ce_loss).sum() / mask.sum()\n acc = outputs[""token_logits""].argmax(-1) == outputs[""video_tokens""]\n acc = (mask * acc).sum() / mask.sum()\n select_probs = jax.nn.softmax(outputs[""token_logits""])\n gt = gt.clip(0, 1).reshape(-1, *gt.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt, recon)).mean()\n _, index_counts_lam = jnp.unique_counts(\n jnp.ravel(outputs[""lam_indices""]), size=args.num_latent_actions, fill_value=0\n )\n _, index_counts_tokenizer = jnp.unique_counts(\n jnp.ravel(outputs[""video_tokens""]), size=args.num_patch_latents, fill_value=0\n )\n codebook_usage_lam = (index_counts_lam != 0).mean()\n codebook_usage_tokenizer = (index_counts_tokenizer != 0).mean()\n metrics = dict(\n cross_entropy_loss=ce_loss,\n masked_token_accuracy=acc,\n select_logit=outputs[""token_logits""].max(-1).mean(),\n select_p=select_probs.max(-1).mean(),\n entropy=jax.scipy.special.entr(select_probs).sum(-1).mean(),\n psnr=psnr,\n ssim=ssim,\n codebook_usage_lam=codebook_usage_lam,\n codebook_usage_tokenizer=codebook_usage_tokenizer,\n )\n return ce_loss, (outputs[""recon""], metrics)\n\n\n@nnx.jit\ndef train_step(\n model: Genie, optimizer: nnx.Optimizer, inputs: dict\n) -> tuple[jax.Array, jax.Array, dict]:\n """"""Update state and compute metrics""""""\n\n def loss_fn(model: Genie) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n return dynamics_loss_fn(model, inputs)\n\n (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(model)\n optimizer.update(grads)\n if args.log_gradients:\n metrics[""gradients_std/""] = jax.tree.map(\n lambda x: x.std(), grads[""params""][""dynamics""]\n )\n return 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.key(args.seed)\n\n # --- Initialize model ---\n rng, _rng = jax.random.split(rng)\n rngs = nnx.Rngs(_rng)\n genie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n tokenizer_ffn_dim=args.tokenizer_ffn_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n lam_ffn_dim=args.lam_ffn_dim,\n latent_action_dim=args.latent_action_dim,\n num_latent_actions=args.num_latent_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n lam_co_train=not args.lam_checkpoint,\n # Dynamics\n dyna_type=args.dyna_type,\n dyna_dim=args.dyna_dim,\n dyna_ffn_dim=args.dyna_ffn_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n dropout=args.dropout,\n mask_limit=args.mask_limit,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n decode=False,\n rngs=rngs,\n )\n\n _, params, _ = nnx.split(genie, nnx.Param, ...)\n param_counts = count_parameters_by_component(params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\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 if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n lr_schedule = get_lr_schedule(\n args.lr_schedule,\n args.init_lr,\n args.max_lr,\n args.decay_end,\n args.num_steps,\n args.warmup_steps,\n args.wsd_decay_steps,\n )\n tx = optax.adamw(\n learning_rate=lr_schedule,\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.dtype,\n )\n optimizer = nnx.Optimizer(genie, tx)\n del genie\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 videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n\n model_state = nnx.state(optimizer.model)\n model_sharded_state = jax.lax.with_sharding_constraint(\n model_state, replicated_sharding\n )\n nnx.update(optimizer.model, model_sharded_state)\n optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)\n optimizer_sharded_state = jax.lax.with_sharding_constraint(\n optimizer_state, replicated_sharding\n )\n nnx.update(optimizer, optimizer_sharded_state)\n\n # --- Initialize checkpoint manager ---\n step = 0\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n\n # --- Create DataLoaderIterator from dataloader ---\n image_shape = (args.image_height, args.image_width, args.image_channels)\n array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n darkness_threshold=args.darkness_threshold,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n\n # --- Restore checkpoint ---\n if args.restore_ckpt:\n abstract_optimizer = nnx.eval_shape(lambda: optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator), # type: ignore\n ),\n )\n restored_optimizer_state = restored[""model_state""]\n nnx.update(optimizer, restored_optimizer_state)\n grain_iterator = restored[""dataloader_state""]\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored dataloader and model state from step {step}"")\n else:\n # Restore from pre-trained tokenizer (and LAM)\n optimizer = restore_genie_components(optimizer, replicated_sharding, rng, args)\n # NOTE: We have to remove the (unused) tokenizer vq dropout due flax.nnx lazily initializing modules.\n # Specifically, the first dynamics model checkpoint will contain the vq dropout module,\n # but the first full restore will fail due to nnx not initializing the module when\n # dropout is set to 0.0.\n del optimizer.model.tokenizer.vq.drop\n\n # --- TRAIN LOOP ---\n dataloader = (\n jax.make_array_from_process_local_data(videos_sharding, elem)\n for elem in grain_iterator\n )\n print(f""Starting training from step {step}..."")\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n rng, _rng_mask = jax.random.split(rng, 2)\n inputs = dict(videos=videos, mask_rng=_rng_mask)\n loss, recon, metrics = train_step(optimizer.model, optimizer, inputs)\n metrics[""lr""] = lr_schedule(step)\n print(f""Step {step}, loss: {loss}"")\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 **metrics,\n }\n )\n if step % args.log_image_interval == 0:\n gt_seq = inputs[""videos""][0].astype(jnp.float32) / 255.0\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 if jax.process_index() == 0:\n log_images = dict(\n image=wandb.Image(np.asarray(gt_seq[args.seq_len - 1])),\n recon=wandb.Image(np.asarray(recon_seq[args.seq_len - 1])),\n true_vs_recon=wandb.Image(\n np.asarray(comparison_seq.astype(np.uint8))\n ),\n )\n wandb.log(log_images)\n # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n optimizer_state = nnx.state(optimizer)\n checkpoint_manager.save(\n step,\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n grain_iterator # type: ignore\n ),\n ),\n )\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n checkpoint_manager.close()\n",python,tab +2631,14283777,"TERMINAL",0,0,"",,terminal_focus +2632,14286008,"TERMINAL",0,0,"source /home/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/bin/activate",,terminal_command +2633,14286027,"TERMINAL",0,0,"]633;E;2025-08-14 14:35:39 source /home/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/bin/activate;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;C]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0]633;P;Cwd=/home/hk-project-p0023960/tum_cte0515/Projects/jafar",,terminal_output +2634,14287602,"TERMINAL",0,0,"queue",,terminal_command +2635,14287690,"TERMINAL",0,0,"]633;E;2025-08-14 14:35:40 queue;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Thu Aug 14 14:35:40 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3415713 accelerat train_dy tum_cte0 PD\t0:00\t 1 (Priority)3412401 accelerat train_to tum_cte0 R 1-20:35:39\t 8 hkn[0412,0416,0425,0433,0718,0732,0803,0807]3418832 accelerat train_dy tum_cte0 R 1-00:46:44\t 8 hkn[0407,0415,0420,0422-0424,0729,0735]3422008 accelerat train_dy tum_cte0 R 2:22:30\t 1 hkn07133421982 accelerat train_to tum_cte0 R 2:34:30\t 4 hkn[0724,0726,0728,0731]3421973 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+6792,19550608,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",1442,0,"",shellscript,selection_mouse +6793,19552223,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",1701,0,"",shellscript,selection_mouse +6794,19552400,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes.sbatch",1695,12,"SLURM_JOB_ID",shellscript,selection_mouse +6795,19564074,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes copy.sbatch",0,0,"",shellscript,tab +6796,19570138,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes_req.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=4\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/%x_%j.log\n#SBATCH --job-name=train_tokenizer_1e-4\n#SBATCH --reservation=llmtum\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=192 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-8-nodes-$slurm_job_id \\n --tags tokenizer big-run 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid",shellscript,tab +6797,19571205,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes.sbatch",0,0,"",shellscript,tab +6798,19572316,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes copy.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/%x_%j.log\n#SBATCH --job-name=train_tokenizer_1e-4\n#SBATCH --reservation=llmtum\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-8-nodes-$slurm_job_id \\n --tags tokenizer big-run 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid",shellscript,tab +6799,19577970,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes_req.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/%x_%j.log\n#SBATCH --job-name=train_tokenizer_1e-4\n#SBATCH --reservation=llmtum\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-8-nodes-$slurm_job_id \\n --tags tokenizer big-run 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid",shellscript,tab +6800,19579807,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes_req.sbatch",460,0,"",shellscript,selection_mouse +6801,19580706,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes_req.sbatch",443,29,"",shellscript,content +6802,19581605,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes_req.sbatch",455,0,"servation=llmtum\n#SBATCH --re",shellscript,content 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slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes_req.sbatch",,terminal_command +6983,19748298,"TERMINAL",0,0,"]633;E;2025-08-14 16:06:41 sbatch slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes_req.sbatch;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;Csbatch: error: Unable to open file slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes_req.sbatch\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_franz/maskgit/dynamics-cotraining]633;D;1",,terminal_output +6984,19754667,"TERMINAL",0,0,"dev",,terminal_command +6985,19756325,"TERMINAL",0,0,"sbatch slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes_req.sbatch",,terminal_command +6986,19757855,"TERMINAL",0,0,"bash",,terminal_focus +6987,19759125,"TERMINAL",0,0,"ls",,terminal_command +6988,19759176,"TERMINAL",0,0,"]633;E;2025-08-14 16:06:52 ls;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;C",,terminal_output +6989,19759418,"TERMINAL",0,0,"atari train_lam_action_space_scaling_50_3329804.log\r\nbig_run train_lam_action_space_scaling_50_3331286.log\r\nbig-runs train_lam_action_space_scaling_6_3318549.log\r\ncausal train_lam_action_space_scaling_6_3320178.log\r\ncoinrun train_lam_action_space_scaling_6_3321528.log\r\nmaskgit train_lam_action_space_scaling_6_3329790.log\r\nmaskgit-maskprob-fix train_lam_action_space_scaling_6_3329805.log\r\npreprocess train_lam_action_space_scaling_6_3331287.log\r\ntrain_dyn_causal_180M_3372931.log train_lam_action_space_scaling_8_3318550.log\r\ntrain_dyn_causal_180M_3372963.log train_lam_action_space_scaling_8_3329791.log\r\ntrain_dyn_causal_180M_3372969.log train_lam_action_space_scaling_8_3329806.log\r\ntrain_dyn_causal_180M_3373107.log train_lam_action_space_scaling_8_3331288.log\r\ntrain_dyn_causal_255M_3372932.log train_lam_minecraft_overfit_sample_3309655.log\r\ntrain_dyn_causal_255M_3372970.log train_lam_model_size_scaling_38M_3317098.log\r\ntrain_dyn_causal_255M_3373108.log train_lam_model_size_scaling_38M_3317115.log\r\ntrain_dyn_causal_356M_3372934.log train_lam_model_size_scaling_38M_3317231.log\r\ntrain_dyn_causal_356M_3372971.log train_tokenizer_batch_size_scaling_16_node_3321526.log\r\ntrain_dyn_causal_356M_3373109.log train_tokenizer_batch_size_scaling_1_node_3318551.log\r\ntrain_dyn_causal_500M_3372936.log train_tokenizer_batch_size_scaling_2_node_3318552.log\r\ntrain_dyn_causal_500M_3372972.log train_tokenizer_batch_size_scaling_2_node_3330806.log\r\ntrain_dyn_causal_500M_3373110.log train_tokenizer_batch_size_scaling_2_node_3330848.log\r\ntrain_dyn_new_arch-bugfixed-spatial-shift_3359343.log train_tokenizer_batch_size_scaling_2_node_3331282.log\r\ntrain_dyn_new_arch-bugfixed-temporal-shift_3359349.log train_tokenizer_batch_size_scaling_4_node_3318553.log\r\ntrain_dyn_yolorun_3333026.log train_tokenizer_batch_size_scaling_4_node_3320175.log\r\ntrain_dyn_yolorun_3333448.log train_tokenizer_batch_size_scaling_4_node_3321524.log\r\ntrain_dyn_yolorun_3335345.log train_tokenizer_batch_size_scaling_8_node_3320176.log\r\ntrain_dyn_yolorun_3335362.log train_tokenizer_batch_size_scaling_8_node_3321525.log\r\ntrain_dyn_yolorun_3348592.log train_tokenizer_minecraft_overfit_sample_3309656.log\r\ntrain_dyn_yolorun_new_arch_3351743.log train_tokenizer_model_size_scaling_127M_3317233.log\r\ntrain_dyn_yolorun_new_arch_3352103.log train_tokenizer_model_size_scaling_127M_3318554.log\r\ntrain_dyn_yolorun_new_arch_3352115.log train_tokenizer_model_size_scaling_140M_3313562.log\r\ntrain_dyn_yolorun_new_arch_3358457.log train_tokenizer_model_size_scaling_140M_3316019.log\r\ntrain_lam_action_space_scaling_10_3320179.log train_tokenizer_model_size_scaling_200M_3313563.log\r\ntrain_lam_action_space_scaling_10_3321529.log train_tokenizer_model_size_scaling_200M_3316020.log\r\ntrain_lam_action_space_scaling_10_3329786.log train_tokenizer_model_size_scaling_227M_3317234.log\r\ntrain_lam_action_space_scaling_10_3329801.log train_tokenizer_model_size_scaling_227M_3318555.log\r\ntrain_lam_action_space_scaling_10_3331283.log train_tokenizer_model_size_scaling_227M_3320173.log\r\ntrain_lam_action_space_scaling_12_3318546.log train_tokenizer_model_size_scaling_227M_3321523.log\r\ntrain_lam_action_space_scaling_12_3320177.log train_tokenizer_model_size_scaling_37M_3313565.log\r\ntrain_lam_action_space_scaling_12_3321527.log train_tokenizer_model_size_scaling_37M_3316022.log\r\ntrain_lam_action_space_scaling_12_3329787.log train_tokenizer_model_size_scaling_37M_3317232.log\r\ntrain_lam_action_space_scaling_12_3329802.log train_tokenizer_model_size_scaling_37M_3317239.log\r\ntrain_lam_action_space_scaling_12_3331284.log train_tokenizer_model_size_scaling_37M_3318556.log\r\ntrain_lam_action_space_scaling_20_3318547.log train_tokenizer_model_size_scaling_74M_3318557.log\r\ntrain_lam_action_space_scaling_20_3329788.log train_tokenizer_model_size_scaling_74M_3320174.log\r\ntrain_lam_action_space_scaling_20_3329803.log train_tokenizer_model_size_scaling_74M_3321522.log\r\ntrain_lam_action_space_scaling_20_3331285.log train_tokenizer_model_size_scaling_80M_3313564.log\r\ntrain_lam_action_space_scaling_50_3320180.log train_tokenizer_model_size_scaling_80M_3316026.log\r\ntrain_lam_action_space_scaling_50_3329789.log yoloruns\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output +6990,19767630,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes.sbatch",0,0,"",shellscript,tab +6991,19771984,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes_req.sbatch",0,0,"",shellscript,tab +6992,19772463,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes_req.sbatch",225,0,"",shellscript,selection_mouse +6993,19775653,"TERMINAL",0,0,"queue",,terminal_command +6994,19775697,"TERMINAL",0,0,"]633;E;2025-08-14 16:07:08 queue;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;C",,terminal_output +6995,19775773,"TERMINAL",0,0,"[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Thu Aug 14 16:07:08 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3421973 accelerat train_to tum_cte0 CG 4:05:29\t 2 hkn[0722-0723]3415713 accelerat train_dy tum_cte0 PD\t0:00\t 1 (Priority)3412401 accelerat train_to tum_cte0 R 1-22:07:07\t 8 hkn[0412,0416,0425,0433,0718,0732,0803,0807]3418832 accelerat train_dy tum_cte0 R 1-02:18:12\t 8 hkn[0407,0415,0420,0422-0424,0729,0735]3423052 accelerat train_to tum_cte0 R\t0:18\t 2 hkn[0714-0715]3422008 accelerat train_dy tum_cte0 R 3:53:58\t 1 hkn07133421982 accelerat train_to tum_cte0 R 4:05:58\t 4 hkn[0724,0726,0728,0731]3421964 accelerat train_to tum_cte0 R 4:06:22\t 1 hkn0720",,terminal_output +6996,19776778,"TERMINAL",0,0,"9839993",,terminal_output +6997,19777817,"TERMINAL",0,0,"10105214:016:015",,terminal_output +6998,19778927,"TERMINAL",0,0,"2162226",,terminal_output +6999,19779935,"TERMINAL",0,0,"3273337",,terminal_output +7000,19780974,"TERMINAL",0,0,"4384448",,terminal_output +7001,19782204,"TERMINAL",0,0,"5495559",,terminal_output +7002,19783139,"TERMINAL",0,0,"\r652066630",,terminal_output +7003,19783214,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output +7004,19785277,"TERMINAL",0,0,"scancel 3421982",,terminal_command +7005,19785305,"TERMINAL",0,0,"]633;E;2025-08-14 16:07:18 scancel 3421982;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;C]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output +7006,19794913,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes.sbatch",0,0,"",shellscript,tab +7007,19802261,"TERMINAL",0,0,"cd big-runs/",,terminal_command +7008,19803309,"TERMINAL",0,0,"ls",,terminal_command +7009,19806236,"TERMINAL",0,0,"cd ..",,terminal_command +7010,19806272,"TERMINAL",0,0,"]633;E;2025-08-14 16:07:39 cd ..;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;C]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output +7011,19807852,"TERMINAL",0,0,"cd big_run/",,terminal_command +7012,19808209,"TERMINAL",0,0,"ls",,terminal_command +7013,19808275,"TERMINAL",0,0,"]633;E;2025-08-14 16:07:41 ls;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;Ctokenizer tokenizer-lr-scaling\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run]633;D;0",,terminal_output +7014,19809914,"TERMINAL",0,0,"cd tokenizer",,terminal_command +7015,19809954,"TERMINAL",0,0,"]633;E;2025-08-14 16:07:43 cd tokenizer;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;C]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer]633;D;0",,terminal_output +7016,19810230,"TERMINAL",0,0,"ls",,terminal_command +7017,19810317,"TERMINAL",0,0,"]633;E;2025-08-14 16:07:43 ls;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;Ctrain_tokenizer_1e-4_3388151.log train_tokenizer_1e-4_3404607.log train_tokenizer_1e-4_3421973.log train_tokenizer_1e-4_3423052.log\r\ntrain_tokenizer_1e-4_3388153.log train_tokenizer_1e-4_3412401.log train_tokenizer_1e-4_3421982.log\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer]633;D;0",,terminal_output +7018,19812786,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes.sbatch",0,0,"",shellscript,tab +7019,19816234,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes_req.sbatch",0,0,"",shellscript,tab +7020,19818645,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes.sbatch",0,0,"",shellscript,tab +7021,19822428,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes_req.sbatch",0,0,"",shellscript,tab +7022,19823035,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_4nodes_req.sbatch",0,0,"",shellscript,tab +7023,19827086,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4_2nodes_req.sbatch",0,0,"",shellscript,tab +7024,19838838,"TERMINAL",0,0,"ls",,terminal_command +7025,19838941,"TERMINAL",0,0,"]633;E;2025-08-14 16:08:12 ls;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;Ctrain_tokenizer_1e-4_3388151.log train_tokenizer_1e-4_3404607.log train_tokenizer_1e-4_3421973.log train_tokenizer_1e-4_3423052.log\r\ntrain_tokenizer_1e-4_3388153.log train_tokenizer_1e-4_3412401.log train_tokenizer_1e-4_3421982.log\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer]633;D;0",,terminal_output +7026,19841148,"TERMINAL",0,0,"queue",,terminal_command +7027,19841215,"TERMINAL",0,0,"]633;E;2025-08-14 16:08:14 queue;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Thu Aug 14 16:08:14 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3415713 accelerat train_dy tum_cte0 PD\t0:00\t 1 (Priority)3412401 accelerat train_to tum_cte0 R 1-22:08:13\t 8 hkn[0412,0416,0425,0433,0718,0732,0803,0807]3418832 accelerat train_dy tum_cte0 R 1-02:19:18\t 8 hkn[0407,0415,0420,0422-0424,0729,0735]3423052 accelerat train_to tum_cte0 R\t1:24\t 2 hkn[0714-0715]3422008 accelerat train_dy tum_cte0 R 3:55:04\t 1 hkn07133421964 accelerat train_to tum_cte0 R 4:07:28\t 1 hkn0720",,terminal_output +7028,19842255,"TERMINAL",0,0,"549559",,terminal_output +7029,19843316,"TERMINAL",0,0,"65206630",,terminal_output +7030,19844100,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer]633;D;0",,terminal_output +7031,19849725,"TERMINAL",0,0,"tail -f train_tokenizer_1e-4_3423052.log",,terminal_command +7032,19849759,"TERMINAL",0,0,"]633;E;2025-08-14 16:08:22 tail -f train_tokenizer_1e-4_3423052.log ;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;CWARNING:absl:Missing metrics for step 14000\r\nWARNING:absl:Missing metrics for step 14000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/014000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 14000\r\nWARNING:absl:Missing metrics for step 14000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/014000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/014000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/014000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 14000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/014000/metrics/metrics not found.\r\n",,terminal_output +7033,19855071,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/train_tokenizer_1e-4_3423052.log",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/%x_%j.log\n#SBATCH --job-name=train_tokenizer_1e-4\n#SBATCH --reservation=llmtum\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=3421973\n\n# CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/$job_name/$slurm_job_id\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973\n\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --save_ckpt \\n --restore_ckpt \\n --wandb_id $slurm_job_id \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-2-nodes-$slurm_job_id \\n --tags tokenizer big-run 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pidSLURM_JOB_USER=tum_cte0515\nSLURM_TASKS_PER_NODE=4(x2)\nSLURM_JOB_UID=999226\nSLURM_TASK_PID=3589358\nSLURM_JOB_GPUS=0,1,2,3\nSLURM_LOCALID=0\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar\nSLURMD_NODENAME=hkn0714\nSLURM_JOB_START_TIME=1755180410\nSLURM_CLUSTER_NAME=hk\nSLURM_JOB_END_TIME=1755353210\nSLURM_CPUS_ON_NODE=24\nSLURM_JOB_CPUS_PER_NODE=24(x2)\nSLURM_GPUS_ON_NODE=4\nSLURM_GTIDS=0\nSLURM_JOB_PARTITION=accelerated\nSLURM_TRES_PER_TASK=cpu=5\nSLURM_OOM_KILL_STEP=0\nSLURM_JOB_NUM_NODES=2\nSLURM_JOBID=3423052\nSLURM_JOB_QOS=normal\nSLURM_PROCID=0\nSLURM_CPUS_PER_TASK=5\nSLURM_JOB_RESERVATION=llmtum\nSLURM_NTASKS=8\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e8.hkn0714\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\nSLURM_SCRIPT_CONTEXT=prolog_task\nSLURM_NODELIST=hkn[0714-0715]\nSLURM_JOB_ACCOUNT=hk-project-p0023960\nSLURM_PRIO_PROCESS=0\nSLURM_NPROCS=8\nSLURM_NNODES=2\nSLURM_SUBMIT_HOST=hkn1993.localdomain\nSLURM_JOB_ID=3423052\nSLURM_NODEID=0\nSLURM_CONF=/etc/slurm/slurm.conf\nSLURM_JOB_NAME=train_tokenizer_1e-4\nSLURM_NTASKS_PER_NODE=4\nSLURM_JOB_GID=502226\nSLURM_JOB_NODELIST=hkn[0714-0715]\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nwandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\nwandb: Tracking run with wandb version 0.19.11\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/wandb/run-20250814_160751-3421973\nwandb: Run `wandb offline` to turn off syncing.\nwandb: Resuming run tokenizer-2-nodes-3421973\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/3421973\nWARNING:absl:Missing metrics for step 13000\nWARNING:absl:Missing metrics for step 13000\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/013000/metrics/metrics not found.\nWARNING:absl:Missing metrics for step 13000\nWARNING:absl:Missing metrics for step 13000\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/013000/metrics/metrics not found.\nWARNING:absl:Missing metrics for step 13000\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/013000/metrics/metrics not found.\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/013000/metrics/metrics not found.\nWARNING:absl:Missing metrics for step 13000\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/013000/metrics/metrics not found.\nWARNING:absl:Missing metrics for step 13000\nWARNING:absl:Missing metrics for step 13000\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/013000/metrics/metrics not found.\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/013000/metrics/metrics not found.\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/013000/metrics/metrics not found.\nWARNING:absl:Missing metrics for step 12000\nWARNING:absl:Missing metrics for step 12000\nWARNING:absl:Missing metrics for step 12000\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/012000/metrics/metrics not found.\nWARNING:absl:Missing metrics for step 12000\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/012000/metrics/metrics not found.\nWARNING:absl:Missing metrics for step 12000\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/012000/metrics/metrics not found.\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/012000/metrics/metrics not found.\nWARNING:absl:Missing metrics for step 12000\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/012000/metrics/metrics not found.\nWARNING:absl:Missing metrics for step 12000\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/012000/metrics/metrics not found.\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/012000/metrics/metrics not found.\nWARNING:absl:Missing metrics for step 14000\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/014000/metrics/metrics not found.\nWARNING:absl:Missing metrics for step 12000\nWARNING:absl:Missing metrics for step 14000\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/014000/metrics/metrics not found.\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/012000/metrics/metrics not found.\nWARNING:absl:Missing metrics for step 14000\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/014000/metrics/metrics not found.\nWARNING:absl:Missing metrics for step 14000\nWARNING:absl:Missing metrics for step 14000\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/014000/metrics/metrics not found.\nWARNING:absl:Missing metrics for step 14000\nWARNING:absl:Missing metrics for step 14000\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/014000/metrics/metrics not found.\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/014000/metrics/metrics not found.\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/014000/metrics/metrics not found.\nWARNING:absl:Missing metrics for step 14000\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3421973/014000/metrics/metrics not found.\n",log,tab +7034,19856638,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/train_tokenizer_1e-4_3423052.log",834,0,"",log,selection_mouse +7035,19856644,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer/train_tokenizer_1e-4_3423052.log",833,0,"",log,selection_command +7036,19897819,"TERMINAL",0,0,"WARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +7037,19898727,"TERMINAL",0,0,"WARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\n",,terminal_output +7038,19899744,"TERMINAL",0,0,"WARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +7039,19900788,"TERMINAL",0,0,"WARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +7040,19904762,"TERMINAL",0,0,"WARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +7041,19906778,"TERMINAL",0,0,"Running on 8 devices.\r\nCounting all components: ['decoder', 'encoder', 'vq']\r\nParameter counts:\r\n{'decoder': 16858736, 'encoder': 16858752, 'vq': 32768, 'total': 33750256}\r\nRestored dataloader and model state from step 14000\r\nStarting training from step 14000...\r\nRunning on 8 devices.\r\nCounting all components: ['decoder', 'encoder', 'vq']\r\nParameter counts:\r\n{'decoder': 16858736, 'encoder': 16858752, 'vq': 32768, 'total': 33750256}\r\nRestored dataloader and model state from step 14000\r\nStarting training from step 14000...\r\nRunning on 8 devices.\r\nCounting all components: ['decoder', 'encoder', 'vq']\r\nParameter counts:\r\n{'decoder': 16858736, 'encoder': 16858752, 'vq': 32768, 'total': 33750256}\r\nRestored dataloader and model state from step 14000\r\nStarting training from step 14000...\r\nRunning on 8 devices.\r\nCounting all components: ['decoder', 'encoder', 'vq']\r\nParameter counts:\r\n{'decoder': 16858736, 'encoder': 16858752, 'vq': 32768, 'total': 33750256}\r\nRestored dataloader and model state from step 14000\r\nStarting training from step 14000...\r\nRunning on 8 devices.\r\nCounting all components: ['decoder', 'encoder', 'vq']\r\nParameter counts:\r\n{'decoder': 16858736, 'encoder': 16858752, 'vq': 32768, 'total': 33750256}\r\nRestored dataloader and model state from step 14000\r\nStarting training from step 14000...\r\nRunning on 8 devices.\r\nCounting all components: ['decoder', 'encoder', 'vq']\r\nParameter counts:\r\n{'decoder': 16858736, 'encoder': 16858752, 'vq': 32768, 'total': 33750256}\r\nRestored dataloader and model state from step 14000\r\nStarting training from step 14000...\r\nRunning on 8 devices.\r\nCounting all components: ['decoder', 'encoder', 'vq']\r\nParameter counts:\r\n{'decoder': 16858736, 'encoder': 16858752, 'vq': 32768, 'total': 33750256}\r\nRestored dataloader and model state from step 14000\r\nStarting training from step 14000...\r\nRunning on 8 devices.\r\nCounting all components: ['decoder', 'encoder', 'vq']\r\nParameter counts:\r\n{'decoder': 16858736, 'encoder': 16858752, 'vq': 32768, 'total': 33750256}\r\nRestored dataloader and model state from step 14000\r\nStarting training from step 14000...\r\n",,terminal_output +7042,19930891,"TERMINAL",0,0,"2025-08-14 16:09:43.161302: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-08-14 16:09:43.161454: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +7043,19932805,"TERMINAL",0,0,"2025-08-14 16:09:45.352547: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +7044,19933860,"TERMINAL",0,0,"2025-08-14 16:09:46.905137: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +7045,20195951,"TERMINAL",0,0,"Step 14000, loss: 0.005812517367303371\r\nStep 14001, loss: 0.005395861808210611\r\nStep 14002, loss: 0.006233721040189266\r\nStep 14003, loss: 0.006271838676184416\r\nStep 14004, loss: 0.0058039287105202675\r\nStep 14005, loss: 0.005527175962924957\r\nStep 14006, loss: 0.0051373583264648914\r\nStep 14007, loss: 0.005339019000530243\r\nStep 14008, loss: 0.005341792479157448\r\nStep 14009, loss: 0.006364726927131414\r\nStep 14010, loss: 0.005711421370506287\r\nStep 14011, loss: 0.005796034820377827\r\nStep 14012, loss: 0.005808668676763773\r\nStep 14013, loss: 0.005712124519050121\r\nStep 14014, loss: 0.005829066503793001\r\nStep 14015, loss: 0.005777937360107899\r\nStep 14016, loss: 0.005655390676110983\r\nStep 14017, loss: 0.005510229617357254\r\nStep 14018, loss: 0.005274210125207901\r\nStep 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+7690,21261384,"TERMINAL",0,0,"43848",,terminal_output +7691,21261940,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer]633;D;0",,terminal_output +7692,21277307,"train_dynamics.py",0,0,"from dataclasses import dataclass, field\nimport os\nfrom typing import cast\n\nimport einops\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\nimport flax.nnx as nnx\n\nfrom genie import Genie, restore_genie_components\nfrom utils.dataloader import get_dataloader\nfrom utils.lr_utils import get_lr_schedule\nfrom utils.parameter_utils import count_parameters_by_component\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n batch_size: int = 36\n init_lr: float = 0.0\n max_lr: float = 3e-5\n decay_end: float = 0.0\n wsd_decay_steps: int = (\n 10000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n )\n warmup_steps: int = 5000\n lr_schedule: str = ""wsd"" # supported options: wsd, cos\n darkness_threshold: float = 0.0\n # Tokenizer\n tokenizer_dim: int = 512\n tokenizer_ffn_dim: int = 2048\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 4\n tokenizer_num_heads: int = 8\n tokenizer_checkpoint: str = """"\n # LAM\n lam_dim: int = 512\n lam_ffn_dim: int = 2048\n latent_action_dim: int = 32\n num_latent_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 4\n lam_num_heads: int = 8\n lam_checkpoint: str = """"\n # Dynamics\n dyna_type: str = ""maskgit"" # supported options: maskgit, causal\n dyna_dim: int = 512\n dyna_ffn_dim: int = 2048\n dyna_num_blocks: int = 6\n dyna_num_heads: int = 8\n dropout: float = 0.0\n mask_limit: float = 0.5\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\n use_flash_attention: bool = True\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n name: str = ""train_dynamics""\n tags: list[str] = field(default_factory=lambda: [""dynamics""])\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 25000\n log_checkpoint_keep_period: int = 20000\n log_gradients: bool = False\n wandb_id: str = """"\n\n\nargs = tyro.cli(Args)\n\n\ndef dynamics_loss_fn(\n model: Genie, inputs: dict\n) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n """"""Compute masked dynamics loss""""""\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n model.train()\n outputs = model(inputs, training=True)\n mask = outputs[""mask""]\n outputs[""token_logits""] = outputs[""token_logits""].astype(jnp.float32)\n ce_loss = optax.softmax_cross_entropy_with_integer_labels(\n outputs[""token_logits""], outputs[""video_tokens""]\n )\n ce_loss = (mask * ce_loss).sum() / mask.sum()\n acc = outputs[""token_logits""].argmax(-1) == outputs[""video_tokens""]\n acc = (mask * acc).sum() / mask.sum()\n select_probs = jax.nn.softmax(outputs[""token_logits""])\n gt = gt.clip(0, 1).reshape(-1, *gt.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt, recon)).mean()\n _, index_counts_lam = jnp.unique_counts(\n jnp.ravel(outputs[""lam_indices""]), size=args.num_latent_actions, fill_value=0\n )\n _, index_counts_tokenizer = jnp.unique_counts(\n jnp.ravel(outputs[""video_tokens""]), size=args.num_patch_latents, fill_value=0\n )\n codebook_usage_lam = (index_counts_lam != 0).mean()\n codebook_usage_tokenizer = (index_counts_tokenizer != 0).mean()\n metrics = dict(\n cross_entropy_loss=ce_loss,\n masked_token_accuracy=acc,\n select_logit=outputs[""token_logits""].max(-1).mean(),\n select_p=select_probs.max(-1).mean(),\n entropy=jax.scipy.special.entr(select_probs).sum(-1).mean(),\n psnr=psnr,\n ssim=ssim,\n codebook_usage_lam=codebook_usage_lam,\n codebook_usage_tokenizer=codebook_usage_tokenizer,\n )\n return ce_loss, (outputs[""recon""], metrics)\n\n\n@nnx.jit\ndef train_step(\n model: Genie, optimizer: nnx.Optimizer, inputs: dict\n) -> tuple[jax.Array, jax.Array, dict]:\n """"""Update state and compute metrics""""""\n\n def loss_fn(model: Genie) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n return dynamics_loss_fn(model, inputs)\n\n (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(model)\n optimizer.update(grads)\n if args.log_gradients:\n metrics[""gradients_std/""] = jax.tree.map(\n lambda x: x.std(), grads[""params""][""dynamics""]\n )\n return 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.key(args.seed)\n\n # --- Initialize model ---\n rng, _rng = jax.random.split(rng)\n rngs = nnx.Rngs(_rng)\n genie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n tokenizer_ffn_dim=args.tokenizer_ffn_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n lam_ffn_dim=args.lam_ffn_dim,\n latent_action_dim=args.latent_action_dim,\n num_latent_actions=args.num_latent_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n lam_co_train=not args.lam_checkpoint,\n # Dynamics\n dyna_type=args.dyna_type,\n dyna_dim=args.dyna_dim,\n dyna_ffn_dim=args.dyna_ffn_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n dropout=args.dropout,\n mask_limit=args.mask_limit,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n decode=False,\n rngs=rngs,\n )\n\n _, params, _ = nnx.split(genie, nnx.Param, ...)\n param_counts = count_parameters_by_component(params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\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 if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n lr_schedule = get_lr_schedule(\n args.lr_schedule,\n args.init_lr,\n args.max_lr,\n args.decay_end,\n args.num_steps,\n args.warmup_steps,\n args.wsd_decay_steps,\n )\n tx = optax.adamw(\n learning_rate=lr_schedule,\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.dtype,\n )\n optimizer = nnx.Optimizer(genie, tx)\n del genie\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 videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n\n model_state = nnx.state(optimizer.model)\n model_sharded_state = jax.lax.with_sharding_constraint(\n model_state, replicated_sharding\n )\n nnx.update(optimizer.model, model_sharded_state)\n optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)\n optimizer_sharded_state = jax.lax.with_sharding_constraint(\n optimizer_state, replicated_sharding\n )\n nnx.update(optimizer, optimizer_sharded_state)\n\n # --- Initialize checkpoint manager ---\n step = 0\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n\n # --- Create DataLoaderIterator from dataloader ---\n image_shape = (args.image_height, args.image_width, args.image_channels)\n array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n darkness_threshold=args.darkness_threshold,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n\n # --- Restore checkpoint ---\n if args.restore_ckpt:\n abstract_optimizer = nnx.eval_shape(lambda: optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator), # type: ignore\n ),\n )\n restored_optimizer_state = restored[""model_state""]\n nnx.update(optimizer, restored_optimizer_state)\n grain_iterator = restored[""dataloader_state""]\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored dataloader and model state from step {step}"")\n else:\n # Restore from pre-trained tokenizer (and LAM)\n optimizer = restore_genie_components(optimizer, replicated_sharding, rng, args)\n # NOTE: We have to remove the (unused) tokenizer vq dropout due flax.nnx lazily initializing modules.\n # Specifically, the first dynamics model checkpoint will contain the vq dropout module,\n # but the first full restore will fail due to nnx not initializing the module when\n # dropout is set to 0.0.\n del optimizer.model.tokenizer.vq.drop\n\n # --- TRAIN LOOP ---\n dataloader = (\n jax.make_array_from_process_local_data(videos_sharding, elem)\n for elem in grain_iterator\n )\n print(f""Starting training from step {step}..."")\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n rng, _rng_mask = jax.random.split(rng, 2)\n inputs = dict(videos=videos, mask_rng=_rng_mask)\n loss, recon, metrics = train_step(optimizer.model, optimizer, inputs)\n metrics[""lr""] = lr_schedule(step)\n print(f""Step {step}, loss: {loss}"")\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 **metrics,\n }\n )\n if step % args.log_image_interval == 0:\n gt_seq = inputs[""videos""][0].astype(jnp.float32) / 255.0\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 if jax.process_index() == 0:\n log_images = dict(\n image=wandb.Image(np.asarray(gt_seq[args.seq_len - 1])),\n recon=wandb.Image(np.asarray(recon_seq[args.seq_len - 1])),\n true_vs_recon=wandb.Image(\n np.asarray(comparison_seq.astype(np.uint8))\n ),\n )\n wandb.log(log_images)\n # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n optimizer_state = nnx.state(optimizer)\n checkpoint_manager.save(\n step,\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n grain_iterator # type: ignore\n ),\n ),\n )\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n checkpoint_manager.close()\n",python,tab +7693,21298766,"train_dynamics.py",3514,0,"",python,selection_mouse +7694,21298931,"train_dynamics.py",3505,16,"index_counts_lam",python,selection_mouse +7695,21307925,"train_dynamics.py",3793,0,"",python,selection_mouse +7696,21308104,"train_dynamics.py",3782,18,"codebook_usage_lam",python,selection_mouse +7697,21316333,"train_dynamics.py",3876,0,"",python,selection_mouse +7698,21316494,"train_dynamics.py",3866,22,"index_counts_tokenizer",python,selection_mouse +7699,21317552,"train_dynamics.py",3853,0,"",python,selection_mouse +7700,21317725,"train_dynamics.py",3838,24,"codebook_usage_tokenizer",python,selection_mouse +7701,21327908,"train_dynamics.py",3872,0,"",python,selection_mouse +7702,21328075,"train_dynamics.py",3866,22,"index_counts_tokenizer",python,selection_mouse +7703,21330660,"train_dynamics.py",3718,0,"",python,selection_mouse +7704,21330832,"train_dynamics.py",3713,12,"video_tokens",python,selection_mouse +7705,21353690,"genie.py",0,0,"from typing import Dict\n\nimport einops\nimport jax\nimport jax.numpy as jnp\nimport flax.nnx as nnx\nimport orbax.checkpoint as ocp\n\nfrom models.dynamics import DynamicsMaskGIT, DynamicsCausal\nfrom models.lam import LatentActionModel\nfrom models.tokenizer import TokenizerVQVAE\n\n\nclass Genie(nnx.Module):\n """"""Genie model""""""\n\n def __init__(\n self,\n in_dim: int,\n tokenizer_dim: int,\n tokenizer_ffn_dim: int,\n latent_patch_dim: int,\n num_patch_latents: int,\n patch_size: int,\n tokenizer_num_blocks: int,\n tokenizer_num_heads: int,\n lam_dim: int,\n lam_ffn_dim: int,\n latent_action_dim: int,\n num_latent_actions: int,\n lam_patch_size: int,\n lam_num_blocks: int,\n lam_num_heads: int,\n lam_co_train: bool,\n dyna_type: str,\n dyna_dim: int,\n dyna_ffn_dim: int,\n dyna_num_blocks: int,\n dyna_num_heads: int,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n decode: bool,\n rngs: nnx.Rngs,\n dropout: float = 0.0,\n mask_limit: float = 0.0,\n ):\n # --- Tokenizer ---\n self.in_dim = in_dim\n self.tokenizer_dim = tokenizer_dim\n self.tokenizer_ffn_dim = tokenizer_ffn_dim\n self.latent_patch_dim = latent_patch_dim\n self.num_patch_latents = num_patch_latents\n self.patch_size = patch_size\n self.tokenizer_num_blocks = tokenizer_num_blocks\n self.tokenizer_num_heads = tokenizer_num_heads\n # --- LAM ---\n self.lam_dim = lam_dim\n self.lam_ffn_dim = lam_ffn_dim\n self.latent_action_dim = latent_action_dim\n self.num_latent_actions = num_latent_actions\n self.lam_patch_size = lam_patch_size\n self.lam_num_blocks = lam_num_blocks\n self.lam_num_heads = lam_num_heads\n self.lam_co_train = lam_co_train\n # --- Dynamics ---\n self.dyna_type = dyna_type\n self.dyna_dim = dyna_dim\n self.dyna_ffn_dim = dyna_ffn_dim\n self.dyna_num_blocks = dyna_num_blocks\n self.dyna_num_heads = dyna_num_heads\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.dropout = dropout\n self.mask_limit = mask_limit\n\n self.tokenizer = TokenizerVQVAE(\n in_dim=self.in_dim,\n model_dim=self.tokenizer_dim,\n ffn_dim=self.tokenizer_ffn_dim,\n latent_dim=self.latent_patch_dim,\n num_latents=self.num_patch_latents,\n patch_size=self.patch_size,\n num_blocks=self.tokenizer_num_blocks,\n num_heads=self.tokenizer_num_heads,\n dropout=0.0,\n codebook_dropout=0.0,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n self.lam = LatentActionModel(\n in_dim=self.in_dim,\n model_dim=self.lam_dim,\n ffn_dim=self.lam_ffn_dim,\n latent_dim=self.latent_patch_dim,\n num_latents=self.num_latent_actions,\n patch_size=self.lam_patch_size,\n num_blocks=self.lam_num_blocks,\n num_heads=self.lam_num_heads,\n dropout=0.0,\n codebook_dropout=0.0,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n if self.dyna_type == ""maskgit"":\n self.dynamics = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n mask_limit=self.mask_limit,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n elif self.dyna_type == ""causal"":\n self.dynamics = DynamicsCausal(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=decode,\n rngs=rngs,\n )\n else:\n raise ValueError(f""Invalid dynamics type: {self.dyna_type}"")\n\n def __call__(\n self, batch: Dict[str, jax.Array], training: bool = True\n ) -> Dict[str, jax.Array]:\n videos_BTHWC = batch[""videos""]\n tokenizer_outputs = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_indices_BTN = tokenizer_outputs[""indices""]\n lam_outputs = self.lam.vq_encode(videos_BTHWC, training=False)\n z_q_BTm11L = lam_outputs[""z_q""]\n action_indices_E = lam_outputs[""indices""]\n latent_actions_BTm11L = jax.lax.cond(\n self.lam_co_train,\n lambda: z_q_BTm11L,\n lambda: jax.lax.stop_gradient(z_q_BTm11L),\n )\n outputs = dict(\n video_tokens=jax.lax.stop_gradient(token_indices_BTN),\n latent_actions=latent_actions_BTm11L,\n )\n outputs[""mask_rng""] = batch[""mask_rng""]\n dyna_logits_BTNV, dyna_mask = self.dynamics(outputs, training)\n outputs[""token_logits""] = dyna_logits_BTNV\n if dyna_mask is not None:\n outputs[""mask""] = dyna_mask\n mle_indices_BTN = jnp.argmax(outputs[""token_logits""], axis=-1)\n H, W = batch[""videos""].shape[2:4]\n outputs[""recon""] = self.tokenizer.decode(mle_indices_BTN, (H, W))\n outputs[""lam_indices""] = action_indices_E\n return outputs\n\n def sample(\n self,\n batch: Dict[str, jax.Array],\n seq_len: int,\n steps: int = 25,\n temperature: float = 1,\n sample_argmax: bool = False,\n ) -> jax.Array:\n """"""\n Autoregressively samples up to `seq_len` future frames, following Figure 8 of the paper.\n\n - Input frames are tokenized once.\n - Future frames are generated autoregressively in token space.\n - All frames are detokenized in a single pass.\n\n Note:\n - For interactive or step-wise sampling, detokenization should occur after each action.\n - To maintain consistent tensor shapes across timesteps, all current and future frames are decoded at every step.\n - Temporal causal structure is preserved by\n a) reapplying the mask before each decoding step.\n b) a temporal causal mask is applied within each ST-transformer block.\n\n Dimension keys:\n B: batch size\n T: number of input (conditioning) frames\n N: number of patches per frame\n M: model dimension\n S: sequence length\n H: height\n W: width\n E: B * (S - 1)\n P: S * N\n """"""\n # --- Encode videos and actions ---\n videos_BTHWC = batch[""videos""]\n latent_actions_E = batch[""latent_actions""]\n tokenizer_out = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_idxs_BTN = tokenizer_out[""indices""]\n B, T, N = token_idxs_BTN.shape\n pad_shape = (B, seq_len - T, N)\n pad = jnp.zeros(pad_shape, dtype=token_idxs_BTN.dtype)\n token_idxs_BSN = jnp.concatenate([token_idxs_BTN, pad], axis=1)\n action_tokens_EL = self.lam.vq.get_codes(latent_actions_E)\n\n def maskgit_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array, jax.Array], step: jax.Array\n ) -> tuple[tuple[jax.Array, jax.Array, jax.Array, jax.Array], None]:\n rng, token_idxs_BSN, mask_BSN, action_tokens_EL = carry\n S, N = token_idxs_BSN.shape[1:]\n L = action_tokens_EL.shape[-1]\n\n # --- Construct + encode video ---\n vid_embed_BSNM = self.dynamics.patch_embed(token_idxs_BSN)\n mask_token_111M = self.dynamics.mask_token.value\n mask_expanded_BSN1 = mask_BSN[..., None]\n vid_embed_BSNM = jnp.where(mask_expanded_BSN1, mask_token_111M, vid_embed_BSNM)\n\n # --- Predict transition ---\n action_tokens_BSm1L = jnp.reshape(action_tokens_EL, (B, S - 1, L))\n act_embed_BSm1M = self.dynamics.action_up(action_tokens_BSm1L)\n act_embed_BSM = jnp.pad(act_embed_BSm1M, ((0, 0), (1, 0), (0, 0)))\n act_embed_BS1M = jnp.reshape(act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1]))\n vid_embed_BSNM += act_embed_BS1M\n unmasked_ratio = jnp.cos(jnp.pi * (step + 1) / (steps * 2))\n step_temp = temperature * (1.0 - unmasked_ratio)\n final_logits_BSNV = self.dynamics.transformer(vid_embed_BSNM) / step_temp\n\n # --- Sample new tokens for final frame ---\n if sample_argmax:\n sampled_token_idxs_BSN = jnp.argmax(final_logits_BSNV, axis=-1)\n else:\n rng, _rng = jax.random.split(rng)\n sampled_token_idxs_BSN = jax.random.categorical(_rng, final_logits_BSNV)\n gather_fn = jax.vmap(jax.vmap(jax.vmap(lambda x, y: x[y])))\n final_token_probs_BSN = gather_fn(\n jax.nn.softmax(final_logits_BSNV), sampled_token_idxs_BSN\n )\n final_token_probs_BSN += ~mask_BSN\n # Update masked tokens only\n token_idxs_BSN = jnp.where(mask_BSN, sampled_token_idxs_BSN, token_idxs_BSN)\n\n # --- Update mask ---\n num_unmasked_tokens = jnp.round(N * (1.0 - unmasked_ratio)).astype(int)\n final_token_probs_flat_BP = einops.rearrange(final_token_probs_BSN, ""b s n -> b (s n)"")\n idx_mask_P = jnp.arange(final_token_probs_flat_BP.shape[-1]) <= N - num_unmasked_tokens\n sorted_idxs_BP = jnp.argsort(final_token_probs_flat_BP, axis=-1)\n mask_update_fn = jax.vmap(lambda msk, ids: msk.at[ids].set(idx_mask_P))\n mask_flat_BP = einops.rearrange(mask_BSN, ""b s n -> b (s n)"")\n new_mask_flat_BP = mask_update_fn(mask_flat_BP, sorted_idxs_BP)\n new_mask_BSN = einops.rearrange(new_mask_flat_BP, ""b (s n) -> b s n"", n=N)\n\n new_carry = (rng, token_idxs_BSN, new_mask_BSN, action_tokens_EL)\n return new_carry, None\n\n def generation_step_fn(\n carry: tuple[jax.Array, jax.Array], step_t: jax.Array\n ) -> tuple[tuple[jax.Array, jax.Array], None]:\n rng, current_token_idxs_BSN = carry\n rng, step_rng = jax.random.split(rng)\n\n # Mask current frame (i.e., t == step_t)\n mask_S = jnp.arange(seq_len) == step_t\n mask_BSN = jnp.broadcast_to(mask_S[None, :, None], (B, seq_len, N)).astype(\n bool\n )\n masked_token_idxs_BSN = current_token_idxs_BSN * ~mask_BSN\n\n # --- Initialize and run MaskGIT loop ---\n init_carry_maskgit = (\n step_rng,\n masked_token_idxs_BSN,\n mask_BSN,\n action_tokens_EL,\n )\n final_carry_maskgit, _ = jax.lax.scan(\n maskgit_step_fn, init_carry_maskgit, jnp.arange(steps)\n )\n updated_token_idxs_BSN = final_carry_maskgit[1]\n new_carry = (rng, updated_token_idxs_BSN)\n return new_carry, None\n\n # --- Run the autoregressive generation using jax.lax.scan ---\n initial_carry = (batch[""rng""], token_idxs_BSN)\n timesteps_to_scan = jnp.arange(T, seq_len)\n final_carry, _ = jax.lax.scan(\n generation_step_fn, initial_carry, timesteps_to_scan\n )\n final_token_idxs_BSN = final_carry[1]\n\n # --- Decode all tokens at once at the end ---\n H, W = batch[""videos""].shape[2:4]\n final_frames_BSHWC = self.tokenizer.decode(\n final_token_idxs_BSN,\n video_hw=(H, W),\n )\n return final_frames_BSHWC\n\n def sample_causal(\n self,\n batch: Dict[str, jax.Array],\n seq_len: int,\n temperature: float = 1,\n sample_argmax: bool = False,\n ) -> jax.Array:\n """"""\n Autoregressively samples up to `seq_len` future frames, following Figure 8 of the paper.\n\n - Input frames are tokenized once.\n - Future frames are generated autoregressively in token space.\n - All frames are detokenized in a single pass.\n\n Note:\n - For interactive or step-wise sampling, detokenization should occur after each action.\n - To maintain consistent tensor shapes across timesteps, all current and future frames are decoded at every step.\n - Temporal causal structure is preserved by\n a) reapplying the mask before each decoding step.\n b) a temporal causal mask is applied within each ST-transformer block.\n\n Dimension keys:\n B: batch size\n T: number of input (conditioning) frames\n N: number of patches per frame\n M: model dimension\n S: sequence length\n H: height\n W: width\n E: B * (S - 1)\n """"""\n assert isinstance(self.dynamics, DynamicsCausal)\n # --- Encode videos and actions ---\n videos_BTHWC = batch[""videos""]\n latent_actions_E = batch[""latent_actions""]\n tokenizer_out = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_idxs_BTN = tokenizer_out[""indices""]\n B, T, N = token_idxs_BTN.shape\n pad_shape = (B, seq_len - T, N)\n pad = jnp.zeros(pad_shape, dtype=token_idxs_BTN.dtype)\n token_idxs_BSN = jnp.concatenate([token_idxs_BTN, pad], axis=1)\n action_tokens_EL = self.lam.vq.get_codes(latent_actions_E)\n dynamics_causal: DynamicsCausal = self.dynamics\n\n def causal_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array, jax.Array], step_n: jax.Array\n ) -> tuple[tuple[jax.Array, jax.Array, jax.Array, jax.Array], None]:\n rng, token_idxs_BSN, action_tokens_EL, step_t = carry\n S, N = token_idxs_BSN.shape[1:]\n L = action_tokens_EL.shape[-1]\n\n # --- Construct + encode video ---\n vid_embed_BSNM = dynamics_causal.patch_embed(token_idxs_BSN)\n\n # --- Predict transition ---\n action_tokens_BSm1L = jnp.reshape(action_tokens_EL, (B, S - 1, L))\n act_embed_BSm1M = dynamics_causal.action_up(action_tokens_BSm1L)\n act_embed_BSM = jnp.pad(act_embed_BSm1M, ((0, 0), (1, 0), (0, 0)))\n act_embed_BS1M = jnp.reshape(act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1]))\n vid_embed_BSNp1M = jnp.concatenate([act_embed_BS1M, vid_embed_BSNM], axis=2)\n final_logits_BTNp1V = dynamics_causal.transformer(vid_embed_BSNp1M, (step_t, step_n)) / temperature\n final_logits_BV = final_logits_BTNp1V[:, step_t, step_n, :]\n\n # --- Sample new tokens for final frame ---\n if sample_argmax:\n sampled_token_idxs_B = jnp.argmax(final_logits_BV, axis=-1)\n else:\n rng, _rng = jax.random.split(rng)\n sampled_token_idxs_B = jax.random.categorical(_rng, final_logits_BV)\n # Update next tokens only\n token_idxs_BSN = token_idxs_BSN.at[:, step_t, step_n].set(sampled_token_idxs_B)\n\n new_carry = (rng, token_idxs_BSN, action_tokens_EL, step_t)\n return new_carry, None\n\n def generation_step_fn(\n carry: tuple[jax.Array, jax.Array], step_t: jax.Array\n ) -> tuple[tuple[jax.Array, jax.Array], None]:\n rng, current_token_idxs_BSN = carry\n rng, step_rng = jax.random.split(rng)\n\n # --- Initialize and run causal loop ---\n init_carry_causal = (\n step_rng,\n current_token_idxs_BSN,\n action_tokens_EL,\n step_t,\n )\n final_carry_causal, _ = jax.lax.scan(\n causal_step_fn, init_carry_causal, jnp.arange(N)\n )\n updated_token_idxs_BSN = final_carry_causal[1]\n new_carry = (rng, updated_token_idxs_BSN)\n return new_carry, None\n\n # --- Run the autoregressive generation using jax.lax.scan ---\n initial_carry = (batch[""rng""], token_idxs_BSN)\n timesteps_to_scan = jnp.arange(T, seq_len)\n final_carry, _ = jax.lax.scan(\n generation_step_fn, initial_carry, timesteps_to_scan\n )\n final_token_idxs_BSN = final_carry[1]\n\n # --- Decode all tokens at once at the end ---\n H, W = batch[""videos""].shape[2:4]\n final_frames_BSHWC = self.tokenizer.decode(\n final_token_idxs_BSN,\n video_hw=(H, W),\n )\n return final_frames_BSHWC\n\n def vq_encode(self, batch: Dict[str, jax.Array], training: bool) -> jax.Array:\n # --- Preprocess videos ---\n video_BTHWC = batch[""videos""]\n lam_output = self.lam.vq_encode(video_BTHWC, training=training)\n lam_indices_E = lam_output[""indices""]\n return lam_indices_E\n\n# FIXME (f.srambical): add conversion script for old checkpoints\ndef restore_genie_components(\n optimizer: nnx.Optimizer,\n sharding: jax.sharding.NamedSharding,\n rng: jax.Array,\n args,\n) -> nnx.Optimizer:\n """"""Restore pre-trained Genie components""""""\n rngs = nnx.Rngs(rng)\n\n tx = optimizer.tx\n model = optimizer.model\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n\n checkpoint_options = ocp.CheckpointManagerOptions(\n step_format_fixed_length=6,\n )\n tokenizer_checkpoint_manager = ocp.CheckpointManager(\n directory=args.tokenizer_checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n dummy_tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.tokenizer_dim,\n ffn_dim=args.tokenizer_ffn_dim,\n latent_dim=args.latent_patch_dim,\n num_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n num_blocks=args.tokenizer_num_blocks,\n num_heads=args.tokenizer_num_heads,\n dropout=args.dropout,\n codebook_dropout=args.dropout,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs,\n )\n dummy_tokenizer_optimizer = nnx.Optimizer(dummy_tokenizer, tx)\n dummy_tokenizer_optimizer_state = nnx.state(dummy_tokenizer_optimizer)\n abstract_sharded_tokenizer_optimizer_state = _create_abstract_sharded_pytree(\n dummy_tokenizer_optimizer_state, sharding\n )\n restored_tokenizer = tokenizer_checkpoint_manager.restore(\n step=tokenizer_checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore( # type: ignore\n abstract_sharded_tokenizer_optimizer_state # type: ignore\n ),\n ),\n )[""model_state""]\n nnx.update(dummy_tokenizer_optimizer.model, restored_tokenizer.model)\n model.tokenizer = dummy_tokenizer_optimizer.model\n tokenizer_checkpoint_manager.close()\n\n if args.lam_checkpoint:\n lam_checkpoint_manager = ocp.CheckpointManager(\n directory=args.lam_checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n dummy_lam = LatentActionModel(\n in_dim=args.image_channels,\n model_dim=args.lam_dim,\n ffn_dim=args.lam_ffn_dim,\n latent_dim=args.latent_patch_dim,\n num_latents=args.num_latent_actions,\n patch_size=args.lam_patch_size,\n num_blocks=args.lam_num_blocks,\n num_heads=args.lam_num_heads,\n dropout=args.dropout,\n codebook_dropout=args.dropout,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs,\n )\n dummy_lam_optimizer = nnx.Optimizer(dummy_lam, tx)\n dummy_lam_optimizer_state = nnx.state(dummy_lam_optimizer)\n abstract_sharded_lam_optimizer_state = _create_abstract_sharded_pytree(\n dummy_lam_optimizer_state, sharding\n )\n restored_lam_optimizer = lam_checkpoint_manager.restore(\n step=lam_checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore( # type: ignore\n abstract_sharded_lam_optimizer_state # type: ignore\n ),\n ),\n )[""model_state""]\n nnx.update(dummy_lam_optimizer.model, restored_lam_optimizer.model)\n model.lam = dummy_lam_optimizer.model\n # Remove the LAM decoder to save memory and avoid unnecessary computation.\n del model.lam.decoder\n lam_checkpoint_manager.close()\n \n # Reinitialize the optimizer states\n optimizer = nnx.Optimizer(model, tx)\n return optimizer\n\n\ndef _create_abstract_sharded_pytree(\n pytree_template: nnx.GraphState, sharding_spec: jax.sharding.NamedSharding\n) -> jax.Array:\n """"""Replaces arrays in a pytree with ShapeDtypeStructs having the given sharding.""""""\n\n def map_fn(leaf_template):\n if hasattr(leaf_template, ""shape"") and hasattr(leaf_template, ""dtype""):\n return jax.ShapeDtypeStruct(\n leaf_template.shape, leaf_template.dtype, sharding=sharding_spec\n )\n return leaf_template\n\n return jax.tree_util.tree_map(map_fn, 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accelerated: 23 nodes idle\rPartition dev_accelerated-h100 :\t 1 nodes idle\rPartition accelerated-h100:\t 1 nodes idle\rPartition large:\t 8 nodes idle",,terminal_output +7771,21681451,"TERMINAL",0,0,"4\t ",,terminal_output +7772,21682048,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar_jobs]633;D;0",,terminal_output +7773,21693470,"TERMINAL",0,0,"queue",,terminal_command +7774,21693527,"TERMINAL",0,0,"]633;E;2025-08-14 16:39:06 queue;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Thu Aug 14 16:39:06 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3415713 accelerat train_dy tum_cte0 PD\t0:00\t 1 (Priority)3423080 accelerat train_to tum_cte0 PD\t0:00\t 4 (Priority)3423082 accelerat train_to tum_cte0 PD\t0:00\t 2 (Priority)3412401 accelerat train_to tum_cte0 R 1-22:39:05\t 8 hkn[0412,0416,0425,0433,0718,0732,0803,0807]3418832 accelerat train_dy tum_cte0 R 1-02:50:10\t 8 hkn[0407,0415,0420,0422-0424,0729,0735]3422008 accelerat train_dy tum_cte0 R 4:25:56\t 1 hkn07133421964 accelerat train_to tum_cte0 R 4:38:20\t 1 hkn0720",,terminal_output +7775,21694689,"TERMINAL",0,0,"76171",,terminal_output +7776,21695666,"TERMINAL",0,0,"87282",,terminal_output +7777,21696694,"TERMINAL",0,0,"98393",,terminal_output +7778,21697741,"TERMINAL",0,0,"10946:004",,terminal_output +7779,21698738,"TERMINAL",0,0,"110515",,terminal_output +7780,21699864,"TERMINAL",0,0,"21626",,terminal_output +7781,21700891,"TERMINAL",0,0,"33848",,terminal_output +7782,21701913,"TERMINAL",0,0,"54959",,terminal_output +7783,21702938,"TERMINAL",0,0,"6520630",,terminal_output +7784,21703951,"TERMINAL",0,0,"76171",,terminal_output +7785,21705011,"TERMINAL",0,0,"87282",,terminal_output +7786,21706050,"TERMINAL",0,0,"98393",,terminal_output +7787,21707088,"TERMINAL",0,0,"2094104",,terminal_output +7788,21708135,"TERMINAL",0,0,"120515",,terminal_output 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+7838,21801547,"TERMINAL",0,0,"\r\n\r\n\r\n\r\n \r\n \r\n Alfred Nguyen\r\n \r\n\r\n\r\n\r\n\r\n

Alfred Nguyen

\r\n

\r\n Hi, I'm Alfred, a researcher in machine learning & foundation models.
\r\n

\r\n\r\n

\r\n currently working at helmholtz munich and p(doom)\r\n

\r\n\r\n

\r\n you can find me here: [\r\n github ·\r\n linkedin ·\r\n email\r\n ]\r\n

\r\n\r\n
\r\n

now

\r\n
    \r\n\r\n
  • \r\n [2025] research assistant at bauer lab, helmholtz munich
    \r\n \r\n
    • \r\n\r\n
  • \r\n [2025] bachelor thesis on scaling test-time training for inference-time adaptation on the arc-agi benchmark
    \r\n \r\n
    • \r\n\r\n
  • \r\n [2025] agi researcher at p(doom)
    \r\n \r\n
    • \r\n\r\n
\r\n
\r\n\r\n
\r\n

selected work

\r\n
    \r\n
  • \r\n hyground: a multi-agent AI system for automated incident response
    \r\n 2024–2025 — developed @ maibornwolff, Python, Multi-agent Systems and more\r\n
    • \r\n
  • \r\n adaptive compute transformer thoughts — blog
    \r\n 2023 — early exploration into dynamic compute scaling\r\n
    • \r\n
  • \r\n matching markets seminar paper — pdf
    \r\n 2022 — undergrad research\r\n
    • \r\n
\r\n
\r\n\r\n
\r\n

previously

\r\n
    \r\n
  • \r\n ai engineer @ maibornwolff
    \r\n 2024–2025 — development of Hyground, a multi-agent AI system for automated incident\r\n response\r\n
    • \r\n
  • \r\n software developer @ arrk engineering
    \r\n 2023–2024 — development of an embedded infotainment system (C++, Python and more)\r\n
    • \r\n
  • \r\n intern @ bosch
    \r\n 2017 — designing switching circuitry and programming microcontrollers using Arduino\r\n
    • \r\n
\r\n
\r\n\r\n\r\n\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer]633;D;0",,terminal_output +7839,21804792,"killer.sh",0,0,"",shellscript,tab +7840,21847853,"killer.sh",0,0,"response=$(curl -s https://home.cit.tum.de/~nguyenal/)\n\n",shellscript,content +7841,21847858,"killer.sh",56,0,"\n",shellscript,content +7842,21847949,"killer.sh",56,0,"if echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n",shellscript,content +7843,21848018,"killer.sh",108,0," echo ""killing all jobs""\n",shellscript,content +7844,21848074,"killer.sh",136,0,"else\n",shellscript,content +7845,21848208,"killer.sh",141,0," echo ""continuing jobs""\n",shellscript,content +7846,21848317,"killer.sh",168,0,"fi\n",shellscript,content +7847,21848320,"killer.sh",171,1,"",shellscript,content +7848,21865399,"TERMINAL",0,0,"dev",,terminal_command +7849,21865437,"TERMINAL",0,0,"]633;E;2025-08-14 16:41:58 dev;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;C]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0]633;P;Cwd=/home/hk-project-p0023960/tum_cte0515/Projects/jafar",,terminal_output +7850,21870035,"TERMINAL",0,0,"sh killer.sh",,terminal_command +7851,21870112,"TERMINAL",0,0,"]633;E;2025-08-14 16:42:03 sh killer.sh ;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;C",,terminal_output +7852,21870182,"TERMINAL",0,0,"continuing jobs\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +7853,21872407,"killer.sh",0,0,"",shellscript,tab +7854,21873260,"killer.sh",153,0,"",shellscript,selection_mouse +7855,21873434,"killer.sh",151,10,"continuing",shellscript,selection_mouse +7856,21874870,"killer.sh",151,10,"",shellscript,content +7857,21875631,"killer.sh",151,0,"c",shellscript,content +7858,21875632,"killer.sh",152,0,"",shellscript,selection_keyboard +7859,21875708,"killer.sh",152,0,"o",shellscript,content +7860,21875709,"killer.sh",153,0,"",shellscript,selection_keyboard 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+7893,21881941,"killer.sh",157,0,"",shellscript,selection_keyboard +7894,21882028,"killer.sh",157,0,"g",shellscript,content +7895,21882029,"killer.sh",158,0,"",shellscript,selection_keyboard +7896,21883476,"killer.sh",163,0," ",shellscript,content +7897,21883477,"killer.sh",164,0,"",shellscript,selection_keyboard +7898,21883601,"killer.sh",164,0,"b",shellscript,content +7899,21883602,"killer.sh",165,0,"",shellscript,selection_keyboard +7900,21883682,"killer.sh",165,0,"e",shellscript,content +7901,21883683,"killer.sh",166,0,"",shellscript,selection_keyboard +7902,21884659,"killer.sh",167,0,"",shellscript,selection_mouse +7903,21885184,"killer.sh",171,0,"",shellscript,selection_mouse +7904,21885370,"killer.sh",170,1,"\n",shellscript,selection_mouse +7905,21885371,"killer.sh",157,14,"g jobs be""\nfi\n",shellscript,selection_mouse +7906,21885388,"killer.sh",140,31,"\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +7907,21885413,"killer.sh",116,55," ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +7908,21885414,"killer.sh",115,56,"o ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +7909,21885433,"killer.sh",60,111,"cho ""$response"" | grep -q ""KILL ALL JOBS""; then\n echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +7910,21885452,"killer.sh",59,112,"echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +7911,21885499,"killer.sh",55,116,"\nif echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +7912,21885543,"killer.sh",0,171,"response=$(curl -s https://home.cit.tum.de/~nguyenal/)\n\nif echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +7913,21886570,"killer.sh",62,0,"",shellscript,selection_mouse +7914,21887616,"killer.sh",88,0,"",shellscript,selection_mouse +7915,21887751,"killer.sh",87,4,"KILL",shellscript,selection_mouse +7916,21887947,"killer.sh",87,5,"KILL ",shellscript,selection_mouse +7917,21887984,"killer.sh",87,8,"KILL ALL",shellscript,selection_mouse +7918,21888019,"killer.sh",87,9,"KILL ALL ",shellscript,selection_mouse +7919,21888381,"killer.sh",87,13,"KILL ALL JOBS",shellscript,selection_mouse +7920,21903475,"killer.sh",171,0,"",shellscript,selection_mouse +7921,21904643,"killer.sh",89,0,"",shellscript,selection_mouse +7922,21904783,"killer.sh",87,4,"KILL",shellscript,selection_mouse +7923,21904980,"killer.sh",87,8,"KILL ALL",shellscript,selection_mouse +7924,21905020,"killer.sh",87,9,"KILL ALL ",shellscript,selection_mouse +7925,21905057,"killer.sh",87,13,"KILL ALL JOBS",shellscript,selection_mouse +7926,21915416,"killer.sh",171,0,"",shellscript,selection_mouse +7927,21920461,"killer.sh",170,1,"\n",shellscript,selection_mouse +7928,21920488,"killer.sh",145,26,"echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +7929,21920525,"killer.sh",140,31,"\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +7930,21920563,"killer.sh",112,59,"echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +7931,21920686,"killer.sh",59,112,"echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +7932,21920724,"killer.sh",55,116,"\nif echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +7933,21920863,"killer.sh",0,171,"response=$(curl -s https://home.cit.tum.de/~nguyenal/)\n\nif echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +7934,21921247,"killer.sh",2,0,"",shellscript,selection_mouse +7935,21921377,"killer.sh",0,8,"response",shellscript,selection_mouse +7936,21921582,"killer.sh",0,55,"response=$(curl -s https://home.cit.tum.de/~nguyenal/)\n",shellscript,selection_mouse +7937,21921612,"killer.sh",0,63,"response=$(curl -s https://home.cit.tum.de/~nguyenal/)\n\nif echo",shellscript,selection_mouse +7938,21921664,"killer.sh",0,116,"response=$(curl -s https://home.cit.tum.de/~nguyenal/)\n\nif echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n echo",shellscript,selection_mouse +7939,21921748,"killer.sh",0,140,"response=$(curl -s https://home.cit.tum.de/~nguyenal/)\n\nif echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n echo ""killing all jobs""\nelse",shellscript,selection_mouse +7940,21921749,"killer.sh",0,150,"response=$(curl -s https://home.cit.tum.de/~nguyenal/)\n\nif echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n echo ""killing all jobs""\nelse\n echo ",shellscript,selection_mouse +7941,21921749,"killer.sh",0,170,"response=$(curl -s https://home.cit.tum.de/~nguyenal/)\n\nif echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi",shellscript,selection_mouse +7942,21921973,"killer.sh",170,0,"",shellscript,selection_mouse +7943,21922167,"killer.sh",168,2,"fi",shellscript,selection_mouse +7944,21922346,"killer.sh",150,20,"""leaving jobs be""\nfi",shellscript,selection_mouse +7945,21922361,"killer.sh",140,30,"\n echo ""leaving jobs be""\nfi",shellscript,selection_mouse +7946,21922381,"killer.sh",112,58,"echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi",shellscript,selection_mouse +7947,21922401,"killer.sh",59,111,"echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi",shellscript,selection_mouse +7948,21922415,"killer.sh",55,115,"\nif echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi",shellscript,selection_mouse +7949,21922498,"killer.sh",0,170,"response=$(curl -s https://home.cit.tum.de/~nguyenal/)\n\nif echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi",shellscript,selection_mouse +7950,21923965,"killer.sh",171,0,"",shellscript,selection_mouse +7951,21926341,"killer.sh",170,1,"\n",shellscript,selection_mouse +7952,21926356,"killer.sh",140,31,"\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +7953,21926392,"killer.sh",110,61," echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +7954,21926428,"killer.sh",57,114,"f echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +7955,21926428,"killer.sh",56,115,"if echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +7956,21926429,"killer.sh",55,116,"\nif echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +7957,21926512,"killer.sh",0,171,"response=$(curl -s https://home.cit.tum.de/~nguyenal/)\n\nif echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +7958,21927210,"killer.sh",0,0,"",shellscript,selection_command +7959,21934406,"killer.sh",44,0,"",shellscript,selection_mouse +7960,21934903,"killer.sh",49,0,"",shellscript,selection_mouse +7961,21935068,"killer.sh",44,8,"nguyenal",shellscript,selection_mouse +7962,21935729,"killer.sh",170,0,"",shellscript,selection_mouse +7963,22026420,"killer.sh",19,0,"",shellscript,selection_mouse +7964,22026537,"killer.sh",19,5,"https",shellscript,selection_mouse +7965,22026704,"killer.sh",19,6,"https:",shellscript,selection_mouse +7966,22026732,"killer.sh",19,8,"https://",shellscript,selection_mouse +7967,22026733,"killer.sh",19,12,"https://home",shellscript,selection_mouse +7968,22026819,"killer.sh",19,13,"https://home.",shellscript,selection_mouse +7969,22026820,"killer.sh",19,16,"https://home.cit",shellscript,selection_mouse +7970,22026904,"killer.sh",19,17,"https://home.cit.",shellscript,selection_mouse +7971,22026923,"killer.sh",19,20,"https://home.cit.tum",shellscript,selection_mouse +7972,22027024,"killer.sh",19,21,"https://home.cit.tum.",shellscript,selection_mouse +7973,22027025,"killer.sh",19,23,"https://home.cit.tum.de",shellscript,selection_mouse +7974,22027056,"killer.sh",19,24,"https://home.cit.tum.de/",shellscript,selection_mouse +7975,22027073,"killer.sh",19,25,"https://home.cit.tum.de/~",shellscript,selection_mouse +7976,22027102,"killer.sh",19,33,"https://home.cit.tum.de/~nguyenal",shellscript,selection_mouse +7977,22027964,"killer.sh",19,33,"",shellscript,content +7978,22028107,"killer.sh",19,1,"",shellscript,content +7979,22028689,"killer.sh",19,0,"https://home.cit.tum.de/~mahajanm/test.html",shellscript,content +7980,22029477,"killer.sh",61,1,"",shellscript,content +7981,22029628,"killer.sh",60,1,"",shellscript,content +7982,22030664,"killer.sh",60,0,"m",shellscript,content +7983,22030664,"killer.sh",61,0,"",shellscript,selection_keyboard +7984,22030912,"killer.sh",61,0,"l",shellscript,content +7985,22030914,"killer.sh",62,0,"",shellscript,selection_keyboard +7986,22031423,"killer.sh",62,0,"\n",shellscript,content +7987,22032022,"killer.sh",62,1,"",shellscript,content +7988,22035944,"TERMINAL",0,0,"sh killer.sh",,terminal_command +7989,22035992,"TERMINAL",0,0,"]633;E;2025-08-14 16:44:49 sh killer.sh ;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;C",,terminal_output +7990,22036143,"TERMINAL",0,0,"leaving jobs be\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +7991,22045473,"TERMINAL",0,0,"watch --help",,terminal_command +7992,22045570,"TERMINAL",0,0,"]633;E;2025-08-14 16:44:58 watch --help;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;C\r\nUsage:\r\n watch [options] command\r\n\r\nOptions:\r\n -b, --beep beep if command has a non-zero exit\r\n -c, --color interpret ANSI color and style sequences\r\n -d, --differences[=]\r\n highlight changes between updates\r\n -e, --errexit exit if command has a non-zero exit\r\n -g, --chgexit exit when output from command changes\r\n -n, --interval seconds to wait between updates\r\n -p, --precise attempt run command in precise intervals\r\n -t, --no-title turn off header\r\n -w, --no-wrap turn off line wrapping\r\n -x, --exec pass command to exec instead of ""sh -c""\r\n\r\n -h, --help display this help and exit\r\n -v, --version output version information and exit\r\n\r\nFor more details see watch(1).\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0]633;P;Cwd=/home/hk-project-p0023960/tum_cte0515/Projects/jafar[?2004h",,terminal_output +7993,22061721,"killer.sh",0,0,"",shellscript,tab +7994,22080293,"TERMINAL",0,0,"watch -n10 ""sh killer.sh""",,terminal_command +7995,22080344,"TERMINAL",0,0,"]633;E;2025-08-14 16:45:33 watch -n10 ""sh killer.sh"";cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;C",,terminal_output +7996,22080492,"TERMINAL",0,0,"[?1049h(B[?7hEvery 10.0s: sh killer.shhkn1993.localdomain: Thu Aug 14 16:45:33 2025leaving jobs be",,terminal_output +7997,22090613,"TERMINAL",0,0,"4\t ",,terminal_output +7998,22096403,"killer.sh",0,0,"",shellscript,tab +7999,22098556,"killer.sh",96,0,"",shellscript,selection_mouse +8000,22098698,"killer.sh",96,4,"KILL",shellscript,selection_mouse +8001,22098872,"killer.sh",96,5,"KILL ",shellscript,selection_mouse +8002,22098896,"killer.sh",96,8,"KILL ALL",shellscript,selection_mouse +8003,22098943,"killer.sh",96,9,"KILL ALL ",shellscript,selection_mouse +8004,22099031,"killer.sh",96,13,"KILL ALL JOBS",shellscript,selection_mouse +8005,22100766,"TERMINAL",0,0,"5\t ",,terminal_output +8006,22111111,"TERMINAL",0,0,"6:0\t ",,terminal_output +8007,22121018,"TERMINAL",0,0,"14\t ",,terminal_output +8008,22131252,"TERMINAL",0,0,"2\rkilling all jobs",,terminal_output +8009,22141319,"TERMINAL",0,0,"3\t ",,terminal_output +8010,22141866,"killer.sh",0,0,"",shellscript,tab +8011,22142574,"killer.sh",180,0,"",shellscript,selection_mouse +8012,22143132,"killer.sh",149,0,"",shellscript,selection_mouse +8013,22143776,"killer.sh",64,0,"",shellscript,selection_mouse +8014,22144101,"killer.sh",64,36,"\nif echo ""$response"" | grep -q ""KILL",shellscript,selection_mouse +8015,22144127,"killer.sh",64,37,"\nif echo ""$response"" | grep -q ""KILL ",shellscript,selection_mouse +8016,22144154,"killer.sh",64,40,"\nif echo ""$response"" | grep -q ""KILL ALL",shellscript,selection_mouse +8017,22144551,"killer.sh",104,0,"",shellscript,selection_mouse +8018,22151454,"TERMINAL",0,0,"4\t ",,terminal_output +8019,22153780,"killer.sh",180,0,"",shellscript,selection_mouse +8020,22161570,"TERMINAL",0,0,"5\t ",,terminal_output +8021,22171672,"TERMINAL",0,0,"7:0\rleaving jobs be",,terminal_output +8022,22181794,"TERMINAL",0,0,"1\t ",,terminal_output +8023,22192006,"TERMINAL",0,0,"2\t ",,terminal_output +8024,22202065,"TERMINAL",0,0,"35\t ",,terminal_output +8025,22212194,"TERMINAL",0,0,"4\t ",,terminal_output +8026,22220942,"killer.sh",57,0,"",shellscript,selection_mouse +8027,22221546,"killer.sh",56,1,"",shellscript,content +8028,22221685,"killer.sh",55,1,"",shellscript,content +8029,22221789,"killer.sh",54,1,"",shellscript,content +8030,22221896,"killer.sh",53,1,"",shellscript,content +8031,22222331,"killer.sh",53,0,"k",shellscript,content +8032,22222332,"killer.sh",54,0,"",shellscript,selection_keyboard +8033,22222407,"TERMINAL",0,0,"5\t ",,terminal_output +8034,22222490,"killer.sh",54,0,"i",shellscript,content +8035,22222491,"killer.sh",55,0,"",shellscript,selection_keyboard +8036,22222670,"killer.sh",55,0,"l",shellscript,content +8037,22222671,"killer.sh",56,0,"",shellscript,selection_keyboard +8038,22222779,"killer.sh",56,0,"l",shellscript,content +8039,22222780,"killer.sh",57,0,"",shellscript,selection_keyboard +8040,22222872,"killer.sh",57,0,"e",shellscript,content +8041,22222873,"killer.sh",58,0,"",shellscript,selection_keyboard +8042,22222978,"killer.sh",58,0,"r",shellscript,content +8043,22222979,"killer.sh",59,0,"",shellscript,selection_keyboard +8044,22226187,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +8045,22230624,"TERMINAL",0,0,"sh killer.sh",,terminal_command +8046,22230678,"TERMINAL",0,0,"]633;E;2025-08-14 16:48:03 sh killer.sh;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;C",,terminal_output +8047,22230785,"TERMINAL",0,0,"killing all jobs\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +8048,22260529,"killer.sh",0,0,"",shellscript,tab +8049,22261082,"killer.sh",182,0,"",shellscript,selection_mouse +8050,22262490,"killer.sh",52,0,"",shellscript,selection_mouse +8051,22262609,"killer.sh",50,2,"nm",shellscript,selection_mouse +8052,22262625,"killer.sh",49,3,"anm",shellscript,selection_mouse +8053,22262641,"killer.sh",48,4,"janm",shellscript,selection_mouse +8054,22262679,"killer.sh",47,5,"ajanm",shellscript,selection_mouse +8055,22262756,"killer.sh",46,6,"hajanm",shellscript,selection_mouse +8056,22262835,"killer.sh",45,7,"ahajanm",shellscript,selection_mouse +8057,22263233,"killer.sh",44,8,"mahajanm",shellscript,selection_mouse +8058,22263860,"killer.sh",44,8,"n",shellscript,content +8059,22263861,"killer.sh",45,0,"",shellscript,selection_keyboard +8060,22264169,"killer.sh",45,0,"g",shellscript,content +8061,22264170,"killer.sh",46,0,"",shellscript,selection_keyboard +8062,22264283,"killer.sh",46,0,"u",shellscript,content +8063,22264284,"killer.sh",47,0,"",shellscript,selection_keyboard +8064,22264493,"killer.sh",47,0,"y",shellscript,content +8065,22264494,"killer.sh",48,0,"",shellscript,selection_keyboard +8066,22264713,"killer.sh",48,0,"e",shellscript,content +8067,22264713,"killer.sh",49,0,"",shellscript,selection_keyboard +8068,22264786,"killer.sh",49,0,"n",shellscript,content +8069,22264787,"killer.sh",50,0,"",shellscript,selection_keyboard +8070,22264956,"killer.sh",50,0,"a",shellscript,content +8071,22264957,"killer.sh",51,0,"",shellscript,selection_keyboard +8072,22265007,"killer.sh",51,0,"l",shellscript,content +8073,22265008,"killer.sh",52,0,"",shellscript,selection_keyboard +8074,22265804,"killer.sh",178,0,"",shellscript,selection_mouse +8075,22273178,"TERMINAL",0,0,"sh killer.sh",,terminal_command +8076,22273236,"TERMINAL",0,0,"]633;E;2025-08-14 16:48:46 sh killer.sh;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;C",,terminal_output +8077,22273382,"TERMINAL",0,0,"leaving jobs be\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +8078,22348376,"killer.sh",0,0,"",shellscript,tab +8079,22350549,"killer.sh",99,0,"",shellscript,selection_mouse +8080,22350692,"killer.sh",98,4,"KILL",shellscript,selection_mouse +8081,22350889,"killer.sh",98,5,"KILL ",shellscript,selection_mouse +8082,22350902,"killer.sh",98,8,"KILL ALL",shellscript,selection_mouse +8083,22350937,"killer.sh",98,9,"KILL ALL ",shellscript,selection_mouse +8084,22350980,"killer.sh",98,13,"KILL ALL JOBS",shellscript,selection_mouse +8085,22432958,"killer.sh",182,0,"",shellscript,selection_mouse +8086,22437487,"TERMINAL",0,0,"bash",,terminal_focus +8087,22492972,"TERMINAL",0,0,"bash",,terminal_focus +8088,22529687,"TERMINAL",0,0,"tmux",,terminal_command +8089,22529775,"TERMINAL",0,0,"]633;E;2025-08-14 16:53:02 tmux;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;C[?1049h[?1h=[?12l[?25h[?1000l[?1002l[?1003l[?1006l[?1005l(B[?12l[?25h[?1006l[?1000l[?1002l[?1003l[?2004l[>c[>q[?25l\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n[0] 0:bash* ""hkn1993.localdomain"" 16:53 14-Aug-25(B[?12l[?25h(B[?12l[?25h[?1006l[?1000l[?1002l[?1003l[?2004l[?25l\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n[0] 0:bash* ""hkn1993.localdomain"" 16:53 14-Aug-25(B[?12l[?25h",,terminal_output +8090,22529874,"TERMINAL",0,0,"[?7727h",,terminal_output +8091,22531549,"TERMINAL",0,0,"[?2004h(jafar) [tum_cte0515@hkn1993 jafar]$ ",,terminal_output +8092,22534383,"TERMINAL",0,0,"w",,terminal_output +8093,22534668,"TERMINAL",0,0,"a",,terminal_output +8094,22534775,"TERMINAL",0,0,"t",,terminal_output +8095,22534847,"TERMINAL",0,0,"c",,terminal_output +8096,22534942,"TERMINAL",0,0,"h",,terminal_output +8097,22535059,"TERMINAL",0,0," ",,terminal_output +8098,22535300,"TERMINAL",0,0,"-",,terminal_output +8099,22535548,"TERMINAL",0,0,"n",,terminal_output +8100,22535927,"TERMINAL",0,0,"1",,terminal_output +8101,22536824,"TERMINAL",0,0," ",,terminal_output +8102,22539577,"TERMINAL",0,0,"""",,terminal_output +8103,22539899,"TERMINAL",0,0,"s",,terminal_output +8104,22539989,"TERMINAL",0,0,"h",,terminal_output +8105,22540106,"TERMINAL",0,0," ",,terminal_output +8106,22540532,"TERMINAL",0,0,"k",,terminal_output +8107,22540739,"TERMINAL",0,0,"i",,terminal_output +8108,22541046,"TERMINAL",0,0,"l",,terminal_output +8109,22541155,"TERMINAL",0,0,"l",,terminal_output +8110,22541241,"TERMINAL",0,0,"e",,terminal_output +8111,22541419,"TERMINAL",0,0,"r.",,terminal_output +8112,22541662,"TERMINAL",0,0,"s",,terminal_output +8113,22541744,"TERMINAL",0,0,"h",,terminal_output +8114,22542114,"TERMINAL",0,0,"""",,terminal_output +8115,22543473,"TERMINAL",0,0,"",,terminal_output +8116,22543688,"TERMINAL",0,0,"",,terminal_output +8117,22543773,"TERMINAL",0,0,"",,terminal_output +8118,22544102,"TERMINAL",0,0,"",,terminal_output +8119,22544737,"TERMINAL",0,0,"",,terminal_output +8120,22544867,"TERMINAL",0,0,"",,terminal_output +8121,22545295,"TERMINAL",0,0,"",,terminal_output +8122,22545837,"TERMINAL",0,0,"[1@5",,terminal_output +8123,22545902,"TERMINAL",0,0,"[1@0",,terminal_output +8124,22546346,"TERMINAL",0,0,"",,terminal_output +8125,22546414,"TERMINAL",0,0,"",,terminal_output +8126,22547078,"TERMINAL",0,0,"[1@6[1@0",,terminal_output +8127,22562217,"killer.sh",0,0,"",shellscript,tab +8128,22566800,"killer.sh",0,0,"",shellscript,tab +8129,22571478,"TERMINAL",0,0,"\r\n[?2004l",,terminal_output +8130,22571619,"TERMINAL",0,0,"[?25lEvery 60.0s: sh killer.shhkn1993.localdomain: Thu Aug 14 16:53:44 2025\r\nleaving jobs be\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n[?12l[?25h",,terminal_output +8131,22574772,"TERMINAL",0,0,"[?25l\r\n[0] 0:watch* ""hkn1993.localdomain"" 16:53 14-Aug-25(B[?12l[?25h",,terminal_output +8132,22576420,"TERMINAL",0,0,"(B[?1l>[?12l[?25h[?1000l[?1002l[?1003l[?1006l[?1005l[?7727l[?1004l[?1049l[detached (from session 0)]\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +8133,22579601,"TERMINAL",0,0,"tmux -q",,terminal_command +8134,22579649,"TERMINAL",0,0,"]633;E;2025-08-14 16:53:52 tmux -q;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;C[?1049h[?1h=[?12l[?25h[?1000l[?1002l[?1003l[?1006l[?1005l(B[?12l[?25h[?1006l[?1000l[?1002l[?1003l[?2004l[>c[>q[?25l\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n[1] 0:tmux* ""hkn1993.localdomain"" 16:53 14-Aug-25(B[?12l[?25h(B[?12l[?25h[?1006l[?1000l[?1002l[?1003l[?2004l[?25l\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n[1] 0:tmux* ""hkn1993.localdomain"" 16:53 14-Aug-25(B[?12l[?25h",,terminal_output +8135,22579723,"TERMINAL",0,0,"[?7727h",,terminal_output +8136,22581232,"TERMINAL",0,0,"(jafar) [tum_cte0515@hkn1993 jafar]$ [?2004h[?25l[1] 0:bash* ""hkn1993.localdomain"" 16:53 14-Aug-25(B[?12l[?25h",,terminal_output +8137,22583047,"TERMINAL",0,0,"\r\nlogout\r\n[?2004l(B[?1l>[?12l[?25h[?1000l[?1002l[?1003l[?1006l[?1005l[?7727l[?1004l[?1049l[exited]\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +8138,22586306,"TERMINAL",0,0,"tmux a",,terminal_command +8139,22586376,"TERMINAL",0,0,"]633;E;2025-08-14 16:53:59 tmux a;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;C[?1049h[?1h=[?12l[?25h[?1000l[?1002l[?1003l[?1006l[?1005l(B[?12l[?25h[?1006l[?1000l[?1002l[?1003l[?2004l[>c[>q[?25lEvery 60.0s: sh killer.shhkn1993.localdomain: Thu Aug 14 16:53:44 2025\r\nleaving jobs be\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n[0] 0:watch* ""hkn1993.localdomain"" 16:53 14-Aug-25(B[?12l[?25h(B[?12l[?25h[?1006l[?1000l[?1002l[?1003l[?2004l[?25lEvery 60.0s: sh killer.shhkn1993.localdomain: Thu Aug 14 16:53:44 2025\r\nleaving jobs be\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n[0] 0:watch* ""hkn1993.localdomain"" 16:53 14-Aug-25(B[?12l[?25h",,terminal_output +8140,22586472,"TERMINAL",0,0,"[?7727h",,terminal_output +8141,22589136,"TERMINAL",0,0,"[?25l(jafar) [tum_cte0515@hkn1993 jafar]$ watch -n60 ""sh killer.sh""\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n[?12l[?25h[?25l[0] 0:bash* ""hkn1993.localdomain"" 16:54 14-Aug-25(B[?12l[?25h(jafar) [tum_cte0515@hkn1993 jafar]$ [?2004h",,terminal_output +8142,22590410,"killer.sh",0,0,"",shellscript,tab +8143,22591702,"killer.sh",129,0,"",shellscript,selection_mouse +8144,22591838,"killer.sh",129,7,"killing",shellscript,selection_mouse +8145,22592092,"killer.sh",129,11,"killing all",shellscript,selection_mouse +8146,22592128,"killer.sh",129,12,"killing all ",shellscript,selection_mouse +8147,22592140,"killer.sh",129,16,"killing all jobs",shellscript,selection_mouse +8148,22592241,"killer.sh",129,17,"killing all jobs""",shellscript,selection_mouse +8149,22592802,"killer.sh",146,0,"",shellscript,selection_mouse +8150,22593348,"killer.sh",118,0,"",shellscript,selection_mouse +8151,22594405,"killer.sh",118,0,"\n",shellscript,content +8152,22595530,"killer.sh",119,0,"s",shellscript,content +8153,22595531,"killer.sh",120,0,"",shellscript,selection_keyboard +8154,22596027,"killer.sh",119,1,"",shellscript,content +8155,22596177,"killer.sh",119,0," ",shellscript,content +8156,22596797,"killer.sh",123,0,"e",shellscript,content +8157,22596798,"killer.sh",124,0,"",shellscript,selection_keyboard +8158,22596991,"killer.sh",124,0,"c",shellscript,content +8159,22596992,"killer.sh",125,0,"",shellscript,selection_keyboard +8160,22597045,"killer.sh",125,0,"h",shellscript,content +8161,22597046,"killer.sh",126,0,"",shellscript,selection_keyboard +8162,22597210,"killer.sh",126,0,"o",shellscript,content +8163,22597210,"killer.sh",127,0,"",shellscript,selection_keyboard +8164,22597325,"killer.sh",127,0," ",shellscript,content +8165,22597326,"killer.sh",128,0,"",shellscript,selection_keyboard +8166,22597672,"killer.sh",128,0,"""",shellscript,content +8167,22597673,"killer.sh",129,0,"",shellscript,selection_keyboard +8168,22598325,"killer.sh",129,0,"h",shellscript,content +8169,22598325,"killer.sh",130,0,"",shellscript,selection_keyboard +8170,22598711,"killer.sh",130,0,"l",shellscript,content +8171,22598712,"killer.sh",131,0,"",shellscript,selection_keyboard +8172,22599061,"killer.sh",130,1,"",shellscript,content +8173,22599571,"killer.sh",130,0,"e",shellscript,content +8174,22599572,"killer.sh",131,0,"",shellscript,selection_keyboard +8175,22599608,"killer.sh",131,0,"l",shellscript,content +8176,22599609,"killer.sh",132,0,"",shellscript,selection_keyboard +8177,22599753,"killer.sh",132,0,"l",shellscript,content +8178,22599754,"killer.sh",133,0,"",shellscript,selection_keyboard +8179,22599911,"killer.sh",133,0,"o",shellscript,content +8180,22599912,"killer.sh",134,0,"",shellscript,selection_keyboard +8181,22599994,"killer.sh",134,0," ",shellscript,content +8182,22599994,"killer.sh",135,0,"",shellscript,selection_keyboard +8183,22600079,"killer.sh",135,0,"w",shellscript,content +8184,22600080,"killer.sh",136,0,"",shellscript,selection_keyboard +8185,22600161,"killer.sh",136,0,"o",shellscript,content +8186,22600162,"killer.sh",137,0,"",shellscript,selection_keyboard +8187,22600302,"killer.sh",137,0,"r",shellscript,content +8188,22600302,"killer.sh",138,0,"",shellscript,selection_keyboard +8189,22600385,"killer.sh",138,0,"l",shellscript,content +8190,22600386,"killer.sh",139,0,"",shellscript,selection_keyboard +8191,22600517,"killer.sh",139,0,"d",shellscript,content +8192,22600518,"killer.sh",140,0,"",shellscript,selection_keyboard +8193,22600760,"killer.sh",140,0,"""",shellscript,content +8194,22600761,"killer.sh",141,0,"",shellscript,selection_keyboard +8195,22601448,"killer.sh",140,0,"",shellscript,selection_command +8196,22601978,"killer.sh",119,23,"",shellscript,content +8197,22602014,"killer.sh",123,0,"",shellscript,selection_command +8198,22602470,"killer.sh",150,0,"",shellscript,selection_command +8199,22602688,"killer.sh",151,0,"\n echo ""hello world""",shellscript,content +8200,22602694,"killer.sh",156,0,"",shellscript,selection_command +8201,22605522,"TERMINAL",0,0,"watch -n60 ""sh killer.sh""",,terminal_output +8202,22606860,"TERMINAL",0,0,"\r\n[?2004l",,terminal_output +8203,22606997,"TERMINAL",0,0,"[?25lEvery 60.0s: sh killer.shhkn1993.localdomain: Thu Aug 14 16:54:20 2025\r\nhello world\r\nleaving jobs be\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n[?12l[?25h",,terminal_output +8204,22609492,"TERMINAL",0,0,"[?25l\r\n[0] 0:watch* ""hkn1993.localdomain"" 16:54 14-Aug-25(B[?12l[?25h[?25l(jafar) [tum_cte0515@hkn1993 jafar]$ watch -n60 ""sh killer.sh""\r\n(jafar) [tum_cte0515@hkn1993 jafar]$ watch -n60 ""sh killer.sh""\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n[?12l[?25h[?25l[0] 0:bash* ""hkn1993.localdomain"" 16:54 14-Aug-25(B[?12l[?25h[?2004h(jafar) [tum_cte0515@hkn1993 jafar]$ ",,terminal_output +8205,22612066,"killer.sh",0,0,"",shellscript,tab +8206,22613654,"killer.sh",164,0,"",shellscript,selection_mouse +8207,22615064,"killer.sh",162,0,"",shellscript,selection_mouse +8208,22615690,"killer.sh",174,0,"",shellscript,selection_mouse +8209,22615700,"killer.sh",173,0,"",shellscript,selection_command +8210,22616110,"killer.sh",174,0,"",shellscript,selection_command +8211,22616406,"killer.sh",174,0," ",shellscript,content +8212,22616407,"killer.sh",175,0,"",shellscript,selection_keyboard +8213,22617459,"killer.sh",175,0,"$",shellscript,content +8214,22617460,"killer.sh",176,0,"",shellscript,selection_keyboard +8215,22618034,"killer.sh",175,1,"",shellscript,content +8216,22619196,"killer.sh",175,0,"&",shellscript,content +8217,22619197,"killer.sh",176,0,"",shellscript,selection_keyboard +8218,22619351,"killer.sh",176,0,"&",shellscript,content +8219,22619352,"killer.sh",177,0,"",shellscript,selection_keyboard +8220,22619692,"killer.sh",177,0," ",shellscript,content +8221,22619692,"killer.sh",178,0,"",shellscript,selection_keyboard +8222,22619948,"killer.sh",178,0,"\",shellscript,content +8223,22619949,"killer.sh",179,0,"",shellscript,selection_keyboard +8224,22622360,"TERMINAL",0,0,"watch -n60 ""sh killer.sh""",,terminal_output +8225,22622716,"TERMINAL",0,0,"\r\n[?2004l",,terminal_output +8226,22622847,"TERMINAL",0,0,"[?25lEvery 60.0s: sh killer.shhkn1993.localdomain: Thu Aug 14 16:54:35 2025\r\nhello world\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n[0] 0:watch* ""hkn1993.localdomain"" 16:54 14-Aug-25(B[?12l[?25hleaving jobs be",,terminal_output +8227,22623954,"TERMINAL",0,0,"[?25l(jafar) [tum_cte0515@hkn1993 jafar]$ watch -n60 ""sh killer.sh""\r\n(jafar) [tum_cte0515@hkn1993 jafar]$ watch -n60 ""sh killer.sh""\r\n(jafar) [tum_cte0515@hkn1993 jafar]$ watch -n60 ""sh killer.sh""\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n[?12l[?25h[?25l[0] 0:bash* ""hkn1993.localdomain"" 16:54 14-Aug-25(B[?12l[?25h(jafar) [tum_cte0515@hkn1993 jafar]$ [?2004h",,terminal_output +8228,22624775,"killer.sh",0,0,"",shellscript,tab +8229,22625608,"killer.sh",167,0,"",shellscript,selection_mouse +8230,22626367,"killer.sh",163,0,"",shellscript,selection_mouse +8231,22626844,"killer.sh",162,0,"",shellscript,selection_command +8232,22627586,"killer.sh",152,28,"",shellscript,content +8233,22627624,"killer.sh",156,0,"",shellscript,selection_command +8234,22627705,"killer.sh",150,0,"",shellscript,selection_command +8235,22627824,"killer.sh",123,0,"",shellscript,selection_command +8236,22628213,"killer.sh",71,0,"",shellscript,selection_command +8237,22628454,"killer.sh",118,0,"\n echo ""hello world"" && \",shellscript,content +8238,22628460,"killer.sh",123,0,"",shellscript,selection_command +8239,22629180,"killer.sh",124,0,"",shellscript,selection_command +8240,22629376,"killer.sh",125,0,"",shellscript,selection_command +8241,22629538,"killer.sh",126,0,"",shellscript,selection_command +8242,22629685,"killer.sh",127,0,"",shellscript,selection_command +8243,22629832,"killer.sh",128,0,"",shellscript,selection_command +8244,22630192,"killer.sh",129,0,"",shellscript,selection_command +8245,22631005,"killer.sh",128,0,"",shellscript,selection_command +8246,22631308,"killer.sh",127,0,"",shellscript,selection_command +8247,22631462,"killer.sh",126,0,"",shellscript,selection_command +8248,22631604,"killer.sh",125,0,"",shellscript,selection_command +8249,22631738,"killer.sh",124,0,"",shellscript,selection_command +8250,22631904,"killer.sh",123,0,"",shellscript,selection_command +8251,22632234,"killer.sh",123,5,"",shellscript,content +8252,22632469,"killer.sh",123,1,"",shellscript,content 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0)]\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +8324,22709304,"killer.sh",0,0,"",shellscript,tab +8325,22710244,"killer.sh",204,0,"",shellscript,selection_mouse +8326,22710861,"killer.sh",203,1,"\n",shellscript,selection_mouse +8327,22710899,"killer.sh",182,22," ""leaving jobs be""\nfi\n",shellscript,selection_mouse +8328,22710899,"killer.sh",173,31,"\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +8329,22710900,"killer.sh",146,58,"cho ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +8330,22710900,"killer.sh",145,59,"echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +8331,22710979,"killer.sh",122,82," scancel --me && \\n echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +8332,22710980,"killer.sh",69,135," echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n scancel --me && \\n echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +8333,22711027,"killer.sh",66,138,"\nif echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n scancel --me && \\n echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +8334,22711075,"killer.sh",0,204,"response=$(curl -s https://home.cit.tum.de/~nguyenal/killer.html)\n\nif echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n scancel --me && \\n echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,selection_mouse +8335,22767206,"killer.sh",204,0,"",shellscript,selection_mouse +8336,23198544,"slurm/jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/%x_%j.log\n#SBATCH --job-name=train_dynamics_maskgit_8_node\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/holiday/maskgit/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=160 \\n --init_lr=0 \\n --darkness_threshold=50 \\n --dyna_type=maskgit \\n --num_latent_actions=100 \\n --max_lr=8e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-maskgit-8-node-darkness-filter-$slurm_job_id \\n --tags dynamics maskgit 8-node post-launch-main darkness-filter \\n --entity instant-uv \\n --project jafar \\n --dyna_dim=1024 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=16 \\n --dyna_ffn_dim=4096 \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n",shellscript,tab +8337,23213674,"TERMINAL",0,0,"bash",,terminal_focus +8338,23215266,"TERMINAL",0,0,"idling",,terminal_command +8339,23215332,"TERMINAL",0,0,"]633;E;2025-08-14 17:04:28 idling;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;C[?1049h(B[?7hEvery 1.0s: sinfo_t_idlehkn1993.localdomain: Thu Aug 14 17:04:28 2025Partition dev_cpuonly: 11 nodes idle\rPartition cpuonly: 255 nodes idle\rPartition dev_accelerated:\t 1 nodes idle\rPartition accelerated: 32 nodes idle\rPartition dev_accelerated-h100 :\t 0 nodes idle\rPartition accelerated-h100:\t 1 nodes idle\rPartition large:\t 8 nodes idle",,terminal_output +8340,23216407,"TERMINAL",0,0,"9\t ",,terminal_output +8341,23216508,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar_jobs]633;D;0",,terminal_output +8342,23226195,"TERMINAL",0,0,"queue",,terminal_command +8343,23226265,"TERMINAL",0,0,"]633;E;2025-08-14 17:04:39 queue;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;C",,terminal_output +8344,23226340,"TERMINAL",0,0,"[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Thu Aug 14 17:04:39 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3415713 accelerat train_dy tum_cte0 PD\t0:00\t 1 (Priority)3423080 accelerat train_to tum_cte0 PD\t0:00\t 4 (Priority)3423082 accelerat train_to tum_cte0 PD\t0:00\t 2 (Priority)3412401 accelerat train_to tum_cte0 R 1-23:04:38\t 8 hkn[0412,0416,0425,0433,0718,0732,0803,0807]3418832 accelerat train_dy tum_cte0 R 1-03:15:43\t 8 hkn[0407,0415,0420,0422-0424,0729,0735]3422008 accelerat train_dy tum_cte0 R 4:51:29\t 1 hkn07133421964 accelerat train_to tum_cte0 R 5:03:53\t 1 hkn0720",,terminal_output +8345,23227484,"TERMINAL",0,0,"4094304",,terminal_output +8346,23228389,"TERMINAL",0,0,"140515",,terminal_output +8347,23229530,"TERMINAL",0,0,"21626",,terminal_output +8348,23230480,"TERMINAL",0,0,"32737",,terminal_output +8349,23231526,"TERMINAL",0,0,"43848",,terminal_output +8350,23232552,"TERMINAL",0,0,"54959",,terminal_output +8351,23233596,"TERMINAL",0,0,"655064:00",,terminal_output +8352,23234650,"TERMINAL",0,0,"76171",,terminal_output +8353,23235691,"TERMINAL",0,0,"87282",,terminal_output +8354,23236881,"TERMINAL",0,0,"98393",,terminal_output +8355,23237827,"TERMINAL",0,0,"5094404",,terminal_output +8356,23238850,"TERMINAL",0,0,"151626",,terminal_output +8357,23239978,"TERMINAL",0,0,"32737",,terminal_output +8358,23241000,"TERMINAL",0,0,"43848",,terminal_output +8359,23242234,"TERMINAL",0,0,"54959",,terminal_output +8360,23242705,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar_jobs]633;D;0",,terminal_output 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+8375,23282038,"slurm/jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",443,0,"",shellscript,selection_mouse +8376,23282704,"slurm/jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",471,0,"\n#SBATCH --reservation=llmtum",shellscript,content +8377,23282735,"slurm/jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",472,0,"",shellscript,selection_command +8378,23303655,"slurm/jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",1753,0,"",shellscript,selection_mouse +8379,23303662,"slurm/jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",1752,0,"",shellscript,selection_command +8380,23397084,"TERMINAL",0,0,"bash",,terminal_focus +8381,23398603,"TERMINAL",0,0,"logs",,terminal_command +8382,23398642,"TERMINAL",0,0,"]633;E;2025-08-14 17:07:31 logs;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;C]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output +8383,23401833,"TERMINAL",0,0,"cd maskgit",,terminal_command +8384,23401862,"TERMINAL",0,0,"]633;E;2025-08-14 17:07:34 cd maskgit;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;C]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit]633;D;0",,terminal_output +8385,23402091,"TERMINAL",0,0,"ls",,terminal_command +8386,23402125,"TERMINAL",0,0,"]633;E;2025-08-14 17:07:35 ls;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;Cdynamics-cotraining\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit]633;D;0",,terminal_output +8387,23403533,"TERMINAL",0,0,"cd dynamics-cotraining/",,terminal_command +8388,23403872,"TERMINAL",0,0,"ls",,terminal_command +8389,23403908,"TERMINAL",0,0,"]633;E;2025-08-14 17:07:37 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denied\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar_jobs]633;D;1",,terminal_output +8608,23733301,"TERMINAL",0,0,"sacct",,terminal_command +8609,23733338,"TERMINAL",0,0,"]633;E;2025-08-14 17:13:06 sacct;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;CJobID JobName Partition Account AllocCPUS State ExitCode \r\n------------ ---------- ---------- ---------- ---------- ---------- -------- \r\n3417227 train_tok+ accelerat+ hk-projec+ 48 COMPLETED 0:0 \r\n3417227.bat+ batch hk-projec+ 24 COMPLETED 0:0 \r\n3417227.ext+ extern hk-projec+ 48 COMPLETED 0:0 \r\n3417227.0 python hk-projec+ 40 COMPLETED 0:0 \r\n3412401 train_tok+ accelerat+ hk-projec+ 192 RUNNING 0:0 \r\n3412401.bat+ batch hk-projec+ 24 RUNNING 0:0 \r\n3412401.ext+ extern hk-projec+ 192 RUNNING 0:0 \r\n3412401.0 python hk-projec+ 160 RUNNING 0:0 \r\n3418832 train_dyn+ accelerat+ hk-projec+ 192 RUNNING 0:0 \r\n3418832.bat+ batch hk-projec+ 24 RUNNING 0:0 \r\n3418832.ext+ extern hk-projec+ 192 RUNNING 0:0 \r\n3418832.0 python hk-projec+ 160 RUNNING 0:0 \r\n3415713 train_dyn+ accelerat+ hk-projec+ 0 PENDING 0:0 \r\n3421964 train_tok+ accelerat+ hk-projec+ 24 RUNNING 0:0 \r\n3421964.bat+ batch hk-projec+ 24 RUNNING 0:0 \r\n3421964.ext+ extern hk-projec+ 24 RUNNING 0:0 \r\n3421964.0 python hk-projec+ 20 RUNNING 0:0 \r\n3421973 train_tok+ accelerat+ hk-projec+ 48 CANCELLED+ 0:0 \r\n3421973.bat+ batch hk-projec+ 24 CANCELLED 0:15 \r\n3421973.ext+ extern hk-projec+ 48 COMPLETED 0:0 \r\n3421973.0 python hk-projec+ 40 CANCELLED 0:9 \r\n3421982 train_tok+ accelerat+ hk-projec+ 96 CANCELLED+ 0:0 \r\n3421982.bat+ batch hk-projec+ 24 CANCELLED 0:15 \r\n3421982.ext+ extern hk-projec+ 96 COMPLETED 0:0 \r\n3421982.0 python hk-projec+ 80 CANCELLED 0:9 \r\n3422007 train_dyn+ accelerat+ hk-projec+ 0 CANCELLED+ 0:0 \r\n3422008 train_dyn+ accelerat+ hk-projec+ 24 RUNNING 0:0 \r\n3422008.bat+ batch hk-projec+ 24 RUNNING 0:0 \r\n3422008.ext+ extern hk-projec+ 24 RUNNING 0:0 \r\n3422008.0 python hk-projec+ 20 RUNNING 0:0 \r\n3422101 train_dyn+ accelerat+ hk-projec+ 0 CANCELLED+ 0:0 \r\n3422103 train_dyn+ accelerat+ hk-projec+ 192 CANCELLED+ 0:0 \r\n3422103.bat+ batch hk-projec+ 24 CANCELLED 0:15 \r\n3422103.ext+ extern hk-projec+ 192 COMPLETED 0:0 \r\n3422103.0 python hk-projec+ 160 CANCELLED 0:9 \r\n3423052 train_tok+ accelerat+ hk-projec+ 48 CANCELLED+ 0:0 \r\n3423052.bat+ batch hk-projec+ 24 CANCELLED 0:15 \r\n3423052.ext+ extern hk-projec+ 48 COMPLETED 0:0 \r\n3423052.0 python hk-projec+ 40 CANCELLED 0:9 \r\n3423080 train_tok+ accelerat+ hk-projec+ 0 PENDING 0:0 \r\n3423082 train_tok+ accelerat+ hk-projec+ 0 PENDING 0:0 \r\n3423221 train_dyn+ accelerat+ hk-projec+ 0 CANCELLED+ 0:0 \r\n3423223 train_dyn+ accelerat+ hk-projec+ 0 PENDING 0:0 \r\n]0;tum_cte0515@hkn1993:~/Projects/jafar_jobs]633;D;0",,terminal_output +8610,23890024,"TERMINAL",0,0,"bash",,terminal_focus +8611,23891197,"TERMINAL",0,0,"bash",,terminal_focus +8612,23892160,"TERMINAL",0,0,"queue",,terminal_command +8613,23892238,"TERMINAL",0,0,"]633;E;2025-08-14 17:15:45 queue;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Thu Aug 14 17:15:45 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3415713 accelerat train_dy tum_cte0 PD\t0:00\t 1 (Priority)3423080 accelerat train_to tum_cte0 PD\t0:00\t 4 (Priority)3423082 accelerat train_to tum_cte0 PD\t0:00\t 2 (Priority)3423223 accelerat train_dy tum_cte0 PD\t0:00\t 8 (Priority)3412401 accelerat train_to tum_cte0 R 1-23:15:44\t 8 hkn[0412,0416,0425,0433,0718,0732,0803,0807]3418832 accelerat train_dy tum_cte0 R 1-03:26:49\t 8 hkn[0407,0415,0420,0422-0424,0729,0735]3422008 accelerat train_dy tum_cte0 R 5:02:35\t 1 hkn07133421964 accelerat train_to tum_cte0 R 5:14:59\t 1 hkn0720",,terminal_output +8614,23893398,"TERMINAL",0,0,"655065:00",,terminal_output +8615,23894420,"TERMINAL",0,0,"76171",,terminal_output +8616,23895443,"TERMINAL",0,0,"87282",,terminal_output +8617,23896505,"TERMINAL",0,0,"98393",,terminal_output 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---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/holiday/maskgit/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=160 \\n --init_lr=0 \\n --darkness_threshold=50 \\n --dyna_type=maskgit \\n --num_latent_actions=100 \\n --max_lr=8e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-maskgit-8-node-darkness-filter-$slurm_job_id \\n --tags dynamics maskgit 8-node post-launch-main darkness-filter \\n --entity instant-uv \\n --project jafar \\n --dyna_dim=1024 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=16 \\n --dyna_ffn_dim=4096 \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n",shellscript,tab +8642,23931088,"TERMINAL",0,0,"bash",,terminal_focus 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PD\t0:00\t 2 (Priority)3412401 accelerat train_to tum_cte0 R 1-23:16:37\t 8 hkn[0412,0416,0425,0433,0718,0732,0803,0807]3418832 accelerat train_dy tum_cte0 R 1-03:27:42\t 8 hkn[0407,0415,0420,0422-0424,0729,0735]3423234 accelerat train_dy tum_cte0 R\t0:00\t 8 hkn[0521-0528]3422008 accelerat train_dy tum_cte0 R 5:03:28\t 1 hkn07133421964 accelerat train_to tum_cte0 R 5:15:52\t 1 hkn0720",,terminal_output +8655,23946718,"TERMINAL",0,0,"983193",,terminal_output +8656,23947779,"TERMINAL",0,0,"40942304",,terminal_output +8657,23948421,"TERMINAL",0,0,"bash",,terminal_focus +8658,23948837,"TERMINAL",0,0,"1405315",,terminal_output +8659,23949862,"TERMINAL",0,0,"227537",,terminal_output +8660,23950905,"TERMINAL",0,0,"438648",,terminal_output +8661,23951977,"TERMINAL",0,0,"549759",,terminal_output +8662,23952991,"TERMINAL",0,0,"6550866:00",,terminal_output +8663,23954231,"TERMINAL",0,0,"761971",,terminal_output +8664,23955072,"TERMINAL",0,0,"8721082",,terminal_output 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train_dynamics_maskgit_8_node_3423234.log;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;CSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn[0521-0528]\r\nGpuFreq=control_disabled\r\nGpuFreq=control_disabled\r\nGpuFreq=control_disabled\r\nGpuFreq=control_disabled\r\nGpuFreq=control_disabled\r\nGpuFreq=control_disabled\r\nGpuFreq=control_disabled\r\nGpuFreq=control_disabled\r\n",,terminal_output +8688,23973810,"TERMINAL",0,0,"65108620",,terminal_output +8689,23974857,"TERMINAL",0,0,"7723082",,terminal_output +8690,23976137,"TERMINAL",0,0,"983193",,terminal_output +8691,23976954,"TERMINAL",0,0,"109424:004",,terminal_output +8692,23978186,"TERMINAL",0,0,"1105315",,terminal_output +8693,23979208,"TERMINAL",0,0,"216426",,terminal_output +8694,23980129,"TERMINAL",0,0,"327537",,terminal_output +8695,23981129,"TERMINAL",0,0,"438648",,terminal_output +8696,23981419,"TERMINAL",0,0,"watch",,terminal_focus +8697,23981907,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +8698,23984124,"TERMINAL",0,0,"idling",,terminal_command +8699,23984191,"TERMINAL",0,0,"]633;E;2025-08-14 17:17:17 idling;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;C[?1049h(B[?7hEvery 1.0s: sinfo_t_idlehkn1993.localdomain: Thu Aug 14 17:17:17 2025Partition dev_cpuonly: 11 nodes idle\rPartition cpuonly: 258 nodes idle\rPartition dev_accelerated:\t 2 nodes idle\rPartition accelerated: 25 nodes idle\rPartition dev_accelerated-h100 :\t 0 nodes idle\rPartition accelerated-h100:\t 1 nodes idle\rPartition large:\t 8 nodes idle",,terminal_output +8700,23985253,"TERMINAL",0,0,"8\t ",,terminal_output +8701,23986273,"TERMINAL",0,0,"9\t ",,terminal_output +8702,23987341,"TERMINAL",0,0,"20\t ",,terminal_output +8703,23988424,"TERMINAL",0,0,"1\t ",,terminal_output +8704,23989655,"TERMINAL",0,0,"2\t ",,terminal_output +8705,23990487,"TERMINAL",0,0,"3\t ",,terminal_output +8706,23991703,"TERMINAL",0,0,"4\t ",,terminal_output +8707,23992502,"TERMINAL",0,0,"5\t ",,terminal_output +8708,23993548,"TERMINAL",0,0,"6\t ",,terminal_output +8709,23994575,"TERMINAL",0,0,"7\t ",,terminal_output +8710,23995619,"TERMINAL",0,0,"8\t ",,terminal_output +8711,23996674,"TERMINAL",0,0,"9\t ",,terminal_output +8712,23997691,"TERMINAL",0,0,"30\t ",,terminal_output +8713,23998768,"TERMINAL",0,0,"1\t ",,terminal_output +8714,23999772,"TERMINAL",0,0,"2\t ",,terminal_output +8715,24000816,"TERMINAL",0,0,"3\t ",,terminal_output +8716,24001851,"TERMINAL",0,0,"47",,terminal_output +8717,24002963,"TERMINAL",0,0,"6\t ",,terminal_output +8718,24003989,"TERMINAL",0,0,"7\t ",,terminal_output +8719,24005013,"TERMINAL",0,0,"8\t ",,terminal_output +8720,24006037,"TERMINAL",0,0,"9\t ",,terminal_output +8721,24006875,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +8722,24007051,"TERMINAL",0,0,"40\t ",,terminal_output +8723,24007882,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.19.11\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/wandb/run-20250814_171739-3423234\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run dynamics-maskgit-8-node-darkness-filter-3423234\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/3423234\r\n",,terminal_output +8724,24008187,"TERMINAL",0,0,"1\t ",,terminal_output +8725,24009313,"TERMINAL",0,0,"2\t ",,terminal_output +8726,24010235,"TERMINAL",0,0,"3\t ",,terminal_output +8727,24011220,"TERMINAL",0,0,"4\t ",,terminal_output +8728,24012258,"TERMINAL",0,0,"5\t ",,terminal_output +8729,24013307,"TERMINAL",0,0,"6\t ",,terminal_output +8730,24014640,"TERMINAL",0,0,"7\t ",,terminal_output 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+8749,24033071,"TERMINAL",0,0,"6\t ",,terminal_output +8750,24034121,"TERMINAL",0,0,"7\t ",,terminal_output +8751,24035153,"TERMINAL",0,0,"8\t ",,terminal_output +8752,24036192,"TERMINAL",0,0,"9\t ",,terminal_output +8753,24037272,"TERMINAL",0,0,"10\t ",,terminal_output +8754,24038277,"TERMINAL",0,0,"1\t ",,terminal_output +8755,24039314,"TERMINAL",0,0,"2\t ",,terminal_output +8756,24040353,"TERMINAL",0,0,"3\t ",,terminal_output +8757,24041398,"TERMINAL",0,0,"4\t ",,terminal_output +8758,24042438,"TERMINAL",0,0,"5\t ",,terminal_output +8759,24043517,"TERMINAL",0,0,"6\t ",,terminal_output +8760,24044539,"TERMINAL",0,0,"7\t ",,terminal_output +8761,24045568,"TERMINAL",0,0,"8\t ",,terminal_output +8762,24046616,"TERMINAL",0,0,"9\t ",,terminal_output +8763,24047655,"TERMINAL",0,0,"20\t ",,terminal_output +8764,24048695,"TERMINAL",0,0,"1\t ",,terminal_output +8765,24049762,"TERMINAL",0,0,"2\t ",,terminal_output +8766,24050835,"TERMINAL",0,0,"3\t ",,terminal_output +8767,24051913,"TERMINAL",0,0,"4\t ",,terminal_output +8768,24052936,"TERMINAL",0,0,"6\t ",,terminal_output +8769,24053961,"TERMINAL",0,0,"7\t ",,terminal_output +8770,24054971,"TERMINAL",0,0,"8\t ",,terminal_output +8771,24056010,"TERMINAL",0,0,"9\t ",,terminal_output +8772,24057143,"TERMINAL",0,0,"30\t ",,terminal_output +8773,24058096,"TERMINAL",0,0,"1\t ",,terminal_output +8774,24059193,"TERMINAL",0,0,"2\t ",,terminal_output +8775,24060183,"TERMINAL",0,0,"3\t ",,terminal_output +8776,24061231,"TERMINAL",0,0,"4\t ",,terminal_output +8777,24062274,"TERMINAL",0,0,"5\t ",,terminal_output +8778,24063396,"TERMINAL",0,0,"6\t ",,terminal_output +8779,24064346,"TERMINAL",0,0,"71",,terminal_output +8780,24065404,"TERMINAL",0,0,"8\t ",,terminal_output +8781,24066457,"TERMINAL",0,0,"9\t ",,terminal_output +8782,24067461,"TERMINAL",0,0,"40\t ",,terminal_output +8783,24068708,"TERMINAL",0,0,"1\t ",,terminal_output +8784,24069732,"TERMINAL",0,0,"2\t ",,terminal_output +8785,24070584,"TERMINAL",0,0,"3\t ",,terminal_output +8786,24071621,"TERMINAL",0,0,"4\t ",,terminal_output +8787,24072667,"TERMINAL",0,0,"5\t ",,terminal_output +8788,24073708,"TERMINAL",0,0,"69",,terminal_output +8789,24074787,"TERMINAL",0,0,"7\t ",,terminal_output +8790,24075873,"TERMINAL",0,0,"8\t ",,terminal_output +8791,24076879,"TERMINAL",0,0,"9\t ",,terminal_output +8792,24077863,"TERMINAL",0,0,"518",,terminal_output +8793,24078911,"TERMINAL",0,0,"2\t ",,terminal_output +8794,24079972,"TERMINAL",0,0,"3\t ",,terminal_output +8795,24081038,"TERMINAL",0,0,"4\t ",,terminal_output +8796,24082123,"TERMINAL",0,0,"5\t ",,terminal_output +8797,24083148,"TERMINAL",0,0,"6\t ",,terminal_output +8798,24084098,"TERMINAL",0,0,"7\t ",,terminal_output +8799,24085192,"TERMINAL",0,0,"8\t ",,terminal_output +8800,24086176,"TERMINAL",0,0,"9\t ",,terminal_output +8801,24087240,"TERMINAL",0,0,"9:00\t ",,terminal_output +8802,24088261,"TERMINAL",0,0,"1\t ",,terminal_output +8803,24088982,"TERMINAL",0,0,"WARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\n",,terminal_output +8804,24089394,"TERMINAL",0,0,"2\t ",,terminal_output +8805,24090416,"TERMINAL",0,0,"3\t ",,terminal_output +8806,24090983,"TERMINAL",0,0,"WARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Dropping 18 examples of 89394 examples (shard 32).\r\nWARNING:absl:Missing metrics for step 220000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nWARNING:absl:Missing metrics for step 120000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nWARNING:absl:Missing metrics for step 140000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 180000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 240000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 140000\r\nWARNING:absl:Missing metrics for step 120000\r\nWARNING:absl:Missing metrics for step 120000\r\nWARNING:absl:Missing metrics for step 200000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 200000\r\nWARNING:absl:Missing metrics for step 60000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 180000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 120000\r\nWARNING:absl:Missing metrics for step 120000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 120000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 200000\r\nWARNING:absl:Missing metrics for step 200000\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nWARNING:absl:Missing metrics for step 180000\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nWARNING:absl:Missing metrics for step 180000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 180000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 220000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 240000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nWARNING:absl:Missing metrics for step 140000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 260000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 140000\r\nWARNING:absl:Missing metrics for step 120000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 120000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 140000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 220000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nWARNING:absl:Missing metrics for step 200000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nWARNING:absl:Missing metrics for step 140000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 260000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 180000\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 180000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 260000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nWARNING:absl:Missing metrics for step 60000\r\nWARNING:absl:Missing metrics for step 140000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 260000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 140000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 260000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 120000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 180000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 240000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 260000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 180000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nWARNING:absl:Missing metrics for step 260000\r\nWARNING:absl:Missing metrics for step 80000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 120000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 120000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 220000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 200000\r\nWARNING:absl:Missing metrics for step 80000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 260000\r\nWARNING:absl:Missing metrics for step 180000\r\nWARNING:absl:Missing metrics for step 200000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 200000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 140000\r\nWARNING:absl:Missing metrics for step 260000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 180000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 120000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 140000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 260000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nWARNING:absl:Missing metrics for step 140000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 160000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 120000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 200000\r\nWARNING:absl:Missing metrics for step 60000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 120000\r\nWARNING:absl:Missing metrics for step 200000\r\nWARNING:absl:Missing metrics for step 80000\r\nWARNING:absl:Missing metrics for step 200000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 120000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nWARNING:absl:Missing metrics for step 289000\r\nWARNING:absl:Missing metrics for step 160000\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 220000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 200000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 220000\r\nWARNING:absl:Missing metrics for step 200000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 200000\r\nWARNING:absl:Missing metrics for step 140000\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 220000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 260000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 120000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 240000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 200000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 180000\r\nWARNING:absl:Missing metrics for step 180000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nWARNING:absl:Missing metrics for step 260000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 200000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 140000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 140000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 140000\r\nWARNING:absl:Missing metrics for step 260000\r\nWARNING:absl:Missing metrics for step 80000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 260000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 180000\r\nWARNING:absl:Missing metrics for step 260000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nWARNING:absl:Missing metrics for step 160000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 140000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nWARNING:absl:Missing metrics for step 289000\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 180000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 290000\r\nWARNING:absl:Missing metrics for step 280000\r\nWARNING:absl:Missing metrics for step 80000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nWARNING:absl:Missing metrics for step 200000\r\nWARNING:absl:Missing metrics for step 260000\r\nWARNING:absl:Missing metrics for step 120000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 200000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 260000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 200000\r\nWARNING:absl:Missing metrics for step 100000\r\nWARNING:absl:Missing metrics for step 60000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 200000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 291000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nWARNING:absl:Missing metrics for step 291000\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 200000\r\nWARNING:absl:Missing metrics for step 291000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nWARNING:absl:Missing metrics for step 291000\r\nWARNING:absl:Missing metrics for step 180000\r\nWARNING:absl:Missing metrics for step 140000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 180000\r\nWARNING:absl:Missing metrics for step 289000\r\nWARNING:absl:Missing metrics for step 260000\r\nWARNING:absl:Missing metrics for step 180000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 160000\r\nWARNING:absl:Missing metrics for step 180000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 120000\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 220000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 240000\r\nWARNING:absl:Missing metrics for step 120000\r\nWARNING:absl:Missing metrics for step 140000\r\nWARNING:absl:Missing metrics for step 240000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 220000\r\nWARNING:absl:Missing metrics for step 240000\r\nWARNING:absl:Missing metrics for step 100000\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 260000\r\nWARNING:absl:Missing metrics for step 180000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 140000\r\nWARNING:absl:Missing metrics for step 240000\r\nWARNING:absl:Missing metrics for step 100000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nWARNING:absl:Missing metrics for step 291000\r\nWARNING:absl:Missing metrics for step 240000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nWARNING:absl:Missing metrics for step 280000\r\nWARNING:absl:Missing metrics for step 120000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 120000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 200000\r\nWARNING:absl:Missing metrics for step 80000\r\nWARNING:absl:Missing metrics for step 291000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 220000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 220000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 290000\r\nWARNING:absl:Missing metrics for step 140000\r\nWARNING:absl:Missing metrics for step 100000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 160000\r\nWARNING:absl:Missing metrics for step 120000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 120000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 160000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 290000\r\nWARNING:absl:Missing metrics for step 260000\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 220000\r\nWARNING:absl:Missing metrics for step 60000\r\nWARNING:absl:Missing metrics for step 180000\r\nWARNING:absl:Missing metrics for step 160000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nWARNING:absl:Missing metrics for step 260000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 289000\r\nWARNING:absl:Missing metrics for step 60000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 120000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 220000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nWARNING:absl:Missing metrics for step 140000\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 160000\r\nWARNING:absl:Missing metrics for step 120000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 290000\r\nWARNING:absl:Missing metrics for step 160000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 291000\r\nWARNING:absl:Missing metrics for step 80000\r\nWARNING:absl:Missing metrics for step 220000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 291000\r\nWARNING:absl:Missing metrics for step 240000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 260000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 180000\r\nWARNING:absl:Missing metrics for step 160000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 140000\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 291000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 291000\r\nWARNING:absl:Missing metrics for step 200000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 260000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 200000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 180000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 240000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 200000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 100000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 289000\r\nWARNING:absl:Missing metrics for step 100000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 240000\r\nWARNING:absl:Missing metrics for step 220000\r\nWARNING:absl:Missing metrics for step 140000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 240000\r\nWARNING:absl:Missing metrics for step 40000\r\nWARNING:absl:Missing metrics for step 260000\r\nWARNING:absl:Missing metrics for step 240000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 290000\r\nWARNING:absl:Missing metrics for step 100000\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 291000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 289000\r\nWARNING:absl:Missing metrics for step 291000\r\nWARNING:absl:Missing metrics for step 200000\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 200000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 160000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 120000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 160000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 240000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 160000\r\nWARNING:absl:Missing metrics for step 140000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 240000\r\nWARNING:absl:Missing metrics for step 260000\r\nWARNING:absl:Missing metrics for step 100000\r\nWARNING:absl:Missing metrics for step 240000\r\nWARNING:absl:Missing metrics for step 291000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 100000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 240000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 160000\r\nWARNING:absl:Missing metrics for step 100000\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 100000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nWARNING:absl:Missing metrics for step 291000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nWARNING:absl:Missing metrics for step 100000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 180000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 100000\r\nWARNING:absl:Missing metrics for step 240000\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 160000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 220000\r\nWARNING:absl:Missing metrics for step 60000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nWARNING:absl:Missing metrics for step 220000\r\nWARNING:absl:Missing metrics for step 291000\r\nWARNING:absl:Missing metrics for step 100000\r\nWARNING:absl:Missing metrics for step 120000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 140000\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nWARNING:absl:Missing metrics for step 140000\r\nWARNING:absl:Missing metrics for step 160000\r\nWARNING:absl:Missing metrics for step 100000\r\nWARNING:absl:Missing metrics for step 220000\r\nWARNING:absl:Missing metrics for step 240000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 260000\r\nWARNING:absl:Missing metrics for step 100000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 220000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 240000\r\nWARNING:absl:Missing metrics for step 100000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 180000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 180000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 100000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 100000\r\nWARNING:absl:Missing metrics for step 291000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 240000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 100000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nWARNING:absl:Missing metrics for step 120000\r\nWARNING:absl:Missing metrics for step 200000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 100000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nWARNING:absl:Missing metrics for step 291000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 240000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 160000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 220000\r\nWARNING:absl:Missing metrics for step 100000\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 140000\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 291000\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 291000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 160000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 220000\r\nWARNING:absl:Missing metrics for step 260000\r\nWARNING:absl:Missing metrics for step 291000\r\nWARNING:absl:Missing metrics for step 120000\r\nWARNING:absl:Missing metrics for step 220000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 291000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 100000\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 160000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 220000\r\nWARNING:absl:Missing metrics for step 220000\r\nWARNING:absl:Missing metrics for step 290000\r\nWARNING:absl:Missing metrics for step 160000\r\nWARNING:absl:Missing metrics for step 160000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 291000\r\nWARNING:absl:Missing metrics for step 220000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nWARNING:absl:Missing metrics for step 220000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 240000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 160000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 100000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 289000\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 160000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 100000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 290000\r\nWARNING:absl:Missing metrics for step 160000\r\nWARNING:absl:Missing metrics for step 220000\r\nWARNING:absl:Missing metrics for step 240000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 180000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 160000\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 120000\r\nWARNING:absl:Missing metrics for step 200000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 140000\r\nWARNING:absl:Missing metrics for step 240000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nWARNING:absl:Missing metrics for step 291000\r\nWARNING:absl:Missing metrics for step 20000\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 140000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 290000\r\nWARNING:absl:Missing metrics for step 291000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nWARNING:absl:Missing metrics for step 240000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 291000\r\nWARNING:absl:Missing metrics for step 260000\r\nWARNING:absl:Missing metrics for step 220000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nWARNING:absl:Missing metrics for step 80000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 260000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 220000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 291000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 100000\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 160000\r\nWARNING:absl:Missing metrics for step 160000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nWARNING:absl:Missing metrics for step 280000\r\nWARNING:absl:Missing metrics for step 120000\r\n",,terminal_output +8807,24091094,"TERMINAL",0,0,"ERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 260000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 291000\r\nWARNING:absl:Missing metrics for step 140000\r\nWARNING:absl:Missing metrics for step 180000\r\nWARNING:absl:Missing metrics for step 100000\r\nWARNING:absl:Missing metrics for step 260000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nWARNING:absl:Missing metrics for step 240000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 140000\r\nWARNING:absl:Missing metrics for step 20000\r\nWARNING:absl:Missing metrics for step 80000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 180000\r\nWARNING:absl:Missing metrics for step 280000\r\nWARNING:absl:Missing metrics for step 240000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 160000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 180000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 100000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 291000\r\nWARNING:absl:Missing metrics for step 220000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 100000\r\nWARNING:absl:Missing metrics for step 289000\r\nWARNING:absl:Missing metrics for step 220000\r\nWARNING:absl:Missing metrics for step 100000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 200000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 291000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 160000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 291000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nWARNING:absl:Missing metrics for step 80000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 240000\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 180000\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nWARNING:absl:Missing metrics for step 200000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 260000\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 160000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 240000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/260000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 120000\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/120000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 291000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 180000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/180000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 290000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 160000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 290000\r\nWARNING:absl:Missing metrics for step 100000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/290000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 140000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/140000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nWARNING:absl:Missing metrics for step 289000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/289000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/040000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 200000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/200000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 220000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/220000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/080000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 240000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/240000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/060000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 291000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/291000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 100000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/100000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 280000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/280000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 160000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/160000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401/020000/metrics/metrics not found.\r\n",,terminal_output +8808,24091421,"TERMINAL",0,0,"4\t ",,terminal_output +8809,24092061,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1251: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +8810,24092462,"TERMINAL",0,0,"5\t ",,terminal_output +8811,24093461,"TERMINAL",0,0,"6\t ",,terminal_output +8812,24094547,"TERMINAL",0,0,"7\t ",,terminal_output +8813,24095060,"TERMINAL",0,0,"Running on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\nRunning on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\n",,terminal_output +8814,24095577,"TERMINAL",0,0,"8\t ",,terminal_output +8815,24096059,"TERMINAL",0,0,"Running on 32 devices.\r\nCounting all components: ['dynamics', 'lam', 'tokenizer']\r\nParameter counts:\r\n{'dynamics': 271933440, 'lam': 35118240, 'tokenizer': 33750256, 'total': 340801936}\r\nStarting training from step 0...\r\n",,terminal_output +8816,24096588,"TERMINAL",0,0,"99",,terminal_output +8817,24097626,"TERMINAL",0,0,"10\t ",,terminal_output +8818,24098671,"TERMINAL",0,0,"1\t ",,terminal_output +8819,24099714,"TERMINAL",0,0,"2\t ",,terminal_output +8820,24100748,"TERMINAL",0,0,"3\t ",,terminal_output +8821,24101787,"TERMINAL",0,0,"4\t ",,terminal_output +8822,24102908,"TERMINAL",0,0,"5\t ",,terminal_output +8823,24103930,"TERMINAL",0,0,"7\t ",,terminal_output +8824,24104913,"TERMINAL",0,0,"8\t ",,terminal_output +8825,24106133,"TERMINAL",0,0,"98",,terminal_output +8826,24107218,"TERMINAL",0,0,"20\t ",,terminal_output +8827,24108232,"TERMINAL",0,0,"1\t ",,terminal_output +8828,24109261,"TERMINAL",0,0,"2\t ",,terminal_output +8829,24110322,"TERMINAL",0,0,"3\t ",,terminal_output +8830,24111545,"TERMINAL",0,0,"4\t ",,terminal_output +8831,24112573,"TERMINAL",0,0,"5\t ",,terminal_output +8832,24113557,"TERMINAL",0,0,"6\t ",,terminal_output +8833,24114582,"TERMINAL",0,0,"7\t ",,terminal_output +8834,24115507,"TERMINAL",0,0,"8\t ",,terminal_output +8835,24116634,"TERMINAL",0,0,"9\t ",,terminal_output +8836,24117576,"TERMINAL",0,0,"30\t ",,terminal_output +8837,24118681,"TERMINAL",0,0,"1\t ",,terminal_output +8838,24119723,"TERMINAL",0,0,"2\t ",,terminal_output +8839,24120706,"TERMINAL",0,0,"3\t ",,terminal_output +8840,24121765,"TERMINAL",0,0,"4\t ",,terminal_output +8841,24122761,"TERMINAL",0,0,"5\t ",,terminal_output +8842,24123808,"TERMINAL",0,0,"6\t ",,terminal_output +8843,24124850,"TERMINAL",0,0,"7\t ",,terminal_output +8844,24125888,"TERMINAL",0,0,"9\t ",,terminal_output +8845,24126926,"TERMINAL",0,0,"40\t ",,terminal_output +8846,24127070,"TERMINAL",0,0,"2025-08-14 17:19:39.267208: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-08-14 17:19:39.377678: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-08-14 17:19:39.377723: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-08-14 17:19:39.458650: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-08-14 17:19:39.491307: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +8847,24127996,"TERMINAL",0,0,"1\t ",,terminal_output +8848,24129021,"TERMINAL",0,0,"2\t ",,terminal_output +8849,24130049,"TERMINAL",0,0,"3\t ",,terminal_output +8850,24131171,"TERMINAL",0,0,"2025-08-14 17:19:44.016889: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +8851,24131207,"TERMINAL",0,0,"4\t ",,terminal_output +8852,24132097,"TERMINAL",0,0,"2025-08-14 17:19:44.269962: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +8853,24132502,"TERMINAL",0,0,"50",,terminal_output +8854,24133566,"TERMINAL",0,0,"6\t ",,terminal_output +8855,24134580,"TERMINAL",0,0,"7\t ",,terminal_output +8856,24135540,"TERMINAL",0,0,"8\t ",,terminal_output +8857,24136701,"TERMINAL",0,0,"9\t ",,terminal_output +8858,24137727,"TERMINAL",0,0,"50\t ",,terminal_output +8859,24138708,"TERMINAL",0,0,"1\t ",,terminal_output +8860,24139775,"TERMINAL",0,0,"2\t ",,terminal_output +8861,24140796,"TERMINAL",0,0,"3\t ",,terminal_output +8862,24141790,"TERMINAL",0,0,"4\t ",,terminal_output +8863,24142825,"TERMINAL",0,0,"5\t ",,terminal_output +8864,24143871,"TERMINAL",0,0,"7\t ",,terminal_output +8865,24144994,"TERMINAL",0,0,"8\t ",,terminal_output +8866,24145956,"TERMINAL",0,0,"9\t ",,terminal_output +8867,24146993,"TERMINAL",0,0,"20:00\t ",,terminal_output 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+9090,24360195,"slurm/jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",874,0,"",shellscript,selection_mouse +9091,24399267,"slurm/jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",906,0,"",shellscript,selection_mouse +9092,24399736,"slurm/jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",781,0,"",shellscript,selection_mouse +9093,24400759,"slurm/jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",517,0,"",shellscript,selection_mouse +9094,24401271,"slurm/jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",541,0,"",shellscript,selection_mouse +9095,24402815,"TERMINAL",0,0,"bash",,terminal_focus +9096,24404778,"TERMINAL",0,0,"idling",,terminal_command +9097,24404837,"TERMINAL",0,0,"]633;E;2025-08-14 17:24:17 idling;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;C[?1049h(B[?7hEvery 1.0s: sinfo_t_idlehkn1993.localdomain: Thu Aug 14 17:24:17 2025Partition dev_cpuonly: 11 nodes idle\rPartition cpuonly: 258 nodes idle\rPartition dev_accelerated:\t 2 nodes idle\rPartition accelerated: 36 nodes idle\rPartition dev_accelerated-h100 :\t 0 nodes idle\rPartition accelerated-h100:\t 1 nodes idle\rPartition large:\t 8 nodes idle",,terminal_output +9098,24405911,"TERMINAL",0,0,"9\t ",,terminal_output +9099,24406923,"TERMINAL",0,0,"20\t ",,terminal_output +9100,24408063,"TERMINAL",0,0,"1\t ",,terminal_output +9101,24409087,"TERMINAL",0,0,"2\t ",,terminal_output +9102,24410122,"TERMINAL",0,0,"3\t ",,terminal_output +9103,24411133,"TERMINAL",0,0,"4\t ",,terminal_output +9104,24412159,"TERMINAL",0,0,"5\t ",,terminal_output +9105,24413184,"TERMINAL",0,0,"6\t ",,terminal_output +9106,24414219,"TERMINAL",0,0,"7\t ",,terminal_output +9107,24415332,"TERMINAL",0,0,"8\t ",,terminal_output +9108,24416461,"TERMINAL",0,0,"9\t ",,terminal_output +9109,24417334,"TERMINAL",0,0,"30\t ",,terminal_output +9110,24418405,"TERMINAL",0,0,"1\t ",,terminal_output +9111,24419430,"TERMINAL",0,0,"2\t ",,terminal_output +9112,24420457,"TERMINAL",0,0,"3\t ",,terminal_output +9113,24421581,"TERMINAL",0,0,"4\t ",,terminal_output +9114,24422636,"TERMINAL",0,0,"5\t ",,terminal_output +9115,24423664,"TERMINAL",0,0,"6\t [?1049l\r[?1l>]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining]633;D;0",,terminal_output +9116,24432319,"killer.sh",0,0,"",shellscript,tab +9117,24433750,"killer copy.sh",0,0,"response=$(curl -s https://home.cit.tum.de/~nguyenal/killer.html)\n\nif echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n scancel --me && \\n echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,tab +9118,24439589,"killer_partition.sh",0,0,"response=$(curl -s https://home.cit.tum.de/~nguyenal/killer.html)\n\nif echo ""$response"" | grep -q ""KILL ALL JOBS""; then\n scancel --me && \\n echo ""killing all jobs""\nelse\n echo ""leaving jobs be""\nfi\n",shellscript,tab +9119,24440747,"killer_partition.sh",93,0,"",shellscript,selection_mouse +9120,24441273,"killer_partition.sh",103,0,"",shellscript,selection_mouse +9121,24442669,"killer_partition.sh",108,0,"",shellscript,selection_mouse +9122,24443617,"killer_partition.sh",104,0,"",shellscript,selection_mouse +9123,24444468,"killer_partition.sh",103,0,"",shellscript,selection_mouse +9124,24445217,"killer_partition.sh",103,4,"",shellscript,content +9125,24445644,"killer_partition.sh",103,0,"P",shellscript,content +9126,24445645,"killer_partition.sh",104,0,"",shellscript,selection_keyboard +9127,24446134,"killer_partition.sh",104,0,"A",shellscript,content +9128,24446135,"killer_partition.sh",105,0,"",shellscript,selection_keyboard +9129,24446556,"killer_partition.sh",105,0,"R",shellscript,content +9130,24446556,"killer_partition.sh",106,0,"",shellscript,selection_keyboard +9131,24446829,"killer_partition.sh",106,0,"T",shellscript,content +9132,24446830,"killer_partition.sh",107,0,"",shellscript,selection_keyboard +9133,24446905,"killer_partition.sh",107,0,"I",shellscript,content +9134,24446906,"killer_partition.sh",108,0,"",shellscript,selection_keyboard +9135,24447043,"killer_partition.sh",108,0,"T",shellscript,content +9136,24447044,"killer_partition.sh",109,0,"",shellscript,selection_keyboard +9137,24447219,"killer_partition.sh",109,0,"I",shellscript,content +9138,24447220,"killer_partition.sh",110,0,"",shellscript,selection_keyboard +9139,24447311,"killer_partition.sh",110,0,"O",shellscript,content +9140,24447311,"killer_partition.sh",111,0,"",shellscript,selection_keyboard +9141,24447469,"killer_partition.sh",111,0,"N",shellscript,content +9142,24447470,"killer_partition.sh",112,0,"",shellscript,selection_keyboard +9143,24447846,"killer_partition.sh",112,0," ",shellscript,content +9144,24447846,"killer_partition.sh",113,0,"",shellscript,selection_keyboard +9145,24448819,"killer_partition.sh",112,0,"",shellscript,selection_command +9146,24449753,"killer_partition.sh",100,0,"",shellscript,selection_mouse +9147,24449885,"killer_partition.sh",98,4,"KILL",shellscript,selection_mouse +9148,24450087,"killer_partition.sh",98,5,"KILL ",shellscript,selection_mouse +9149,24450124,"killer_partition.sh",98,14,"KILL PARTITION",shellscript,selection_mouse +9150,24450406,"killer_partition.sh",98,15,"KILL PARTITION ",shellscript,selection_mouse +9151,24450612,"killer_partition.sh",98,19,"KILL PARTITION JOBS",shellscript,selection_mouse +9152,24451546,"killer_partition.sh",170,0,"",shellscript,selection_mouse +9153,24452586,"killer_partition.sh",137,0,"",shellscript,selection_mouse +9154,24452747,"killer_partition.sh",137,1,"-",shellscript,selection_mouse +9155,24452789,"killer_partition.sh",137,2,"--",shellscript,selection_mouse +9156,24452869,"killer_partition.sh",137,3,"--m",shellscript,selection_mouse +9157,24452998,"killer_partition.sh",137,4,"--me",shellscript,selection_mouse +9158,24453524,"killer_partition.sh",137,4,"",shellscript,content +9159,24455350,"TERMINAL",0,0,"bash",,terminal_focus +9160,24456254,"TERMINAL",0,0,"queue",,terminal_command +9161,24456318,"TERMINAL",0,0,"]633;E;2025-08-14 17:25:09 queue;7bbc766d-cb8c-45e4-8c85-2cc7c12fa702]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Thu Aug 14 17:25:09 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3415713 accelerat train_dy tum_cte0 PD\t0:00\t 1 (Priority)3423080 accelerat train_to tum_cte0 PD\t0:00\t 4 (Priority)3423082 accelerat train_to tum_cte0 PD\t0:00\t 2 (Priority)3423250 accelerat train_dy tum_cte0 PD\t0:00\t 8 (Priority)3412401 accelerat train_to tum_cte0 R 1-23:25:08\t 8 hkn[0412,0416,0425,0433,0718,0732,0803,0807]3418832 accelerat train_dy tum_cte0 R 1-03:36:13\t 8 hkn[0407,0415,0420,0422-0424,0729,0735]3422008 accelerat train_dy tum_cte0 R 5:11:59\t 1 hkn07133421964 accelerat train_to tum_cte0 R 5:24:23\t 1 hkn0720",,terminal_output +9162,24457356,"TERMINAL",0,0,"10942:004",,terminal_output +9163,24458402,"TERMINAL",0,0,"110515",,terminal_output +9164,24459472,"TERMINAL",0,0,"21626",,terminal_output +9165,24460475,"TERMINAL",0,0,"32737",,terminal_output +9166,24460853,"killer_partition.sh",136,0,"",shellscript,selection_mouse +9167,24461543,"TERMINAL",0,0,"43848",,terminal_output +9168,24462200,"killer_partition.sh",137,0,"",shellscript,selection_command +9169,24462465,"killer_partition.sh",137,0,"3421964",shellscript,content +9170,24462597,"TERMINAL",0,0,"54959",,terminal_output +9171,24463196,"killer_partition.sh",144,0," ",shellscript,content +9172,24463197,"killer_partition.sh",145,0,"",shellscript,selection_keyboard +9173,24463660,"TERMINAL",0,0,"6520630",,terminal_output +9174,24464701,"TERMINAL",0,0,"76171",,terminal_output +9175,24465749,"TERMINAL",0,0,"87282",,terminal_output +9176,24466842,"TERMINAL",0,0,"98393",,terminal_output +9177,24467776,"TERMINAL",0,0,"2094104",,terminal_output +9178,24467812,"killer_partition.sh",145,0,"3422008",shellscript,content +9179,24468822,"TERMINAL",0,0,"121626",,terminal_output +9180,24469602,"killer_partition.sh",190,0,"",shellscript,selection_mouse +9181,24469866,"TERMINAL",0,0,"32737",,terminal_output +9182,24470967,"TERMINAL",0,0,"43848",,terminal_output +9183,24471960,"TERMINAL",0,0,"54959",,terminal_output +9184,24473091,"TERMINAL",0,0,"6530640",,terminal_output +9185,24474146,"TERMINAL",0,0,"76171",,terminal_output +9186,24475025,"killer_partition.sh",221,0,"",shellscript,selection_mouse +9187,24475075,"TERMINAL",0,0,"87282",,terminal_output +9188,24476160,"TERMINAL",0,0,"98393",,terminal_output +9189,24477176,"TERMINAL",0,0,"3094204",,terminal_output +9190,24478225,"TERMINAL",0,0,"130515",,terminal_output +9191,24479268,"TERMINAL",0,0,"21626",,terminal_output +9192,24480315,"TERMINAL",0,0,"32737",,terminal_output +9193,24481384,"TERMINAL",0,0,"43848",,terminal_output +9194,24482660,"TERMINAL",0,0,"54959",,terminal_output +9195,24483532,"TERMINAL",0,0,"6540650",,terminal_output +9196,24484558,"TERMINAL",0,0,"76171",,terminal_output +9197,24485535,"TERMINAL",0,0,"87282",,terminal_output +9198,24486604,"TERMINAL",0,0,"98393",,terminal_output +9199,24487729,"TERMINAL",0,0,"4094304",,terminal_output +9200,24488149,"TERMINAL",0,0,"bash",,terminal_focus +9201,24488695,"TERMINAL",0,0,"140515",,terminal_output +9202,24489762,"TERMINAL",0,0,"21626",,terminal_output +9203,24490747,"TERMINAL",0,0,"32737",,terminal_output +9204,24491802,"TERMINAL",0,0,"43848",,terminal_output +9205,24492778,"TERMINAL",0,0,"tmux a",,terminal_command +9206,24492836,"TERMINAL",0,0,"]633;E;2025-08-14 17:25:45 tmux a;cc3c0b2f-e52c-4c7f-81cf-c8a8bbae24c8]633;C[?1049h[?1h=[?12l[?25h[?1000l[?1002l[?1003l[?1006l[?1005l(B[?12l[?25h[?1006l[?1000l[?1002l[?1003l[?2004l[>c[>q[?25lEvery 60.0s: sh killer.shhkn1993.localdomain: Thu Aug 14 17:24:57 2025\r\nleaving jobs be\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n \r\n[0] 0:watch* ""hkn1993.localdomain"" 17:25 14-Aug-25(B[?12l[?25h(B[?12l[?25h[?1006l[?1000l[?1002l[?1003l[?2004l[?25lEvery 60.0s: sh killer.shhkn1993.localdomain: Thu Aug 14 17:24:57 2025\r\nleaving jobs be\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n \r\n[0] 0:watch* ""hkn1993.localdomain"" 17:25 14-Aug-25(B[?12l[?25h",,terminal_output +9207,24492872,"TERMINAL",0,0,"555065:00",,terminal_output +9208,24492904,"TERMINAL",0,0,"[?7727h",,terminal_output +9209,24493976,"TERMINAL",0,0,"76171",,terminal_output +9210,24494943,"TERMINAL",0,0,"87282",,terminal_output +9211,24495009,"TERMINAL",0,0,"[?25l(jafar) [tum_cte0515@hkn1993 jafar]$ watch -n60 ""sh killer.sh""\r\n(jafar) [tum_cte0515@hkn1993 jafar]$ watch -n60 ""sh killer.sh""\r\n(jafar) [tum_cte0515@hkn1993 jafar]$ watch -n60 ""sh killer.sh""\r\n(jafar) [tum_cte0515@hkn1993 jafar]$ watch -n60 ""sh killer.sh""\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n[?12l[?25h[?25l[0] 0:bash* ""hkn1993.localdomain"" 17:25 14-Aug-25(B[?12l[?25h(jafar) [tum_cte0515@hkn1993 jafar]$ [?2004h",,terminal_output +9212,24496025,"TERMINAL",0,0,"98393",,terminal_output +9213,24497153,"TERMINAL",0,0,"5094404",,terminal_output +9214,24498074,"TERMINAL",0,0,"150515",,terminal_output +9215,24499199,"TERMINAL",0,0,"21626",,terminal_output +9216,24500225,"TERMINAL",0,0,"32737",,terminal_output +9217,24501216,"TERMINAL",0,0,"43848",,terminal_output +9218,24502249,"TERMINAL",0,0,"54959",,terminal_output +9219,24503445,"TERMINAL",0,0,"657:00610",,terminal_output +9220,24504334,"TERMINAL",0,0,"76171",,terminal_output +9221,24505378,"TERMINAL",0,0,"87282",,terminal_output +9222,24506417,"TERMINAL",0,0,"98393",,terminal_output +9223,24507470,"TERMINAL",0,0,"6:0094504",,terminal_output +9224,24507791,"TERMINAL",0,0,"[?25l[0] 0:bash* ""hkn1993.localdomain"" 17:26 14-Aug-25(B[?12l[?25h",,terminal_output +9225,24508510,"TERMINAL",0,0,"16:00515",,terminal_output +9226,24509628,"TERMINAL",0,0,"21626",,terminal_output +9227,24510603,"TERMINAL",0,0,"32737",,terminal_output +9228,24511061,"TERMINAL",0,0,"[?25l│││││││││││││││││││(B \r\n(jafar) [tum_cte0515@hkn1993 jafar]$ watch -n60 ""sh killer.sh""\r\n(jafar) [tum_cte0515@hkn1993 jafar]$ watch -n60 ""sh killer.sh""\r\n(jafar) [tum_cte0515@hkn1993 jafar]$ watch -n60 ""sh killer.sh""\r\n(jafar) [tum_cte0515@hkn1993 jafar]$ \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\r\n[0] 0:bash* ""hkn1993.localdomain"" 17:26 14-Aug-25(B[?12l[?25h[?2004l[?25l│││││││││││││││││││(B\r\n[0] 0:tmux* ""hkn1993.localdomain"" 17:26 14-Aug-25(B[?12l[?25h(jafar) [tum_cte0515@hkn1993 jafar]$ ",,terminal_output +9229,24511702,"TERMINAL",0,0,"43848",,terminal_output +9230,24512716,"TERMINAL",0,0,"54959",,terminal_output +9231,24512725,"TERMINAL",0,0,"[?25l│││││││││││││││││││(B\r\n[0] 0:bash* ""hkn1993.localdomain"" 17:26 14-Aug-25(B[?12l[?25h[?2004h(jafar) [tum_cte0515@hkn1993 jafar]$ ",,terminal_output +9232,24513725,"TERMINAL",0,0,"6510620",,terminal_output +9233,24513934,"TERMINAL",0,0,"[?25l│││││││││││││││││││(B[?12l[?25h",,terminal_output +9234,24514765,"TERMINAL",0,0,"76171",,terminal_output +9235,24514934,"TERMINAL",0,0,"watch -n60 ""sh killer.sh""",,terminal_output +9236,24515809,"TERMINAL",0,0,"87282",,terminal_output +9237,24516864,"TERMINAL",0,0,"9943:004",,terminal_output +9238,24517910,"TERMINAL",0,0,"1110515",,terminal_output +9239,24519064,"TERMINAL",0,0,"21626",,terminal_output +9240,24519997,"TERMINAL",0,0,"32737",,terminal_output +9241,24521122,"TERMINAL",0,0,"43848",,terminal_output +9242,24522088,"TERMINAL",0,0,"54959",,terminal_output +9243,24522491,"TERMINAL",0,0,"[?25l│││││││││││││││││││(B[?12l[?25h",,terminal_output +9244,24523164,"TERMINAL",0,0,"6520630",,terminal_output +9245,24524217,"TERMINAL",0,0,"76171",,terminal_output +9246,24524253,"TERMINAL",0,0,"watch -n60 ""sh 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o.sh""",,terminal_output +9262,24527819,"TERMINAL",0,0,"n.sh""",,terminal_output +9263,24528486,"TERMINAL",0,0,"120515",,terminal_output +9264,24529402,"TERMINAL",0,0,"21626",,terminal_output +9265,24530464,"TERMINAL",0,0,"32737",,terminal_output +9266,24531343,"TERMINAL",0,0,"\n[?2004l",,terminal_output +9267,24531489,"TERMINAL",0,0,"[?25lEvery 60.0s: sh ki... hkn1993.localdomain: Thu Aug 14 17:26:24 2025\nleaving jobs be\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n[?12l[?25h",,terminal_output +9268,24531514,"TERMINAL",0,0,"43848",,terminal_output +9269,24532625,"TERMINAL",0,0,"54959",,terminal_output +9270,24533272,"TERMINAL",0,0,"[?25l│││││││││││││││││││(B\r\n[0] 0:watch* ""hkn1993.localdomain"" 17:26 14-Aug-25(B[?12l[?25h",,terminal_output +9271,24533616,"TERMINAL",0,0,"[?25l│││││││││││││││││││(B[?12l[?25h[?2004h[?25l│││││││││││││││││││(B\r\n[0] 0:bash* ""hkn1993.localdomain"" 17:26 14-Aug-25(B[?12l[?25h",,terminal_output +9272,24533617,"TERMINAL",0,0,"6530640",,terminal_output 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preprocess_coinrun_chunked_3541783.log\r\n]0;tum_cte0515@hkn1990:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/preprocess/coinrun",,terminal_output +36,45296,"TERMINAL",0,0,"tail -f preprocess_coinrun_chunked_3541783.log",,terminal_command +37,45340,"TERMINAL",0,0,"]633;C warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/data/.venv/lib/python3.10/site-packages/stable_baselines3/common/save_util.py:167: UserWarning: Could not deserialize object clip_range. Consider using `custom_objects` argument to replace this object.\r\nException: code expected at most 16 arguments, got 18\r\n warnings.warn(\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/data/.venv/lib/python3.10/site-packages/stable_baselines3/common/save_util.py:167: UserWarning: Could not deserialize object lr_schedule. Consider using `custom_objects` argument to replace this object.\r\nException: code expected at most 16 arguments, got 18\r\n warnings.warn(\r\nAL lib: (WW) alc_initconfig: Failed to initialize backend ""pulse""\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/data/.venv/lib/python3.10/site-packages/stable_baselines3/common/vec_env/patch_gym.py:49: UserWarning: You provided an OpenAI Gym environment. We strongly recommend transitioning to Gymnasium environments. Stable-Baselines3 is automatically wrapping your environments in a compatibility layer, which could potentially cause issues.\r\n warnings.warn(\r\n",,terminal_output +38,149567,"TERMINAL",0,0,"bash",,terminal_focus +39,161968,"data/jasmine_data/ViZDoomPPO/common/envs.py",0,0,"import typing as t\n\nimport cv2\nimport numpy as np\nimport vizdoom\nfrom gym import Env\nfrom gym import spaces\nfrom stable_baselines3.common import vec_env\nfrom stable_baselines3.common.callbacks import EvalCallback\nfrom stable_baselines3.ppo import ppo, policies\nfrom vizdoom import GameVariable\n\nfrom common.models import init_model\nfrom common.utils import get_available_actions\n\nfrom tqdm import tqdm\nimport time\nfrom stable_baselines3.common.callbacks import BaseCallback\nfrom stable_baselines3.common.vec_env import SubprocVecEnv, VecTransposeImage\n\nimport torch\nimport os \nimport sys\n\nFrame = np.ndarray\n\nDOOM_ENV_WITH_BOTS_ARGS = """"""\n -host 1 \n -deathmatch \n +viz_nocheat 0 \n +cl_run 1 \n +name AGENT \n +colorset 0 \n +sv_forcerespawn 1 \n +sv_respawnprotect 1 \n +sv_nocrouch 1 \n +sv_noexit 1\n """"""\n\n\nclass DoomEnv(Env):\n """"""Wrapper environment following OpenAI's gym interface for a VizDoom game instance.""""""\n\n def __init__(self,\n game: vizdoom.DoomGame,\n frame_processor: t.Callable,\n frame_skip: int = 4):\n super().__init__()\n\n # Determine action space\n self.action_space = spaces.Discrete(game.get_available_buttons_size())\n\n # Determine observation space\n h, w, c = game.get_screen_height(), game.get_screen_width(), game.get_screen_channels()\n new_h, new_w, new_c = frame_processor(np.zeros((h, w, c))).shape\n self.observation_space = spaces.Box(low=0, high=255, shape=(new_h, new_w, new_c), dtype=np.uint8)\n\n # Assign other variables\n self.game = game\n self.possible_actions = np.eye(self.action_space.n).tolist() # VizDoom needs a list of buttons states.\n self.frame_skip = frame_skip\n self.frame_processor = frame_processor\n\n self.empty_frame = np.zeros(self.observation_space.shape, dtype=np.uint8)\n self.state = self.empty_frame\n\n def step(self, action: int) -> t.Tuple[Frame, int, bool, t.Dict]:\n """"""Apply an action to the environment.\n\n Args:\n action:\n\n Returns:\n A tuple containing:\n - A numpy ndarray containing the current environment state.\n - The reward obtained by applying the provided action.\n - A boolean flag indicating whether the episode has ended.\n - An empty info dict.\n """"""\n reward = self.game.make_action(self.possible_actions[action], self.frame_skip)\n done = self.game.is_episode_finished()\n self.state = self._get_frame(done)\n\n return self.state, reward, done, {}\n\n def reset(self) -> Frame:\n """"""Resets the environment.\n\n Returns:\n The initial state of the new environment.\n """"""\n self.game.new_episode()\n self.state = self._get_frame()\n\n return self.state\n\n def close(self) -> None:\n self.game.close()\n\n def render(self, mode='human'):\n pass\n\n def _get_frame(self, done: bool = False) -> Frame:\n return self.frame_processor(\n self.game.get_state().screen_buffer) if not done else self.empty_frame\n\n\nclass DoomWithBots(DoomEnv):\n\n def __init__(self, game, frame_processor, frame_skip, n_bots):\n super().__init__(game, frame_processor, frame_skip)\n self.n_bots = n_bots\n self.last_frags = 0\n self._reset_bots()\n\n # Redefine the action space using combinations.\n self.possible_actions = get_available_actions(np.array(game.get_available_buttons()))\n self.action_space = spaces.Discrete(len(self.possible_actions))\n\n def step(self, action):\n self.game.make_action(self.possible_actions[action], self.frame_skip)\n\n # Compute rewards.\n frags = self.game.get_game_variable(GameVariable.FRAGCOUNT)\n reward = frags - self.last_frags\n self.last_frags = frags\n\n # Check for episode end.\n self._respawn_if_dead()\n done = self.game.is_episode_finished()\n self.state = self._get_frame(done)\n\n return self.state, reward, done, {}\n\n def reset(self):\n self._reset_bots()\n self.last_frags = 0\n\n return super().reset()\n\n def _respawn_if_dead(self):\n if not self.game.is_episode_finished():\n if self.game.is_player_dead():\n self.game.respawn_player()\n\n def _reset_bots(self):\n # Make sure you have the bots.cfg file next to the program entry point.\n self.game.send_game_command('removebots')\n for i in range(self.n_bots):\n self.game.send_game_command('addbot')\n\n def _print_state(self):\n server_state = self.game.get_server_state()\n player_scores = list(zip(\n server_state.players_names,\n server_state.players_frags,\n server_state.players_in_game))\n player_scores = sorted(player_scores, key=lambda tup: tup[1])\n\n # print('*** DEATHMATCH RESULTS ***')\n # for player_name, player_score, player_ingame in player_scores:\n # if player_ingame:\n # print(f' - {player_name}: {player_score}')\n\n\ndef default_frame_processor(frame: Frame) -> Frame:\n return cv2.resize(frame[40:, 4:-4], None, fx=.5, fy=.5, interpolation=cv2.INTER_AREA)\n\n\ndef create_env(scenario: str, **kwargs) -> DoomEnv:\n # Create a VizDoom instance.\n game = vizdoom.DoomGame()\n game.load_config(f'scenarios/{scenario}.cfg')\n game.set_window_visible(False) \n game.init()\n\n # Wrap the game with the Gym adapter.\n return DoomEnv(game, **kwargs)\n\n\ndef create_env_with_bots(scenario: str, **kwargs) -> DoomEnv:\n # Create a VizDoom instance.\n game = vizdoom.DoomGame()\n game.load_config(f'scenarios/{scenario}.cfg')\n game.add_game_args(DOOM_ENV_WITH_BOTS_ARGS)\n game.set_window_visible(False) \n game.init()\n\n return DoomWithBots(game, **kwargs)\n\ndef create_vec_env(n_envs: int = 1, **kwargs) -> VecTransposeImage:\n return VecTransposeImage(SubprocVecEnv([lambda: create_env(**kwargs) for _ in range(n_envs)]))\n\ndef vec_env_with_bots(n_envs: int = 1, **kwargs) -> VecTransposeImage:\n return VecTransposeImage(SubprocVecEnv([lambda: create_env_with_bots(**kwargs) for _ in range(n_envs)]))\n\n\ndef create_eval_vec_env(**kwargs) -> vec_env.VecTransposeImage:\n return create_vec_env(n_envs=1, **kwargs)\n\n\n\ndef solve_env(env: vec_env.VecTransposeImage, eval_env: vec_env.VecTransposeImage, scenario: str, agent_args: t.Dict, resume: bool = False, load_path: str = None):\n device = torch.device(""cuda"" if torch.cuda.is_available() else ""cpu"")\n \n if resume:\n # Load the existing model\n if load_path != """":\n agent = ppo.PPO.load(load_path, env=env, tensorboard_log='logs/tensorboard', **agent_args)\n print(f""Resumed training from {load_path}"")\n else:\n print(""Resume selected but no path provided"")\n sys.exit()\n else:\n # Create a new agent\n agent = ppo.PPO(policies.ActorCriticCnnPolicy, env, tensorboard_log='logs/tensorboard', seed=0, **agent_args)\n init_model(agent)\n\n agent.policy.to(device)\n\n # Create callbacks.\n eval_callback = EvalCallback(\n eval_env,\n n_eval_episodes=5,\n eval_freq=4000,\n log_path=f'logs/evaluations/{scenario}',\n best_model_save_path=f'logs/models/{scenario}'\n )\n\n # Set up progress bar\n total_timesteps = 10_000_000\n pbar = tqdm(total=total_timesteps, desc=""Training Progress"")\n class ProgressBarCallback(BaseCallback):\n def __init__(self, pbar):\n super().__init__()\n self.pbar = pbar\n self.last_time = time.time()\n self.last_timesteps = 0\n \n def _on_step(self):\n current_timesteps = self.num_timesteps - self.last_timesteps\n self.pbar.update(current_timesteps)\n self.last_timesteps = self.num_timesteps\n \n current_time = time.time()\n steps_per_second = current_timesteps / (current_time - self.last_time)\n self.pbar.set_postfix({""steps/s"": f""{steps_per_second:.2f}""})\n self.last_time = current_time\n \n return True\n \n def on_training_end(self):\n self.pbar.close()\n\n progress_callback = ProgressBarCallback(pbar)\n\n # Start the training process.\n try:\n agent.learn(\n total_timesteps=10_000_000, \n tb_log_name=scenario, \n callback=[eval_callback, progress_callback],\n reset_num_timesteps=not resume # Don't reset timesteps if resuming\n )\n finally:\n pbar.close()\n env.close()\n eval_env.close()\n\n return agent\n\ndef save_model(agent: ppo.PPO, scenario: str):\n """"""Save the trained model.""""""\n save_path = f'logs/models/{scenario}/final_model'\n if not os.path.exists(os.path.dirname(save_path)):\n os.makedirs(os.path.dirname(save_path))\n agent.save(save_path)\n print(f""Model saved to {save_path}"")\n\n\ndef load_model(load_path: str, env: vec_env.VecTransposeImage) -> ppo.PPO:\n """"""Load a trained model.""""""\n if not os.path.exists(os.path.dirname(load_path)):\n os.makedirs(os.path.dirname(load_path))\n agent = ppo.PPO.load(load_path, env=env)\n print(f""Model loaded from {load_path}"")\n return agent",python,tab +40,161971,"data/jasmine_data/ViZDoomPPO/common/envs.py",8130,0,"",python,selection_mouse +41,162562,"data/jasmine_data/ViZDoomPPO/common/envs.py",8038,0,"",python,selection_mouse 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# # # # # # # # # # # # # \n \n# FORK OF LEANDRO KIELIGER'S DOOM PPO TUTORIAL: https://lkieliger.medium.com/deep-reinforcement-learning-in-practice-by-playing-doom-part-1-getting-started-618c99075c77 \n\n# SCRIPT TO TRAIN A PPO AGENT ON THE ViZDOOM DEATHMATCH ENVIRONMENT. \n\nimport numpy as np\nimport os\nimport vizdoom\nfrom common import envs\nfrom common.models import CustomCNN\nfrom stable_baselines3.common.vec_env import VecTransposeImage, SubprocVecEnv\nimport random\nimport string\nfrom vizdoom.vizdoom import GameVariable\nfrom collections import deque\nimport torch\n\ndevice = torch.device(""cuda"" if torch.cuda.is_available() else ""cpu"")\n# device=torch.device(""mps"")\nprint(f""Using device: {device}"")\n\n# Rewards\n# 1 per kill\nreward_factor_frag = 1.0\nreward_factor_damage = 0.01\n\n# Player can move at ~16.66 units per tick\nreward_factor_distance = 5e-4\npenalty_factor_distance = -2.5e-3\nreward_threshold_distance = 3.0\n\n# Pistol clips have 10 bullets\nreward_factor_ammo_increment = 0.02\nreward_factor_ammo_decrement = -0.01\n\n# Player starts at 100 health\nreward_factor_health_increment = 0.02\nreward_factor_health_decrement = -0.01\nreward_factor_armor_increment = 0.01\n\n\n# List of game variables storing ammunition information. Used for keeping track of ammunition-related rewards.\nAMMO_VARIABLES = [\n GameVariable.AMMO0,\n GameVariable.AMMO1,\n GameVariable.AMMO2,\n GameVariable.AMMO3,\n GameVariable.AMMO4,\n GameVariable.AMMO5,\n GameVariable.AMMO6,\n GameVariable.AMMO7,\n GameVariable.AMMO8,\n GameVariable.AMMO9,\n]\n\n# List of game variables storing weapon information. Used for keeping track of ammunition-related rewards.\nWEAPON_VARIABLES = [\n GameVariable.WEAPON0,\n GameVariable.WEAPON1,\n GameVariable.WEAPON2,\n GameVariable.WEAPON3,\n GameVariable.WEAPON4,\n GameVariable.WEAPON5,\n GameVariable.WEAPON6,\n GameVariable.WEAPON7,\n GameVariable.WEAPON8,\n GameVariable.WEAPON9,\n]\n\n\nclass DoomWithBotsShaped(envs.DoomWithBots):\n """"""An environment wrapper for a Doom deathmatch game with bots.\n\n Rewards are shaped according to the multipliers defined in the notebook.\n """"""\n\n def __init__(self, game, frame_processor, frame_skip, n_bots, shaping):\n super().__init__(game, frame_processor, frame_skip, n_bots)\n\n # Give a random two-letter name to the agent for identifying instances in parallel learning.\n self.name = """".join(random.choices(string.ascii_uppercase + string.digits, k=2))\n self.shaping = shaping\n\n # Internal states\n self.last_health = 100\n self.last_x, self.last_y = self._get_player_pos()\n self.ammo_state = self._get_ammo_state()\n self.weapon_state = self._get_weapon_state()\n self.total_rew = self.last_damage_dealt = self.deaths = self.last_frags = (\n self.last_armor\n ) = 0\n\n # Store individual reward contributions for logging purposes\n self.rewards_stats = {\n ""frag"": 0,\n ""damage"": 0,\n ""ammo"": 0,\n ""health"": 0,\n ""armor"": 0,\n ""distance"": 0,\n }\n\n def step(self, action, array=False):\n # Perform the action as usual\n state, reward, done, info = super().step(action)\n\n self._log_reward_stat(""frag"", reward)\n\n # Adjust the reward based on the shaping table\n if self.shaping:\n shaped_reward = reward + self.shape_rewards()\n else:\n shaped_reward = reward\n\n self.total_rew += shaped_reward\n\n return state, shaped_reward, done, info\n\n def reset(self):\n self._print_state()\n\n state = super().reset()\n\n self.last_health = 100\n self.last_x, self.last_y = self._get_player_pos()\n self.last_armor = self.last_frags = self.total_rew = self.deaths = 0\n\n # Damage count is not cleared when starting a new episode: https://github.com/mwydmuch/ViZDoom/issues/399\n # self.last_damage_dealt = 0\n\n # Reset reward stats\n for k in self.rewards_stats.keys():\n self.rewards_stats[k] = 0\n\n return state\n\n def seed(self, seed=None):\n if seed is not None:\n random.seed(seed)\n np.random.seed(seed)\n return [seed]\n\n def shape_rewards(self):\n reward_contributions = [\n self._compute_damage_reward(),\n self._compute_ammo_reward(),\n self._compute_health_reward(),\n self._compute_armor_reward(),\n self._compute_distance_reward(*self._get_player_pos()),\n ]\n\n return sum(reward_contributions)\n\n def _respawn_if_dead(self):\n if not self.game.is_episode_finished():\n # Check if player is dead\n if self.game.is_player_dead():\n self.deaths += 1\n self._reset_player()\n\n def _compute_distance_reward(self, x, y):\n """"""Computes a reward/penalty based on the distance travelled since last update.""""""\n dx = self.last_x - x\n dy = self.last_y - y\n\n distance = np.sqrt(dx**2 + dy**2)\n\n if distance - reward_threshold_distance > 0:\n reward = reward_factor_distance\n else:\n reward = -reward_factor_distance\n\n self.last_x = x\n self.last_y = y\n self._log_reward_stat(""distance"", reward)\n\n return reward\n\n def _compute_damage_reward(self):\n """"""Computes a reward based on total damage inflicted to enemies since last update.""""""\n damage_dealt = self.game.get_game_variable(GameVariable.DAMAGECOUNT)\n reward = reward_factor_damage * (damage_dealt - self.last_damage_dealt)\n\n self.last_damage_dealt = damage_dealt\n self._log_reward_stat(""damage"", reward)\n\n return reward\n\n def _compute_health_reward(self):\n """"""Computes a reward/penalty based on total health change since last update.""""""\n # When the player is dead, the health game variable can be -999900\n health = max(self.game.get_game_variable(GameVariable.HEALTH), 0)\n\n health_reward = reward_factor_health_increment * max(\n 0, health - self.last_health\n )\n health_penalty = reward_factor_health_decrement * min(\n 0, health - self.last_health\n )\n reward = health_reward - health_penalty\n\n self.last_health = health\n self._log_reward_stat(""health"", reward)\n\n return reward\n\n def _compute_armor_reward(self):\n """"""Computes a reward/penalty based on total armor change since last update.""""""\n armor = self.game.get_game_variable(GameVariable.ARMOR)\n reward = reward_factor_armor_increment * max(0, armor - self.last_armor)\n\n self.last_armor = armor\n self._log_reward_stat(""armor"", reward)\n\n return reward\n\n def _compute_ammo_reward(self):\n """"""Computes a reward/penalty based on total ammunition change since last update.""""""\n self.weapon_state = self._get_weapon_state()\n\n new_ammo_state = self._get_ammo_state()\n ammo_diffs = (new_ammo_state - self.ammo_state) * self.weapon_state\n ammo_reward = reward_factor_ammo_increment * max(0, np.sum(ammo_diffs))\n ammo_penalty = reward_factor_ammo_decrement * min(0, np.sum(ammo_diffs))\n reward = ammo_reward - ammo_penalty\n\n self.ammo_state = new_ammo_state\n self._log_reward_stat(""ammo"", reward)\n\n return reward\n\n def _get_player_pos(self):\n """"""Returns the player X- and Y- coordinates.""""""\n return self.game.get_game_variable(\n GameVariable.POSITION_X\n ), self.game.get_game_variable(GameVariable.POSITION_Y)\n\n def _get_ammo_state(self):\n """"""Returns the total available ammunition per weapon slot.""""""\n ammo_state = np.zeros(10)\n\n for i in range(10):\n ammo_state[i] = self.game.get_game_variable(AMMO_VARIABLES[i])\n\n return ammo_state\n\n def _get_weapon_state(self):\n """"""Returns which weapon slots can be used. Available weapons are encoded as ones.""""""\n weapon_state = np.zeros(10)\n\n for i in range(10):\n weapon_state[i] = self.game.get_game_variable(WEAPON_VARIABLES[i])\n\n return weapon_state\n\n def _log_reward_stat(self, kind: str, reward: float):\n self.rewards_stats[kind] += reward\n\n def _reset_player(self):\n self.last_health = 100\n self.last_armor = 0\n self.game.respawn_player()\n self.last_x, self.last_y = self._get_player_pos()\n self.ammo_state = self._get_ammo_state()\n\n def _print_state(self):\n super()._print_state()\n\n\nREWARD_THRESHOLDS = [5, 10, 15, 20, 25, 25]\n\n# Curriculum based learning\nclass DoomWithBotsCurriculum(DoomWithBotsShaped):\n def __init__(\n self,\n game,\n frame_processor,\n frame_skip,\n n_bots,\n shaping,\n initial_level=0,\n max_level=5,\n rolling_mean_length=10,\n ):\n super().__init__(game, frame_processor, frame_skip, n_bots, shaping)\n\n # Initialize ACS script difficulty level\n game.send_game_command(""pukename change_difficulty 0"")\n\n # Internal state\n self.level = initial_level\n self.max_level = max_level\n self.rolling_mean_length = rolling_mean_length\n self.last_rewards = deque(maxlen=rolling_mean_length)\n\n def step(self, action, array=False):\n # Perform action step as usual\n state, reward, done, infos = super().step(action, array)\n\n # After an episode, check whether difficulty should be increased.\n if done:\n self.last_rewards.append(self.total_rew)\n run_mean = np.mean(self.last_rewards)\n print(\n ""Avg. last 10 runs of {}: {:.2f}. Current difficulty level: {}"".format(\n self.name, run_mean, self.level\n )\n )\n if (\n run_mean > REWARD_THRESHOLDS[self.level]\n and len(self.last_rewards) >= self.rolling_mean_length\n ):\n self._change_difficulty()\n\n return state, reward, done, infos\n\n def reset(self):\n state = super().reset()\n self.game.send_game_command(f""pukename change_difficulty {self.level}"")\n\n return state\n\n def _change_difficulty(self):\n """"""Adjusts the difficulty by setting the difficulty level in the ACS script.""""""\n if self.level < self.max_level:\n self.level += 1\n print(f""Changing difficulty for {self.name} to {self.level}"")\n self.game.send_game_command(f""pukename change_difficulty {self.level}"")\n self.last_rewards = deque(maxlen=self.rolling_mean_length)\n else:\n print(f""{self.name} already at max level!"")\n\n\ndef game_instance(scenario):\n """"""Creates a Doom game instance.""""""\n game = vizdoom.DoomGame()\n dir_path = os.path.dirname(os.path.realpath(__file__))\n cfg_pth = os.path.join(dir_path, ""scenarios"", f""{scenario}.cfg"")\n game.load_config(cfg_pth)\n game.add_game_args(envs.DOOM_ENV_WITH_BOTS_ARGS)\n game.set_window_visible(False)\n game.init()\n\n return game\n\n\ndef env_with_bots_shaped(scenario, **kwargs) -> envs.DoomEnv:\n """"""Wraps a Doom game instance in an environment with shaped rewards.""""""\n game = game_instance(scenario)\n return DoomWithBotsShaped(game, **kwargs)\n\n\ndef vec_env_with_bots_shaped(n_envs=1, **kwargs) -> VecTransposeImage:\n """"""Wraps Doom game instances in a vectorized environment with shaped rewards using true parallelism.""""""\n return VecTransposeImage(\n SubprocVecEnv([lambda: env_with_bots_shaped(**kwargs) for _ in range(n_envs)])\n )\n\n\ndef env_with_bots_curriculum(scenario, **kwargs) -> envs.DoomEnv:\n """"""Wraps a Doom game instance in an environment with shaped rewards and curriculum.""""""\n game = game_instance(scenario)\n return DoomWithBotsCurriculum(game, **kwargs)\n\n\ndef vec_env_with_bots_curriculum(n_envs=1, **kwargs) -> VecTransposeImage:\n """"""Wraps a Doom game instance in a vectorized environment with shaped rewards and curriculum.""""""\n return VecTransposeImage(\n SubprocVecEnv([lambda: env_with_bots_curriculum(**kwargs) for _ in range(n_envs)])\n )\n\n\nif __name__ == ""__main__"":\n scenario = ""deathmatch_simple""\n\n # Agent parameters.\n agent_args = {\n ""n_steps"": 4096,\n ""learning_rate"": 1e-4,\n ""batch_size"": 64,\n ""policy_kwargs"": {""features_extractor_class"": CustomCNN},\n ""device"": device,\n }\n\n # Environment parameters.\n env_args = {\n ""scenario"": scenario,\n ""frame_skip"": 4,\n ""frame_processor"": envs.default_frame_processor,\n ""n_bots"": 6,\n ""shaping"": True,\n ""initial_level"": 1,\n ""max_level"": 5,\n }\n\n # In the evaluation environment we measure frags only.\n eval_env_args = dict(env_args)\n eval_env_args[""shaping""] = False\n\n n_envs = 32\n # Create environments with bots and shaping.\n env = vec_env_with_bots_curriculum(\n n_envs, **env_args\n ) # You can increase the number of parallel environments\n eval_env = vec_env_with_bots_curriculum(n_envs, **eval_env_args)\n\n agent = envs.solve_env(\n env,\n eval_env,\n scenario,\n agent_args,\n resume=False,\n )\n envs.save_model(agent, ""agent_test"")\n",python,tab +68,219592,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",0,0,"# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n# /$$ /$$ /$$ /$$$$$$$ /$$$$$$$ /$$$$$$$ /$$$$$$ #\n# | $$ | $$|__/ | $$__ $$ | $$__ $$| $$__ $$ /$$__ $$ #\n# | $$ | $$ /$$ /$$$$$$$$| $$ \ $$ /$$$$$$ /$$$$$$ /$$$$$$/$$$$ | $$ \ $$| $$ \ $$| $$ \ $$ #\n# | $$ / $$/| $$|____ /$$/| $$ | $$ /$$__ $$ /$$__ $$| $$_ $$_ $$ | $$$$$$$/| $$$$$$$/| $$ | $$ #\n# \ $$ $$/ | $$ /$$$$/ | $$ | $$| $$ \ $$| $$ \ $$| $$ \ $$ \ $$ | $$____/ | $$____/ | $$ | $$ #\n# \ $$$/ | $$ /$$__/ | $$ | $$| $$ | $$| $$ | $$| $$ | $$ | $$ | $$ | $$ | $$ | $$ #\n# \ $/ | $$ /$$$$$$$$| $$$$$$$/| $$$$$$/| $$$$$$/| $$ | $$ | $$ | $$ | $$ | $$$$$$/ #\n# \_/ |__/|________/|_______/ \______/ \______/ |__/ |__/ |__/ |__/ |__/ \______/ #\n# #\n# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n \n# FORK OF LEANDRO KIELIGER'S DOOM PPO TUTORIAL: https://lkieliger.medium.com/deep-reinforcement-learning-in-practice-by-playing-doom-part-1-getting-started-618c99075c77 \n\n# SCRIPT TO RUN PPO AGENT AND GENERATE DATASET FOR DOOM ENVIRONMENT. \n\nfrom dataclasses import dataclass\nimport imageio\nfrom common import envs\nimport torch\nfrom vizdoom.vizdoom import GameVariable\n\n\nimport os\n\nimport numpy as np\nfrom train_ppo_parallel import DoomWithBotsCurriculum, game_instance\nfrom stable_baselines3.common.vec_env import (\n VecTransposeImage,\n DummyVecEnv\n)\nfrom loguru import logger\nimport tyro\nfrom jasmine_data.utils import save_chunks\n\n# To replicate frame_skip in the environment\nACTION_REPEAT = 4\n\n@dataclass\nclass Args:\n num_episodes_train: int = 1000\n num_episodes_val: int = 100\n num_episodes_test: int = 100\n min_episode_length: int = 100\n max_episode_length: int = 1000\n chunk_size: int = 160\n chunks_per_file: int = 100\n agent_path: str = """"\n seed: int = 0\n output_dir: str = ""data/vizdoom_episodes""\n generate_gif: bool = False\n\nargs = tyro.cli(Args)\ndevice = torch.device(""cuda"" if torch.cuda.is_available() else ""cpu"")\nlogger.info(f""Using device: {device}"")\n\ndef dummy_vec_env_with_bots_curriculum(n_envs=1, **kwargs) -> VecTransposeImage:\n """"""Wraps a Doom game instance in a vectorized environment with shaped rewards and curriculum.""""""\n scenario = kwargs.pop(""scenario"") # Remove 'scenario' from kwargs\n return VecTransposeImage(\n DummyVecEnv(\n [lambda: DoomWithBotsCurriculum(game_instance(scenario), **kwargs)] * n_envs\n )\n )\n\ndef make_gif(agent, eval_env_args):\n """"""Generate a GIF by running the agent in the environment.\n\n Args:\n agent: The trained PPO agent.\n file_path (str): Path to save the generated GIF.\n eval_env_args (dict): Arguments for the evaluation environment.\n num_episodes (int): Number of episodes to run.\n \n Returns:\n list: Collected health values for analysis.\n """"""\n # Set frame_skip to 1 to capture all frames\n eval_env_args['frame_skip'] = 1\n env = dummy_vec_env_with_bots_curriculum(1, **eval_env_args)\n\n images = []\n actions = []\n health_values = []\n current_action = None\n frame_counter = 0\n\n obs = env.reset()\n\n done = False\n while not done and frame_counter < args.max_episode_length:\n if frame_counter % ACTION_REPEAT == 0:\n current_action, _ = agent.predict(obs)\n \n obs, _, done, _ = env.step(current_action)\n\n # Get the raw screen buffer from the Doom game instance\n screen = env.venv.envs[0].game.get_state().screen_buffer\n\n # Get the current health value\n health = env.venv.envs[0].game.get_game_variable(GameVariable.HEALTH)\n health_values.append(health) # Store the health value\n\n actions.append(current_action)\n images.append(screen)\n\n frame_counter += 1\n\n print(""Health values:"", health_values)\n print(""Number of health values:"", len(health_values))\n print(""Number of actions:"", len(actions))\n print(""Number of images:"", len(images))\n\n # Save only the first 1000 frames to avoid large file size\n imageio.mimsave(args.output_dir, images, fps=20)\n env.close()\n logger.info(f""GIF saved to {args.output_dir}"")\n \n return health_values\n\ndef make_array_records_dataset(agent, eval_env_args, num_episodes, split):\n """"""Generate a dataset by running the agent in the environment and saving the data as array record files.\n\n Args:\n agent: The trained PPO agent.\n output_dir (str): Directory to save the array record files.\n eval_env_args (dict): Arguments for the evaluation environment.\n num_episodes (int): Number of episodes to run.\n """"""\n # Set frame_skip to 1 to capture all frames\n eval_env_args['frame_skip'] = 1\n env = dummy_vec_env_with_bots_curriculum(1, **eval_env_args)\n\n current_action = None\n episode_idx = 0\n episode_metadata = []\n obs_chunks = []\n act_chunks = []\n file_idx = 0\n output_dir_split = os.path.join(args.output_dir, split)\n os.makedirs(output_dir_split, exist_ok=True)\n\n while episode_idx < num_episodes:\n obs = env.reset()\n done = False\n frame_counter = 0\n\n observations_seq = []\n actions_seq = []\n health_values_seq = []\n episode_obs_chunks = []\n episode_act_chunks = []\n\n # --- Run episode ---\n while not done and frame_counter < args.max_episode_length:\n screen = env.venv.envs[0].game.get_state().screen_buffer\n health = env.venv.envs[0].game.get_game_variable(GameVariable.HEALTH)\n if frame_counter % ACTION_REPEAT == 0:\n current_action, _ = agent.predict(obs)\n\n obs, _, done, _ = env.step(current_action)\n\n observations_seq.append(screen)\n actions_seq.append(int(current_action.item()))\n health_values_seq.append(int(health))\n\n if len(observations_seq) == args.chunk_size:\n episode_obs_chunks.append(observations_seq)\n episode_act_chunks.append(actions_seq)\n observations_seq = []\n actions_seq = []\n\n frame_counter += 1\n\n # --- Save episode ---\n if frame_counter >= args.min_episode_length:\n if observations_seq:\n if len(observations_seq) < args.chunk_size:\n print(\n f""Warning: Inconsistent chunk_sizes. Episode has {len(observations_seq)} frames, ""\n f""which is smaller than the requested chunk_size: {args.chunk_size}. ""\n ""This might lead to performance degradation during training.""\n )\n episode_obs_chunks.append(observations_seq)\n episode_act_chunks.append(actions_seq)\n\n obs_chunks_data = [\n np.stack(seq).astype(np.uint8)\n for seq in episode_obs_chunks\n ]\n act_chunks_data = [\n np.stack(act).astype(np.uint8) for act in episode_act_chunks\n ]\n obs_chunks.extend(obs_chunks_data)\n act_chunks.extend(act_chunks_data)\n\n ep_metadata, file_idx, obs_chunks, act_chunks = save_chunks(\n file_idx, args.chunks_per_file, output_dir_split, obs_chunks, act_chunks\n )\n episode_metadata.extend(ep_metadata)\n\n print(f""Episode {episode_idx} completed, length: {frame_counter}."")\n episode_idx += 1\n else:\n print(f""Episode too short ({frame_counter}), resampling..."")\n env.close()\n\ndef main():\n scenario = ""deathmatch_simple""\n\n env_args = {\n ""scenario"": scenario,\n ""frame_skip"": 1,\n ""frame_processor"": envs.default_frame_processor,\n ""n_bots"": 8,\n ""shaping"": True,\n ""initial_level"": 5,\n ""max_level"": 5,\n ""rolling_mean_length"": 10,\n }\n\n eval_env_args = dict(env_args)\n new_env = dummy_vec_env_with_bots_curriculum(1, **env_args)\n agent = envs.load_model(\n args.agent_path,\n new_env,\n )\n\n if args.generate_gif:\n make_gif(agent, eval_env_args)\n else:\n make_array_records_dataset(agent, num_episodes=args.num_episodes_train, eval_env_args=eval_env_args, split=""train"")\n make_array_records_dataset(agent, num_episodes=args.num_episodes_val, eval_env_args=eval_env_args, split=""val"")\n make_array_records_dataset(agent, num_episodes=args.num_episodes_test, eval_env_args=eval_env_args, split=""test"")\n\nif __name__ == ""__main__"":\n main()\n",python,tab +69,226400,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",0,0,"""""""\nGenerates a dataset of random-action CoinRun episodes.\nEpisodes are saved individually as memory-mapped files for efficient loading.\n""""""\n\nfrom dataclasses import dataclass\n\nfrom gym3 import types_np\nimport numpy as np\nfrom procgen import ProcgenGym3Env\nimport tyro\nimport json\nimport os\nfrom jasmine_data.utils import save_chunks\n\n\n@dataclass\nclass Args:\n num_episodes_train: int = 10000\n num_episodes_val: int = 500\n num_episodes_test: int = 500\n output_dir: str = ""data/coinrun_episodes""\n min_episode_length: int = 1000\n max_episode_length: int = 1000\n chunk_size: int = 160\n chunks_per_file: int = 100\n seed: int = 0\n\n\nargs = tyro.cli(Args)\nassert (\n args.max_episode_length >= args.min_episode_length\n), ""Maximum episode length must be greater than or equal to minimum episode length.""\n\nif args.min_episode_length < args.chunk_size:\n print(\n ""Warning: Minimum episode length is smaller than chunk size. Note that episodes shorter than the chunk size will be discarded.""\n )\n\n\n# --- Generate episodes ---\ndef generate_episodes(num_episodes, split):\n episode_idx = 0\n episode_metadata = []\n obs_chunks = []\n act_chunks = []\n file_idx = 0\n output_dir_split = os.path.join(args.output_dir, split)\n while episode_idx < num_episodes:\n seed = np.random.randint(0, 10000)\n env = ProcgenGym3Env(num=1, env_name=""coinrun"", start_level=seed)\n\n observations_seq = []\n actions_seq = []\n episode_obs_chunks = []\n episode_act_chunks = []\n\n # --- Run episode ---\n step_t = 0\n first_obs = True\n for step_t in range(args.max_episode_length):\n _, obs, first = env.observe()\n action = types_np.sample(env.ac_space, bshape=(env.num,))\n env.act(action)\n observations_seq.append(obs[""rgb""])\n actions_seq.append(action)\n if len(observations_seq) == args.chunk_size:\n episode_obs_chunks.append(observations_seq)\n episode_act_chunks.append(actions_seq)\n observations_seq = []\n actions_seq = []\n if first and not first_obs:\n break\n first_obs = False\n\n # --- Save episode ---\n if step_t + 1 >= args.min_episode_length:\n if observations_seq:\n if len(observations_seq) < args.chunk_size:\n print(\n f""Warning: Inconsistent chunk_sizes. Episode has {len(observations_seq)} frames, ""\n f""which is smaller than the requested chunk_size: {args.chunk_size}. ""\n ""This might lead to performance degradation during training.""\n )\n episode_obs_chunks.append(observations_seq)\n episode_act_chunks.append(actions_seq)\n\n obs_chunks_data = [\n np.concatenate(seq, axis=0).astype(np.uint8)\n for seq in episode_obs_chunks\n ]\n act_chunks_data = [\n np.concatenate(act, axis=0) for act in episode_act_chunks\n ]\n obs_chunks.extend(obs_chunks_data)\n act_chunks.extend(act_chunks_data)\n\n ep_metadata, file_idx, obs_chunks, act_chunks = save_chunks(\n file_idx, args.chunks_per_file, output_dir_split, obs_chunks, act_chunks\n )\n episode_metadata.extend(ep_metadata)\n\n print(f""Episode {episode_idx} completed, length: {step_t + 1}."")\n episode_idx += 1\n else:\n print(f""Episode too short ({step_t + 1}), resampling..."")\n\n if len(obs_chunks) > 0:\n print(\n f""Warning: Dropping {len(obs_chunks)} chunks for consistent number of chunks per file."",\n ""Consider changing the chunk_size and chunks_per_file parameters to prevent data-loss."",\n )\n\n print(f""Done generating {split} split"")\n return episode_metadata\n\n\ndef get_action_space():\n env = ProcgenGym3Env(num=1, env_name=""coinrun"", start_level=0)\n return env.ac_space.eltype.n\n\n\ndef main():\n # Set random seed and create dataset directories\n np.random.seed(args.seed)\n # --- Generate episodes ---\n train_episode_metadata = generate_episodes(args.num_episodes_train, ""train"")\n val_episode_metadata = generate_episodes(args.num_episodes_val, ""val"")\n test_episode_metadata = generate_episodes(args.num_episodes_test, ""test"")\n\n # --- Save metadata ---\n metadata = {\n ""env"": ""coinrun"",\n ""num_actions"": get_action_space(),\n ""num_episodes_train"": args.num_episodes_train,\n ""num_episodes_val"": args.num_episodes_val,\n ""num_episodes_test"": args.num_episodes_test,\n ""avg_episode_len_train"": np.mean(\n [ep[""avg_seq_len""] for ep in train_episode_metadata]\n ),\n ""avg_episode_len_val"": np.mean(\n [ep[""avg_seq_len""] for ep in val_episode_metadata]\n ),\n ""avg_episode_len_test"": np.mean(\n [ep[""avg_seq_len""] for ep in test_episode_metadata]\n ),\n ""episode_metadata_train"": train_episode_metadata,\n ""episode_metadata_val"": val_episode_metadata,\n ""episode_metadata_test"": test_episode_metadata,\n }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")\n\n\nif __name__ == ""__main__"":\n main()\n",python,tab +70,247266,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",0,0,"",python,tab +71,247268,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",8034,0,"",python,selection_mouse +72,247900,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",8026,16,"episode_metadata",python,selection_mouse +73,257800,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",8275,0,"",python,selection_mouse +74,257808,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",8274,0,"",python,selection_command 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+273,310234,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9186,0,"_",python,content +274,310235,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9187,0,"",python,selection_keyboard +275,310594,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9187,0,"m",python,content +276,310595,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9188,0,"",python,selection_keyboard +277,310764,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9188,0,"e",python,content +278,310764,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9189,0,"",python,selection_keyboard +279,310931,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9189,0,"t",python,content +280,310932,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9190,0,"",python,selection_keyboard +281,310972,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9190,0,"a",python,content +282,310973,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9191,0,"",python,selection_keyboard +283,311151,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9191,0,"d",python,content +284,311152,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9192,0,"",python,selection_keyboard +285,311286,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9192,0,"a",python,content +286,311287,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9193,0,"",python,selection_keyboard +287,311359,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9193,0,"t",python,content +288,311360,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9194,0,"",python,selection_keyboard +289,311463,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9194,0,"a",python,content +290,311464,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9195,0,"",python,selection_keyboard +291,311734,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9195,0," ",python,content +292,311735,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9196,0,"",python,selection_keyboard +293,311953,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9196,0,"=",python,content +294,311954,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9197,0,"",python,selection_keyboard +295,311999,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9197,0," ",python,content +296,312000,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9198,0,"",python,selection_keyboard +297,312628,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9197,0,"",python,selection_command +298,314589,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9050,0,"",python,selection_mouse +299,316800,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",0,0,"",python,tab +300,316801,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",5397,0,"",python,selection_mouse +301,317210,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",5381,0,"",python,selection_mouse +302,317737,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",5358,38," print(f""Done generating dataset."")",python,selection_command +303,317937,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",5357,39,"\n print(f""Done generating dataset."")",python,selection_command +304,318461,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",5326,70," json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,selection_command +305,318477,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",5253,143," with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,selection_command +306,318520,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",5247,149," }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,selection_command +307,318543,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",5191,205," ""episode_metadata_test"": test_episode_metadata,\n }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,selection_command +308,318578,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",5137,259," ""episode_metadata_val"": val_episode_metadata,\n ""episode_metadata_test"": test_episode_metadata,\n }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,selection_command +309,318763,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",5079,317," ""episode_metadata_train"": train_episode_metadata,\n ""episode_metadata_val"": val_episode_metadata,\n ""episode_metadata_test"": test_episode_metadata,\n }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,selection_command +310,318764,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",5068,328," ),\n ""episode_metadata_train"": train_episode_metadata,\n ""episode_metadata_val"": val_episode_metadata,\n ""episode_metadata_test"": test_episode_metadata,\n }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,selection_command +311,318764,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",5004,392," [ep[""avg_seq_len""] for ep in test_episode_metadata]\n ),\n ""episode_metadata_train"": train_episode_metadata,\n ""episode_metadata_val"": val_episode_metadata,\n ""episode_metadata_test"": test_episode_metadata,\n }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,selection_command +312,318765,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",4963,433," ""avg_episode_len_test"": np.mean(\n [ep[""avg_seq_len""] for ep in test_episode_metadata]\n ),\n ""episode_metadata_train"": train_episode_metadata,\n ""episode_metadata_val"": val_episode_metadata,\n ""episode_metadata_test"": test_episode_metadata,\n }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,selection_command +313,318765,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",4952,444," ),\n ""avg_episode_len_test"": np.mean(\n [ep[""avg_seq_len""] for ep in test_episode_metadata]\n ),\n ""episode_metadata_train"": train_episode_metadata,\n ""episode_metadata_val"": val_episode_metadata,\n ""episode_metadata_test"": test_episode_metadata,\n }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,selection_command +314,318765,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",4889,507," [ep[""avg_seq_len""] for ep in val_episode_metadata]\n ),\n ""avg_episode_len_test"": np.mean(\n [ep[""avg_seq_len""] for ep in test_episode_metadata]\n ),\n ""episode_metadata_train"": train_episode_metadata,\n ""episode_metadata_val"": val_episode_metadata,\n ""episode_metadata_test"": test_episode_metadata,\n }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,selection_command +315,318766,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",4849,547," ""avg_episode_len_val"": np.mean(\n [ep[""avg_seq_len""] for ep in val_episode_metadata]\n ),\n ""avg_episode_len_test"": np.mean(\n [ep[""avg_seq_len""] for ep in test_episode_metadata]\n ),\n ""episode_metadata_train"": train_episode_metadata,\n ""episode_metadata_val"": val_episode_metadata,\n ""episode_metadata_test"": test_episode_metadata,\n }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,selection_command +316,318787,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",4838,558," ),\n ""avg_episode_len_val"": np.mean(\n [ep[""avg_seq_len""] for ep in val_episode_metadata]\n ),\n ""avg_episode_len_test"": np.mean(\n [ep[""avg_seq_len""] for ep in test_episode_metadata]\n ),\n ""episode_metadata_train"": train_episode_metadata,\n ""episode_metadata_val"": val_episode_metadata,\n ""episode_metadata_test"": test_episode_metadata,\n }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,selection_command +317,318827,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",4773,623," [ep[""avg_seq_len""] for ep in train_episode_metadata]\n ),\n ""avg_episode_len_val"": np.mean(\n [ep[""avg_seq_len""] for ep in val_episode_metadata]\n ),\n ""avg_episode_len_test"": np.mean(\n [ep[""avg_seq_len""] for ep in test_episode_metadata]\n ),\n ""episode_metadata_train"": train_episode_metadata,\n ""episode_metadata_val"": val_episode_metadata,\n ""episode_metadata_test"": test_episode_metadata,\n }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,selection_command +318,318851,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",4731,665," ""avg_episode_len_train"": np.mean(\n [ep[""avg_seq_len""] for ep in train_episode_metadata]\n ),\n ""avg_episode_len_val"": np.mean(\n [ep[""avg_seq_len""] for ep in val_episode_metadata]\n ),\n ""avg_episode_len_test"": np.mean(\n [ep[""avg_seq_len""] for ep in test_episode_metadata]\n ),\n ""episode_metadata_train"": train_episode_metadata,\n ""episode_metadata_val"": val_episode_metadata,\n ""episode_metadata_test"": test_episode_metadata,\n }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,selection_command +319,318892,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",4678,718," ""num_episodes_test"": args.num_episodes_test,\n ""avg_episode_len_train"": np.mean(\n [ep[""avg_seq_len""] for ep in train_episode_metadata]\n ),\n ""avg_episode_len_val"": np.mean(\n [ep[""avg_seq_len""] for ep in val_episode_metadata]\n ),\n ""avg_episode_len_test"": np.mean(\n [ep[""avg_seq_len""] for ep in test_episode_metadata]\n ),\n ""episode_metadata_train"": train_episode_metadata,\n ""episode_metadata_val"": val_episode_metadata,\n ""episode_metadata_test"": test_episode_metadata,\n }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,selection_command +320,318936,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",4627,769," ""num_episodes_val"": args.num_episodes_val,\n ""num_episodes_test"": args.num_episodes_test,\n ""avg_episode_len_train"": np.mean(\n [ep[""avg_seq_len""] for ep in train_episode_metadata]\n ),\n ""avg_episode_len_val"": np.mean(\n [ep[""avg_seq_len""] for ep in val_episode_metadata]\n ),\n ""avg_episode_len_test"": np.mean(\n [ep[""avg_seq_len""] for ep in test_episode_metadata]\n ),\n ""episode_metadata_train"": train_episode_metadata,\n ""episode_metadata_val"": val_episode_metadata,\n ""episode_metadata_test"": test_episode_metadata,\n }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,selection_command +321,318963,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",4572,824," ""num_episodes_train"": args.num_episodes_train,\n ""num_episodes_val"": args.num_episodes_val,\n ""num_episodes_test"": args.num_episodes_test,\n ""avg_episode_len_train"": np.mean(\n [ep[""avg_seq_len""] for ep in train_episode_metadata]\n ),\n ""avg_episode_len_val"": np.mean(\n [ep[""avg_seq_len""] for ep in val_episode_metadata]\n ),\n ""avg_episode_len_test"": np.mean(\n [ep[""avg_seq_len""] for ep in test_episode_metadata]\n ),\n ""episode_metadata_train"": train_episode_metadata,\n ""episode_metadata_val"": val_episode_metadata,\n ""episode_metadata_test"": test_episode_metadata,\n }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,selection_command +322,318979,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",4529,867," ""num_actions"": get_action_space(),\n ""num_episodes_train"": args.num_episodes_train,\n ""num_episodes_val"": args.num_episodes_val,\n ""num_episodes_test"": args.num_episodes_test,\n ""avg_episode_len_train"": np.mean(\n [ep[""avg_seq_len""] for ep in train_episode_metadata]\n ),\n ""avg_episode_len_val"": np.mean(\n [ep[""avg_seq_len""] for ep in val_episode_metadata]\n ),\n ""avg_episode_len_test"": np.mean(\n [ep[""avg_seq_len""] for ep in test_episode_metadata]\n ),\n ""episode_metadata_train"": train_episode_metadata,\n ""episode_metadata_val"": val_episode_metadata,\n ""episode_metadata_test"": test_episode_metadata,\n }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,selection_command +323,319307,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",4503,893," ""env"": ""coinrun"",\n ""num_actions"": get_action_space(),\n ""num_episodes_train"": args.num_episodes_train,\n ""num_episodes_val"": args.num_episodes_val,\n ""num_episodes_test"": args.num_episodes_test,\n ""avg_episode_len_train"": np.mean(\n [ep[""avg_seq_len""] for ep in train_episode_metadata]\n ),\n ""avg_episode_len_val"": np.mean(\n [ep[""avg_seq_len""] for ep in val_episode_metadata]\n ),\n ""avg_episode_len_test"": np.mean(\n [ep[""avg_seq_len""] for ep in test_episode_metadata]\n ),\n ""episode_metadata_train"": train_episode_metadata,\n ""episode_metadata_val"": val_episode_metadata,\n ""episode_metadata_test"": test_episode_metadata,\n }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,selection_command +324,319475,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",4486,910," metadata = {\n ""env"": ""coinrun"",\n ""num_actions"": get_action_space(),\n ""num_episodes_train"": args.num_episodes_train,\n ""num_episodes_val"": args.num_episodes_val,\n ""num_episodes_test"": args.num_episodes_test,\n ""avg_episode_len_train"": np.mean(\n [ep[""avg_seq_len""] for ep in train_episode_metadata]\n ),\n ""avg_episode_len_val"": np.mean(\n [ep[""avg_seq_len""] for ep in val_episode_metadata]\n ),\n ""avg_episode_len_test"": np.mean(\n [ep[""avg_seq_len""] for ep in test_episode_metadata]\n ),\n ""episode_metadata_train"": train_episode_metadata,\n ""episode_metadata_val"": val_episode_metadata,\n ""episode_metadata_test"": test_episode_metadata,\n }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,selection_command +325,319611,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",4458,938," # --- Save metadata ---\n metadata = {\n ""env"": ""coinrun"",\n ""num_actions"": get_action_space(),\n ""num_episodes_train"": args.num_episodes_train,\n ""num_episodes_val"": args.num_episodes_val,\n ""num_episodes_test"": args.num_episodes_test,\n ""avg_episode_len_train"": np.mean(\n [ep[""avg_seq_len""] for ep in train_episode_metadata]\n ),\n ""avg_episode_len_val"": np.mean(\n [ep[""avg_seq_len""] for ep in val_episode_metadata]\n ),\n ""avg_episode_len_test"": np.mean(\n [ep[""avg_seq_len""] for ep in test_episode_metadata]\n ),\n ""episode_metadata_train"": train_episode_metadata,\n ""episode_metadata_val"": val_episode_metadata,\n ""episode_metadata_test"": test_episode_metadata,\n }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,selection_command +326,319819,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",4458,0,"",python,selection_command +327,321737,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",0,0,"",python,tab +328,321738,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9181,0,"",python,selection_mouse +329,322358,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9311,0,"\n # --- Save metadata ---\n metadata = {\n ""env"": ""coinrun"",\n ""num_actions"": get_action_space(),\n ""num_episodes_train"": args.num_episodes_train,\n ""num_episodes_val"": args.num_episodes_val,\n ""num_episodes_test"": args.num_episodes_test,\n ""avg_episode_len_train"": np.mean(\n [ep[""avg_seq_len""] for ep in train_episode_metadata]\n ),\n ""avg_episode_len_val"": np.mean(\n [ep[""avg_seq_len""] for ep in val_episode_metadata]\n ),\n ""avg_episode_len_test"": np.mean(\n [ep[""avg_seq_len""] for ep in test_episode_metadata]\n ),\n ""episode_metadata_train"": train_episode_metadata,\n ""episode_metadata_val"": val_episode_metadata,\n ""episode_metadata_test"": test_episode_metadata,\n }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")",python,content +330,322398,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9316,0,"",python,selection_command +331,324957,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9312,27," # --- Save metadata ---",python,selection_command +332,326121,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9316,0,"",python,selection_command +333,326312,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9170,0,"",python,selection_command +334,326471,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9027,0,"",python,selection_command +335,326611,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",8878,0,"",python,selection_command +336,327130,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",8874,148," train_episode_metadata = make_array_records_dataset(agent, num_episodes=args.num_episodes_train, eval_env_args=eval_env_args, split=""train"")",python,selection_command +337,327320,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",8874,291," train_episode_metadata = make_array_records_dataset(agent, num_episodes=args.num_episodes_train, eval_env_args=eval_env_args, split=""train"")\n val_episode_metadata = make_array_records_dataset(agent, num_episodes=args.num_episodes_val, eval_env_args=eval_env_args, split=""val"")",python,selection_command +338,327472,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",8874,437," train_episode_metadata = make_array_records_dataset(agent, num_episodes=args.num_episodes_train, eval_env_args=eval_env_args, split=""train"")\n val_episode_metadata = make_array_records_dataset(agent, num_episodes=args.num_episodes_val, eval_env_args=eval_env_args, split=""val"")\n test_episode_metadata = make_array_records_dataset(agent, num_episodes=args.num_episodes_test, eval_env_args=eval_env_args, split=""test"")",python,selection_command +339,327740,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",8882,0,"",python,selection_command +340,327973,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9170,4,"",python,content +341,327973,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",9027,4,"",python,content +342,327973,"data/jasmine_data/ViZDoomPPO/load_model_generate_dataset.py",8878,4,"",python,content 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| $$__ $$| $$__ $$ /$$__ $$ #\n# | $$ | $$ /$$ /$$$$$$$$| $$ \ $$ /$$$$$$ /$$$$$$ /$$$$$$/$$$$ | $$ \ $$| $$ \ $$| $$ \ $$ #\n# | $$ / $$/| $$|____ /$$/| $$ | $$ /$$__ $$ /$$__ $$| $$_ $$_ $$ | $$$$$$$/| $$$$$$$/| $$ | $$ #\n# \ $$ $$/ | $$ /$$$$/ | $$ | $$| $$ \ $$| $$ \ $$| $$ \ $$ \ $$ | $$____/ | $$____/ | $$ | $$ #\n# \ $$$/ | $$ /$$__/ | $$ | $$| $$ | $$| $$ | $$| $$ | $$ | $$ | $$ | $$ | $$ | $$ #\n# \ $/ | $$ /$$$$$$$$| $$$$$$$/| $$$$$$/| $$$$$$/| $$ | $$ | $$ | $$ | $$ | $$$$$$/ #\n# \_/ |__/|________/|_______/ \______/ \______/ |__/ |__/ |__/ |__/ |__/ \______/ #\n# #\n# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n \n# FORK OF LEANDRO KIELIGER'S DOOM PPO TUTORIAL: https://lkieliger.medium.com/deep-reinforcement-learning-in-practice-by-playing-doom-part-1-getting-started-618c99075c77 \n\n# SCRIPT TO TRAIN A PPO AGENT ON THE ViZDOOM DEATHMATCH ENVIRONMENT. \n\nimport numpy as np\nimport os\nimport vizdoom\nfrom common import envs\nfrom common.models import CustomCNN\nfrom stable_baselines3.common.vec_env import VecTransposeImage, SubprocVecEnv\nimport random\nimport string\nfrom vizdoom.vizdoom import GameVariable\nfrom collections import deque\nimport torch\n\ndevice = torch.device(""cuda"" if torch.cuda.is_available() else ""cpu"")\n# device=torch.device(""mps"")\nprint(f""Using device: {device}"")\n\n# Rewards\n# 1 per kill\nreward_factor_frag = 1.0\nreward_factor_damage = 0.01\n\n# Player can move at ~16.66 units per tick\nreward_factor_distance = 5e-4\npenalty_factor_distance = -2.5e-3\nreward_threshold_distance = 3.0\n\n# Pistol clips have 10 bullets\nreward_factor_ammo_increment = 0.02\nreward_factor_ammo_decrement = -0.01\n\n# Player starts at 100 health\nreward_factor_health_increment = 0.02\nreward_factor_health_decrement = -0.01\nreward_factor_armor_increment = 0.01\n\n\n# List of game variables storing ammunition information. Used for keeping track of ammunition-related rewards.\nAMMO_VARIABLES = [\n GameVariable.AMMO0,\n GameVariable.AMMO1,\n GameVariable.AMMO2,\n GameVariable.AMMO3,\n GameVariable.AMMO4,\n GameVariable.AMMO5,\n GameVariable.AMMO6,\n GameVariable.AMMO7,\n GameVariable.AMMO8,\n GameVariable.AMMO9,\n]\n\n# List of game variables storing weapon information. Used for keeping track of ammunition-related rewards.\nWEAPON_VARIABLES = [\n GameVariable.WEAPON0,\n GameVariable.WEAPON1,\n GameVariable.WEAPON2,\n GameVariable.WEAPON3,\n GameVariable.WEAPON4,\n GameVariable.WEAPON5,\n GameVariable.WEAPON6,\n GameVariable.WEAPON7,\n GameVariable.WEAPON8,\n GameVariable.WEAPON9,\n]\n\n\nclass DoomWithBotsShaped(envs.DoomWithBots):\n """"""An environment wrapper for a Doom deathmatch game with bots.\n\n Rewards are shaped according to the multipliers defined in the notebook.\n """"""\n\n def __init__(self, game, frame_processor, frame_skip, n_bots, shaping):\n super().__init__(game, frame_processor, frame_skip, n_bots)\n\n # Give a random two-letter name to the agent for identifying instances in parallel learning.\n self.name = """".join(random.choices(string.ascii_uppercase + string.digits, k=2))\n self.shaping = shaping\n\n # Internal states\n self.last_health = 100\n self.last_x, self.last_y = self._get_player_pos()\n self.ammo_state = self._get_ammo_state()\n self.weapon_state = self._get_weapon_state()\n self.total_rew = self.last_damage_dealt = self.deaths = self.last_frags = (\n self.last_armor\n ) = 0\n\n # Store individual reward contributions for logging purposes\n self.rewards_stats = {\n ""frag"": 0,\n ""damage"": 0,\n ""ammo"": 0,\n ""health"": 0,\n ""armor"": 0,\n ""distance"": 0,\n }\n\n def step(self, action, array=False):\n # Perform the action as usual\n state, reward, done, info = super().step(action)\n\n self._log_reward_stat(""frag"", reward)\n\n # Adjust the reward based on the shaping table\n if self.shaping:\n shaped_reward = reward + self.shape_rewards()\n else:\n shaped_reward = reward\n\n self.total_rew += shaped_reward\n\n return state, shaped_reward, done, info\n\n def reset(self):\n self._print_state()\n\n state = super().reset()\n\n self.last_health = 100\n self.last_x, self.last_y = self._get_player_pos()\n self.last_armor = self.last_frags = self.total_rew = self.deaths = 0\n\n # Damage count is not cleared when starting a new episode: https://github.com/mwydmuch/ViZDoom/issues/399\n # self.last_damage_dealt = 0\n\n # Reset reward stats\n for k in self.rewards_stats.keys():\n self.rewards_stats[k] = 0\n\n return state\n\n def seed(self, seed=None):\n if seed is not None:\n random.seed(seed)\n np.random.seed(seed)\n return [seed]\n\n def shape_rewards(self):\n reward_contributions = [\n self._compute_damage_reward(),\n self._compute_ammo_reward(),\n self._compute_health_reward(),\n self._compute_armor_reward(),\n self._compute_distance_reward(*self._get_player_pos()),\n ]\n\n return sum(reward_contributions)\n\n def _respawn_if_dead(self):\n if not self.game.is_episode_finished():\n # Check if player is dead\n if self.game.is_player_dead():\n self.deaths += 1\n self._reset_player()\n\n def _compute_distance_reward(self, x, y):\n """"""Computes a reward/penalty based on the distance travelled since last update.""""""\n dx = self.last_x - x\n dy = self.last_y - y\n\n distance = np.sqrt(dx**2 + dy**2)\n\n if distance - reward_threshold_distance > 0:\n reward = reward_factor_distance\n else:\n reward = -reward_factor_distance\n\n self.last_x = x\n self.last_y = y\n self._log_reward_stat(""distance"", reward)\n\n return reward\n\n def _compute_damage_reward(self):\n """"""Computes a reward based on total damage inflicted to enemies since last update.""""""\n damage_dealt = self.game.get_game_variable(GameVariable.DAMAGECOUNT)\n reward = reward_factor_damage * (damage_dealt - self.last_damage_dealt)\n\n self.last_damage_dealt = damage_dealt\n self._log_reward_stat(""damage"", reward)\n\n return reward\n\n def _compute_health_reward(self):\n """"""Computes a reward/penalty based on total health change since last update.""""""\n # When the player is dead, the health game variable can be -999900\n health = max(self.game.get_game_variable(GameVariable.HEALTH), 0)\n\n health_reward = reward_factor_health_increment * max(\n 0, health - self.last_health\n )\n health_penalty = reward_factor_health_decrement * min(\n 0, health - self.last_health\n )\n reward = health_reward - health_penalty\n\n self.last_health = health\n self._log_reward_stat(""health"", reward)\n\n return reward\n\n def _compute_armor_reward(self):\n """"""Computes a reward/penalty based on total armor change since last update.""""""\n armor = self.game.get_game_variable(GameVariable.ARMOR)\n reward = reward_factor_armor_increment * max(0, armor - self.last_armor)\n\n self.last_armor = armor\n self._log_reward_stat(""armor"", reward)\n\n return reward\n\n def _compute_ammo_reward(self):\n """"""Computes a reward/penalty based on total ammunition change since last update.""""""\n self.weapon_state = self._get_weapon_state()\n\n new_ammo_state = self._get_ammo_state()\n ammo_diffs = (new_ammo_state - self.ammo_state) * self.weapon_state\n ammo_reward = reward_factor_ammo_increment * max(0, np.sum(ammo_diffs))\n ammo_penalty = reward_factor_ammo_decrement * min(0, np.sum(ammo_diffs))\n reward = ammo_reward - ammo_penalty\n\n self.ammo_state = new_ammo_state\n self._log_reward_stat(""ammo"", reward)\n\n return reward\n\n def _get_player_pos(self):\n """"""Returns the player X- and Y- coordinates.""""""\n return self.game.get_game_variable(\n GameVariable.POSITION_X\n ), self.game.get_game_variable(GameVariable.POSITION_Y)\n\n def _get_ammo_state(self):\n """"""Returns the total available ammunition per weapon slot.""""""\n ammo_state = np.zeros(10)\n\n for i in range(10):\n ammo_state[i] = self.game.get_game_variable(AMMO_VARIABLES[i])\n\n return ammo_state\n\n def _get_weapon_state(self):\n """"""Returns which weapon slots can be used. Available weapons are encoded as ones.""""""\n weapon_state = np.zeros(10)\n\n for i in range(10):\n weapon_state[i] = self.game.get_game_variable(WEAPON_VARIABLES[i])\n\n return weapon_state\n\n def _log_reward_stat(self, kind: str, reward: float):\n self.rewards_stats[kind] += reward\n\n def _reset_player(self):\n self.last_health = 100\n self.last_armor = 0\n self.game.respawn_player()\n self.last_x, self.last_y = self._get_player_pos()\n self.ammo_state = self._get_ammo_state()\n\n def _print_state(self):\n super()._print_state()\n\n\nREWARD_THRESHOLDS = [5, 10, 15, 20, 25, 25]\n\n# Curriculum based learning\nclass DoomWithBotsCurriculum(DoomWithBotsShaped):\n def __init__(\n self,\n game,\n frame_processor,\n frame_skip,\n n_bots,\n shaping,\n initial_level=0,\n max_level=5,\n rolling_mean_length=10,\n ):\n super().__init__(game, frame_processor, frame_skip, n_bots, shaping)\n\n # Initialize ACS script difficulty level\n game.send_game_command(""pukename change_difficulty 0"")\n\n # Internal state\n self.level = initial_level\n self.max_level = max_level\n self.rolling_mean_length = rolling_mean_length\n self.last_rewards = deque(maxlen=rolling_mean_length)\n\n def step(self, action, array=False):\n # Perform action step as usual\n state, reward, done, infos = super().step(action, array)\n\n # After an episode, check whether difficulty should be increased.\n if done:\n self.last_rewards.append(self.total_rew)\n run_mean = np.mean(self.last_rewards)\n print(\n ""Avg. last 10 runs of {}: {:.2f}. Current difficulty level: {}"".format(\n self.name, run_mean, self.level\n )\n )\n if (\n run_mean > REWARD_THRESHOLDS[self.level]\n and len(self.last_rewards) >= self.rolling_mean_length\n ):\n self._change_difficulty()\n\n return state, reward, done, infos\n\n def reset(self):\n state = super().reset()\n self.game.send_game_command(f""pukename change_difficulty {self.level}"")\n\n return state\n\n def _change_difficulty(self):\n """"""Adjusts the difficulty by setting the difficulty level in the ACS script.""""""\n if self.level < self.max_level:\n self.level += 1\n print(f""Changing difficulty for {self.name} to {self.level}"")\n self.game.send_game_command(f""pukename change_difficulty {self.level}"")\n self.last_rewards = deque(maxlen=self.rolling_mean_length)\n else:\n print(f""{self.name} already at max level!"")\n\n\ndef game_instance(scenario):\n """"""Creates a Doom game instance.""""""\n game = vizdoom.DoomGame()\n dir_path = os.path.dirname(os.path.realpath(__file__))\n cfg_pth = os.path.join(dir_path, ""scenarios"", f""{scenario}.cfg"")\n game.load_config(cfg_pth)\n game.add_game_args(envs.DOOM_ENV_WITH_BOTS_ARGS)\n game.set_window_visible(False)\n game.init()\n\n return game\n\n\ndef env_with_bots_shaped(scenario, **kwargs) -> envs.DoomEnv:\n """"""Wraps a Doom game instance in an environment with shaped rewards.""""""\n game = game_instance(scenario)\n return DoomWithBotsShaped(game, **kwargs)\n\n\ndef vec_env_with_bots_shaped(n_envs=1, **kwargs) -> VecTransposeImage:\n """"""Wraps Doom game instances in a vectorized environment with shaped rewards using true parallelism.""""""\n return VecTransposeImage(\n SubprocVecEnv([lambda: env_with_bots_shaped(**kwargs) for _ in range(n_envs)])\n )\n\n\ndef env_with_bots_curriculum(scenario, **kwargs) -> envs.DoomEnv:\n """"""Wraps a Doom game instance in an environment with shaped rewards and curriculum.""""""\n game = game_instance(scenario)\n return DoomWithBotsCurriculum(game, **kwargs)\n\n\ndef vec_env_with_bots_curriculum(n_envs=1, **kwargs) -> VecTransposeImage:\n """"""Wraps a Doom game instance in a vectorized environment with shaped rewards and curriculum.""""""\n return VecTransposeImage(\n SubprocVecEnv([lambda: env_with_bots_curriculum(**kwargs) for _ in range(n_envs)])\n )\n\n\nif __name__ == ""__main__"":\n scenario = ""deathmatch_simple""\n\n # Agent parameters.\n agent_args = {\n ""n_steps"": 4096,\n ""learning_rate"": 1e-4,\n ""batch_size"": 64,\n ""policy_kwargs"": {""features_extractor_class"": CustomCNN},\n ""device"": device,\n }\n\n # Environment parameters.\n env_args = {\n ""scenario"": scenario,\n ""frame_skip"": 4,\n ""frame_processor"": envs.default_frame_processor,\n ""n_bots"": 6,\n ""shaping"": True,\n ""initial_level"": 1,\n ""max_level"": 5,\n }\n\n # In the evaluation environment we measure frags only.\n eval_env_args = dict(env_args)\n eval_env_args[""shaping""] = False\n\n n_envs = 32\n # Create environments with bots and shaping.\n env = vec_env_with_bots_curriculum(\n n_envs, **env_args\n ) # You can increase the number of parallel environments\n eval_env = vec_env_with_bots_curriculum(n_envs, **eval_env_args)\n\n agent = envs.solve_env(\n env,\n eval_env,\n scenario,\n agent_args,\n resume=False,\n )\n envs.save_model(agent, ""agent_test"")\n",python,tab +423,365585,"data/jasmine_data/ViZDoomPPO/train_ppo_parallel.py",4384,0,"",python,selection_mouse +424,369913,"data/jasmine_data/ViZDoomPPO/train_ppo_parallel.py",4637,0,"",python,selection_command +425,370881,"data/jasmine_data/ViZDoomPPO/train_ppo_parallel.py",4681,0,"",python,selection_command +426,371274,"data/jasmine_data/ViZDoomPPO/train_ppo_parallel.py",4746,0,"",python,selection_command +427,372422,"data/jasmine_data/ViZDoomPPO/train_ppo_parallel.py",10875,0,"",python,selection_command +428,374580,"data/jasmine_data/ViZDoomPPO/train_ppo_parallel.py",10915,0,"",python,selection_command 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--cpus-per-task=16\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/preprocess/doom/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/preprocess/doom/%x_%j.log\n#SBATCH --job-name=preprocess_coinrun_chunked\n\n# export PYTHONUNBUFFERED=1\n\n\n# source .venv/bin/activate\n\nsource .venv/bin/activate\n\npython jasmine_data/ViZDoomPPO/load_model_generate_dataset.py \\n --output_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_doom/doom_episodes_50m \\n --agent_path /home/hk-project-p0023960/tum_cte0515/Projects/jasmine/data/jasmine_data/ViZDoomPPO/logs/models/deathmatch_simple_bak/best_model.zip \\n --num_episodes_train 50_000 \\n \n\n",shellscript,tab +457,461940,"TERMINAL",0,0,"bash",,terminal_focus +458,463242,"TERMINAL",0,0,"python",,terminal_command +459,463289,"TERMINAL",0,0,"]633;C",,terminal_output +460,463354,"TERMINAL",0,0,"Python 3.9.18 (main, Sep 4 2025, 00:00:00) \r\n[GCC 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data_0172.array_record\r\ndata_0037.array_record data_0105.array_record data_0173.array_record\r\ndata_0038.array_record data_0106.array_record data_0174.array_record\r\ndata_0039.array_record data_0107.array_record data_0175.array_record\r\ndata_0040.array_record data_0108.array_record data_0176.array_record\r\ndata_0041.array_record data_0109.array_record data_0177.array_record\r\ndata_0042.array_record data_0110.array_record data_0178.array_record\r\ndata_0043.array_record data_0111.array_record data_0179.array_record\r\ndata_0044.array_record data_0112.array_record data_0180.array_record\r\ndata_0045.array_record data_0113.array_record data_0181.array_record\r\ndata_0046.array_record data_0114.array_record data_0182.array_record\r\ndata_0047.array_record data_0115.array_record data_0183.array_record\r\ndata_0048.array_record data_0116.array_record data_0184.array_record\r\ndata_0049.array_record data_0117.array_record data_0185.array_record\r\ndata_0050.array_record data_0118.array_record data_0186.array_record\r\ndata_0051.array_record data_0119.array_record data_0187.array_record\r\ndata_0052.array_record data_0120.array_record data_0188.array_record\r\ndata_0053.array_record data_0121.array_record data_0189.array_record\r\ndata_0054.array_record data_0122.array_record data_0190.array_record\r\ndata_0055.array_record data_0123.array_record data_0191.array_record\r\ndata_0056.array_record data_0124.array_record data_0192.array_record\r\ndata_0057.array_record data_0125.array_record data_0193.array_record\r\ndata_0058.array_record data_0126.array_record data_0194.array_record\r\ndata_0059.array_record data_0127.array_record data_0195.array_record\r\ndata_0060.array_record data_0128.array_record data_0196.array_record\r\ndata_0061.array_record data_0129.array_record data_0197.array_record\r\ndata_0062.array_record data_0130.array_record data_0198.array_record\r\ndata_0063.array_record data_0131.array_record data_0199.array_record\r\ndata_0064.array_record data_0132.array_record data_0200.array_record\r\ndata_0065.array_record data_0133.array_record data_0201.array_record\r\ndata_0066.array_record data_0134.array_record\r\ndata_0067.array_record data_0135.array_record\r\n]0;tum_cte0515@hkn1990:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_doom/doom_episodes_50m/train",,terminal_output +553,594583,"TERMINAL",0,0,"python",,terminal_focus +554,595629,"TERMINAL",0,0,"",,terminal_output +555,596085,"TERMINAL",0,0,"16000 * 186",,terminal_output +556,596356,"TERMINAL",0,0," * 100",,terminal_output +557,597754,"TERMINAL",0,0,"",,terminal_output +558,598187,"TERMINAL",0,0,"\r",,terminal_output +559,599200,"TERMINAL",0,0,"",,terminal_output +560,599570,"TERMINAL",0,0,"",,terminal_output +561,603397,"TERMINAL",0,0," ",,terminal_output +562,603594,"TERMINAL",0,0,"*",,terminal_output +563,603736,"TERMINAL",0,0," ",,terminal_output +564,604304,"TERMINAL",0,0,"2",,terminal_output +565,604487,"TERMINAL",0,0,"0",,terminal_output +566,604636,"TERMINAL",0,0,"0",,terminal_output +567,606624,"TERMINAL",0,0,"\r\n3200000\r\n>>> ",,terminal_output +568,616526,"TERMINAL",0,0,"bash",,terminal_focus +569,617806,"TERMINAL",0,0,"queue",,terminal_command +570,617873,"TERMINAL",0,0,"]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1990.localdomain: Sat Oct 4 18:17:42 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3541783 accelerat preproce tum_cte0 R 1:31:13\t 1 hkn0517",,terminal_output +571,618979,"TERMINAL",0,0,"34",,terminal_output +572,619990,"TERMINAL",0,0,"45",,terminal_output +573,621027,"TERMINAL",0,0,"56",,terminal_output +574,622022,"TERMINAL",0,0,"67",,terminal_output +575,623078,"TERMINAL",0,0,"78",,terminal_output +576,624208,"TERMINAL",0,0,"89",,terminal_output +577,625131,"TERMINAL",0,0,"920",,terminal_output +578,626166,"TERMINAL",0,0,"501",,terminal_output +579,627273,"TERMINAL",0,0,"12",,terminal_output +580,628295,"TERMINAL",0,0,"23",,terminal_output +581,629320,"TERMINAL",0,0,"35",,terminal_output +582,630446,"TERMINAL",0,0,"56",,terminal_output +583,631369,"TERMINAL",0,0,"67",,terminal_output +584,632395,"TERMINAL",0,0,"78",,terminal_output +585,633435,"TERMINAL",0,0,"89",,terminal_output +586,634473,"TERMINAL",0,0,"930",,terminal_output +587,635509,"TERMINAL",0,0,"8:001",,terminal_output +588,636590,"TERMINAL",0,0,"12",,terminal_output +589,637582,"TERMINAL",0,0,"23",,terminal_output +590,638614,"TERMINAL",0,0,"34",,terminal_output +591,639654,"TERMINAL",0,0,"45",,terminal_output +592,640690,"TERMINAL",0,0,"56",,terminal_output +593,641788,"TERMINAL",0,0,"67",,terminal_output +594,642946,"dataset_duplicates.ipynb",0,0,"\nprint(f""\nFound {len(duplicates)} sets of duplicate videos."")\nif duplicates:\n # Print the first 5 duplicate sets as an example\n for i, (h, paths) in enumerate(duplicates.items()):\n print(f"" - Hash: {h[:10]}... ({len(paths)} files)"")\n for path in paths:\n print(f"" - {os.path.basename(path)}"")\n\n",python,tab +595,643037,"TERMINAL",0,0,"78",,terminal_output +596,643832,"TERMINAL",0,0,"89",,terminal_output +597,644076,"TERMINAL",0,0,"python",,terminal_focus +598,644940,"TERMINAL",0,0,"940",,terminal_output +599,645067,"TERMINAL",0,0,"\r\nKeyboardInterrupt\r\n>>> ",,terminal_output +600,645886,"TERMINAL",0,0,"\r\n]0;tum_cte0515@hkn1990:~/Projects/jasmine",,terminal_output +601,645927,"TERMINAL",0,0,"101",,terminal_output +602,647053,"TERMINAL",0,0,"watch",,terminal_focus +603,647056,"TERMINAL",0,0,"12",,terminal_output +604,647727,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1990:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_doom/doom_episodes_50m/train",,terminal_output +605,648847,"TERMINAL",0,0,"pwd",,terminal_command +606,650439,"TERMINAL",0,0,"cd ..",,terminal_command +607,650949,"TERMINAL",0,0,"pwd",,terminal_command +608,654076,"dataset_duplicates.ipynb",0,0,"",python,tab +609,658821,"dataset_duplicates.ipynb",0,0,"base = ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_doom/dev""\n\ntrain_dir = os.path.join(base, ""train"")\ntest_dir = os.path.join(base, ""test"")\nval_dir = os.path.join(base, ""val"")\n\ntrain_array_record_files = get_array_record_files(train_dir)\ntest_array_record_files = get_array_record_files(test_dir)\nval_array_record_files = get_array_record_files(val_dir)\n\narray_record_files = train_array_record_files + test_array_record_files + val_array_record_files\nprint(f""Found {len(array_record_files)} files to process."")\n\nnum_processes = multiprocessing.cpu_count()\nprint(f""Using {num_processes} worker processes."")\n",python,tab +610,658822,"dataset_duplicates.ipynb",8,0,"",python,selection_mouse +611,658969,"dataset_duplicates.ipynb",7,2,"""/",python,selection_mouse +612,659019,"dataset_duplicates.ipynb",7,6,"""/hkfs",python,selection_mouse +613,659059,"dataset_duplicates.ipynb",7,11,"""/hkfs/work",python,selection_mouse +614,659060,"dataset_duplicates.ipynb",7,12,"""/hkfs/work/",python,selection_mouse +615,659068,"dataset_duplicates.ipynb",7,21,"""/hkfs/work/workspace",python,selection_mouse +616,659115,"dataset_duplicates.ipynb",7,29,"""/hkfs/work/workspace/scratch",python,selection_mouse +617,659133,"dataset_duplicates.ipynb",7,30,"""/hkfs/work/workspace/scratch/",python,selection_mouse +618,659160,"dataset_duplicates.ipynb",7,41,"""/hkfs/work/workspace/scratch/tum_ind3695",python,selection_mouse +619,659226,"dataset_duplicates.ipynb",7,72,"""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_doom/dev""\n",python,selection_mouse +620,659756,"dataset_duplicates.ipynb",7,42,"""/hkfs/work/workspace/scratch/tum_ind3695-",python,selection_mouse +621,659780,"dataset_duplicates.ipynb",7,41,"""/hkfs/work/workspace/scratch/tum_ind3695",python,selection_mouse +622,659825,"dataset_duplicates.ipynb",0,9,"base = ""/",python,selection_mouse +623,660553,"dataset_duplicates.ipynb",7,71,"""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_doom/dev""",python,selection_mouse +624,661415,"dataset_duplicates.ipynb",7,70,"""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_doom/dev",python,selection_mouse +625,662213,"dataset_duplicates.ipynb",7,70,"",python,content +626,662646,"dataset_duplicates.ipynb",7,0,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_doom/doom_episodes_50m",python,content +627,665373,"dataset_duplicates.ipynb",7,0,"",python,selection_mouse +628,666191,"dataset_duplicates.ipynb",7,0,"""",python,content +629,666193,"dataset_duplicates.ipynb",8,0,"",python,selection_keyboard +630,667178,"dataset_duplicates.ipynb",137,0,"",python,selection_mouse +631,668968,"dataset_duplicates.ipynb",291,0,"",python,selection_mouse +632,670755,"dataset_duplicates.ipynb",432,0,"",python,selection_mouse +633,671897,"dataset_duplicates.ipynb",432,0,"#",python,content +634,671899,"dataset_duplicates.ipynb",433,0,"",python,selection_keyboard +635,677266,"dataset_duplicates.ipynb",0,0,"",python,tab +636,678978,"dataset_duplicates.ipynb",0,0,"import os\nimport pickle\nimport hashlib\nimport numpy as np\nfrom collections import defaultdict\nfrom array_record.python.array_record_module import ArrayRecordReader\nimport multiprocessing\nfrom tqdm import tqdm # Using tqdm for a nice progress bar\n",python,tab +637,680407,"dataset_duplicates.ipynb",0,0,"# --- Helper Functions ---\n\ndef hash_byte_data(bytes_data: bytes) -> str:\n """"""Calculates the SHA256 hash of a byte string.""""""\n return hashlib.sha256(bytes_data).hexdigest()\n\n\ndef hash_numpy_frame(frame: np.ndarray) -> str:\n """"""Calculates the SHA256 hash of a numpy array.""""""\n return hashlib.sha256(np.ascontiguousarray(frame)).hexdigest()\n\n\ndef get_episode_level_hash(video_path: str) -> tuple[str, str] | None:\n """"""\n Reads a single array_record file, extracts the video, hashes it,\n and returns the (hash, video_path) tuple.\n """"""\n try:\n reader = ArrayRecordReader(video_path)\n record_data = reader.read()\n record_unpickled = pickle.loads(record_data)\n video_bytes = record_unpickled[""raw_video""]\n video_hash = hash_byte_data(video_bytes)\n return video_hash, video_path\n except Exception as e:\n print(f""Error processing file {video_path}: {e}"")\n return None\n\n\ndef get_frame_level_hashes(video_path: str) -> tuple[list[str], str] | None:\n """"""\n Reads a single array_record file, extracts the frames, hashes each frame,\n and returns the (list of hashes, video_path) tuple.\n """"""\n try:\n reader = ArrayRecordReader(video_path)\n record_data = pickle.loads(reader.read())\n\n # video shape (seq_len, 64, 64, 3)\n video_shape = ( record_data[""sequence_length""], 64, 64, 3)\n episode_tensor = np.frombuffer(record_data[""raw_video""], dtype=np.uint8)\n episode_tensor = episode_tensor.reshape(video_shape) \n\n frame_hashes = [hash_numpy_frame(frame) for frame in episode_tensor]\n\n return frame_hashes, video_path\n except Exception as e:\n print(f""Error processing file {video_path}: {e}"")\n return None\n\ndef get_array_record_files(dir):\n return [\n os.path.join(dir, x)\n for x in os.listdir(dir)\n if x.endswith("".array_record"")\n ]\n",python,tab +638,684141,"dataset_duplicates.ipynb",0,0,"# Find episode level duplicates\nduplicate_episode = defaultdict(list)\n\n# The 'with' statement ensures the pool is properly closed\nwith multiprocessing.Pool(processes=num_processes) as pool:\n # Use pool.imap_unordered for efficiency. It processes items as they are submitted\n # and returns results as they complete, which is perfect for progress bars.\n # We wrap the result iterator with tqdm to show progress.\n results = pool.imap_unordered(get_episode_level_hash, array_record_files)\n \n print(""Processing files and calculating hashes..."")\n for result in tqdm(results, total=len(array_record_files)):\n if result: # Ensure the worker didn't return None due to an error\n video_hash, video_path = result\n duplicate_episode[video_hash].append(video_path)\n",python,tab +639,688637,"dataset_duplicates.ipynb",0,0,"",python,tab +640,700131,"dataset_duplicates.ipynb",365,0,"",python,selection_mouse +641,701262,"dataset_duplicates.ipynb",347,0,"",python,selection_mouse +642,701994,"dataset_duplicates.ipynb",353,0,"",python,selection_mouse +643,702600,"dataset_duplicates.ipynb",352,0,"",python,selection_command +644,703232,"dataset_duplicates.ipynb",329,56,"val_array_record_files = get_array_record_files(val_dir)",python,selection_command +645,703424,"dataset_duplicates.ipynb",270,115,"test_array_record_files = get_array_record_files(test_dir)\nval_array_record_files = get_array_record_files(val_dir)",python,selection_command +646,703672,"dataset_duplicates.ipynb",270,0,"",python,selection_command +647,704314,"dataset_duplicates.ipynb",329,0,"#",python,content +648,704314,"dataset_duplicates.ipynb",270,0,"#",python,content +649,704316,"dataset_duplicates.ipynb",271,0,"",python,selection_keyboard +650,704358,"dataset_duplicates.ipynb",331,0," ",python,content +651,704358,"dataset_duplicates.ipynb",271,0," ",python,content +652,704360,"dataset_duplicates.ipynb",272,0,"",python,selection_keyboard +653,704749,"dataset_duplicates.ipynb",271,0,"",python,selection_command +654,709671,"dataset_duplicates.ipynb",0,0,"",python,tab diff --git a/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-1103382d-4338-4569-ad1a-fd99664b131a1756899628806-2025_09_03-13.40.59.36/source.csv b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-1103382d-4338-4569-ad1a-fd99664b131a1756899628806-2025_09_03-13.40.59.36/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..b7ae549a3324aeafc27e0420174e23f34d81fb09 --- /dev/null +++ b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-1103382d-4338-4569-ad1a-fd99664b131a1756899628806-2025_09_03-13.40.59.36/source.csv @@ -0,0 +1,551 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +1,4,"models/dynamics.py",0,0,"from typing import Dict\n\nimport jax\nimport jax.numpy as jnp\nimport flax.nnx as nnx\nimport einops\n\nfrom utils.nn import STTransformer, Transformer\n\n\nclass DynamicsMaskGIT(nnx.Module):\n """"""\n MaskGIT dynamics model\n \n Dimension keys:\n B: batch size\n T: sequence length\n N: number of patches per frame\n L: latent dimension\n V: vocabulary size (number of latents)\n """"""\n\n def __init__(\n self,\n model_dim: int,\n ffn_dim: int,\n num_latents: int,\n latent_action_dim: int,\n num_blocks: int,\n num_heads: int,\n dropout: float,\n mask_limit: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n rngs: nnx.Rngs,\n ):\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.num_latents = num_latents\n self.latent_action_dim = latent_action_dim\n self.num_blocks = num_blocks\n self.num_heads = num_heads\n self.dropout = dropout\n self.mask_limit = mask_limit\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n\n self.transformer = STTransformer(\n self.model_dim,\n self.model_dim,\n self.ffn_dim,\n self.num_latents,\n self.num_blocks,\n self.num_heads,\n self.dropout,\n self.param_dtype,\n self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)\n self.mask_token = nnx.Param(\n nnx.initializers.lecun_uniform()(rngs.params(), (1, 1, 1, self.model_dim))\n )\n self.action_up = nnx.Linear(\n self.latent_action_dim,\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n def __call__(\n self, batch: Dict[str, jax.Array], training: bool = True\n ) -> tuple[jax.Array, jax.Array | None]:\n # --- Mask videos ---\n video_tokens_BTN = batch[""video_tokens""]\n latent_actions_BTm11L = batch[""latent_actions""]\n vid_embed_BTNM = self.patch_embed(video_tokens_BTN)\n if training:\n batch_size = vid_embed_BTNM.shape[0]\n _rng_prob, *_rngs_mask = jax.random.split(batch[""mask_rng""], batch_size + 1)\n mask_prob = jax.random.uniform(\n _rng_prob, shape=(batch_size,), minval=self.mask_limit\n )\n per_sample_shape = vid_embed_BTNM.shape[1:-1]\n mask = jax.vmap(\n lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),\n in_axes=(0, 0),\n )(jnp.asarray(_rngs_mask), mask_prob)\n mask = mask.at[:, 0].set(False)\n vid_embed_BTNM = jnp.where(\n jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM\n )\n else:\n mask = None\n\n # --- Predict transition ---\n act_embed_BTm11M = self.action_up(latent_actions_BTm11L)\n padded_act_embed_BT1M = jnp.pad(act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0)))\n padded_act_embed_BTNM = jnp.broadcast_to(padded_act_embed_BT1M, vid_embed_BTNM.shape)\n vid_embed_BTNM += padded_act_embed_BTNM\n logits_BTNV = self.transformer(vid_embed_BTNM)\n return logits_BTNV, mask\n\nclass DynamicsCausal(nnx.Module):\n """"""Causal dynamics model""""""\n\n def __init__(\n self,\n model_dim: int,\n ffn_dim: int,\n num_latents: int,\n latent_action_dim: int,\n num_blocks: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n decode: bool,\n rngs: nnx.Rngs,\n ):\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.num_latents = num_latents\n self.latent_action_dim = latent_action_dim\n self.num_blocks = num_blocks\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n\n self.transformer = Transformer(\n self.model_dim,\n self.model_dim,\n self.ffn_dim,\n self.num_latents,\n self.num_blocks,\n self.num_heads,\n self.dropout,\n self.param_dtype,\n self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=decode,\n rngs=rngs,\n )\n self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)\n self.action_up = nnx.Linear(\n self.latent_action_dim,\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n def __call__(\n self, batch: Dict[str, jax.Array], training: bool = True\n ) -> tuple[jax.Array, jax.Array | None]:\n video_tokens_BTN = batch[""video_tokens""]\n latent_actions_BTm11L = batch[""latent_actions""]\n\n vid_embed_BTNM = self.patch_embed(video_tokens_BTN)\n act_embed_BTm11M = self.action_up(latent_actions_BTm11L)\n padded_act_embed_BT1M = jnp.pad(act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0)))\n vid_embed_BTNp1M = jnp.concatenate([padded_act_embed_BT1M, vid_embed_BTNM], axis=2)\n\n logits_BTNp1V = self.transformer(vid_embed_BTNp1M)\n logits_BTNV = logits_BTNp1V[:, :, :-1]\n\n return logits_BTNV, jnp.ones_like(video_tokens_BTN)\n",python,tab +2,312,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"1:40:58 PM [info] Activating crowd-code\n1:40:59 PM [info] Recording started\n1:40:59 PM [info] Initializing git provider using file system watchers...\n1:40:59 PM [info] Git repository found\n1:40:59 PM [info] Git provider initialized successfully\n",Log,tab +3,487,"extension-output-pdoom-org.crowd-code-#1-crowd-code",245,0,"1:40:59 PM [info] Initial git state: [object Object]\n",Log,content +4,9066,"models/dynamics.py",0,0,"",python,tab +5,10669,"train_dynamics.py",0,0,"from dataclasses import dataclass, field\nimport os\nfrom typing import cast\n\nimport einops\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\nimport flax.nnx as nnx\n\nfrom genie import Genie, restore_genie_components\nfrom utils.dataloader import get_dataloader\nfrom utils.lr_utils import get_lr_schedule\nfrom utils.parameter_utils import count_parameters_by_component\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n batch_size: int = 36\n init_lr: float = 0.0\n max_lr: float = 3e-5\n decay_end: float = 0.0\n wsd_decay_steps: int = (\n 10000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n )\n warmup_steps: int = 5000\n lr_schedule: str = ""wsd"" # supported options: wsd, cos\n darkness_threshold: float = 0.0\n # Tokenizer\n tokenizer_dim: int = 512\n tokenizer_ffn_dim: int = 2048\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 4\n tokenizer_num_heads: int = 8\n tokenizer_checkpoint: str = """"\n # LAM\n lam_dim: int = 512\n lam_ffn_dim: int = 2048\n latent_action_dim: int = 32\n num_latent_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 4\n lam_num_heads: int = 8\n lam_checkpoint: str = """"\n # Dynamics\n dyna_type: str = ""maskgit"" # supported options: maskgit, causal\n dyna_dim: int = 512\n dyna_ffn_dim: int = 2048\n dyna_num_blocks: int = 6\n dyna_num_heads: int = 8\n dropout: float = 0.0\n mask_limit: float = 0.5\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\n use_flash_attention: bool = True\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n name: str = ""train_dynamics""\n tags: list[str] = field(default_factory=lambda: [""dynamics""])\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 25000\n log_checkpoint_keep_period: int = 20000\n log_gradients: bool = False\n wandb_id: str = """"\n\n\nargs = tyro.cli(Args)\n\n\ndef dynamics_loss_fn(\n model: Genie, inputs: dict\n) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n """"""Compute masked dynamics loss""""""\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n model.train()\n outputs = model(inputs, training=True)\n mask = outputs[""mask""]\n outputs[""token_logits""] = outputs[""token_logits""].astype(jnp.float32)\n ce_loss = optax.softmax_cross_entropy_with_integer_labels(\n outputs[""token_logits""], outputs[""video_tokens""]\n )\n ce_loss = (mask * ce_loss).sum() / mask.sum()\n acc = outputs[""token_logits""].argmax(-1) == outputs[""video_tokens""]\n acc = (mask * acc).sum() / mask.sum()\n select_probs = jax.nn.softmax(outputs[""token_logits""])\n gt = gt.clip(0, 1).reshape(-1, *gt.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt, recon)).mean()\n _, index_counts_lam = jnp.unique_counts(\n jnp.ravel(outputs[""lam_indices""]), size=args.num_latent_actions, fill_value=0\n )\n _, index_counts_tokenizer = jnp.unique_counts(\n jnp.ravel(outputs[""video_tokens""]), size=args.num_patch_latents, fill_value=0\n )\n codebook_usage_lam = (index_counts_lam != 0).mean()\n codebook_usage_tokenizer = (index_counts_tokenizer != 0).mean()\n metrics = dict(\n cross_entropy_loss=ce_loss,\n masked_token_accuracy=acc,\n select_logit=outputs[""token_logits""].max(-1).mean(),\n select_p=select_probs.max(-1).mean(),\n entropy=jax.scipy.special.entr(select_probs).sum(-1).mean(),\n psnr=psnr,\n ssim=ssim,\n codebook_usage_lam=codebook_usage_lam,\n codebook_usage_tokenizer=codebook_usage_tokenizer,\n )\n return ce_loss, (outputs[""recon""], metrics)\n\n\n@nnx.jit\ndef train_step(\n model: Genie, optimizer: nnx.Optimizer, inputs: dict\n) -> tuple[jax.Array, jax.Array, dict]:\n """"""Update state and compute metrics""""""\n\n def loss_fn(model: Genie) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n return dynamics_loss_fn(model, inputs)\n\n (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(model)\n optimizer.update(grads)\n if args.log_gradients:\n metrics[""gradients_std/""] = jax.tree.map(\n lambda x: x.std(), grads[""params""][""dynamics""]\n )\n return 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.key(args.seed)\n\n # --- Initialize model ---\n rng, _rng = jax.random.split(rng)\n rngs = nnx.Rngs(_rng)\n genie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n tokenizer_ffn_dim=args.tokenizer_ffn_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n lam_ffn_dim=args.lam_ffn_dim,\n latent_action_dim=args.latent_action_dim,\n num_latent_actions=args.num_latent_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n lam_co_train=not args.lam_checkpoint,\n # Dynamics\n dyna_type=args.dyna_type,\n dyna_dim=args.dyna_dim,\n dyna_ffn_dim=args.dyna_ffn_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n dropout=args.dropout,\n mask_limit=args.mask_limit,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n decode=False,\n rngs=rngs,\n )\n\n _, params, _ = nnx.split(genie, nnx.Param, ...)\n param_counts = count_parameters_by_component(params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\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 if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n lr_schedule = get_lr_schedule(\n args.lr_schedule,\n args.init_lr,\n args.max_lr,\n args.decay_end,\n args.num_steps,\n args.warmup_steps,\n args.wsd_decay_steps,\n )\n tx = optax.adamw(\n learning_rate=lr_schedule,\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.dtype,\n )\n optimizer = nnx.Optimizer(genie, tx)\n del genie\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 videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n\n model_state = nnx.state(optimizer.model)\n model_sharded_state = jax.lax.with_sharding_constraint(\n model_state, replicated_sharding\n )\n nnx.update(optimizer.model, model_sharded_state)\n optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)\n optimizer_sharded_state = jax.lax.with_sharding_constraint(\n optimizer_state, replicated_sharding\n )\n nnx.update(optimizer, optimizer_sharded_state)\n\n # --- Initialize checkpoint manager ---\n step = 0\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n\n # --- Create DataLoaderIterator from dataloader ---\n image_shape = (args.image_height, args.image_width, args.image_channels)\n array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n darkness_threshold=args.darkness_threshold,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n\n # --- Restore checkpoint ---\n if args.restore_ckpt:\n abstract_optimizer = nnx.eval_shape(lambda: optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator), # type: ignore\n ),\n )\n restored_optimizer_state = restored[""model_state""]\n nnx.update(optimizer, restored_optimizer_state)\n grain_iterator = restored[""dataloader_state""]\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored dataloader and model state from step {step}"")\n else:\n # Restore from pre-trained tokenizer (and LAM)\n optimizer = restore_genie_components(optimizer, replicated_sharding, rng, args)\n # NOTE: We have to remove the (unused) tokenizer vq dropout due flax.nnx lazily initializing modules.\n # Specifically, the first dynamics model checkpoint will contain the vq dropout module,\n # but the first full restore will fail due to nnx not initializing the module when\n # dropout is set to 0.0.\n del optimizer.model.tokenizer.vq.drop\n\n # --- TRAIN LOOP ---\n dataloader = (\n jax.make_array_from_process_local_data(videos_sharding, elem)\n for elem in grain_iterator\n )\n print(f""Starting training from step {step}..."")\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n rng, _rng_mask = jax.random.split(rng, 2)\n inputs = dict(videos=videos, mask_rng=_rng_mask)\n loss, recon, metrics = train_step(optimizer.model, optimizer, inputs)\n metrics[""lr""] = lr_schedule(step)\n print(f""Step {step}, loss: {loss}"")\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 **metrics,\n }\n )\n if step % args.log_image_interval == 0:\n gt_seq = inputs[""videos""][0].astype(jnp.float32) / 255.0\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 if jax.process_index() == 0:\n log_images = dict(\n image=wandb.Image(np.asarray(gt_seq[args.seq_len - 1])),\n recon=wandb.Image(np.asarray(recon_seq[args.seq_len - 1])),\n true_vs_recon=wandb.Image(\n np.asarray(comparison_seq.astype(np.uint8))\n ),\n )\n wandb.log(log_images)\n # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n optimizer_state = nnx.state(optimizer)\n checkpoint_manager.save(\n step,\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n grain_iterator # type: ignore\n ),\n ),\n )\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n checkpoint_manager.close()\n",python,tab +6,15765,"genie.py",0,0,"from typing import Dict\n\nimport einops\nimport jax\nimport jax.numpy as jnp\nimport flax.nnx as nnx\nimport orbax.checkpoint as ocp\n\nfrom models.dynamics import DynamicsMaskGIT, DynamicsCausal\nfrom models.lam import LatentActionModel\nfrom models.tokenizer import TokenizerVQVAE\n\n\nclass Genie(nnx.Module):\n """"""Genie model""""""\n\n def __init__(\n self,\n in_dim: int,\n tokenizer_dim: int,\n tokenizer_ffn_dim: int,\n latent_patch_dim: int,\n num_patch_latents: int,\n patch_size: int,\n tokenizer_num_blocks: int,\n tokenizer_num_heads: int,\n lam_dim: int,\n lam_ffn_dim: int,\n latent_action_dim: int,\n num_latent_actions: int,\n lam_patch_size: int,\n lam_num_blocks: int,\n lam_num_heads: int,\n lam_co_train: bool,\n dyna_type: str,\n dyna_dim: int,\n dyna_ffn_dim: int,\n dyna_num_blocks: int,\n dyna_num_heads: int,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n decode: bool,\n rngs: nnx.Rngs,\n dropout: float = 0.0,\n mask_limit: float = 0.0,\n ):\n # --- Tokenizer ---\n self.in_dim = in_dim\n self.tokenizer_dim = tokenizer_dim\n self.tokenizer_ffn_dim = tokenizer_ffn_dim\n self.latent_patch_dim = latent_patch_dim\n self.num_patch_latents = num_patch_latents\n self.patch_size = patch_size\n self.tokenizer_num_blocks = tokenizer_num_blocks\n self.tokenizer_num_heads = tokenizer_num_heads\n # --- LAM ---\n self.lam_dim = lam_dim\n self.lam_ffn_dim = lam_ffn_dim\n self.latent_action_dim = latent_action_dim\n self.num_latent_actions = num_latent_actions\n self.lam_patch_size = lam_patch_size\n self.lam_num_blocks = lam_num_blocks\n self.lam_num_heads = lam_num_heads\n self.lam_co_train = lam_co_train\n # --- Dynamics ---\n self.dyna_type = dyna_type\n self.dyna_dim = dyna_dim\n self.dyna_ffn_dim = dyna_ffn_dim\n self.dyna_num_blocks = dyna_num_blocks\n self.dyna_num_heads = dyna_num_heads\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.dropout = dropout\n self.mask_limit = mask_limit\n\n self.tokenizer = TokenizerVQVAE(\n in_dim=self.in_dim,\n model_dim=self.tokenizer_dim,\n ffn_dim=self.tokenizer_ffn_dim,\n latent_dim=self.latent_patch_dim,\n num_latents=self.num_patch_latents,\n patch_size=self.patch_size,\n num_blocks=self.tokenizer_num_blocks,\n num_heads=self.tokenizer_num_heads,\n dropout=0.0,\n codebook_dropout=0.0,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n self.lam = LatentActionModel(\n in_dim=self.in_dim,\n model_dim=self.lam_dim,\n ffn_dim=self.lam_ffn_dim,\n latent_dim=self.latent_patch_dim,\n num_latents=self.num_latent_actions,\n patch_size=self.lam_patch_size,\n num_blocks=self.lam_num_blocks,\n num_heads=self.lam_num_heads,\n dropout=0.0,\n codebook_dropout=0.0,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n if self.dyna_type == ""maskgit"":\n self.dynamics = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n mask_limit=self.mask_limit,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n elif self.dyna_type == ""causal"":\n self.dynamics = DynamicsCausal(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=decode,\n rngs=rngs,\n )\n else:\n raise ValueError(f""Invalid dynamics type: {self.dyna_type}"")\n\n def __call__(\n self, batch: Dict[str, jax.Array], training: bool = True\n ) -> Dict[str, jax.Array]:\n videos_BTHWC = batch[""videos""]\n tokenizer_outputs = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_indices_BTN = tokenizer_outputs[""indices""]\n lam_outputs = self.lam.vq_encode(videos_BTHWC, training=False)\n z_q_BTm11L = lam_outputs[""z_q""]\n action_indices_E = lam_outputs[""indices""]\n latent_actions_BTm11L = jax.lax.cond(\n self.lam_co_train,\n lambda: z_q_BTm11L,\n lambda: jax.lax.stop_gradient(z_q_BTm11L),\n )\n outputs = dict(\n video_tokens=jax.lax.stop_gradient(token_indices_BTN),\n latent_actions=latent_actions_BTm11L,\n )\n outputs[""mask_rng""] = batch[""mask_rng""]\n dyna_logits_BTNV, dyna_mask = self.dynamics(outputs, training)\n outputs[""token_logits""] = dyna_logits_BTNV\n if dyna_mask is not None:\n outputs[""mask""] = dyna_mask\n mle_indices_BTN = jnp.argmax(outputs[""token_logits""], axis=-1)\n H, W = batch[""videos""].shape[2:4]\n outputs[""recon""] = self.tokenizer.decode(mle_indices_BTN, (H, W))\n outputs[""lam_indices""] = action_indices_E\n return outputs\n\n def sample(\n self,\n batch: Dict[str, jax.Array],\n seq_len: int,\n steps: int = 25,\n temperature: float = 1,\n sample_argmax: bool = False,\n ) -> jax.Array:\n """"""\n Autoregressively samples up to `seq_len` future frames, following Figure 8 of the paper.\n\n - Input frames are tokenized once.\n - Future frames are generated autoregressively in token space.\n - All frames are detokenized in a single pass.\n\n Note:\n - For interactive or step-wise sampling, detokenization should occur after each action.\n - To maintain consistent tensor shapes across timesteps, all current and future frames are decoded at every step.\n - Temporal causal structure is preserved by\n a) reapplying the mask before each decoding step.\n b) a temporal causal mask is applied within each ST-transformer block.\n\n Dimension keys:\n B: batch size\n T: number of input (conditioning) frames\n N: number of patches per frame\n M: model dimension\n S: sequence length\n H: height\n W: width\n E: B * (S - 1)\n P: S * N\n """"""\n # --- Encode videos and actions ---\n videos_BTHWC = batch[""videos""]\n latent_actions_E = batch[""latent_actions""]\n tokenizer_out = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_idxs_BTN = tokenizer_out[""indices""]\n B, T, N = token_idxs_BTN.shape\n pad_shape = (B, seq_len - T, N)\n pad = jnp.zeros(pad_shape, dtype=token_idxs_BTN.dtype)\n token_idxs_BSN = jnp.concatenate([token_idxs_BTN, pad], axis=1)\n action_tokens_EL = self.lam.vq.get_codes(latent_actions_E)\n\n def maskgit_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array, jax.Array], step: jax.Array\n ) -> tuple[tuple[jax.Array, jax.Array, jax.Array, jax.Array], None]:\n rng, token_idxs_BSN, mask_BSN, action_tokens_EL = carry\n S, N = token_idxs_BSN.shape[1:]\n L = action_tokens_EL.shape[-1]\n\n # --- Construct + encode video ---\n vid_embed_BSNM = self.dynamics.patch_embed(token_idxs_BSN)\n mask_token_111M = self.dynamics.mask_token.value\n mask_expanded_BSN1 = mask_BSN[..., None]\n vid_embed_BSNM = jnp.where(mask_expanded_BSN1, mask_token_111M, vid_embed_BSNM)\n\n # --- Predict transition ---\n action_tokens_BSm1L = jnp.reshape(action_tokens_EL, (B, S - 1, L))\n act_embed_BSm1M = self.dynamics.action_up(action_tokens_BSm1L)\n act_embed_BSM = jnp.pad(act_embed_BSm1M, ((0, 0), (1, 0), (0, 0)))\n act_embed_BS1M = jnp.reshape(act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1]))\n vid_embed_BSNM += act_embed_BS1M\n unmasked_ratio = jnp.cos(jnp.pi * (step + 1) / (steps * 2))\n step_temp = temperature * (1.0 - unmasked_ratio)\n final_logits_BSNV = self.dynamics.transformer(vid_embed_BSNM) / step_temp\n\n # --- Sample new tokens for final frame ---\n if sample_argmax:\n sampled_token_idxs_BSN = jnp.argmax(final_logits_BSNV, axis=-1)\n else:\n rng, _rng = jax.random.split(rng)\n sampled_token_idxs_BSN = jax.random.categorical(_rng, final_logits_BSNV)\n gather_fn = jax.vmap(jax.vmap(jax.vmap(lambda x, y: x[y])))\n final_token_probs_BSN = gather_fn(\n jax.nn.softmax(final_logits_BSNV), sampled_token_idxs_BSN\n )\n final_token_probs_BSN += ~mask_BSN\n # Update masked tokens only\n token_idxs_BSN = jnp.where(mask_BSN, sampled_token_idxs_BSN, token_idxs_BSN)\n\n # --- Update mask ---\n num_unmasked_tokens = jnp.round(N * (1.0 - unmasked_ratio)).astype(int)\n final_token_probs_flat_BP = einops.rearrange(final_token_probs_BSN, ""b s n -> b (s n)"")\n idx_mask_P = jnp.arange(final_token_probs_flat_BP.shape[-1]) <= N - num_unmasked_tokens\n sorted_idxs_BP = jnp.argsort(final_token_probs_flat_BP, axis=-1)\n mask_update_fn = jax.vmap(lambda msk, ids: msk.at[ids].set(idx_mask_P))\n mask_flat_BP = einops.rearrange(mask_BSN, ""b s n -> b (s n)"")\n new_mask_flat_BP = mask_update_fn(mask_flat_BP, sorted_idxs_BP)\n new_mask_BSN = einops.rearrange(new_mask_flat_BP, ""b (s n) -> b s n"", n=N)\n\n new_carry = (rng, token_idxs_BSN, new_mask_BSN, action_tokens_EL)\n return new_carry, None\n\n def generation_step_fn(\n carry: tuple[jax.Array, jax.Array], step_t: jax.Array\n ) -> tuple[tuple[jax.Array, jax.Array], None]:\n rng, current_token_idxs_BSN = carry\n rng, step_rng = jax.random.split(rng)\n\n # Mask current frame (i.e., t == step_t)\n mask_S = jnp.arange(seq_len) == step_t\n mask_BSN = jnp.broadcast_to(mask_S[None, :, None], (B, seq_len, N)).astype(\n bool\n )\n masked_token_idxs_BSN = current_token_idxs_BSN * ~mask_BSN\n\n # --- Initialize and run MaskGIT loop ---\n init_carry_maskgit = (\n step_rng,\n masked_token_idxs_BSN,\n mask_BSN,\n action_tokens_EL,\n )\n final_carry_maskgit, _ = jax.lax.scan(\n maskgit_step_fn, init_carry_maskgit, jnp.arange(steps)\n )\n updated_token_idxs_BSN = final_carry_maskgit[1]\n new_carry = (rng, updated_token_idxs_BSN)\n return new_carry, None\n\n # --- Run the autoregressive generation using jax.lax.scan ---\n initial_carry = (batch[""rng""], token_idxs_BSN)\n timesteps_to_scan = jnp.arange(T, seq_len)\n final_carry, _ = jax.lax.scan(\n generation_step_fn, initial_carry, timesteps_to_scan\n )\n final_token_idxs_BSN = final_carry[1]\n\n # --- Decode all tokens at once at the end ---\n H, W = batch[""videos""].shape[2:4]\n final_frames_BSHWC = self.tokenizer.decode(\n final_token_idxs_BSN,\n video_hw=(H, W),\n )\n return final_frames_BSHWC\n\n def sample_causal(\n self,\n batch: Dict[str, jax.Array],\n seq_len: int,\n temperature: float = 1,\n sample_argmax: bool = False,\n ) -> jax.Array:\n """"""\n Autoregressively samples up to `seq_len` future frames, following Figure 8 of the paper.\n\n - Input frames are tokenized once.\n - Future frames are generated autoregressively in token space.\n - All frames are detokenized in a single pass.\n\n Note:\n - For interactive or step-wise sampling, detokenization should occur after each action.\n - To maintain consistent tensor shapes across timesteps, all current and future frames are decoded at every step.\n - Temporal causal structure is preserved by\n a) reapplying the mask before each decoding step.\n b) a temporal causal mask is applied within each ST-transformer block.\n\n Dimension keys:\n B: batch size\n T: number of input (conditioning) frames\n N: number of patches per frame\n M: model dimension\n S: sequence length\n H: height\n W: width\n E: B * (S - 1)\n """"""\n assert isinstance(self.dynamics, DynamicsCausal)\n # --- Encode videos and actions ---\n videos_BTHWC = batch[""videos""]\n latent_actions_E = batch[""latent_actions""]\n tokenizer_out = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_idxs_BTN = tokenizer_out[""indices""]\n B, T, N = token_idxs_BTN.shape\n pad_shape = (B, seq_len - T, N)\n pad = jnp.zeros(pad_shape, dtype=token_idxs_BTN.dtype)\n token_idxs_BSN = jnp.concatenate([token_idxs_BTN, pad], axis=1)\n action_tokens_EL = self.lam.vq.get_codes(latent_actions_E)\n dynamics_causal: DynamicsCausal = self.dynamics\n\n def causal_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array, jax.Array], step_n: jax.Array\n ) -> tuple[tuple[jax.Array, jax.Array, jax.Array, jax.Array], None]:\n rng, token_idxs_BSN, action_tokens_EL, step_t = carry\n S, N = token_idxs_BSN.shape[1:]\n L = action_tokens_EL.shape[-1]\n\n # --- Construct + encode video ---\n vid_embed_BSNM = dynamics_causal.patch_embed(token_idxs_BSN)\n\n # --- Predict transition ---\n action_tokens_BSm1L = jnp.reshape(action_tokens_EL, (B, S - 1, L))\n act_embed_BSm1M = dynamics_causal.action_up(action_tokens_BSm1L)\n act_embed_BSM = jnp.pad(act_embed_BSm1M, ((0, 0), (1, 0), (0, 0)))\n act_embed_BS1M = jnp.reshape(act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1]))\n vid_embed_BSNp1M = jnp.concatenate([act_embed_BS1M, vid_embed_BSNM], axis=2)\n final_logits_BTNp1V = dynamics_causal.transformer(vid_embed_BSNp1M, (step_t, step_n)) / temperature\n final_logits_BV = final_logits_BTNp1V[:, step_t, step_n, :]\n\n # --- Sample new tokens for final frame ---\n if sample_argmax:\n sampled_token_idxs_B = jnp.argmax(final_logits_BV, axis=-1)\n else:\n rng, _rng = jax.random.split(rng)\n sampled_token_idxs_B = jax.random.categorical(_rng, final_logits_BV)\n # Update next tokens only\n token_idxs_BSN = token_idxs_BSN.at[:, step_t, step_n].set(sampled_token_idxs_B)\n\n new_carry = (rng, token_idxs_BSN, action_tokens_EL, step_t)\n return new_carry, None\n\n def generation_step_fn(\n carry: tuple[jax.Array, jax.Array], step_t: jax.Array\n ) -> tuple[tuple[jax.Array, jax.Array], None]:\n rng, current_token_idxs_BSN = carry\n rng, step_rng = jax.random.split(rng)\n\n # --- Initialize and run causal loop ---\n init_carry_causal = (\n step_rng,\n current_token_idxs_BSN,\n action_tokens_EL,\n step_t,\n )\n final_carry_causal, _ = jax.lax.scan(\n causal_step_fn, init_carry_causal, jnp.arange(N)\n )\n updated_token_idxs_BSN = final_carry_causal[1]\n new_carry = (rng, updated_token_idxs_BSN)\n return new_carry, None\n\n # --- Run the autoregressive generation using jax.lax.scan ---\n initial_carry = (batch[""rng""], token_idxs_BSN)\n timesteps_to_scan = jnp.arange(T, seq_len)\n final_carry, _ = jax.lax.scan(\n generation_step_fn, initial_carry, timesteps_to_scan\n )\n final_token_idxs_BSN = final_carry[1]\n\n # --- Decode all tokens at once at the end ---\n H, W = batch[""videos""].shape[2:4]\n final_frames_BSHWC = self.tokenizer.decode(\n final_token_idxs_BSN,\n video_hw=(H, W),\n )\n return final_frames_BSHWC\n\n def vq_encode(self, batch: Dict[str, jax.Array], training: bool) -> jax.Array:\n # --- Preprocess videos ---\n video_BTHWC = batch[""videos""]\n lam_output = self.lam.vq_encode(video_BTHWC, training=training)\n lam_indices_E = lam_output[""indices""]\n return lam_indices_E\n\n# FIXME (f.srambical): add conversion script for old checkpoints\ndef restore_genie_components(\n optimizer: nnx.Optimizer,\n sharding: jax.sharding.NamedSharding,\n rng: jax.Array,\n args,\n) -> nnx.Optimizer:\n """"""Restore pre-trained Genie components""""""\n rngs = nnx.Rngs(rng)\n\n tx = optimizer.tx\n model = optimizer.model\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n\n checkpoint_options = ocp.CheckpointManagerOptions(\n step_format_fixed_length=6,\n )\n tokenizer_checkpoint_manager = ocp.CheckpointManager(\n directory=args.tokenizer_checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n dummy_tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.tokenizer_dim,\n ffn_dim=args.tokenizer_ffn_dim,\n latent_dim=args.latent_patch_dim,\n num_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n num_blocks=args.tokenizer_num_blocks,\n num_heads=args.tokenizer_num_heads,\n dropout=args.dropout,\n codebook_dropout=args.dropout,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs,\n )\n dummy_tokenizer_optimizer = nnx.Optimizer(dummy_tokenizer, tx)\n dummy_tokenizer_optimizer_state = nnx.state(dummy_tokenizer_optimizer)\n abstract_sharded_tokenizer_optimizer_state = _create_abstract_sharded_pytree(\n dummy_tokenizer_optimizer_state, sharding\n )\n restored_tokenizer = tokenizer_checkpoint_manager.restore(\n step=tokenizer_checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore( # type: ignore\n abstract_sharded_tokenizer_optimizer_state # type: ignore\n ),\n ),\n )[""model_state""]\n nnx.update(dummy_tokenizer_optimizer.model, restored_tokenizer.model)\n model.tokenizer = dummy_tokenizer_optimizer.model\n tokenizer_checkpoint_manager.close()\n\n if args.lam_checkpoint:\n lam_checkpoint_manager = ocp.CheckpointManager(\n directory=args.lam_checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n dummy_lam = LatentActionModel(\n in_dim=args.image_channels,\n model_dim=args.lam_dim,\n ffn_dim=args.lam_ffn_dim,\n latent_dim=args.latent_patch_dim,\n num_latents=args.num_latent_actions,\n patch_size=args.lam_patch_size,\n num_blocks=args.lam_num_blocks,\n num_heads=args.lam_num_heads,\n dropout=args.dropout,\n codebook_dropout=args.dropout,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs,\n )\n dummy_lam_optimizer = nnx.Optimizer(dummy_lam, tx)\n dummy_lam_optimizer_state = nnx.state(dummy_lam_optimizer)\n abstract_sharded_lam_optimizer_state = _create_abstract_sharded_pytree(\n dummy_lam_optimizer_state, sharding\n )\n restored_lam_optimizer = lam_checkpoint_manager.restore(\n step=lam_checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore( # type: ignore\n abstract_sharded_lam_optimizer_state # type: ignore\n ),\n ),\n )[""model_state""]\n nnx.update(dummy_lam_optimizer.model, restored_lam_optimizer.model)\n model.lam = dummy_lam_optimizer.model\n # Remove the LAM decoder to save memory and avoid unnecessary computation.\n del model.lam.decoder\n lam_checkpoint_manager.close()\n \n # Reinitialize the optimizer states\n optimizer = nnx.Optimizer(model, tx)\n return optimizer\n\n\ndef _create_abstract_sharded_pytree(\n pytree_template: nnx.GraphState, sharding_spec: jax.sharding.NamedSharding\n) -> jax.Array:\n """"""Replaces arrays in a pytree with ShapeDtypeStructs having the given sharding.""""""\n\n def map_fn(leaf_template):\n if hasattr(leaf_template, ""shape"") and hasattr(leaf_template, ""dtype""):\n return jax.ShapeDtypeStruct(\n leaf_template.shape, leaf_template.dtype, sharding=sharding_spec\n )\n return leaf_template\n\n return jax.tree_util.tree_map(map_fn, pytree_template)\n",python,tab +7,21363,"utils/nn.py",0,0,"import math\nfrom typing import Tuple, Callable, List\n\nfrom flax import nnx\nimport jax\nimport jax.numpy as jnp\nimport einops\n\n\nclass SpatioTemporalPositionalEncoding(nnx.Module):\n """"""\n Applies separate sinusoidal positional encodings to the temporal and spatial dimensions.\n """"""\n def __init__(self, d_model: int, max_len: int = 5000):\n self.d_model = d_model\n self.max_len = max_len\n\n pe = jnp.zeros((self.max_len, self.d_model))\n position = jnp.arange(0, self.max_len, dtype=jnp.float32)[:, None]\n div_term = jnp.exp(\n jnp.arange(0, self.d_model, 2) * (-math.log(10000.0) / self.d_model)\n )\n pe = pe.at[:, 0::2].set(jnp.sin(position * div_term))\n pe = pe.at[:, 1::2].set(jnp.cos(position * div_term))\n self.pe = nnx.Variable(pe)\n\n def __call__(self, x: jax.Array) -> jax.Array:\n """"""\n Args:\n x: The input tensor of shape (Batch, Time, Space, Dimension).\n\n Returns:\n The input tensor with positional encodings added.\n """"""\n assert x.ndim == 4, f""Input must be 4-dimensional, but got shape {x.shape}""\n\n num_timesteps = x.shape[1]\n num_spatial_patches = x.shape[2]\n\n # Temporal positional encoding: (1, T, 1, D)\n temporal_pe = self.pe.value[None, :num_timesteps, None, :]\n x = x + temporal_pe\n\n # Spatial positional encoding: (1, 1, S, D)\n spatial_pe = self.pe.value[None, None, :num_spatial_patches, :]\n x = x + spatial_pe\n\n return x\n\n\nclass STBlock(nnx.Module):\n def __init__(\n self,\n dim: int,\n ffn_dim: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n rngs: nnx.Rngs,\n ):\n self.dim = dim\n self.ffn_dim = ffn_dim\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n\n self.spatial_norm = nnx.LayerNorm(\n num_features=self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.spatial_attention = nnx.MultiHeadAttention(\n num_heads=self.num_heads,\n in_features=self.dim,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n attention_fn=_create_flash_attention_fn(\n self.use_flash_attention, is_causal=False\n ),\n rngs=rngs,\n decode=False,\n )\n\n self.temporal_norm = nnx.LayerNorm(\n num_features=self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.temporal_attention = nnx.MultiHeadAttention(\n num_heads=self.num_heads,\n in_features=self.dim,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n attention_fn=_create_flash_attention_fn(\n self.use_flash_attention, is_causal=True\n ),\n rngs=rngs,\n decode=False,\n )\n\n self.ffn_norm = nnx.LayerNorm(\n num_features=self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.ffn_dense1 = nnx.Linear(\n in_features=self.dim,\n out_features=self.ffn_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.ffn_dense2 = nnx.Linear(\n in_features=self.ffn_dim,\n out_features=self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n @nnx.remat\n def __call__(self, x_BTNM: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z_BTNM = self.spatial_norm(x_BTNM)\n z_BTNM = self.spatial_attention(z_BTNM)\n x_BTNM = x_BTNM + z_BTNM\n\n # --- Temporal attention ---\n x_BNTM = x_BTNM.swapaxes(1, 2)\n z_BNTM = self.temporal_norm(x_BNTM)\n z_BNTM = self.temporal_attention(z_BNTM)\n x_BNTM = x_BNTM + z_BNTM\n x_BTNM = x_BNTM.swapaxes(1, 2)\n\n # --- Feedforward ---\n z_BTNM = self.ffn_norm(x_BTNM)\n z_BTND = self.ffn_dense1(z_BTNM)\n z_BTND = jax.nn.gelu(z_BTND)\n z_BTNM = self.ffn_dense2(z_BTND)\n x_BTNM = x_BTNM + z_BTNM\n\n return x_BTNM\n\n\nclass STTransformer(nnx.Module):\n """"""\n Dimension keys:\n B: batch size\n T: number of frames\n N: number of patches per frame\n I: number of input features\n M: model dimension\n D: FFN dimension\n O: number of output features\n """"""\n def __init__(\n self,\n input_dim: int,\n model_dim: int,\n ffn_dim: int,\n out_dim: int,\n num_blocks: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n rngs: nnx.Rngs,\n max_len: int = 5000,\n ):\n self.input_dim = input_dim\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.out_dim = out_dim\n self.num_blocks = num_blocks\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n\n self.input_norm1 = nnx.LayerNorm(\n num_features=self.input_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.input_dense = nnx.Linear(\n in_features=self.input_dim,\n out_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.input_norm2 = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n self.pos_enc = SpatioTemporalPositionalEncoding(self.model_dim, max_len=max_len)\n\n self.blocks = []\n for _ in range(self.num_blocks):\n self.blocks.append(\n STBlock(\n dim=self.model_dim,\n ffn_dim=self.ffn_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n )\n\n self.output_dense = nnx.Linear(\n in_features=self.model_dim,\n out_features=self.out_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n def __call__(self, x_BTNI: jax.Array) -> jax.Array:\n x_BTNI = self.input_norm1(x_BTNI)\n x_BTNM = self.input_dense(x_BTNI)\n x_BTNM = self.input_norm2(x_BTNM)\n x_BTNM = self.pos_enc(x_BTNM)\n for block in self.blocks:\n x_BTNM = block(x_BTNM)\n\n x_BTNO = self.output_dense(x_BTNM)\n return x_BTNO\n\nclass TransformerBlock(nnx.Module):\n def __init__(\n self,\n model_dim: int,\n ffn_dim: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n decode: bool,\n rngs: nnx.Rngs,\n ):\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.decode = decode\n\n self.temporal_norm = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.spatial_norm = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.ffn_norm = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.temporal_attention = nnx.MultiHeadAttention(\n num_heads=self.num_heads,\n in_features=self.model_dim,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n attention_fn=_create_flash_attention_fn(\n self.use_flash_attention, is_causal=True\n ),\n rngs=rngs,\n decode=self.decode,\n )\n self.spatial_attention = nnx.MultiHeadAttention(\n num_heads=self.num_heads,\n in_features=self.model_dim,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n attention_fn=_create_flash_attention_fn(\n self.use_flash_attention, is_causal=True\n ),\n rngs=rngs,\n decode=self.decode,\n )\n self.ffn_dense1 = nnx.Linear(\n in_features=self.model_dim,\n out_features=self.ffn_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.ffn_dense2 = nnx.Linear(\n in_features=self.ffn_dim,\n out_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n @nnx.remat\n def __call__(self, x_BTNM: jax.Array, pos_index: Tuple[jax.Array, jax.Array] | None = None) -> jax.Array:\n # --- Spatial attention ---\n B, T, N, M = x_BTNM.shape\n z_FNM = einops.rearrange(x_BTNM, ""b t n m -> (b t) n m"")\n z_FNM = self.spatial_norm(z_FNM)\n z_FNM = self.spatial_attention(z_FNM)\n z_BTNM = einops.rearrange(z_FNM, ""(b t) n m -> b t n m"", t=T)\n x_BTNM = x_BTNM + z_BTNM\n # --- Temporal attention ---\n z_PTM = einops.rearrange(x_BTNM, ""b t n m -> (b n) t m"")\n z_PTM = self.temporal_norm(z_PTM)\n z_PTM = self.temporal_attention(z_PTM)\n z_BTNM = einops.rearrange(z_PTM, ""(b n) t m -> b t n m"", n=N)\n x_BTNM = x_BTNM + z_BTNM\n # --- Feedforward ---\n z_BTNM = self.ffn_norm(x_BTNM)\n z_BTND = self.ffn_dense1(z_BTNM)\n z_BTND = jax.nn.gelu(z_BTND)\n z_BTNM = self.ffn_dense2(z_BTND)\n x_BTNM = x_BTNM + z_BTNM\n\n return x_BTNM\n\nclass Transformer(nnx.Module):\n """"""\n Dimension keys:\n B: batch size\n T: number of frames\n N: number of patches per frame\n I: number of input features\n M: model dimension\n D: FFN dimension\n O: number of output features\n F: number of frames in batch\n P: number of patch positions in batch\n """"""\n def __init__(\n self,\n input_dim: int,\n model_dim: int,\n ffn_dim: int,\n out_dim: int,\n num_blocks: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n decode: bool,\n rngs: nnx.Rngs,\n max_len: int = 5000,\n ):\n self.input_dim = input_dim\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.out_dim = out_dim\n self.num_blocks = num_blocks\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n\n self.input_norm1 = nnx.LayerNorm(\n num_features=self.input_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.input_dense = nnx.Linear(\n in_features=self.input_dim,\n out_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.input_norm2 = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n self.pos_enc = SpatioTemporalPositionalEncoding(self.model_dim, max_len=max_len)\n\n self.blocks: List[TransformerBlock] = []\n for _ in range(self.num_blocks):\n self.blocks.append(\n TransformerBlock(\n model_dim=self.model_dim,\n ffn_dim=self.ffn_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=decode,\n rngs=rngs,\n )\n )\n self.output_dense = nnx.Linear(\n in_features=self.model_dim,\n out_features=self.out_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n def __call__(self, x_BTNI: jax.Array, pos_index: Tuple[jax.Array, jax.Array] | None = None) -> jax.Array:\n x_BTNI = self.input_norm1(x_BTNI)\n x_BTNM = self.input_dense(x_BTNI)\n x_BTNM = self.input_norm2(x_BTNM)\n x_BTNM = self.pos_enc(x_BTNM)\n for block in self.blocks:\n x_BTNM = block(x_BTNM, pos_index)\n\n x_BTNV = self.output_dense(x_BTNM)\n return x_BTNV\n\ndef normalize(x: jax.Array) -> jax.Array:\n return x / (jnp.linalg.norm(x, ord=2, axis=-1, keepdims=True) + 1e-8)\n\n\nclass VectorQuantizer(nnx.Module):\n """"""\n Dimension keys:\n D: B * T * N\n K: number of latents\n L: latent dimension\n """"""\n def __init__(\n self, latent_dim: int, num_latents: int, dropout: float, rngs: nnx.Rngs\n ):\n self.latent_dim = latent_dim\n self.num_latents = num_latents\n self.dropout = dropout\n\n self.codebook = nnx.Param(\n normalize(\n nnx.initializers.lecun_uniform()(\n rngs.params(), (self.num_latents, self.latent_dim)\n )\n )\n )\n self.drop = nnx.Dropout(self.dropout, rngs=rngs)\n\n def __call__(\n self, x_DL: jax.Array, training: bool\n ) -> Tuple[jax.Array, jax.Array, jax.Array, jax.Array]:\n # --- Compute distances ---\n x_DL = normalize(x_DL)\n normalized_codebook_KL = normalize(self.codebook.value)\n distance_DK = -jnp.matmul(x_DL, normalized_codebook_KL.T)\n if training:\n distance_DK = self.drop(distance_DK)\n\n # --- Get indices and embeddings ---\n indices_D = jnp.argmin(distance_DK, axis=-1)\n z_DL = self.codebook[indices_D]\n\n # --- Straight through estimator ---\n z_q_DL = x_DL + jax.lax.stop_gradient(z_DL - x_DL)\n return z_q_DL, z_DL, x_DL, indices_D\n\n def get_codes(self, indices_E: jax.Array) -> jax.Array:\n return self.codebook[indices_E]\n\n\ndef _create_flash_attention_fn(use_flash_attention: bool, is_causal: bool) -> Callable:\n """"""\n Create an attention function that uses flash attention if enabled.\n\n flax.nnx.MultiHeadAttention provides tensors with shape (batch..., length, num_heads, head_dim),\n but jax.nn.dot_product_attention expects (batch, length, num_heads, head_dim). We reshape to\n ensure compatibility. cuDNN's flash attention additionally requires a sequence length that\n is a multiple of 4. We pad the sequence length to the nearest multiple of 4 and mask\n accordingly. Note that cuDNN requires the mask to be broadcast before calling the attention\n function due to strict shape checking.\n """"""\n\n def attention_fn(query_BTHD, key_BSHD, value_BSHD, bias=None, mask_B111=None, **kwargs):\n implementation = ""cudnn"" if use_flash_attention else None\n\n def _merge_batch_dims(x):\n return einops.rearrange(x, ""... l h k -> (...) l h k"")\n\n def _pad(x, pad_size):\n return jnp.pad(x, ((0, 0), (0, pad_size), (0, 0), (0, 0)))\n\n original_shape = query_BTHD.shape\n T = query_BTHD.shape[-3]\n S = key_BSHD.shape[-3]\n\n # Pad to nearest multiple of 4\n Q = ((T + 3) // 4) * 4\n pad_size_Q = Q - T\n K = ((S + 3) // 4) * 4\n pad_size_K = K - S\n\n query_BQHD = _pad(_merge_batch_dims(query_BTHD), pad_size_Q)\n key_BKHD = _pad(_merge_batch_dims(key_BSHD), pad_size_K)\n value_BKHD = _pad(_merge_batch_dims(value_BSHD), pad_size_K)\n\n attention_mask = jnp.ones((Q, K), dtype=jnp.bool_)\n attention_mask = attention_mask.at[T:, :].set(False)\n attention_mask = attention_mask.at[:, S:].set(False)\n\n mask_11TS = attention_mask[jnp.newaxis, jnp.newaxis, :, :]\n\n bias_4d = jnp.pad(_merge_batch_dims(bias), ((0, 0), (0, 0), (0, pad_size_Q), (0, pad_size_K))) if bias is not None else None\n\n # NOTE: jax.nn.dot_product_attention does not support dropout\n output_4d = jax.nn.dot_product_attention(\n query=query_BQHD,\n key=key_BKHD,\n value=value_BKHD,\n bias=bias_4d,\n mask=mask_11TS,\n implementation=implementation,\n is_causal=is_causal,\n )\n return output_4d[..., :T, :, :].reshape(original_shape)\n\n return attention_fn\n",python,tab +8,84155,"utils/nn.py",16785,0,"",python,selection_mouse +9,138465,"utils/nn.py",16789,0,"",python,selection_mouse +10,138480,"utils/nn.py",16788,0,"",python,selection_command +11,139099,"utils/nn.py",16718,0,"",python,selection_mouse +12,139109,"utils/nn.py",16717,0,"",python,selection_command +13,140678,"utils/nn.py",16874,0,"",python,selection_mouse +14,140693,"utils/nn.py",16873,0,"",python,selection_command +15,146771,"utils/nn.py",16647,0,"",python,selection_mouse +16,147465,"utils/nn.py",16719,0,"",python,selection_mouse +17,149012,"utils/nn.py",16758,0,"",python,selection_mouse +18,149017,"utils/nn.py",16757,0,"",python,selection_command +19,151452,"utils/nn.py",16719,0,"",python,selection_command +20,151603,"utils/nn.py",16717,0,"",python,selection_command +21,151965,"utils/nn.py",16719,0,"",python,selection_command +22,152096,"utils/nn.py",16757,0,"",python,selection_command +23,152242,"utils/nn.py",16788,0,"",python,selection_command +24,152388,"utils/nn.py",16815,0,"",python,selection_command +25,825613,"TERMINAL",0,0,"neofetch",,terminal_command +26,825662,"TERMINAL",0,0,"]633;E;2025-09-03 13:54:44 neofetch;6a06e7a8-9391-4d17-9f3a-a329f1fb7609]633;Cbash: neofetch: command not found...\r\n",,terminal_output +27,826759,"TERMINAL",0,0,"]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;127",,terminal_output +28,827175,"TERMINAL",0,0,"^C",,terminal_command +29,830682,"TERMINAL",0,0,"clear",,terminal_command +30,830700,"TERMINAL",0,0,"]633;E;2025-09-03 13:54:49 clear;6a06e7a8-9391-4d17-9f3a-a329f1fb7609]633;C]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0]633;P;Cwd=/home/hk-project-p0023960/tum_cte0515/Projects/jafar",,terminal_output +31,927507,"utils/nn.py",16509,0,"",python,selection_mouse +32,1174031,"utils/nn.py",15860,0,"",python,selection_mouse +33,1210327,"utils/nn.py",15366,0,"",python,selection_mouse +34,1224242,"utils/nn.py",15859,0,"",python,selection_mouse +35,1579802,"utils/nn.py",16437,0,"",python,selection_mouse +36,1580754,"utils/nn.py",16441,0,"",python,selection_mouse +37,1580757,"utils/nn.py",16440,0,"",python,selection_command +38,1581460,"utils/nn.py",16407,0,"",python,selection_mouse +39,2723196,"train_tokenizer.py",0,0,"from dataclasses import dataclass, field\nimport os\nfrom typing import cast\n\nimport einops\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\nimport flax.nnx as nnx\n\nfrom models.tokenizer import TokenizerVQVAE\nfrom utils.dataloader import get_dataloader\nfrom utils.lr_utils import get_lr_schedule\nfrom utils.parameter_utils import count_parameters_by_component\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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n vq_beta: float = 0.25\n batch_size: int = 48\n init_lr: float = 0.0\n max_lr: float = 3e-4\n decay_end: float = 0.0\n wsd_decay_steps: int = (\n 20000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n )\n lr_schedule: str = ""wsd"" # supported options: wsd, cos\n warmup_steps: int = 10000\n darkness_threshold: float = 0.0\n # Tokenizer\n model_dim: int = 512\n ffn_dim: int = 2048\n latent_dim: int = 32\n num_latents: int = 1024\n patch_size: int = 4\n num_blocks: int = 4\n num_heads: int = 8\n dropout: float = 0.0\n codebook_dropout: float = 0.01\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\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_checkpoint_keep_period: int = 20000\n log_gradients: bool = False\n wandb_id: str = """"\n use_flash_attention: bool = True\n\n\nargs = tyro.cli(Args)\n\n\ndef tokenizer_loss_fn(\n model: TokenizerVQVAE, inputs: dict\n) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n # --- Compute loss ---\n # FIXME (f.srambical): Can we even do native int8 training without casting the video at all?\n # FIXME (f.srambical): If the tokenizer is the reason for the dynamics model being memory-bound,\n # should we at least train the tokenizer natively in int8?\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n model.train()\n outputs = model(inputs, training=True)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n mse = jnp.square(gt - 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 = gt.clip(0, 1).reshape(-1, *gt.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(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@nnx.jit\ndef train_step(\n tokenizer: TokenizerVQVAE, optimizer: nnx.Optimizer, inputs: dict\n) -> tuple[jax.Array, jax.Array, dict]:\n def loss_fn(model: TokenizerVQVAE) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n return tokenizer_loss_fn(model, inputs)\n\n (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(\n tokenizer\n )\n optimizer.update(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 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.key(args.seed)\n\n # --- Initialize model ---\n rng, _rng = jax.random.split(rng)\n rngs = nnx.Rngs(_rng)\n tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.model_dim,\n ffn_dim=args.ffn_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 param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs,\n )\n\n _, params, _ = nnx.split(tokenizer, nnx.Param, ...)\n param_counts = count_parameters_by_component(params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\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 if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n lr_schedule = get_lr_schedule(\n args.lr_schedule,\n args.init_lr,\n args.max_lr,\n args.decay_end,\n args.num_steps,\n args.warmup_steps,\n args.wsd_decay_steps,\n )\n tx = optax.adamw(\n learning_rate=lr_schedule,\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.dtype,\n )\n optimizer = nnx.Optimizer(tokenizer, 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 videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n\n model_state = nnx.state(optimizer.model)\n model_sharded_state = jax.lax.with_sharding_constraint(\n model_state, replicated_sharding\n )\n nnx.update(optimizer.model, model_sharded_state)\n optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)\n optimizer_sharded_state = jax.lax.with_sharding_constraint(\n optimizer_state, replicated_sharding\n )\n nnx.update(optimizer, optimizer_sharded_state)\n\n # --- Initialize checkpoint manager ---\n step = 0\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n\n # --- Create DataLoaderIterator from dataloader ---\n image_shape = (args.image_height, args.image_width, args.image_channels)\n array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n darkness_threshold=args.darkness_threshold,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n\n # --- Restore checkpoint ---\n if args.restore_ckpt:\n abstract_optimizer = nnx.eval_shape(lambda: optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator), # type: ignore\n ),\n )\n restored_optimizer_state = restored[""model_state""]\n nnx.update(optimizer, restored_optimizer_state)\n grain_iterator = restored[""dataloader_state""]\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored dataloader and model state from step {step}"")\n\n # --- TRAIN LOOP ---\n dataloader = (\n jax.make_array_from_process_local_data(videos_sharding, elem)\n for elem in grain_iterator\n )\n print(f""Starting training from step {step}..."")\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n inputs = dict(videos=videos)\n loss, recon, metrics = train_step(tokenizer, optimizer, inputs)\n metrics[""lr""] = lr_schedule(step)\n print(f""Step {step}, loss: {loss}"")\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 **metrics,\n }\n )\n if step % args.log_image_interval == 0:\n gt_seq = inputs[""videos""][0].astype(jnp.float32) / 255.0\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 # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n optimizer_state = nnx.state(optimizer)\n checkpoint_manager.save(\n step,\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n grain_iterator # type: ignore\n ),\n ),\n )\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n checkpoint_manager.close()\n",python,tab +40,2728858,"train_tokenizer.py",1380,0,"",python,selection_mouse +41,2729005,"train_tokenizer.py",1373,10,"patch_size",python,selection_mouse +42,2746713,"train_lam.py",0,0,"from dataclasses import dataclass, field\nimport os\nfrom typing import cast\n\nimport einops\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\nimport flax.nnx as nnx\n\nfrom models.lam import LatentActionModel\nfrom utils.dataloader import get_dataloader\nfrom utils.lr_utils import get_lr_schedule\nfrom utils.parameter_utils import count_parameters_by_component\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n batch_size: int = 36\n vq_beta: float = 0.25\n init_lr: float = 0.0\n max_lr: float = 3e-5\n decay_end: float = 0.0\n wsd_decay_steps: int = (\n 10000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n )\n warmup_steps: int = 5000\n lr_schedule: str = ""wsd"" # supported options: wsd, cos\n vq_reset_thresh: int = 50\n darkness_threshold: float = 0.0\n # LAM\n model_dim: int = 512\n ffn_dim: int = 2048\n latent_dim: int = 32\n num_latents: int = 6\n patch_size: int = 16\n num_blocks: int = 4\n num_heads: int = 8\n dropout: float = 0.0\n codebook_dropout: float = 0.0\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n name: str = ""train_lam""\n tags: list[str] = field(default_factory=lambda: [""lam""])\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 10000\n log_checkpoint_keep_period: int = 20000\n wandb_id: str = """"\n use_flash_attention: bool = True\n\n\nargs = tyro.cli(Args)\n\n\ndef lam_loss_fn(\n model: LatentActionModel, inputs: dict\n) -> tuple[jax.Array, tuple[jax.Array, jax.Array, dict]]:\n # --- Compute loss ---\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n model.train()\n outputs = model(inputs, training=True)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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 = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt, recon)).mean()\n count_fn = jax.vmap(lambda i: (outputs[""indices""] == i).sum())\n index_counts = count_fn(jnp.arange(args.num_latents))\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=(index_counts != 0).mean(),\n )\n return loss, (outputs[""recon""], index_counts, metrics)\n\n\n@nnx.jit\ndef train_step(\n lam: LatentActionModel,\n optimizer: nnx.Optimizer,\n inputs: dict,\n action_last_active: jax.Array,\n rng: jax.Array,\n) -> tuple[jax.Array, jax.Array, jax.Array, dict]:\n def loss_fn(\n model: LatentActionModel,\n ) -> tuple[jax.Array, tuple[jax.Array, jax.Array, dict]]:\n return lam_loss_fn(model, inputs)\n\n # --- Update model ---\n (loss, (recon, idx_counts, metrics)), grads = nnx.value_and_grad(\n loss_fn, has_aux=True\n )(lam)\n optimizer.update(grads)\n\n # --- Reset inactive latent actions ---\n codebook = lam.vq.codebook\n num_codes = len(codebook)\n active_codes = idx_counts != 0.0\n action_last_active = jnp.where(active_codes, 0, action_last_active + 1)\n p_code = active_codes / active_codes.sum()\n reset_idxs = jax.random.choice(rng, num_codes, shape=(num_codes,), p=p_code)\n do_reset = action_last_active >= args.vq_reset_thresh\n new_codebook = jnp.where(\n jnp.expand_dims(do_reset, -1), codebook[reset_idxs], codebook.value\n )\n lam.vq.codebook.value = new_codebook\n action_last_active = jnp.where(do_reset, 0, action_last_active)\n return loss, recon, action_last_active, 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.key(args.seed)\n\n # --- Initialize model ---\n rng, _rng = jax.random.split(rng)\n rngs = nnx.Rngs(_rng)\n lam = LatentActionModel(\n in_dim=args.image_channels,\n model_dim=args.model_dim,\n ffn_dim=args.ffn_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 param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs,\n )\n\n # Count parameters\n _, params, _ = nnx.split(lam, nnx.Param, ...)\n param_counts = count_parameters_by_component(params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\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 if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n lr_schedule = get_lr_schedule(\n args.lr_schedule,\n args.init_lr,\n args.max_lr,\n args.decay_end,\n args.num_steps,\n args.warmup_steps,\n args.wsd_decay_steps,\n )\n tx = optax.adamw(\n learning_rate=lr_schedule,\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.dtype,\n )\n optimizer = nnx.Optimizer(lam, 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 videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n\n model_state = nnx.state(optimizer.model)\n model_sharded_state = jax.lax.with_sharding_constraint(\n model_state, replicated_sharding\n )\n nnx.update(optimizer.model, model_sharded_state)\n optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)\n optimizer_sharded_state = jax.lax.with_sharding_constraint(\n optimizer_state, replicated_sharding\n )\n nnx.update(optimizer, optimizer_sharded_state)\n\n # --- Initialize checkpoint manager ---\n step = 0\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n\n # --- Create DataLoaderIterator from dataloader ---\n image_shape = (args.image_height, args.image_width, args.image_channels)\n array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n darkness_threshold=args.darkness_threshold,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n\n # --- Restore checkpoint ---\n if args.restore_ckpt:\n abstract_optimizer = nnx.eval_shape(lambda: optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator), # type: ignore\n ),\n )\n restored_optimizer_state = restored[""model_state""]\n nnx.update(optimizer, restored_optimizer_state)\n grain_iterator = restored[""dataloader_state""]\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored dataloader and model state from step {step}"")\n\n # --- TRAIN LOOP ---\n dataloader = (\n jax.make_array_from_process_local_data(videos_sharding, elem)\n for elem in grain_iterator\n )\n print(f""Starting training from step {step}..."")\n action_last_active = jnp.zeros(args.num_latents, dtype=jnp.int32)\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n rng, _rng = jax.random.split(rng)\n\n inputs = dict(videos=videos, rng=_rng)\n rng, _rng = jax.random.split(rng)\n loss, recon, action_last_active, metrics = train_step(\n lam, optimizer, inputs, action_last_active, _rng\n )\n metrics[""lr""] = lr_schedule(step)\n print(f""Step {step}, loss: {loss}"")\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 **metrics,\n }\n )\n if step % args.log_image_interval == 0:\n gt_seq = inputs[""videos""][0, 1:].astype(jnp.float32) / 255.0\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 # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n optimizer_state = nnx.state(optimizer)\n checkpoint_manager.save(\n step,\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n grain_iterator # type: ignore\n ),\n ),\n )\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n checkpoint_manager.close()\n",python,tab +43,2753802,"train_lam.py",1406,0,"",python,selection_mouse +44,5002152,"train_lam.py",1423,0,"",python,selection_mouse +45,5007085,"train_tokenizer.py",0,0,"",python,tab +46,5012806,"train_tokenizer.py",1403,0,"",python,selection_mouse +47,11335024,"TERMINAL",0,0,"",,terminal_focus +48,11337086,"TERMINAL",0,0,"source /home/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/bin/activate",,terminal_command +49,11337136,"TERMINAL",0,0,"]633;E;2025-09-03 16:49:56 source /home/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/bin/activate;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0]633;P;Cwd=/home/hk-project-p0023960/tum_cte0515/Projects/jafar",,terminal_output +50,11338099,"TERMINAL",0,0,"queue",,terminal_command +51,11338144,"TERMINAL",0,0,"]633;E;2025-09-03 16:49:57 queue;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C",,terminal_output +52,11338216,"TERMINAL",0,0,"[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Wed Sep 3 16:49:57 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3457966 accelerat train_la tum_cte0 PD\t0:00\t 8 (Resources)3457967 accelerat train_la tum_cte0 PD\t0:00\t 8 (Priority)3457968 accelerat train_la tum_cte0 PD\t0:00\t 8 (Priority)",,terminal_output +53,11339316,"TERMINAL",0,0,"8",,terminal_output +54,11339485,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +55,11342233,"TERMINAL",0,0,"idling",,terminal_command +56,11342301,"TERMINAL",0,0,"]633;E;2025-09-03 16:50:01 idling;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C[?1049h(B[?7hEvery 1.0s: sinfo_t_idlehkn1993.localdomain: Wed Sep 3 16:50:01 2025Partition dev_cpuonly:\t 8 nodes idle\rPartition cpuonly: 19 nodes idle\rPartition dev_accelerated:\t 1 nodes idle\rPartition accelerated:\t 0 nodes idle\rPartition dev_accelerated-h100 :\t 0 nodes idle\rPartition accelerated-h100:\t 0 nodes idle\rPartition large:\t 0 nodes idle\rPartition accelerated-h200:\t 0 nodes idle",,terminal_output +57,11343412,"TERMINAL",0,0,"2",,terminal_output +58,11344436,"TERMINAL",0,0,"3",,terminal_output +59,11345136,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +60,11348557,"TERMINAL",0,0,"idling",,terminal_command +61,11348642,"TERMINAL",0,0,"]633;E;2025-09-03 16:50:07 idling;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C[?1049h(B[?7hEvery 1.0s: sinfo_t_idlehkn1993.localdomain: Wed Sep 3 16:50:07 2025Partition dev_cpuonly:\t 8 nodes idle\rPartition cpuonly: 19 nodes idle\rPartition dev_accelerated:\t 1 nodes idle\rPartition accelerated:\t 0 nodes idle\rPartition dev_accelerated-h100 :\t 0 nodes idle\rPartition accelerated-h100:\t 0 nodes idle\rPartition large:\t 0 nodes idle\rPartition accelerated-h200:\t 0 nodes idle",,terminal_output +62,11349761,"TERMINAL",0,0,"8",,terminal_output +63,11350591,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +64,13842370,"TERMINAL",0,0,"queue",,terminal_command +65,13842462,"TERMINAL",0,0,"]633;E;2025-09-03 17:31:41 queue;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Wed Sep 3 17:31:41 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3457966 accelerat train_la tum_cte0 PD\t0:00\t 8 (Resources)3457967 accelerat train_la tum_cte0 PD\t0:00\t 8 (Priority)3457968 accelerat train_la tum_cte0 PD\t0:00\t 8 (Priority)",,terminal_output +66,13843118,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +67,13884392,"TERMINAL",0,0,"queue",,terminal_command +68,13884443,"TERMINAL",0,0,"]633;E;2025-09-03 17:32:23 queue;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C",,terminal_output +69,13884514,"TERMINAL",0,0,"[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Wed Sep 3 17:32:23 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3457966 accelerat train_la tum_cte0 PD\t0:00\t 8 (Resources)3457967 accelerat train_la tum_cte0 PD\t0:00\t 8 (Priority)3457968 accelerat train_la tum_cte0 PD\t0:00\t 8 (Priority)",,terminal_output +70,13885540,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +71,13888862,"TERMINAL",0,0,"fsacct_week",,terminal_command +72,13888895,"TERMINAL",0,0,"]633;E;2025-09-03 17:32:27 fsacct_week;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C JobID JobName Partition All State Elapsed Timelimit \r\n--------------- ------------------------------ ---------------- --- ------------ ---------- ---------- \r\n 3454890 train_lam_minecraft_1node_dev accelerated 24 REQUEUED 00:04:42 00:10:00 \r\n 3454917 train_lam_minecraft_1node_dev accelerated 24 REQUEUED 00:04:36 00:10:00 \r\n 3457966 train_lam_minecraft_8node_dar+ accelerated 0 PENDING 00:00:00 2-00:00:00 \r\n 3457967 train_lam_minecraft_8node_dar+ accelerated 0 PENDING 00:00:00 2-00:00:00 \r\n 3457968 train_lam_minecraft_8node_dar+ accelerated 0 PENDING 00:00:00 2-00:00:00 \r\n 3457969 train_lam_minecraft_8node_dar+ accelerated 192 FAILED 00:05:59 2-00:00:00 \r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +73,13893779,"TERMINAL",0,0,"logs",,terminal_command +74,13893806,"TERMINAL",0,0,"]633;E;2025-09-03 17:32:32 logs;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output +75,13898899,"TERMINAL",0,0,"cd coinrun/",,terminal_command +76,13899210,"TERMINAL",0,0,"ls",,terminal_command +77,13899233,"TERMINAL",0,0,"]633;E;2025-09-03 17:32:38 ls;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;Cdynamics tokenizer\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun]633;D;0",,terminal_output +78,13903031,"TERMINAL",0,0,"cd ..",,terminal_command +79,13903047,"TERMINAL",0,0,"]633;E;2025-09-03 17:32:41 cd ..;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output +80,13903314,"TERMINAL",0,0,"ls",,terminal_command +81,13903395,"TERMINAL",0,0,"]633;E;2025-09-03 17:32:42 ls;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;Catari train_dyn_causal_500M_3372972.log train_lam_action_space_scaling_12_3321527.log train_lam_action_space_scaling_8_3329806.log train_tokenizer_model_size_scaling_140M_3313562.log\r\nbig_run train_dyn_causal_500M_3373110.log train_lam_action_space_scaling_12_3329787.log train_lam_action_space_scaling_8_3331288.log train_tokenizer_model_size_scaling_140M_3316019.log\r\nbig-runs train_dyn_new_arch-bugfixed-spatial-shift_3359343.log train_lam_action_space_scaling_12_3329802.log train_lam_minecraft_overfit_sample_3309655.log train_tokenizer_model_size_scaling_200M_3313563.log\r\ncausal train_dyn_new_arch-bugfixed-temporal-shift_3359349.log train_lam_action_space_scaling_12_3331284.log train_lam_model_size_scaling_38M_3317098.log train_tokenizer_model_size_scaling_200M_3316020.log\r\ncoinrun train_dyn_yolorun_3333026.log train_lam_action_space_scaling_20_3318547.log train_lam_model_size_scaling_38M_3317115.log train_tokenizer_model_size_scaling_227M_3317234.log\r\nlam train_dyn_yolorun_3333448.log train_lam_action_space_scaling_20_3329788.log train_lam_model_size_scaling_38M_3317231.log train_tokenizer_model_size_scaling_227M_3318555.log\r\nmaskgit train_dyn_yolorun_3335345.log train_lam_action_space_scaling_20_3329803.log train_tokenizer_batch_size_scaling_16_node_3321526.log train_tokenizer_model_size_scaling_227M_3320173.log\r\nmaskgit-maskprob-fix train_dyn_yolorun_3335362.log train_lam_action_space_scaling_20_3331285.log train_tokenizer_batch_size_scaling_1_node_3318551.log train_tokenizer_model_size_scaling_227M_3321523.log\r\npreprocess train_dyn_yolorun_3348592.log train_lam_action_space_scaling_50_3320180.log train_tokenizer_batch_size_scaling_2_node_3318552.log train_tokenizer_model_size_scaling_37M_3313565.log\r\ntrain_dyn_causal_180M_3372931.log train_dyn_yolorun_new_arch_3351743.log train_lam_action_space_scaling_50_3329789.log train_tokenizer_batch_size_scaling_2_node_3330806.log train_tokenizer_model_size_scaling_37M_3316022.log\r\ntrain_dyn_causal_180M_3372963.log train_dyn_yolorun_new_arch_3352103.log train_lam_action_space_scaling_50_3329804.log train_tokenizer_batch_size_scaling_2_node_3330848.log train_tokenizer_model_size_scaling_37M_3317232.log\r\ntrain_dyn_causal_180M_3372969.log train_dyn_yolorun_new_arch_3352115.log train_lam_action_space_scaling_50_3331286.log train_tokenizer_batch_size_scaling_2_node_3331282.log train_tokenizer_model_size_scaling_37M_3317239.log\r\ntrain_dyn_causal_180M_3373107.log train_dyn_yolorun_new_arch_3358457.log train_lam_action_space_scaling_6_3318549.log train_tokenizer_batch_size_scaling_4_node_3318553.log train_tokenizer_model_size_scaling_37M_3318556.log\r\ntrain_dyn_causal_255M_3372932.log train_lam_action_space_scaling_10_3320179.log train_lam_action_space_scaling_6_3320178.log train_tokenizer_batch_size_scaling_4_node_3320175.log train_tokenizer_model_size_scaling_74M_3318557.log\r\ntrain_dyn_causal_255M_3372970.log train_lam_action_space_scaling_10_3321529.log train_lam_action_space_scaling_6_3321528.log train_tokenizer_batch_size_scaling_4_node_3321524.log train_tokenizer_model_size_scaling_74M_3320174.log\r\ntrain_dyn_causal_255M_3373108.log train_lam_action_space_scaling_10_3329786.log train_lam_action_space_scaling_6_3329790.log train_tokenizer_batch_size_scaling_8_node_3320176.log train_tokenizer_model_size_scaling_74M_3321522.log\r\ntrain_dyn_causal_356M_3372934.log train_lam_action_space_scaling_10_3329801.log train_lam_action_space_scaling_6_3329805.log train_tokenizer_batch_size_scaling_8_node_3321525.log train_tokenizer_model_size_scaling_80M_3313564.log\r\ntrain_dyn_causal_356M_3372971.log train_lam_action_space_scaling_10_3331283.log train_lam_action_space_scaling_6_3331287.log train_tokenizer_minecraft_overfit_sample_3309656.log train_tokenizer_model_size_scaling_80M_3316026.log\r\ntrain_dyn_causal_356M_3373109.log train_lam_action_space_scaling_12_3318546.log train_lam_action_space_scaling_8_3318550.log train_tokenizer_model_size_scaling_127M_3317233.log yoloruns\r\ntrain_dyn_causal_500M_3372936.log train_lam_action_space_scaling_12_3320177.log train_lam_action_space_scaling_8_3329791.log train_tokenizer_model_size_scaling_127M_3318554.log\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output +82,13905306,"TERMINAL",0,0,"cd lam/",,terminal_command +83,13905646,"TERMINAL",0,0,"ls",,terminal_command +84,13905684,"TERMINAL",0,0,"]633;E;2025-09-03 17:32:44 ls;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C",,terminal_output +85,13905977,"TERMINAL",0,0,"train_lam_minecraft_8node_3431870.log train_lam_minecraft_8node_3431885.log train_lam_minecraft_8node_3454917.log train_lam_minecraft_8node_3454948.log train_lam_minecraft_8node_darkness_filter_37M_3454953.log\r\ntrain_lam_minecraft_8node_3431875.log train_lam_minecraft_8node_3431895.log train_lam_minecraft_8node_3454941.log train_lam_minecraft_8node_darkness_filter_133M_3454956.log train_lam_minecraft_8node_darkness_filter_37M_3457969.log\r\ntrain_lam_minecraft_8node_3431876.log train_lam_minecraft_8node_3454890.log train_lam_minecraft_8node_3454944.log train_lam_minecraft_8node_darkness_filter_311M_3454955.log train_lam_minecraft_8node_darkness_filter_400M_3454954.log\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam]633;D;0",,terminal_output +86,13931971,"TERMINAL",0,0,"ls -l | grep 3457969",,terminal_command +87,13932008,"TERMINAL",0,0,"]633;E;2025-09-03 17:33:10 ls -l | grep 3457969;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C-rw-rw----+ 1 tum_cte0515 hk-project-p0023960 58963 Sep 2 16:56 train_lam_minecraft_8node_darkness_filter_37M_3457969.log\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam]633;D;0",,terminal_output +88,13935482,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/train_lam_minecraft_8node_darkness_filter_37M_3457969.log",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/%x_%j.log\n#SBATCH --job-name=train_lam_minecraft_8node_darkness_filter_37M\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\n# slurm_job_id=$SLURM_JOB_ID\nslurm_job_id=3454953\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/lam/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_lam.py \\n --save_ckpt \\n --restore_ckpt \\n --wandb_id $slurm_job_id \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=160 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --darkness_threshold=50 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=lam-minecraft-8-node-darkness-filter-37M-$slurm_job_id \\n --tags lam minecraft 8-node darkness-filter 37M \\n --entity instant-uv \\n --project jafar \\n --num_latents=100 \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n/var/spool/slurmd/job3457969/slurm_script: line 38: .venv/bin/activate: No such file or directory\nSLURM_JOB_USER=tum_cte0515\nSLURM_TASKS_PER_NODE=4(x8)\nSLURM_JOB_UID=999226\nSLURM_TASK_PID=1440498\nSLURM_JOB_GPUS=0,1,2,3\nSLURM_LOCALID=0\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs\nSLURMD_NODENAME=hkn0404\nSLURM_JOB_START_TIME=1756824635\nSLURM_CLUSTER_NAME=hk\nSLURM_JOB_END_TIME=1756997435\nSLURM_CPUS_ON_NODE=24\nSLURM_JOB_CPUS_PER_NODE=24(x8)\nSLURM_GPUS_ON_NODE=4\nSLURM_GTIDS=0\nSLURM_JOB_PARTITION=accelerated\nSLURM_TRES_PER_TASK=cpu=5\nSLURM_OOM_KILL_STEP=0\nSLURM_JOB_NUM_NODES=8\nSLURM_JOBID=3457969\nSLURM_JOB_QOS=normal\nSLURM_PROCID=0\nSLURM_CPUS_PER_TASK=5\nSLURM_NTASKS=32\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0404\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\nSLURM_SCRIPT_CONTEXT=prolog_task\nSLURM_NODELIST=hkn[0404,0407-0408,0417,0503,0725,0729,0732]\nSLURM_JOB_ACCOUNT=hk-project-p0023960\nSLURM_PRIO_PROCESS=0\nSLURM_NPROCS=32\nSLURM_NNODES=8\nSLURM_SUBMIT_HOST=hkn1990.localdomain\nSLURM_JOB_ID=3457969\nSLURM_NODEID=0\nSLURM_CONF=/etc/slurm/slurm.conf\nSLURM_JOB_NAME=train_lam_minecraft_8node_darkness_filter_37M\nSLURM_NTASKS_PER_NODE=4\nSLURM_JOB_GID=502226\nSLURM_JOB_NODELIST=hkn[0404,0407-0408,0417,0503,0725,0729,0732]\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\n2025-09-02 16:51:32.979826: E external/xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\n2025-09-02 16:51:32.979716: E external/xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\n2025-09-02 16:51:32.979963: E external/xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\n2025-09-02 16:51:32.979958: E external/xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 742, in backends\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = _init_backend(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 828, in _init_backend\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n backend = registration.factory()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 528, in factory\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jaxlib/xla_client.py"", line 153, in make_c_api_client\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_lam.py"", line 153, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_lam.py"", line 153, in \n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_lam.py"", line 153, in \n num_devices = jax.device_count()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 907, in device_count\n return _xla.get_c_api_client(plugin_name, options, distributed_client)\njaxlib._jax.XlaRuntimeError: FAILED_PRECONDITION: No visible GPU devices.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/train_lam.py"", line 153, in \n num_devices = jax.device_count()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 907, in device_count\n num_devices = jax.device_count()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 907, in device_count\n num_devices = jax.device_count()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 907, in device_count\n return int(get_backend(backend).device_count())\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 876, in get_backend\n return int(get_backend(backend).device_count())\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 876, in get_backend\n return _get_backend_uncached(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 855, in _get_backend_uncached\n return int(get_backend(backend).device_count())\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 876, in get_backend\n return int(get_backend(backend).device_count())\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 876, in get_backend\n return _get_backend_uncached(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 855, in _get_backend_uncached\n return _get_backend_uncached(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 855, in _get_backend_uncached\n bs = backends()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 758, in backends\n return _get_backend_uncached(platform)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 855, in _get_backend_uncached\n bs = backends()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 758, in backends\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': FAILED_PRECONDITION: No visible GPU devices. (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\n bs = backends()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 758, in backends\n bs = backends()\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 758, in backends\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': FAILED_PRECONDITION: No visible GPU devices. (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': FAILED_PRECONDITION: No visible GPU devices. (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': FAILED_PRECONDITION: No visible GPU devices. (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\n2025-09-02 16:56:33.847340: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service.cc:1798] Shutdown barrier in coordination service has failed:\nDEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\nThis suggests that the workers are out of sync. Either at least one worker (a) crashed early due to program error or scheduler events (e.g. preemption, eviction), (b) was too fast in its execution, or (c) too slow / hanging. Check the logs (both the program and scheduler events) for an earlier error to identify the root cause.\n2025-09-02 16:56:33.847379: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service.cc:1839] Use error polling to propagate the following error to all tasks: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-09-02 16:56:33.847562: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.847711: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.847756: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-09-02 16:56:33.847765: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-09-02 16:56:33.847814: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.847865: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-09-02 16:56:33.848049: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848309: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\n2025-09-02 16:56:33.848088: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848204: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848026: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-09-02 16:56:33.848100: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848254: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848027: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848062: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848225: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848211: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848202: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848085: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848183: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848217: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848280: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848218: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848263: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848329: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848260: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848395: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848441: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848796: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\n2025-09-02 16:56:33.848399: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848611: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-09-02 16:56:33.848439: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848749: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848760: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\n2025-09-02 16:56:33.848398: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.848833: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-09-02 16:56:33.849068: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\n2025-09-02 16:56:33.849200: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-09-02 16:56:33.849066: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\n2025-09-02 16:56:33.849198: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\n2025-09-02 16:56:33.849003: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-09-02 16:56:33.849035: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\n2025-09-02 16:56:33.849080: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-09-02 16:56:33.849229: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\n2025-09-02 16:56:33.849107: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\n2025-09-02 16:56:33.849359: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\n2025-09-02 16:56:33.849266: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-09-02 16:56:33.849228: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\n2025-09-02 16:56:33.849209: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\n2025-09-02 16:56:33.849270: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-09-02 16:56:33.849223: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\n2025-09-02 16:56:33.849264: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-09-02 16:56:33.849408: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-09-02 16:56:33.849271: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-09-02 16:56:33.849221: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-09-02 16:56:33.849411: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-09-02 16:56:33.849434: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-09-02 16:56:33.849435: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-09-02 16:56:33.849434: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::2947760685903380055::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 4/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:1. Some timed out task names:\n/job:jax_worker/replica:0/task:13\n/job:jax_worker/replica:0/task:20\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1dShutdown::2947760685903380055']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-09-02 16:56:33.931086: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.931108: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n:{""created"":""@1756824993.930994068"",""description"":""Error received from peer ipv4:10.0.1.36:62385"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\n2025-09-02 16:56:33.931120: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:429] Polled an error from coordination service (this can be an error from this or another task).\n2025-09-02 16:56:33.931151: F external/xla/xla/pjrt/distributed/client.h:88] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: Failed to send RPC to coordination service. Either the leader task was preempted/died/restarted unexpectedly or this task is experiencing network issues. Check earlier logs from 1) this task, 2) the leader (usually slice 0 task 0), and 3) cluster scheduler to debug further.\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/PollForError:\n:{""created"":""@1756824993.931007203"",""description"":""Error received from peer ipv4:10.0.1.36:62385"",""file"":""external/com_github_grpc_grpc/src/core/lib/surface/call.cc"",""file_line"":1056,""grpc_message"":""Socket closed"",""grpc_status"":14}\nsrun: error: hkn0404: tasks 0-3: Aborted (core dumped)\nsrun: error: hkn0732: tasks 29-30: Aborted (core dumped)\nsrun: error: hkn0407: tasks 4,7: Aborted (core dumped)\nsrun: error: hkn0725: tasks 21,23: Aborted (core dumped)\nsrun: error: hkn0408: tasks 9-10: Aborted (core dumped)\nsrun: error: hkn0729: tasks 24-25: Aborted (core dumped)\nsrun: error: hkn0417: tasks 14-15: Aborted (core dumped)\nsrun: error: hkn0503: tasks 16,18: Aborted (core dumped)\nsrun: error: hkn0407: task 6: Aborted (core dumped)\nsrun: error: hkn0732: task 31: Aborted (core dumped)\nsrun: error: hkn0725: task 22: Aborted (core dumped)\nsrun: error: hkn0729: task 26: Aborted (core dumped)\nsrun: error: hkn0503: task 17: Aborted (core dumped)\nsrun: error: hkn0408: task 11: Aborted (core dumped)\nsrun: error: hkn0417: task 12: Aborted (core dumped)\nsrun: error: hkn0732: task 28: Aborted (core dumped)\nsrun: error: hkn0725: task 20: Aborted (core dumped)\nsrun: error: hkn0407: task 5: Aborted (core dumped)\nsrun: error: hkn0408: task 8: Aborted (core dumped)\nsrun: error: hkn0417: task 13: Aborted (core dumped)\nsrun: error: hkn0729: task 27: Aborted (core dumped)\nsrun: error: hkn0503: task 19: Aborted (core dumped)\n\n============================= JOB FEEDBACK =============================\n\nJob ID: 3457969\nCluster: hk\nUser/Group: tum_cte0515/hk-project-p0023960\nAccount: hk-project-p0023960\nState: FAILED (exit code 134)\nPartition: accelerated\nNodes: 8\nCores per node: 24\nNodelist: hkn[0404,0407-0408,0417,0503,0725,0729,0732]\nCPU Utilized: 00:02:37\nCPU Efficiency: 0.23% of 19:08:48 core-walltime\nJob Wall-clock time: 00:05:59\nStarttime: Tue Sep 2 16:50:35 2025\nEndtime: Tue Sep 2 16:56:34 2025\nMemory Utilized: 12.17 GB (estimated maximum)\nMemory Efficiency: 0.00% of 0.00 MB (0.00 MB/node)\nEnergy Consumed: 3157333 Joule / 877.036944444444 Watthours\nAverage node power draw: 8794.79944289694 Watt\n",log,tab +89,13935894,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/train_lam_minecraft_8node_darkness_filter_37M_3457969.log",508,0,"",log,selection_mouse +90,13935910,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/train_lam_minecraft_8node_darkness_filter_37M_3457969.log",507,0,"",log,selection_command +91,13936594,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/train_lam_minecraft_8node_darkness_filter_37M_3457969.log",58963,0,"",log,selection_command +92,13967121,"TERMINAL",0,0,"runner",,terminal_command +93,13979199,"TERMINAL",0,0,"sbatch slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-^Crkness-filter-base.sbatch",,terminal_command +94,13979221,"TERMINAL",0,0,"^C[?2004l\r[?2004h[?2004l\r\r\n]633;E;;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C]0;tum_cte0515@hkn1993:~/Projects/jafar_jobs]633;D",,terminal_output +95,13987679,"TERMINAL",0,0,"cat slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-base.sbatch",,terminal_command +96,13987708,"TERMINAL",0,0,"]633;E;2025-09-03 17:34:06 cat slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-base.sbatch;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=8\r\n#SBATCH --ntasks-per-node=4\r\n#SBATCH --time=48:00:00\r\n#SBATCH --partition=accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:4\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/%x_%j.log\r\n#SBATCH --job-name=train_lam_minecraft_8node_darkness_filter_37M\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# --- signal trap to requeue job before timeout ---\r\nrequeue_job() {\r\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\r\n scontrol requeue $SLURM_JOB_ID\r\n exit 0\r\n}\r\n\r\ntrap requeue_job sigusr1\r\n\r\n# set checkpoint flag based on restart count\r\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\r\n\r\nif [ $restart_count -eq 0 ]; then\r\n restore_ckpt_flag=""--no-restore-ckpt""\r\nelse\r\n restore_ckpt_flag=""--restore-ckpt""\r\nfi\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\r\n\r\njob_name=$SLURM_JOB_NAME\r\n# slurm_job_id=$SLURM_JOB_ID\r\nslurm_job_id=3454953\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/lam/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\nenv | grep SLURM\r\n\r\nsrun python train_lam.py \\r\n --save_ckpt \\r\n --restore_ckpt \\r\n --wandb_id $slurm_job_id \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=160 \\r\n --init_lr=0 \\r\n --max_lr=1e-4 \\r\n --darkness_threshold=50 \\r\n --log_image_interval=1000 \\r\n --log \\r\n --log_checkpoint_interval=1000 \\r\n --name=lam-minecraft-8-node-darkness-filter-37M-$slurm_job_id \\r\n --tags lam minecraft 8-node darkness-filter 37M \\r\n --entity instant-uv \\r\n --project jafar \\r\n --num_latents=100 \\r\n --data_dir $array_records_dir &\r\n\r\nchild_pid=$!\r\n\r\nwait $child_pid\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar_jobs]633;D;0",,terminal_output +97,13996678,"TERMINAL",0,0,"sbatch slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-base.sbatch",,terminal_command +98,13996728,"TERMINAL",0,0,"]633;E;2025-09-03 17:34:15 sbatch slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-base.sbatch;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C",,terminal_output +99,13996951,"TERMINAL",0,0,"Submitted batch job 3463210\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar_jobs]633;D;0",,terminal_output +100,13999265,"TERMINAL",0,0,"queue",,terminal_command +101,13999316,"TERMINAL",0,0,"]633;E;2025-09-03 17:34:18 queue;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C",,terminal_output +102,13999389,"TERMINAL",0,0,"[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Wed Sep 3 17:34:18 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3457966 accelerat train_la tum_cte0 PD\t0:00\t 8 (Resources)3457967 accelerat train_la tum_cte0 PD\t0:00\t 8 (Priority)3457968 accelerat train_la tum_cte0 PD\t0:00\t 8 (Priority)3463210 accelerat train_la tum_cte0 PD\t0:00\t 8 (Priority)",,terminal_output +103,14000077,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar_jobs]633;D;0",,terminal_output +104,14215026,"TERMINAL",0,0,"cd ..",,terminal_command +105,14215299,"TERMINAL",0,0,"ls",,terminal_command +106,14215337,"TERMINAL",0,0,"]633;E;2025-09-03 17:37:54 ls;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;Ccheckpoints jafar jafar_jobs jafar_jobs_2 jafar_jobs_3 tmp\r\n]0;tum_cte0515@hkn1993:~/Projects]633;D;0",,terminal_output +107,14222012,"TERMINAL",0,0,"cd ls",,terminal_command +108,14223194,"TERMINAL",0,0,"ls",,terminal_command +109,14227747,"TERMINAL",0,0,"mv jafar jasmine",,terminal_command +110,14227770,"TERMINAL",0,0,"]633;E;2025-09-03 17:38:06 mv jafar jasmine;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C]0;tum_cte0515@hkn1993:~/Projects]633;D;0]633;P;Cwd=/home/hk-project-p0023960/tum_cte0515/Projects",,terminal_output +111,14228805,"TERMINAL",0,0,"ls",,terminal_command +112,14228820,"TERMINAL",0,0,"]633;E;2025-09-03 17:38:07 ls;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;Ccheckpoints jafar_jobs jafar_jobs_2 jafar_jobs_3 jasmine tmp\r\n]0;tum_cte0515@hkn1993:~/Projects]633;D;0]633;P;Cwd=/home/hk-project-p0023960/tum_cte0515/Projects",,terminal_output +113,14241517,"TERMINAL",0,0,"mv jafar_jobs jasmine_jobs",,terminal_command +114,14241539,"TERMINAL",0,0,"]633;E;2025-09-03 17:38:20 mv jafar_jobs jasmine_jobs;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C]0;tum_cte0515@hkn1993:~/Projects]633;D;0]633;P;Cwd=/home/hk-project-p0023960/tum_cte0515/Projects",,terminal_output +115,14247458,"TERMINAL",0,0,"mv jafar_jobs_2 jasmine_jobs_2",,terminal_command +116,14247470,"TERMINAL",0,0,"]633;E;2025-09-03 17:38:26 mv jafar_jobs_2 jasmine_jobs_2;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C]0;tum_cte0515@hkn1993:~/Projects]633;D;0]633;P;Cwd=/home/hk-project-p0023960/tum_cte0515/Projects",,terminal_output +117,14252310,"TERMINAL",0,0,"mv jafar_jobs_3 jasmine_jobs_3",,terminal_command +118,14252339,"TERMINAL",0,0,"]633;E;2025-09-03 17:38:31 mv jafar_jobs_3 jasmine_jobs_3;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C]0;tum_cte0515@hkn1993:~/Projects]633;D;0",,terminal_output +119,14269977,"TERMINAL",0,0,"vim ~/.bashrc",,terminal_command +120,14270165,"TERMINAL",0,0,"]633;E;2025-09-03 17:38:48 vim ~/.bashrc;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C[?1049h[>4;2m[?1h=[?2004h[?1004h[?12h[?12l[?25l""~/.bashrc"" 45L, 2218B▽ Pzz\[0%m [>c]10;?]11;?# .bashrc# Source global definitionsif [ -f /etc/bashrc ]; then\r\n . /etc/bashrc\r\nfi\r\n\r\n# User specific environment\r\nif ! [[ ""$PATH"" =~ ""$HOME/.local/bin:$HOME/bin:"" ]]\r\nthen\r\n PATH=""$HOME/.local/bin:$HOME/bin:$PATH""\r\nfi\r\nexport PATH\r\n\r\nalias idle='sinfo_t_idle'\r\nexport ws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\r\nalias salloc_node='salloc --time=01:00:00 --partition=dev_accelerated --nodes=1 --gres=gpu:1 --cpus-per-task=8'\r\nalias salloc_cpu='salloc --time=01:00:00 --partition=dev_cpuonly --nodes=1 --cpus-per-task=128'\r\nalias sync-runner=""sh /home/hk-project-p0023960/tum_cte0515/sync_runner.sh /home/hk-project-p0023960/tum_cte0515/Projects/jafar /home/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/""\r\nalias sync-runner-2=""sh /home/hk-project-p0023960/tum_cte0515/sync_runner.sh /home/hk-project-p0023960/tum_cte0515/Projects/jafar /home/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs_2/""\r\nalias sync-runner-3=""sh /home/hk-project-p0023960/tum_cte0515/sync_runner.sh /home/hk-project-p0023960/tum_cte0515/Projects/jafar /home/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs_3/""\r\nalias runner=""cd /home/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/""\r\nalias runner-2=""cd /home/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs_2/""\r\nalias runner-3=""cd /home/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs_3/""\r\nalias dev=""cd /home/hk-project-p0023960/tum_cte0515/Projects/jafar/""\r\nalias smi=""watch -n1 nvidia-smi""\r\nalias idling=""watch -n1 sinfo_t_idle""\r\nalias queue='watch -n1 squeue --me'\r\nalias fqueue='watch -n 1 ""squeue -o \""%.10i %.16P %.30j %.8u %.8T %.10M %.9l %.6D %R\""""'\r\nalias fsacct_week='sacct --format=""JobID%15,JobName%30,Partition%16,AllocCPUS%3,State%12,Elapsed%10,Timelimit%10"" --starttime $(date -d ""last week"" +%Y-%m-%d) | grep -vE ""*.batch|*.extern|*.inter|bash|python|CANCELLED|echo""'\r\nalias logs=""cd /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir""overlapper() {\r\n if [ -z ""$1"" ]; thenecho ""Usage: overlap 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9[?25h[?25ll 10[?25h[?25ll 1[?25h[?25ll 2[?25h[?25ll 3[?25h[?25ll 4[?25h[?25ll 5[?25h[?25ll 6[?25h[?25ll 7[?25h[?25ll 8[?25h[?25ll 9[?25h[?25ll 20[?25h[?25ll 1[?25h",,terminal_output +144,14277342,"TERMINAL",0,0,"[?25ll 2[?25h",,terminal_output +145,14277515,"TERMINAL",0,0,"[?25ll 3[?25h",,terminal_output +146,14277645,"TERMINAL",0,0,"[?25ll 4[?25h",,terminal_output +147,14277808,"TERMINAL",0,0,"[?25ll 5[?25h",,terminal_output +148,14278061,"TERMINAL",0,0,"[?25ld[?25h",,terminal_output +149,14278146,"TERMINAL",0,0,"[?25lw /home/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/""[?25h",,terminal_output +150,14278283,"TERMINAL",0,0,"[?25li -- INSERT --19,125Top[?25h",,terminal_output +151,14278835,"TERMINAL",0,0,"[?25ls/home/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/""6[?25h",,terminal_output +152,14279315,"TERMINAL",0,0,"[?25lm/home/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/""7[?25h",,terminal_output 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/home/hk-project-p0023960/tum_cte0515/Projects/jafar/""6[?25h",,terminal_output +465,14395163,"TERMINAL",0,0,"[?25ln=""cd /home/hk-project-p0023960/tum_cte0515/Projects/jafar/""7[?25h[?25le=""cd /home/hk-project-p0023960/tum_cte0515/Projects/jafar/""8[?25h",,terminal_output +466,14395695,"TERMINAL",0,0,"[?25lr=""cd /home/hk-project-p0023960/tum_cte0515/Projects/jafar/""9[?25h",,terminal_output +467,14396218,"TERMINAL",0,0,"[?25l^[",,terminal_output +468,14396328,"TERMINAL",0,0," 27,18Top[?25h",,terminal_output +469,14396693,"TERMINAL",0,0,"[?25ll 9[?25h",,terminal_output +470,14397176,"TERMINAL",0,0,"[?25ll 20[?25h[?25ll 1[?25h",,terminal_output +471,14397257,"TERMINAL",0,0,"[?25ll 2[?25h[?25ll 3[?25h",,terminal_output +472,14397533,"TERMINAL",0,0,"[?25ll 4[?25h[?25l5[?25h[?25ll 6[?25h[?25ll 7[?25h[?25ll 8[?25h[?25ll 9[?25h[?25ll 30[?25h",,terminal_output +473,14397544,"TERMINAL",0,0,"[?25ll 1[?25h[?25ll 2[?25h",,terminal_output +474,14398045,"TERMINAL",0,0,"[?25ll 3[?25h[?25l4[?25h[?25ll 5[?25h[?25ll 6[?25h[?25ll 7[?25h[?25ll 8[?25h[?25ll 9[?25h[?25ll 40[?25h[?25ll 1[?25h[?25ll 2[?25h[?25ll 3[?25h[?25ll 4[?25h[?25ll 5[?25h[?25ll 6[?25h[?25ll 7[?25h[?25ll 8[?25h[?25ll 9[?25h[?25ll 50[?25h",,terminal_output +475,14398477,"TERMINAL",0,0,"[?25lh 49[?25h",,terminal_output +476,14398645,"TERMINAL",0,0,"[?25lh 8[?25h",,terminal_output +477,14398860,"TERMINAL",0,0,"[?25lh 7[?25h",,terminal_output +478,14399108,"TERMINAL",0,0,"[?25ll 8[?25h",,terminal_output +479,14399834,"TERMINAL",0,0,"[?25ll 9[?25h[?25ll 50[?25h[?25ll 1[?25h[?25ll 2[?25h[?25ll 3[?25h[?25ll 4[?25h[?25ll 5[?25h[?25ll 6[?25h[?25ll 7[?25h",,terminal_output +480,14400314,"TERMINAL",0,0,"[?25ll 8[?25h[?25ll 9[?25h[?25ll 60[?25h[?25ll 1[?25h[?25ll 2[?25h[?25ll 3[?25h[?25ll 4[?25h[?25ll 5[?25h[?25ll 6[?25h[?25ll 7[?25h[?25ll 8[?25h[?25ll 9[?25h[?25ll 70[?25h",,terminal_output +481,14400329,"TERMINAL",0,0,"[?25ll 1[?25h",,terminal_output +482,14400453,"TERMINAL",0,0,"[?25ll 2[?25h",,terminal_output +483,14400611,"TERMINAL",0,0,"[?25ll 3[?25h",,terminal_output +484,14400798,"TERMINAL",0,0,"[?25ll 4[?25h",,terminal_output +485,14400941,"TERMINAL",0,0,"[?25ll 5[?25h",,terminal_output +486,14401049,"TERMINAL",0,0,"[?25ll 6[?25h",,terminal_output +487,14401346,"TERMINAL",0,0,"[?25li -- INSERT --27,76Top[?25h",,terminal_output +488,14404749,"TERMINAL",0,0,"[?25l_/""7[?25h",,terminal_output +489,14405162,"TERMINAL",0,0,"[?25lj/""8[?25h",,terminal_output +490,14405316,"TERMINAL",0,0,"[?25lo/""9[?25h",,terminal_output +491,14405531,"TERMINAL",0,0,"[?25lb/""80[?25h",,terminal_output +492,14405599,"TERMINAL",0,0,"[?25ls/""1[?25h",,terminal_output +493,14406023,"TERMINAL",0,0,"[?25l^[",,terminal_output +494,14406132,"TERMINAL",0,0," 27,80Top[?25h",,terminal_output +495,14407393,"TERMINAL",0,0,"[?25l::[?25h",,terminal_output +496,14407643,"TERMINAL",0,0,"w",,terminal_output +497,14407710,"TERMINAL",0,0,"q",,terminal_output +498,14410463,"TERMINAL",0,0,"\r[?25l[?2004l[>4;m""~/.bashrc"" 48L, 2579B written\r\r\r\n[?1004l[?2004l[?1l>[?25h[>4;m[?1049l]0;tum_cte0515@hkn1993:~/Projects]633;D;0",,terminal_output +499,14414478,"TERMINAL",0,0,"source ~/.bashrc",,terminal_command +500,14417268,"TERMINAL",0,0,"dev",,terminal_command +501,14417277,"TERMINAL",0,0,"]633;E;2025-09-03 17:41:16 dev;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C]0;tum_cte0515@hkn1993:~/Projects/jasmine]633;D;0",,terminal_output +502,14418906,"TERMINAL",0,0,"runner",,terminal_command +503,14423535,"TERMINAL",0,0,"runner-jafar",,terminal_command +504,14423585,"TERMINAL",0,0,"]633;E;2025-09-03 17:41:22 runner-jafar;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;Cbash: runner-jafar: command not found...\r\n",,terminal_output +505,14424639,"TERMINAL",0,0,"^C\r\n]0;tum_cte0515@hkn1993:~/Projects/jasmine_jobs]633;D;130",,terminal_output +506,14427421,"TERMINAL",0,0,"runner_jafar",,terminal_command +507,14427455,"TERMINAL",0,0,"]633;E;2025-09-03 17:41:26 runner_jafar;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;Cbash: runner_jafar: command not found...\r\n",,terminal_output +508,14427534,"TERMINAL",0,0,"]0;tum_cte0515@hkn1993:~/Projects/jasmine_jobs]633;D;127",,terminal_output +509,14432135,"TERMINAL",0,0,"vim ~/.bashrc",,terminal_command +510,14432431,"TERMINAL",0,0,"]633;E;2025-09-03 17:41:31 vim ~/.bashrc;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C[?1049h[>4;2m[?1h=[?2004h[?1004h[?12h[?12l[?25l""~/.bashrc"" 48L, 2579B▽ Pzz\[0%m [>c]10;?]11;?# .bashrc# Source global definitionsif [ -f /etc/bashrc ]; then\r\n . /etc/bashrc\r\nfi\r\n\r\n# User specific environment\r\nif ! [[ ""$PATH"" =~ ""$HOME/.local/bin:$HOME/bin:"" ]]\r\nthen\r\n PATH=""$HOME/.local/bin:$HOME/bin:$PATH""\r\nfi\r\nexport PATH\r\n\r\nalias idle='sinfo_t_idle'\r\nexport ws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\r\nalias salloc_node='salloc --time=01:00:00 --partition=dev_accelerated --nodes=1 --gres=gpu:1 --cpus-per-task=8'\r\nalias salloc_cpu='salloc --time=01:00:00 --partition=dev_cpuonly --nodes=1 --cpus-per-task=128'\r\nalias sync-runner=""sh /home/hk-project-p0023960/tum_cte0515/sync_runner.sh /home/hk-project-p0023960/tum_cte0515/Projects/jasmine /home/hk-project-p0023960/tum_cte0515/Projects/jasmine_jobs/""\r\nalias sync-jafar=""sh /home/hk-project-p0023960/tum_cte0515/sync_runner.sh /home/hk-project-p0023960/tum_cte0515/Projects/jafar /home/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/""\r\nalias sync-runner-2=""sh /home/hk-project-p0023960/tum_cte0515/sync_runner.sh /home/hk-project-p0023960/tum_cte0515/Projects/jasmine /home/hk-project-p0023960/tum_cte0515/Projects/jasmine_jobs_2/""\r\nalias sync-runner-3=""sh /home/hk-project-p0023960/tum_cte0515/sync_runner.sh /home/hk-project-p0023960/tum_cte0515/Projects/jasmine /home/hk-project-p0023960/tum_cte0515/Projects/jasmine_jobs_3/""\r\nalias runner=""cd /home/hk-project-p0023960/tum_cte0515/Projects/jasmine_jobs/""\r\nalias runner-2=""cd /home/hk-project-p0023960/tum_cte0515/Projects/jasmine_jobs_2/""\r\nalias runner-3=""cd /home/hk-project-p0023960/tum_cte0515/Projects/jasmine_jobs_3/""\r\nalias jafar=""cd /home/hk-project-p0023960/tum_cte0515/Projects/jafar/""\r\nalias jafar-runner=""cd /home/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/""\r\nalias dev=""cd /home/hk-project-p0023960/tum_cte0515/Projects/jasmine/""\r\nalias smi=""watch -n1 nvidia-smi""\r\nalias idling=""watch -n1 sinfo_t_idle""\r\nalias queue='watch -n1 squeue --me'\r\nalias fqueue='watch -n 1 ""squeue -o \""%.10i %.16P %.30j %.8u %.8T %.10M %.9l %.6D %R\""""'\r\nalias fsacct_week='sacct --format=""JobID%15,JobName%30,Partition%16,AllocCPUS%3,State%12,Elapsed%10,Timelimit%10"" --starttime $(date -d ""last week"" +%Y-%m-%d) | grep -vE ""*.batch|*.extern|*.inter|bash|python|CANCELLED|echo""'\r\nalias logs=""cd /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir""27,80Top[?25hP+q436f\P+q6b75\P+q6b64\P+q6b72\P+q6b6c\P+q2332\P+q2334\P+q2569\P+q2a37\P+q6b31\[?12$p[?25l/3333/3333 [?25h[?25l/f6f6/e3e3 [?25h",,terminal_output +511,14434131,"TERMINAL",0,0,"[?25l^[",,terminal_output +512,14434231,"TERMINAL",0,0," ^[ [?25h",,terminal_output +513,14435468,"TERMINAL",0,0,"[?25lh 79[?25h",,terminal_output +514,14437466,"TERMINAL",0,0,"[?25lh 8[?25h[?25lh 7[?25h[?25lh 6[?25h[?25lh 5[?25h[?25lh 4[?25h[?25lh 3[?25h[?25lh 2[?25h[?25lh 1[?25h[?25lh 0[?25h[?25lh 69[?25h[?25lh 8[?25h[?25lh 7[?25h[?25lh 6[?25h[?25lh 5[?25h[?25lh 4[?25h[?25lh 3[?25h[?25lh 2[?25h[?25lh 1[?25h[?25lh 0[?25h[?25lh 59[?25h[?25lh 8[?25h[?25lh 7[?25h[?25lh 6[?25h[?25lh 5[?25h[?25lh 4[?25h[?25lh 3[?25h[?25lh 2[?25h[?25lh 1[?25h[?25lh 0[?25h[?25lh 49[?25h[?25lh 8[?25h[?25lh 7[?25h[?25lh 6[?25h[?25lh 5[?25h[?25lh 4[?25h[?25lh 3[?25h[?25lh 2[?25h[?25lh 1[?25h[?25lh 0[?25h[?25lh 39[?25h[?25lh 8[?25h[?25lh 7[?25h[?25lh 6[?25h[?25lh 5[?25h[?25lh 4[?25h[?25lh 3[?25h[?25lh 2[?25h[?25lh 1[?25h[?25lh 0[?25h[?25lh 29[?25h[?25lh 8[?25h",,terminal_output +515,14437750,"TERMINAL",0,0,"[?25lh 7[?25h[?25lh 6[?25h[?25lh 5[?25h[?25lh 4[?25h[?25lh 3[?25h[?25lh 2[?25h[?25lh 1[?25h[?25lh 0[?25h[?25lh 19[?25h[?25lh 8[?25h",,terminal_output +516,14437861,"TERMINAL",0,0,"[?25lh 7[?25h[?25lh 6[?25h[?25lh 5[?25h[?25lh 4[?25h",,terminal_output +517,14438181,"TERMINAL",0,0,"[?25lh 3[?25h",,terminal_output +518,14438407,"TERMINAL",0,0,"[?25lh 2[?25h",,terminal_output +519,14440737,"TERMINAL",0,0,"[?25l^[",,terminal_output +520,14440803,"TERMINAL",0,0," ^[ [?25h",,terminal_output +521,14441075,"TERMINAL",0,0,"[?25l::[?25h",,terminal_output +522,14441141,"TERMINAL",0,0,"w",,terminal_output +523,14441343,"TERMINAL",0,0,"q",,terminal_output +524,14442739,"TERMINAL",0,0,"\r[?25l[?2004l[>4;m""~/.bashrc"" 48L, 2579B written\r\r\r\n[?1004l[?2004l[?1l>[?25h[>4;m[?1049l]0;tum_cte0515@hkn1993:~/Projects/jasmine_jobs]633;D;0",,terminal_output +525,14446598,"TERMINAL",0,0,"jafar-runner",,terminal_command +526,14557860,"TERMINAL",0,0,"dev",,terminal_command +527,14558629,"TERMINAL",0,0,"cd ..",,terminal_command +528,14587698,"TERMINAL",0,0,"git clone git@github.com:FLAIROx/jafar.git",,terminal_command +529,14587755,"TERMINAL",0,0,"]633;E;2025-09-03 17:44:06 git clone git@github.com:FLAIROx/jafar.git;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;CCloning into 'jafar'...\r\n",,terminal_output +530,14589372,"TERMINAL",0,0,"remote: Enumerating objects: 71, done.\r\nremote: Counting objects: 2% (1/43)\rremote: Counting objects: 4% (2/43)\rremote: Counting objects: 6% (3/43)\rremote: Counting objects: 9% (4/43)\rremote: Counting objects: 11% (5/43)\rremote: Counting objects: 13% (6/43)\rremote: Counting objects: 16% (7/43)\rremote: Counting objects: 18% (8/43)\rremote: Counting objects: 20% (9/43)\rremote: Counting objects: 23% (10/43)\rremote: Counting objects: 25% (11/43)\rremote: Counting objects: 27% (12/43)\rremote: Counting objects: 30% (13/43)\rremote: Counting objects: 32% (14/43)\rremote: Counting objects: 34% (15/43)\rremote: Counting objects: 37% (16/43)\rremote: Counting objects: 39% (17/43)\rremote: Counting objects: 41% (18/43)\rremote: Counting objects: 44% (19/43)\rremote: Counting objects: 46% (20/43)\rremote: Counting objects: 48% (21/43)\rremote: Counting objects: 51% (22/43)\rremote: Counting objects: 53% (23/43)\rremote: Counting objects: 55% (24/43)\rremote: Counting objects: 58% (25/43)\rremote: Counting objects: 60% (26/43)\rremote: Counting objects: 62% (27/43)\rremote: Counting objects: 65% (28/43)\rremote: Counting objects: 67% (29/43)\rremote: Counting objects: 69% (30/43)\rremote: Counting objects: 72% (31/43)\rremote: Counting objects: 74% (32/43)\rremote: Counting objects: 76% (33/43)\rremote: Counting objects: 79% (34/43)\rremote: Counting objects: 81% (35/43)\rremote: Counting objects: 83% (36/43)\rremote: Counting objects: 86% (37/43)\rremote: Counting objects: 88% (38/43)\rremote: Counting objects: 90% (39/43)\rremote: Counting objects: 93% (40/43)\rremote: Counting objects: 95% (41/43)\rremote: Counting objects: 97% (42/43)\rremote: Counting objects: 100% (43/43)\rremote: Counting objects: 100% (43/43), done.\r\nremote: Compressing objects: 4% (1/22)\rremote: Compressing objects: 9% (2/22)\rremote: Compressing objects: 13% (3/22)\rremote: Compressing objects: 18% (4/22)\rremote: Compressing objects: 22% (5/22)\rremote: Compressing objects: 27% (6/22)\rremote: Compressing objects: 31% (7/22)\r",,terminal_output +531,14589487,"TERMINAL",0,0,"remote: Compressing objects: 36% (8/22)\rremote: Compressing objects: 40% (9/22)\rremote: Compressing objects: 45% (10/22)\rremote: Compressing objects: 50% (11/22)\rremote: Compressing objects: 54% (12/22)\rremote: Compressing objects: 59% (13/22)\rremote: Compressing objects: 63% (14/22)\rremote: Compressing objects: 68% (15/22)\rremote: Compressing objects: 72% (16/22)\rremote: Compressing objects: 77% (17/22)\rremote: Compressing objects: 81% (18/22)\rremote: Compressing objects: 86% (19/22)\rremote: Compressing objects: 90% (20/22)\rremote: Compressing objects: 95% (21/22)\rremote: Compressing objects: 100% (22/22)\rremote: Compressing objects: 100% (22/22), done.\r\nReceiving objects: 1% (1/71)\rReceiving objects: 2% (2/71)\rReceiving objects: 4% (3/71)\rReceiving objects: 5% (4/71)\rReceiving objects: 7% (5/71)\rReceiving objects: 8% (6/71)\rReceiving objects: 9% (7/71)\rReceiving objects: 11% (8/71)\rReceiving objects: 12% (9/71)\rReceiving objects: 14% (10/71)\rReceiving objects: 15% (11/71)\rReceiving objects: 16% (12/71)\rReceiving objects: 18% (13/71)\rReceiving objects: 19% (14/71)\rReceiving objects: 21% (15/71)\r",,terminal_output +532,14589571,"TERMINAL",0,0,"Receiving objects: 22% (16/71)\rReceiving objects: 23% (17/71)\rReceiving objects: 25% (18/71)\rReceiving objects: 26% (19/71)\rReceiving objects: 28% (20/71)\rReceiving objects: 29% (21/71)\rReceiving objects: 30% (22/71)\rReceiving objects: 32% (23/71)\rReceiving objects: 33% (24/71)\rReceiving objects: 35% (25/71)\rReceiving objects: 36% (26/71)\rReceiving objects: 38% (27/71)\rReceiving objects: 39% (28/71)\rReceiving objects: 40% (29/71)\rReceiving objects: 42% (30/71)\rReceiving objects: 43% (31/71)\rReceiving objects: 45% (32/71)\rReceiving objects: 46% (33/71)\rReceiving objects: 47% (34/71)\rReceiving objects: 49% (35/71)\rReceiving objects: 50% (36/71)\rReceiving objects: 52% (37/71)\rReceiving objects: 53% (38/71)\rReceiving objects: 54% (39/71)\rReceiving objects: 56% (40/71)\rReceiving objects: 57% (41/71)\rReceiving objects: 59% (42/71)\rReceiving objects: 60% (43/71)\rReceiving objects: 61% (44/71)\rReceiving objects: 63% (45/71)\rReceiving objects: 64% (46/71)\rReceiving objects: 66% (47/71)\rReceiving objects: 67% (48/71)\rReceiving objects: 69% (49/71)\rReceiving objects: 70% (50/71)\rReceiving objects: 71% (51/71)\rReceiving objects: 73% (52/71)\rReceiving objects: 74% (53/71)\rReceiving objects: 76% (54/71)\rReceiving objects: 77% (55/71)\rReceiving objects: 78% (56/71)\rReceiving objects: 80% (57/71)\rReceiving objects: 81% (58/71)\rReceiving objects: 83% (59/71)\rReceiving objects: 84% (60/71)\rReceiving objects: 85% (61/71)\rReceiving objects: 87% (62/71)\rReceiving objects: 88% (63/71)\rReceiving objects: 90% (64/71)\rReceiving objects: 91% (65/71)\rReceiving objects: 92% (66/71)\rReceiving objects: 94% (67/71)\rReceiving objects: 95% (68/71)\rReceiving objects: 97% (69/71)\rReceiving objects: 98% (70/71)\rReceiving objects: 100% (71/71)\rReceiving objects: 100% (71/71), 81.99 KiB | 792.00 KiB/s, done.\r\nResolving deltas: 0% (0/34)\rResolving deltas: 2% (1/34)\rResolving deltas: 5% (2/34)\rResolving deltas: 11% (4/34)\rResolving deltas: 14% (5/34)\rResolving deltas: 20% (7/34)\rResolving deltas: 23% (8/34)\rResolving deltas: 26% (9/34)\rResolving deltas: 32% (11/34)\rResolving deltas: 35% (12/34)\rResolving deltas: 41% (14/34)\rResolving deltas: 44% (15/34)\rResolving deltas: 47% (16/34)\rResolving deltas: 52% (18/34)\rResolving deltas: 55% (19/34)\rResolving deltas: 61% (21/34)\rResolving deltas: 67% (23/34)\rResolving deltas: 70% (24/34)\rResolving deltas: 73% (25/34)\rResolving deltas: 76% (26/34)\rResolving deltas: 79% (27/34)\rResolving deltas: 82% (28/34)\rResolving deltas: 85% (29/34)\rResolving deltas: 88% (30/34)\rResolving deltas: 91% (31/34)\rResolving deltas: 94% (32/34)\rResolving deltas: 97% (33/34)\rResolving deltas: 100% (34/34)\rResolving deltas: 100% (34/34), done.\r\n",,terminal_output +533,14589666,"TERMINAL",0,0,"remote: Total 71 (delta 33), reused 21 (delta 21), pack-reused 28 (from 1)\r\n",,terminal_output +534,14589730,"TERMINAL",0,0,"]0;tum_cte0515@hkn1993:~/Projects]633;D;0]633;P;Cwd=/home/hk-project-p0023960/tum_cte0515/Projects",,terminal_output +535,14591447,"",0,0,"Switched from branch 'feat/darkness-filter' to 'main'",,git_branch_checkout +536,14638061,"TERMINAL",0,0,"jafar",,terminal_command +537,14639618,"TERMINAL",0,0,"ls",,terminal_command +538,14674257,"TERMINAL",0,0,"dev",,terminal_command +539,14675452,"TERMINAL",0,0,"cd slurm/",,terminal_command +540,14675794,"TERMINAL",0,0,"ls",,terminal_command +541,14675846,"TERMINAL",0,0,"]633;E;2025-09-03 17:45:34 ls;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C",,terminal_output +542,14675928,"TERMINAL",0,0,"common dev jobs README.md templates utils\r\n]0;tum_cte0515@hkn1993:~/Projects/jasmine/slurm]633;D;0",,terminal_output +543,14677718,"TERMINAL",0,0,"git pull",,terminal_command +544,14677788,"TERMINAL",0,0,"]633;E;2025-09-03 17:45:36 git pull;135ca8e6-3085-4cb9-9ca6-3533ce46ed81]633;C",,terminal_output +545,14679568,"TERMINAL",0,0,"remote: Enumerating objects: 381, done.\r\nremote: Counting objects: 0% (1/217)\rremote: Counting objects: 1% (3/217)\rremote: Counting objects: 2% (5/217)\rremote: Counting objects: 3% (7/217)\rremote: Counting objects: 4% (9/217)\rremote: Counting objects: 5% (11/217)\rremote: Counting objects: 6% (14/217)\rremote: Counting objects: 7% (16/217)\rremote: Counting objects: 8% (18/217)\rremote: Counting objects: 9% (20/217)\rremote: Counting objects: 10% (22/217)\rremote: Counting objects: 11% (24/217)\rremote: Counting objects: 12% (27/217)\rremote: Counting objects: 13% (29/217)\rremote: Counting objects: 14% (31/217)\rremote: Counting objects: 15% (33/217)\rremote: Counting objects: 16% (35/217)\rremote: Counting objects: 17% (37/217)\rremote: Counting objects: 18% (40/217)\rremote: Counting objects: 19% (42/217)\rremote: Counting objects: 20% (44/217)\rremote: Counting objects: 21% (46/217)\rremote: Counting objects: 22% (48/217)\rremote: Counting objects: 23% (50/217)\rremote: Counting objects: 24% (53/217)\rremote: Counting objects: 25% (55/217)\rremote: Counting objects: 26% (57/217)\rremote: Counting objects: 27% (59/217)\rremote: Counting objects: 28% (61/217)\rremote: Counting objects: 29% (63/217)\rremote: Counting objects: 30% (66/217)\rremote: Counting objects: 31% (68/217)\rremote: Counting objects: 32% (70/217)\rremote: Counting objects: 33% (72/217)\rremote: Counting objects: 34% (74/217)\rremote: Counting objects: 35% (76/217)\rremote: Counting objects: 36% (79/217)\rremote: Counting objects: 37% (81/217)\rremote: Counting objects: 38% (83/217)\r",,terminal_output +546,14680234,"TERMINAL",0,0,"remote: Counting objects: 39% (85/217)\rremote: Counting objects: 40% (87/217)\rremote: Counting objects: 41% (89/217)\rremote: Counting objects: 42% (92/217)\rremote: Counting objects: 43% (94/217)\rremote: Counting objects: 44% (96/217)\rremote: Counting objects: 45% (98/217)\rremote: Counting objects: 46% (100/217)\rremote: Counting objects: 47% (102/217)\rremote: Counting objects: 48% (105/217)\rremote: Counting objects: 49% (107/217)\rremote: Counting objects: 50% (109/217)\rremote: Counting objects: 51% (111/217)\rremote: Counting objects: 52% (113/217)\rremote: Counting objects: 53% (116/217)\rremote: Counting objects: 54% (118/217)\rremote: Counting objects: 55% (120/217)\rremote: Counting objects: 56% (122/217)\rremote: Counting objects: 57% (124/217)\rremote: Counting objects: 58% (126/217)\rremote: Counting objects: 59% (129/217)\rremote: Counting objects: 60% (131/217)\rremote: Counting objects: 61% (133/217)\rremote: Counting objects: 62% (135/217)\rremote: Counting objects: 63% (137/217)\rremote: Counting objects: 64% (139/217)\rremote: Counting objects: 65% (142/217)\rremote: Counting objects: 66% (144/217)\rremote: Counting objects: 67% (146/217)\rremote: Counting objects: 68% (148/217)\rremote: Counting objects: 69% (150/217)\rremote: Counting objects: 70% (152/217)\rremote: Counting objects: 71% (155/217)\rremote: Counting objects: 72% (157/217)\rremote: Counting objects: 73% (159/217)\rremote: Counting objects: 74% (161/217)\rremote: Counting objects: 75% (163/217)\rremote: Counting objects: 76% (165/217)\rremote: Counting objects: 77% (168/217)\rremote: Counting objects: 78% (170/217)\rremote: Counting objects: 79% (172/217)\rremote: Counting objects: 80% (174/217)\rremote: Counting objects: 81% (176/217)\rremote: Counting objects: 82% (178/217)\rremote: Counting objects: 83% (181/217)\rremote: Counting objects: 84% (183/217)\rremote: Counting objects: 85% (185/217)\rremote: Counting objects: 86% (187/217)\rremote: Counting objects: 87% (189/217)\rremote: Counting objects: 88% (191/217)\rremote: Counting objects: 89% (194/217)\rremote: Counting objects: 90% (196/217)\rremote: Counting objects: 91% (198/217)\rremote: Counting objects: 92% (200/217)\rremote: Counting objects: 93% (202/217)\rremote: Counting objects: 94% (204/217)\rremote: Counting objects: 95% (207/217)\rremote: Counting objects: 96% (209/217)\rremote: Counting objects: 97% (211/217)\rremote: Counting objects: 98% (213/217)\rremote: Counting objects: 99% (215/217)\rremote: Counting objects: 100% (217/217)\rremote: Counting objects: 100% (217/217), done.\r\nremote: Compressing objects: 0% (1/108)\rremote: Compressing objects: 1% (2/108)\rremote: Compressing objects: 2% (3/108)\rremote: Compressing objects: 3% (4/108)\rremote: Compressing objects: 4% (5/108)\rremote: Compressing objects: 5% (6/108)\rremote: Compressing objects: 6% (7/108)\rremote: Compressing objects: 7% (8/108)\rremote: Compressing objects: 8% (9/108)\rremote: Compressing objects: 9% (10/108)\rremote: Compressing objects: 10% (11/108)\rremote: Compressing objects: 11% (12/108)\rremote: Compressing objects: 12% (13/108)\rremote: Compressing objects: 13% (15/108)\rremote: Compressing objects: 14% (16/108)\rremote: Compressing objects: 15% (17/108)\rremote: Compressing objects: 16% (18/108)\rremote: Compressing objects: 17% (19/108)\rremote: Compressing objects: 18% (20/108)\rremote: Compressing objects: 19% (21/108)\rremote: Compressing objects: 20% (22/108)\rremote: Compressing objects: 21% (23/108)\rremote: Compressing objects: 22% (24/108)\rremote: Compressing objects: 23% (25/108)\rremote: Compressing objects: 24% (26/108)\rremote: Compressing objects: 25% (27/108)\rremote: Compressing objects: 26% (29/108)\rremote: Compressing objects: 27% (30/108)\rremote: Compressing objects: 28% (31/108)\rremote: Compressing objects: 29% (32/108)\rremote: Compressing objects: 30% (33/108)\rremote: Compressing objects: 31% (34/108)\rremote: Compressing objects: 32% (35/108)\rremote: Compressing objects: 33% (36/108)\rremote: Compressing objects: 34% (37/108)\rremote: Compressing objects: 35% (38/108)\rremote: Compressing objects: 36% (39/108)\rremote: Compressing objects: 37% (40/108)\rremote: Compressing objects: 38% (42/108)\rremote: Compressing objects: 39% (43/108)\rremote: Compressing objects: 40% (44/108)\rremote: Compressing objects: 41% (45/108)\rremote: Compressing objects: 42% (46/108)\rremote: Compressing objects: 43% (47/108)\rremote: Compressing objects: 44% (48/108)\rremote: Compressing objects: 45% (49/108)\rremote: Compressing objects: 46% (50/108)\rremote: Compressing objects: 47% (51/108)\rremote: Compressing objects: 48% (52/108)\rremote: Compressing objects: 49% (53/108)\rremote: Compressing objects: 50% (54/108)\rremote: Compressing objects: 51% (56/108)\rremote: Compressing objects: 52% (57/108)\rremote: Compressing objects: 53% (58/108)\rremote: Compressing objects: 54% (59/108)\rremote: Compressing objects: 55% (60/108)\rremote: Compressing objects: 56% (61/108)\rremote: Compressing objects: 57% (62/108)\rremote: Compressing objects: 58% (63/108)\rremote: Compressing objects: 59% (64/108)\rremote: Compressing objects: 60% (65/108)\rremote: Compressing objects: 61% (66/108)\rremote: Compressing objects: 62% (67/108)\rremote: Compressing objects: 63% (69/108)\rremote: Compressing objects: 64% (70/108)\rremote: Compressing objects: 65% (71/108)\rremote: Compressing objects: 66% (72/108)\rremote: Compressing objects: 67% (73/108)\rremote: Compressing objects: 68% (74/108)\rremote: Compressing objects: 69% (75/108)\rremote: Compressing objects: 70% (76/108)\rremote: Compressing objects: 71% (77/108)\rremote: Compressing objects: 72% (78/108)\rremote: Compressing objects: 73% (79/108)\rremote: Compressing objects: 74% (80/108)\rremote: Compressing objects: 75% (81/108)\rremote: Compressing objects: 76% (83/108)\rremote: Compressing objects: 77% (84/108)\rremote: Compressing objects: 78% (85/108)\rremote: Compressing objects: 79% (86/108)\rremote: Compressing objects: 80% (87/108)\rremote: Compressing objects: 81% (88/108)\rremote: Compressing objects: 82% (89/108)\rremote: Compressing objects: 83% (90/108)\rremote: Compressing objects: 84% (91/108)\rremote: Compressing objects: 85% (92/108)\rremote: Compressing objects: 86% (93/108)\rremote: Compressing objects: 87% (94/108)\rremote: Compressing objects: 88% (96/108)\rremote: Compressing objects: 89% (97/108)\rremote: Compressing objects: 90% (98/108)\rremote: Compressing objects: 91% (99/108)\rremote: Compressing objects: 92% (100/108)\rremote: Compressing objects: 93% (101/108)\rremote: Compressing objects: 94% (102/108)\rremote: Compressing objects: 95% (103/108)\rremote: Compressing objects: 96% (104/108)\rremote: Compressing objects: 97% (105/108)\rremote: Compressing objects: 98% (106/108)\rremote: Compressing objects: 99% (107/108)\rremote: Compressing objects: 100% (108/108)\rremote: Compressing objects: 100% (108/108), done.\r\nReceiving objects: 0% (1/381)\rReceiving objects: 1% (4/381)\rReceiving objects: 2% (8/381)\rReceiving objects: 3% (12/381)\rReceiving objects: 4% (16/381)\rReceiving objects: 5% (20/381)\rReceiving objects: 6% (23/381)\rReceiving objects: 7% (27/381)\rReceiving objects: 8% (31/381)\rReceiving objects: 9% (35/381)\rReceiving objects: 10% (39/381)\rReceiving objects: 11% (42/381)\rReceiving objects: 12% (46/381)\rReceiving objects: 13% (50/381)\rReceiving objects: 14% (54/381)\rReceiving objects: 15% (58/381)\rReceiving objects: 16% (61/381)\rReceiving objects: 17% (65/381)\rReceiving objects: 18% (69/381)\rReceiving objects: 19% (73/381)\rremote: Total 381 (delta 127), reused 188 (delta 102), pack-reused 164 (from 1)\r\nReceiving objects: 20% (77/381)\rReceiving objects: 21% (81/381)\rReceiving objects: 22% (84/381)\rReceiving objects: 23% (88/381)\rReceiving objects: 24% (92/381)\rReceiving objects: 25% (96/381)\rReceiving objects: 26% (100/381)\rReceiving objects: 27% (103/381)\rReceiving objects: 28% (107/381)\rReceiving objects: 29% (111/381)\rReceiving objects: 30% (115/381)\rReceiving objects: 31% (119/381)\rReceiving objects: 32% (122/381)\rReceiving objects: 33% (126/381)\rReceiving objects: 34% (130/381)\rReceiving objects: 35% (134/381)\rReceiving objects: 36% (138/381)\rReceiving objects: 37% (141/381)\rReceiving objects: 38% (145/381)\rReceiving objects: 39% (149/381)\rReceiving objects: 40% (153/381)\rReceiving objects: 41% (157/381)\rReceiving objects: 42% (161/381)\rReceiving objects: 43% (164/381)\rReceiving objects: 44% (168/381)\rReceiving objects: 45% (172/381)\rReceiving objects: 46% (176/381)\rReceiving objects: 47% (180/381)\rReceiving objects: 48% (183/381)\rReceiving objects: 49% (187/381)\rReceiving objects: 50% (191/381)\rReceiving objects: 51% (195/381)\rReceiving objects: 52% (199/381)\rReceiving objects: 53% (202/381)\rReceiving objects: 54% (206/381)\rReceiving objects: 55% (210/381)\rReceiving objects: 56% (214/381)\rReceiving objects: 57% (218/381)\rReceiving objects: 58% (221/381)\rReceiving objects: 59% (225/381)\rReceiving objects: 60% (229/381)\rReceiving objects: 61% (233/381)\rReceiving objects: 62% (237/381)\rReceiving objects: 63% (241/381)\rReceiving objects: 64% (244/381)\rReceiving objects: 65% (248/381)\rReceiving objects: 66% (252/381)\rReceiving objects: 67% (256/381)\rReceiving objects: 68% (260/381)\rReceiving objects: 69% (263/381)\rReceiving objects: 70% (267/381)\rReceiving objects: 71% (271/381)\rReceiving objects: 72% (275/381)\rReceiving objects: 73% (279/381)\rReceiving objects: 74% (282/381)\rReceiving objects: 75% (286/381)\rReceiving objects: 76% (290/381)\rReceiving objects: 77% (294/381)\rReceiving objects: 78% (298/381)\rReceiving objects: 79% (301/381)\rReceiving objects: 80% (305/381)\rReceiving objects: 81% (309/381)\rReceiving objects: 82% (313/381)\rReceiving objects: 83% (317/381)\rReceiving objects: 84% (321/381)\rReceiving objects: 85% (324/381)\rReceiving objects: 86% (328/381)\rReceiving objects: 87% (332/381)\rReceiving objects: 88% (336/381)\rReceiving objects: 89% (340/381)\rReceiving objects: 90% (343/381)\rReceiving objects: 91% (347/381)\rReceiving objects: 92% (351/381)\rReceiving objects: 93% (355/381)\rReceiving objects: 94% (359/381)\rReceiving objects: 95% (362/381)\rReceiving objects: 96% (366/381)\rReceiving objects: 97% (370/381)\rReceiving objects: 98% (374/381)\rReceiving objects: 99% (378/381)\rReceiving objects: 100% (381/381)\rReceiving objects: 100% (381/381), 71.62 KiB | 261.00 KiB/s, done.\r\nResolving deltas: 0% (0/211)\rResolving deltas: 1% (3/211)\rResolving deltas: 2% (5/211)\rResolving deltas: 3% (7/211)\rResolving deltas: 4% (9/211)\rResolving deltas: 5% (11/211)\rResolving deltas: 6% (13/211)\rResolving deltas: 7% (15/211)\rResolving deltas: 8% (17/211)\rResolving deltas: 9% (19/211)\rResolving deltas: 10% (22/211)\rResolving deltas: 11% (24/211)\rResolving deltas: 12% (26/211)\rResolving deltas: 13% (28/211)\rResolving deltas: 14% (30/211)\rResolving deltas: 15% (32/211)\rResolving deltas: 16% (34/211)\rResolving deltas: 17% (36/211)\rResolving deltas: 18% (38/211)\rResolving deltas: 19% (41/211)\rResolving deltas: 20% (43/211)\rResolving deltas: 21% (45/211)\rResolving deltas: 22% (47/211)\rResolving deltas: 23% (49/211)\rResolving deltas: 24% (51/211)\rResolving deltas: 25% (53/211)\rResolving deltas: 26% (55/211)\rResolving deltas: 27% (57/211)\rResolving deltas: 28% (60/211)\rResolving deltas: 29% (62/211)\rResolving deltas: 30% (64/211)\rResolving deltas: 31% (66/211)\rResolving deltas: 32% (68/211)\rResolving deltas: 33% (70/211)\rResolving deltas: 34% (72/211)\rResolving deltas: 35% (74/211)\rResolving deltas: 36% (76/211)\rResolving deltas: 37% (79/211)\rResolving deltas: 38% (81/211)\rResolving deltas: 39% (83/211)\rResolving deltas: 40% (85/211)\rResolving deltas: 41% (87/211)\rResolving deltas: 42% (89/211)\rResolving deltas: 43% (91/211)\rResolving deltas: 44% (93/211)\rResolving deltas: 45% (95/211)\rResolving deltas: 46% (98/211)\rResolving deltas: 47% (100/211)\rResolving deltas: 48% (102/211)\rResolving deltas: 49% (104/211)\rResolving deltas: 50% (106/211)\rResolving deltas: 51% (108/211)\rResolving deltas: 52% (110/211)\rResolving deltas: 53% (112/211)\rResolving deltas: 54% (114/211)\rResolving deltas: 55% (117/211)\rResolving deltas: 56% (119/211)\rResolving deltas: 57% (121/211)\rResolving deltas: 58% (123/211)\rResolving deltas: 59% (125/211)\rResolving deltas: 60% (127/211)\rResolving deltas: 61% (129/211)\rResolving deltas: 62% (131/211)\rResolving deltas: 63% (133/211)\rResolving deltas: 64% (136/211)\rResolving deltas: 65% (138/211)\rResolving deltas: 66% (140/211)\rResolving deltas: 67% (142/211)\rResolving deltas: 68% (144/211)\rResolving deltas: 69% (146/211)\rResolving deltas: 70% (148/211)\rResolving deltas: 71% (150/211)\rResolving deltas: 72% (152/211)\rResolving deltas: 73% (155/211)\rResolving deltas: 74% (157/211)\rResolving deltas: 75% (159/211)\rResolving deltas: 76% (161/211)\rResolving deltas: 77% (163/211)\rResolving deltas: 78% (165/211)\rResolving deltas: 79% (167/211)\rResolving deltas: 80% (169/211)\rResolving deltas: 81% (171/211)\rResolving deltas: 82% (174/211)\rResolving deltas: 83% (176/211)\rResolving deltas: 84% (178/211)\rResolving deltas: 85% (180/211)\rResolving deltas: 86% (182/211)\rResolving deltas: 87% (184/211)\rResolving deltas: 88% (186/211)\rResolving deltas: 89% (188/211)\rResolving deltas: 90% (190/211)\rResolving deltas: 91% (193/211)\rResolving deltas: 92% (195/211)\rResolving deltas: 93% (197/211)\rResolving deltas: 94% (199/211)\rResolving deltas: 95% (201/211)\rResolving deltas: 96% (203/211)\rResolving deltas: 97% (205/211)\rResolving deltas: 98% (207/211)\rResolving deltas: 99% (209/211)\rResolving deltas: 100% (211/211)\rResolving deltas: 100% (211/211), completed with 13 local objects.\r\n",,terminal_output +547,14680608,"TERMINAL",0,0,"From github.com:p-doom/slurm\r\n 94549c5..63f0e09 main -> origin/main\r\n",,terminal_output +548,14680751,"TERMINAL",0,0,"Updating 94549c5..63f0e09\r\n",,terminal_output +549,14682075,"TERMINAL",0,0,"Fast-forward\r\n",,terminal_output +550,14682247,"TERMINAL",0,0," dev/alfred/berlin/gt_actions/sample_causal_32gpus.sbatch | 29 +++++++++++++++++++++++++++++\r\n dev/alfred/berlin/gt_actions/sample_darkness_filter.sbatch | 33 +++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/gt_actions/train_dynacmis_on_3nodes_2gpu.sbatch | 52 ++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/gt_actions/train_dynacmis_on_8gpu.sbatch | 52 ++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/gt_actions/train_dynacmis_on_more_than4.sbatch | 52 ++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/gt_actions/train_dynacmis_overfit.sbatch | 53 +++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/gt_actions/train_dynacmis_overfit_2gpus.sbatch | 51 +++++++++++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/gt_actions/train_dynacmis_overfit_4gpu.sbatch | 53 +++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/gt_actions/train_dynacmis_overfit_4gpu_to_8gpu.sbatch | 52 ++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/gt_actions/train_tok_topology_one_gpu copy.sbatch | 45 +++++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/gt_actions/train_tok_topology_one_gpu.sbatch | 45 +++++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/gt_actions/train_tok_topology_restore_to_on_gpu.sbatch | 48 ++++++++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/gt_actions/train_tok_topology_two_gpus.sbatch | 45 +++++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/gt_sampling/sampling_mock.sbatch | 29 +++++++++++++++++++++++++++++\r\n dev/alfred/berlin/job_requeueing/notes.md | 47 -----------------------------------------------\r\n dev/alfred/berlin/test_franz_pr/train_dynacmis_overfit.sbatch | 52 ++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/test_franz_pr/train_lam_overfit.sbatch | 40 ++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/test_franz_pr/train_tokenizer_overfit.sbatch | 40 ++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/topology/sample_overfit_single_gpu.sbatch | 30 ++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/topology/train_dynacmis_overfit_1.sbatch | 50 ++++++++++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/topology/train_dynacmis_overfit_1_gt_actions.sbatch | 51 +++++++++++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/topology/train_dynacmis_overfit_1_gt_actions_noise.sbatch | 51 +++++++++++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/topology/train_dynacmis_overfit_1_noise.sbatch | 50 ++++++++++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/topology/train_dynacmis_overfit_2_nodes_2_gpu.sbatch | 51 +++++++++++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/topology/train_dynacmis_overfit_2_nodes_4_gpu.sbatch | 51 +++++++++++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/topology/train_dynacmis_overfit_4gpu.sbatch | 52 ++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/topology/train_lam_overfit.sbatch | 40 ++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/topology/train_tokenizer_overfit_4_gpu.sbatch | 41 +++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/topology/train_tokenizer_overfit_single_record.sbatch | 42 ++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/topology/train_tokenizer_overfit_single_record_requeue.sbatch | 40 ++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/topology_restore_fix/train_tokenizer_overfit_4_gpu.sbatch | 42 ++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/topology_restore_fix/train_tokenizer_overfit_4_to_1_gpu.sbatch | 48 ++++++++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/berlin/train_dyn_dev/{train_dynacmis.sbatch => train_dynacmis_overfit.sbatch} | 26 ++++++++++++--------------\r\n dev/alfred/berlin/train_lam_dev/train_lam.sbatch | 19 ++++++++++++++++---\r\n dev/alfred/berlin/train_tok_dev/train_tok.sbatch | 7 ++++---\r\n dev/alfred/berlin/train_tok_dev/train_tok_overfit.sbatch | 43 +++++++++++++++++++++++++++++++++++++++++++\r\n dev/alfred/helmholtz_cluster/train_tok_dev/train_tok.sbatch | 30 ++++++++++++++++++++++++++++++\r\n dev/alfred/helmholtz_cluster/train_tok_dev/train_tok_overfit.sbatch | 43 +++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/coinrun/coinrun_dynamics/coinrun_dynamics.sbatch | 2 +-\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction/coinrun_dynamics_reproduction.sbatch | 3 ++-\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction/coinrun_lam_reproduction_full_prec.sbatch | 69 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction/coinrun_lam_reproduction_full_prec_cosine.sbatch | 70 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction/coinrun_lam_reproduction_full_prec_w_restore.sbatch | 70 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction/coinrun_lam_reproduction_mix_prec_cosine.sbatch | 70 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction/coinrun_lam_reproduction_mix_prec_cosine_min_init_lt_3e-6.sbatch | 72 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction/sample.sbatch | 30 ++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction/sample_cotrain.sbatch | 28 ++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_dynamics_reproduction.sbatch | 81 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_dynamics_reproduction_cotrain.sbatch | 76 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_lam_reproduction.sbatch | 76 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_lam_reproduction_dc_0.sbatch | 76 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_lam_reproduction_ffn_512.sbatch | 77 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_lam_reproduction_ffn_512_num_blocks_8.sbatch | 78 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_lam_reproduction_lower_lr.sbatch | 76 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_lam_reproduction_lower_lr_0.5x.sbatch | 77 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_lam_reproduction_lower_lr_3e6_1e5_0.sbatch | 76 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_lam_reproduction_lower_lr_3e6_3e5_0.sbatch | 76 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_tokenizer_repoduction.sbatch | 75 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_tokenizer_repoduction_ffn_512_n_blocks_8.sbatch | 77 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/generate_data.sbatch | 15 +++++++++++++++\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/sample.sbatch | 30 ++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/sample_cotrain.sbatch | 28 ++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch | 42 ++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/jafar_og_reproduction/og_coinrun_dynamics_reproduction_requeue.sbatch | 47 +++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/jafar_og_reproduction/og_coinrun_dynamics_reproduction_requeue_2.0.sbatch | 47 +++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/jafar_og_reproduction/og_coinrun_tokenizer_repoduction_requeue.sbatch | 47 +++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/jafar_og_reproduction/og_coinrun_tokenizer_repoduction_requeue_2.0.sbatch | 48 ++++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/berlin/jafar_og_reproduction/sample.sbatch | 25 +++++++++++++++++++++++++\r\n jobs/alfred/berlin/jafar_og_reproduction/sample_og_150k.sbatch | 27 +++++++++++++++++++++++++++\r\n jobs/alfred/berlin/jafar_og_reproduction/sample_og_175k.sbatch | 27 +++++++++++++++++++++++++++\r\n jobs/alfred/berlin/jafar_og_reproduction/sample_og_200k.sbatch | 26 ++++++++++++++++++++++++++\r\n jobs/alfred/berlin/jafar_og_reproduction/sample_og_spawner.sh | 10 ++++++++++\r\n jobs/alfred/berlin/jafar_og_reproduction/spawner_sample.sh | 11 +++++++++++\r\n jobs/alfred/helmholtz_cluster/jafar_og_reproduction/generate_dataset.sbatch | 18 ++++++++++++++++++\r\n jobs/alfred/helmholtz_cluster/jafar_og_reproduction/generate_dataset_10m.sbatch | 21 +++++++++++++++++++++\r\n jobs/alfred/helmholtz_cluster/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch | 44 ++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/helmholtz_cluster/jafar_og_reproduction/og_coinrun_lam_reproduction.sbatch | 40 ++++++++++++++++++++++++++++++++++++++++\r\n jobs/alfred/helmholtz_cluster/jafar_og_reproduction/og_coinrun_tokenizer_repoduction.sbatch | 41 +++++++++++++++++++++++++++++++++++++++++\r\n jobs/franz/berlin/coinrun/coinrun_dynamics/coinrun_dynamics_fp32_adam_moments.sh | 84 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/franz/berlin/coinrun/coinrun_dynamics/coinrun_dynamics_fp32_layernorm.sh | 84 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/franz/berlin/coinrun/coinrun_lam/lam_coinrun_nan_investigation_100k_to_107k.sbatch | 49 +++++++++++++++++++++++++++++++++++++++++++++++++\r\n jobs/mihir/horeka/causal_big_runs/train_dynamics_8_nodes_filter_dark_req.sbatch | 1 +\r\n utils/alfred/scp_scripts/copy_to_local.sh | 7 +++++++\r\n utils/exclude.txt | 4 ++++\r\n 84 files changed, 3714 insertions(+), 69 deletions(-)\r\n create mode 100644 dev/alfred/berlin/gt_actions/sample_causal_32gpus.sbatch\r\n create mode 100644 dev/alfred/berlin/gt_actions/sample_darkness_filter.sbatch\r\n create mode 100644 dev/alfred/berlin/gt_actions/train_dynacmis_on_3nodes_2gpu.sbatch\r\n create mode 100644 dev/alfred/berlin/gt_actions/train_dynacmis_on_8gpu.sbatch\r\n create mode 100644 dev/alfred/berlin/gt_actions/train_dynacmis_on_more_than4.sbatch\r\n create mode 100644 dev/alfred/berlin/gt_actions/train_dynacmis_overfit.sbatch\r\n create mode 100644 dev/alfred/berlin/gt_actions/train_dynacmis_overfit_2gpus.sbatch\r\n create mode 100644 dev/alfred/berlin/gt_actions/train_dynacmis_overfit_4gpu.sbatch\r\n create mode 100644 dev/alfred/berlin/gt_actions/train_dynacmis_overfit_4gpu_to_8gpu.sbatch\r\n create mode 100644 dev/alfred/berlin/gt_actions/train_tok_topology_one_gpu copy.sbatch\r\n create mode 100644 dev/alfred/berlin/gt_actions/train_tok_topology_one_gpu.sbatch\r\n create mode 100644 dev/alfred/berlin/gt_actions/train_tok_topology_restore_to_on_gpu.sbatch\r\n create mode 100644 dev/alfred/berlin/gt_actions/train_tok_topology_two_gpus.sbatch\r\n create mode 100644 dev/alfred/berlin/gt_sampling/sampling_mock.sbatch\r\n delete mode 100644 dev/alfred/berlin/job_requeueing/notes.md\r\n create mode 100644 dev/alfred/berlin/test_franz_pr/train_dynacmis_overfit.sbatch\r\n create mode 100644 dev/alfred/berlin/test_franz_pr/train_lam_overfit.sbatch\r\n create mode 100644 dev/alfred/berlin/test_franz_pr/train_tokenizer_overfit.sbatch\r\n create mode 100644 dev/alfred/berlin/topology/sample_overfit_single_gpu.sbatch\r\n create mode 100644 dev/alfred/berlin/topology/train_dynacmis_overfit_1.sbatch\r\n create mode 100644 dev/alfred/berlin/topology/train_dynacmis_overfit_1_gt_actions.sbatch\r\n create mode 100644 dev/alfred/berlin/topology/train_dynacmis_overfit_1_gt_actions_noise.sbatch\r\n create mode 100644 dev/alfred/berlin/topology/train_dynacmis_overfit_1_noise.sbatch\r\n create mode 100644 dev/alfred/berlin/topology/train_dynacmis_overfit_2_nodes_2_gpu.sbatch\r\n create mode 100644 dev/alfred/berlin/topology/train_dynacmis_overfit_2_nodes_4_gpu.sbatch\r\n create mode 100644 dev/alfred/berlin/topology/train_dynacmis_overfit_4gpu.sbatch\r\n create mode 100644 dev/alfred/berlin/topology/train_lam_overfit.sbatch\r\n create mode 100644 dev/alfred/berlin/topology/train_tokenizer_overfit_4_gpu.sbatch\r\n create mode 100644 dev/alfred/berlin/topology/train_tokenizer_overfit_single_record.sbatch\r\n create mode 100644 dev/alfred/berlin/topology/train_tokenizer_overfit_single_record_requeue.sbatch\r\n create mode 100644 dev/alfred/berlin/topology_restore_fix/train_tokenizer_overfit_4_gpu.sbatch\r\n create mode 100644 dev/alfred/berlin/topology_restore_fix/train_tokenizer_overfit_4_to_1_gpu.sbatch\r\n rename dev/alfred/berlin/train_dyn_dev/{train_dynacmis.sbatch => train_dynacmis_overfit.sbatch} (71%)\r\n mode change 100644 => 100755 dev/alfred/berlin/train_lam_dev/train_lam.sbatch\r\n create mode 100644 dev/alfred/berlin/train_tok_dev/train_tok_overfit.sbatch\r\n create mode 100644 dev/alfred/helmholtz_cluster/train_tok_dev/train_tok.sbatch\r\n create mode 100644 dev/alfred/helmholtz_cluster/train_tok_dev/train_tok_overfit.sbatch\r\n create mode 100644 jobs/alfred/berlin/coinrun/coinrun_reproduction/coinrun_lam_reproduction_full_prec.sbatch\r\n create mode 100644 jobs/alfred/berlin/coinrun/coinrun_reproduction/coinrun_lam_reproduction_full_prec_cosine.sbatch\r\n create mode 100644 jobs/alfred/berlin/coinrun/coinrun_reproduction/coinrun_lam_reproduction_full_prec_w_restore.sbatch\r\n create mode 100644 jobs/alfred/berlin/coinrun/coinrun_reproduction/coinrun_lam_reproduction_mix_prec_cosine.sbatch\r\n create mode 100644 jobs/alfred/berlin/coinrun/coinrun_reproduction/coinrun_lam_reproduction_mix_prec_cosine_min_init_lt_3e-6.sbatch\r\n create mode 100644 jobs/alfred/berlin/coinrun/coinrun_reproduction/sample.sbatch\r\n create mode 100644 jobs/alfred/berlin/coinrun/coinrun_reproduction/sample_cotrain.sbatch\r\n create mode 100644 jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_dynamics_reproduction.sbatch\r\n create mode 100644 jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_dynamics_reproduction_cotrain.sbatch\r\n create mode 100644 jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_lam_reproduction.sbatch\r\n create mode 100644 jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_lam_reproduction_dc_0.sbatch\r\n create mode 100644 jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_lam_reproduction_ffn_512.sbatch\r\n create mode 100644 jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_lam_reproduction_ffn_512_num_blocks_8.sbatch\r\n create mode 100644 jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_lam_reproduction_lower_lr.sbatch\r\n create mode 100644 jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_lam_reproduction_lower_lr_0.5x.sbatch\r\n create mode 100644 jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_lam_reproduction_lower_lr_3e6_1e5_0.sbatch\r\n create mode 100644 jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_lam_reproduction_lower_lr_3e6_3e5_0.sbatch\r\n create mode 100644 jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_tokenizer_repoduction.sbatch\r\n create mode 100644 jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/coinrun_tokenizer_repoduction_ffn_512_n_blocks_8.sbatch\r\n create mode 100644 jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/generate_data.sbatch\r\n create mode 100644 jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/sample.sbatch\r\n create mode 100644 jobs/alfred/berlin/coinrun/coinrun_reproduction_10k/sample_cotrain.sbatch\r\n create mode 100644 jobs/alfred/berlin/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch\r\n create mode 100644 jobs/alfred/berlin/jafar_og_reproduction/og_coinrun_dynamics_reproduction_requeue.sbatch\r\n create mode 100644 jobs/alfred/berlin/jafar_og_reproduction/og_coinrun_dynamics_reproduction_requeue_2.0.sbatch\r\n create mode 100644 jobs/alfred/berlin/jafar_og_reproduction/og_coinrun_tokenizer_repoduction_requeue.sbatch\r\n create mode 100644 jobs/alfred/berlin/jafar_og_reproduction/og_coinrun_tokenizer_repoduction_requeue_2.0.sbatch\r\n create mode 100644 jobs/alfred/berlin/jafar_og_reproduction/sample.sbatch\r\n create mode 100644 jobs/alfred/berlin/jafar_og_reproduction/sample_og_150k.sbatch\r\n create mode 100644 jobs/alfred/berlin/jafar_og_reproduction/sample_og_175k.sbatch\r\n create mode 100644 jobs/alfred/berlin/jafar_og_reproduction/sample_og_200k.sbatch\r\n create mode 100644 jobs/alfred/berlin/jafar_og_reproduction/sample_og_spawner.sh\r\n create mode 100644 jobs/alfred/berlin/jafar_og_reproduction/spawner_sample.sh\r\n create mode 100644 jobs/alfred/helmholtz_cluster/jafar_og_reproduction/generate_dataset.sbatch\r\n create mode 100644 jobs/alfred/helmholtz_cluster/jafar_og_reproduction/generate_dataset_10m.sbatch\r\n create mode 100644 jobs/alfred/helmholtz_cluster/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch\r\n create mode 100644 jobs/alfred/helmholtz_cluster/jafar_og_reproduction/og_coinrun_lam_reproduction.sbatch\r\n create mode 100644 jobs/alfred/helmholtz_cluster/jafar_og_reproduction/og_coinrun_tokenizer_repoduction.sbatch\r\n create mode 100644 jobs/franz/berlin/coinrun/coinrun_dynamics/coinrun_dynamics_fp32_adam_moments.sh\r\n create mode 100644 jobs/franz/berlin/coinrun/coinrun_dynamics/coinrun_dynamics_fp32_layernorm.sh\r\n create mode 100644 jobs/franz/berlin/coinrun/coinrun_lam/lam_coinrun_nan_investigation_100k_to_107k.sbatch\r\n create mode 100644 utils/alfred/scp_scripts/copy_to_local.sh\r\n]0;tum_cte0515@hkn1993:~/Projects/jasmine/slurm",,terminal_output diff --git a/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-33d627dc-2555-472b-a3d4-76d3b06878631756723263490-2025_09_01-12.41.38.380/source.csv b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-33d627dc-2555-472b-a3d4-76d3b06878631756723263490-2025_09_01-12.41.38.380/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..97197aaea46aa8e34487c7a794363867d0a8d5c9 --- /dev/null +++ b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-33d627dc-2555-472b-a3d4-76d3b06878631756723263490-2025_09_01-12.41.38.380/source.csv @@ -0,0 +1,3463 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +1,4,"train_lam.py",0,0,"from dataclasses import dataclass, field\nimport os\nfrom typing import cast\n\nimport einops\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\nimport flax.nnx as nnx\n\nfrom models.lam import LatentActionModel\nfrom utils.dataloader import get_dataloader\nfrom utils.lr_utils import get_lr_schedule\nfrom utils.parameter_utils import count_parameters_by_component\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n batch_size: int = 36\n vq_beta: float = 0.25\n init_lr: float = 0.0\n max_lr: float = 3e-5\n decay_end: float = 0.0\n wsd_decay_steps: int = (\n 10000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n )\n warmup_steps: int = 5000\n lr_schedule: str = ""wsd"" # supported options: wsd, cos\n vq_reset_thresh: int = 50\n darkness_threshold: float = 0.0\n # LAM\n model_dim: int = 512\n ffn_dim: int = 2048\n latent_dim: int = 32\n num_latents: int = 6\n patch_size: int = 16\n num_blocks: int = 4\n num_heads: int = 8\n dropout: float = 0.0\n codebook_dropout: float = 0.0\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n name: str = ""train_lam""\n tags: list[str] = field(default_factory=lambda: [""lam""])\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 10000\n log_checkpoint_keep_period: int = 20000\n wandb_id: str = """"\n use_flash_attention: bool = True\n\n\nargs = tyro.cli(Args)\n\n\ndef lam_loss_fn(\n model: LatentActionModel, inputs: dict\n) -> tuple[jax.Array, tuple[jax.Array, jax.Array, dict]]:\n # --- Compute loss ---\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n model.train()\n outputs = model(inputs, training=True)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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 = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt, recon)).mean()\n count_fn = jax.vmap(lambda i: (outputs[""indices""] == i).sum())\n index_counts = count_fn(jnp.arange(args.num_latents))\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=(index_counts != 0).mean(),\n )\n return loss, (outputs[""recon""], index_counts, metrics)\n\n\n@nnx.jit\ndef train_step(\n lam: LatentActionModel,\n optimizer: nnx.Optimizer,\n inputs: dict,\n action_last_active: jax.Array,\n rng: jax.Array,\n) -> tuple[jax.Array, jax.Array, jax.Array, dict]:\n def loss_fn(\n model: LatentActionModel,\n ) -> tuple[jax.Array, tuple[jax.Array, jax.Array, dict]]:\n return lam_loss_fn(model, inputs)\n\n # --- Update model ---\n (loss, (recon, idx_counts, metrics)), grads = nnx.value_and_grad(\n loss_fn, has_aux=True\n )(lam)\n optimizer.update(grads)\n\n # --- Reset inactive latent actions ---\n codebook = lam.vq.codebook\n num_codes = len(codebook)\n active_codes = idx_counts != 0.0\n action_last_active = jnp.where(active_codes, 0, action_last_active + 1)\n p_code = active_codes / active_codes.sum()\n reset_idxs = jax.random.choice(rng, num_codes, shape=(num_codes,), p=p_code)\n do_reset = action_last_active >= args.vq_reset_thresh\n new_codebook = jnp.where(\n jnp.expand_dims(do_reset, -1), codebook[reset_idxs], codebook.value\n )\n lam.vq.codebook.value = new_codebook\n action_last_active = jnp.where(do_reset, 0, action_last_active)\n return loss, recon, action_last_active, 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.key(args.seed)\n\n # --- Initialize model ---\n rng, _rng = jax.random.split(rng)\n rngs = nnx.Rngs(_rng)\n lam = LatentActionModel(\n in_dim=args.image_channels,\n model_dim=args.model_dim,\n ffn_dim=args.ffn_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 param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs,\n )\n\n # Count parameters\n _, params, _ = nnx.split(lam, nnx.Param, ...)\n param_counts = count_parameters_by_component(params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\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 if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n lr_schedule = get_lr_schedule(\n args.lr_schedule,\n args.init_lr,\n args.max_lr,\n args.decay_end,\n args.num_steps,\n args.warmup_steps,\n args.wsd_decay_steps,\n )\n tx = optax.adamw(\n learning_rate=lr_schedule,\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.dtype,\n )\n optimizer = nnx.Optimizer(lam, 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 videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n\n model_state = nnx.state(optimizer.model)\n model_sharded_state = jax.lax.with_sharding_constraint(\n model_state, replicated_sharding\n )\n nnx.update(optimizer.model, model_sharded_state)\n optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)\n optimizer_sharded_state = jax.lax.with_sharding_constraint(\n optimizer_state, replicated_sharding\n )\n nnx.update(optimizer, optimizer_sharded_state)\n\n # --- Initialize checkpoint manager ---\n step = 0\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n\n # --- Create DataLoaderIterator from dataloader ---\n image_shape = (args.image_height, args.image_width, args.image_channels)\n array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n darkness_threshold=args.darkness_threshold,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n\n # --- Restore checkpoint ---\n if args.restore_ckpt:\n abstract_optimizer = nnx.eval_shape(lambda: optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator), # type: ignore\n ),\n )\n restored_optimizer_state = restored[""model_state""]\n nnx.update(optimizer, restored_optimizer_state)\n grain_iterator = restored[""dataloader_state""]\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored dataloader and model state from step {step}"")\n\n # --- TRAIN LOOP ---\n dataloader = (\n jax.make_array_from_process_local_data(videos_sharding, elem)\n for elem in grain_iterator\n )\n print(f""Starting training from step {step}..."")\n action_last_active = jnp.zeros(args.num_latents, dtype=jnp.int32)\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n rng, _rng = jax.random.split(rng)\n\n inputs = dict(videos=videos, rng=_rng)\n rng, _rng = jax.random.split(rng)\n loss, recon, action_last_active, metrics = train_step(\n lam, optimizer, inputs, action_last_active, _rng\n )\n metrics[""lr""] = lr_schedule(step)\n print(f""Step {step}, loss: {loss}"")\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 **metrics,\n }\n )\n if step % args.log_image_interval == 0:\n gt_seq = inputs[""videos""][0, 1:].astype(jnp.float32) / 255.0\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 # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n optimizer_state = nnx.state(optimizer)\n checkpoint_manager.save(\n step,\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n grain_iterator # type: ignore\n ),\n ),\n )\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n checkpoint_manager.close()\n",python,tab +2,512,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"12:41:38 PM [info] Activating crowd-code\n12:41:38 PM [info] Recording started\n12:41:38 PM [info] Initializing git provider using file system watchers...\n12:41:38 PM [info] Git repository found\n12:41:38 PM [info] Git provider initialized successfully\n12:41:38 PM [info] Initial git state: [object Object]\n",Log,tab +3,37789,"train_lam.py",0,0,"",python,tab +4,38854,"train_lam.py",9464,0,"",python,selection_mouse +5,40228,"train_lam.py",9405,0,"",python,selection_mouse +6,40759,"train_lam.py",9466,0,"",python,selection_command +7,40917,"train_lam.py",9488,0,"",python,selection_command +8,41316,"train_lam.py",9517,0,"",python,selection_command +9,41537,"train_lam.py",9563,0,"",python,selection_command +10,41833,"train_lam.py",9517,0,"",python,selection_command +11,41985,"train_lam.py",9563,0,"",python,selection_command +12,42137,"train_lam.py",9517,0,"",python,selection_command +13,47931,"TERMINAL",0,0,"",,terminal_focus +14,50060,"TERMINAL",0,0,"source /home/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/bin/activate",,terminal_command +15,50088,"TERMINAL",0,0,"]633;E;2025-09-01 12:42:28 source /home/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/bin/activate;2bb8f881-ac5e-40fe-80c9-f1201978c519]633;C]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +16,52744,"TERMINAL",0,0,"clear",,terminal_command +17,52778,"TERMINAL",0,0,"]633;E;2025-09-01 12:42:31 clear;2bb8f881-ac5e-40fe-80c9-f1201978c519]633;C]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +18,54926,"TERMINAL",0,0,"idling",,terminal_command +19,54984,"TERMINAL",0,0,"]633;E;2025-09-01 12:42:33 idling;2bb8f881-ac5e-40fe-80c9-f1201978c519]633;C[?1049h(B[?7hEvery 1.0s: sinfo_t_idlehkn1993.localdomain: Mon Sep 1 12:42:33 2025Partition dev_cpuonly: 11 nodes idle\rPartition cpuonly: 131 nodes idle\rPartition dev_accelerated:\t 2 nodes idle\rPartition accelerated: 33 nodes idle\rPartition dev_accelerated-h100 :\t 0 nodes idle\rPartition accelerated-h100:\t 0 nodes idle\rPartition large:\t 0 nodes idle\rPartition accelerated-h200:\t 0 nodes idle",,terminal_output +20,56067,"TERMINAL",0,0,"4\t ",,terminal_output +21,57070,"TERMINAL",0,0,"5\t ",,terminal_output +22,58129,"TERMINAL",0,0,"6\t ",,terminal_output +23,59188,"TERMINAL",0,0,"7\t ",,terminal_output +24,64570,"train_dynamics.py",0,0,"from dataclasses import dataclass, field\nimport os\nfrom typing import cast\n\nimport einops\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\nimport flax.nnx as nnx\n\nfrom genie import Genie, restore_genie_components\nfrom utils.dataloader import get_dataloader\nfrom utils.lr_utils import get_lr_schedule\nfrom utils.parameter_utils import count_parameters_by_component\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n batch_size: int = 36\n init_lr: float = 0.0\n max_lr: float = 3e-5\n decay_end: float = 0.0\n wsd_decay_steps: int = (\n 10000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n )\n warmup_steps: int = 5000\n lr_schedule: str = ""wsd"" # supported options: wsd, cos\n darkness_threshold: float = 0.0\n # Tokenizer\n tokenizer_dim: int = 512\n tokenizer_ffn_dim: int = 2048\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 4\n tokenizer_num_heads: int = 8\n tokenizer_checkpoint: str = """"\n # LAM\n lam_dim: int = 512\n lam_ffn_dim: int = 2048\n latent_action_dim: int = 32\n num_latent_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 4\n lam_num_heads: int = 8\n lam_checkpoint: str = """"\n # Dynamics\n dyna_type: str = ""maskgit"" # supported options: maskgit, causal\n dyna_dim: int = 512\n dyna_ffn_dim: int = 2048\n dyna_num_blocks: int = 6\n dyna_num_heads: int = 8\n dropout: float = 0.0\n mask_limit: float = 0.5\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\n use_flash_attention: bool = True\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n name: str = ""train_dynamics""\n tags: list[str] = field(default_factory=lambda: [""dynamics""])\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 25000\n log_checkpoint_keep_period: int = 20000\n log_gradients: bool = False\n wandb_id: str = """"\n\n\nargs = tyro.cli(Args)\n\n\ndef dynamics_loss_fn(\n model: Genie, inputs: dict\n) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n """"""Compute masked dynamics loss""""""\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n model.train()\n outputs = model(inputs, training=True)\n mask = outputs[""mask""]\n outputs[""token_logits""] = outputs[""token_logits""].astype(jnp.float32)\n ce_loss = optax.softmax_cross_entropy_with_integer_labels(\n outputs[""token_logits""], outputs[""video_tokens""]\n )\n ce_loss = (mask * ce_loss).sum() / mask.sum()\n acc = outputs[""token_logits""].argmax(-1) == outputs[""video_tokens""]\n acc = (mask * acc).sum() / mask.sum()\n select_probs = jax.nn.softmax(outputs[""token_logits""])\n gt = gt.clip(0, 1).reshape(-1, *gt.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt, recon)).mean()\n _, index_counts_lam = jnp.unique_counts(\n jnp.ravel(outputs[""lam_indices""]), size=args.num_latent_actions, fill_value=0\n )\n _, index_counts_tokenizer = jnp.unique_counts(\n jnp.ravel(outputs[""video_tokens""]), size=args.num_patch_latents, fill_value=0\n )\n codebook_usage_lam = (index_counts_lam != 0).mean()\n codebook_usage_tokenizer = (index_counts_tokenizer != 0).mean()\n metrics = dict(\n cross_entropy_loss=ce_loss,\n masked_token_accuracy=acc,\n select_logit=outputs[""token_logits""].max(-1).mean(),\n select_p=select_probs.max(-1).mean(),\n entropy=jax.scipy.special.entr(select_probs).sum(-1).mean(),\n psnr=psnr,\n ssim=ssim,\n codebook_usage_lam=codebook_usage_lam,\n codebook_usage_tokenizer=codebook_usage_tokenizer,\n )\n return ce_loss, (outputs[""recon""], metrics)\n\n\n@nnx.jit\ndef train_step(\n model: Genie, optimizer: nnx.Optimizer, inputs: dict\n) -> tuple[jax.Array, jax.Array, dict]:\n """"""Update state and compute metrics""""""\n\n def loss_fn(model: Genie) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n return dynamics_loss_fn(model, inputs)\n\n (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(model)\n optimizer.update(grads)\n if args.log_gradients:\n metrics[""gradients_std/""] = jax.tree.map(\n lambda x: x.std(), grads[""params""][""dynamics""]\n )\n return 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.key(args.seed)\n\n # --- Initialize model ---\n rng, _rng = jax.random.split(rng)\n rngs = nnx.Rngs(_rng)\n genie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n tokenizer_ffn_dim=args.tokenizer_ffn_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n lam_ffn_dim=args.lam_ffn_dim,\n latent_action_dim=args.latent_action_dim,\n num_latent_actions=args.num_latent_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n lam_co_train=not args.lam_checkpoint,\n # Dynamics\n dyna_type=args.dyna_type,\n dyna_dim=args.dyna_dim,\n dyna_ffn_dim=args.dyna_ffn_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n dropout=args.dropout,\n mask_limit=args.mask_limit,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n decode=False,\n rngs=rngs,\n )\n\n _, params, _ = nnx.split(genie, nnx.Param, ...)\n param_counts = count_parameters_by_component(params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\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 if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n lr_schedule = get_lr_schedule(\n args.lr_schedule,\n args.init_lr,\n args.max_lr,\n args.decay_end,\n args.num_steps,\n args.warmup_steps,\n args.wsd_decay_steps,\n )\n tx = optax.adamw(\n learning_rate=lr_schedule,\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.dtype,\n )\n optimizer = nnx.Optimizer(genie, tx)\n del genie\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 videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n\n model_state = nnx.state(optimizer.model)\n model_sharded_state = jax.lax.with_sharding_constraint(\n model_state, replicated_sharding\n )\n nnx.update(optimizer.model, model_sharded_state)\n optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)\n optimizer_sharded_state = jax.lax.with_sharding_constraint(\n optimizer_state, replicated_sharding\n )\n nnx.update(optimizer, optimizer_sharded_state)\n\n # --- Initialize checkpoint manager ---\n step = 0\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n\n # --- Create DataLoaderIterator from dataloader ---\n image_shape = (args.image_height, args.image_width, args.image_channels)\n array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n darkness_threshold=args.darkness_threshold,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n\n # --- Restore checkpoint ---\n if args.restore_ckpt:\n abstract_optimizer = nnx.eval_shape(lambda: optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator), # type: ignore\n ),\n )\n restored_optimizer_state = restored[""model_state""]\n nnx.update(optimizer, restored_optimizer_state)\n grain_iterator = restored[""dataloader_state""]\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored dataloader and model state from step {step}"")\n else:\n # Restore from pre-trained tokenizer (and LAM)\n optimizer = restore_genie_components(optimizer, replicated_sharding, rng, args)\n # NOTE: We have to remove the (unused) tokenizer vq dropout due flax.nnx lazily initializing modules.\n # Specifically, the first dynamics model checkpoint will contain the vq dropout module,\n # but the first full restore will fail due to nnx not initializing the module when\n # dropout is set to 0.0.\n del optimizer.model.tokenizer.vq.drop\n\n # --- TRAIN LOOP ---\n dataloader = (\n jax.make_array_from_process_local_data(videos_sharding, elem)\n for elem in grain_iterator\n )\n print(f""Starting training from step {step}..."")\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n rng, _rng_mask = jax.random.split(rng, 2)\n inputs = dict(videos=videos, mask_rng=_rng_mask)\n loss, recon, metrics = train_step(optimizer.model, optimizer, inputs)\n metrics[""lr""] = lr_schedule(step)\n print(f""Step {step}, loss: {loss}"")\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 **metrics,\n }\n )\n if step % args.log_image_interval == 0:\n gt_seq = inputs[""videos""][0].astype(jnp.float32) / 255.0\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 if jax.process_index() == 0:\n log_images = dict(\n image=wandb.Image(np.asarray(gt_seq[args.seq_len - 1])),\n recon=wandb.Image(np.asarray(recon_seq[args.seq_len - 1])),\n true_vs_recon=wandb.Image(\n np.asarray(comparison_seq.astype(np.uint8))\n ),\n )\n wandb.log(log_images)\n # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n optimizer_state = nnx.state(optimizer)\n checkpoint_manager.save(\n step,\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n grain_iterator # type: ignore\n ),\n ),\n )\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n checkpoint_manager.close()\n",python,tab +25,406654,"TERMINAL",0,0,"idle",,terminal_command +26,406694,"TERMINAL",0,0,"]633;E;2025-09-01 12:48:24 idle;2bb8f881-ac5e-40fe-80c9-f1201978c519]633;CPartition dev_cpuonly : 2 nodes idle\r\nPartition cpuonly : 132 nodes idle\r\nPartition dev_accelerated : 1 nodes idle\r\nPartition accelerated : 33 nodes idle\r\nPartition dev_accelerated-h100 : 0 nodes idle\r\nPartition accelerated-h100 : 0 nodes idle\r\nPartition large : 0 nodes idle\r\nPartition accelerated-h200 : 0 nodes idle\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +27,413321,"slurm/jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/%x_%j.log\n#SBATCH --job-name=train_dynamics_maskgit_8_node\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/holiday/maskgit/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=160 \\n --init_lr=0 \\n --darkness_threshold=50 \\n --dyna_type=maskgit \\n --num_latent_actions=100 \\n --max_lr=8e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-maskgit-8-node-darkness-filter-$slurm_job_id \\n --tags dynamics maskgit 8-node post-launch-main darkness-filter \\n --entity instant-uv \\n --project jafar \\n --dyna_dim=1024 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=16 \\n --dyna_ffn_dim=4096 \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n",shellscript,tab +28,416914,"slurm/jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",0,0,"",shellscript,tab +29,420545,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=00:10:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/train_lam_minecraft_8node_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/train_lam_minecraft_8node_%j.log\n#SBATCH --job-name=train_lam_minecraft_1node_dev\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/lam/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_lam.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $slurm_job_id \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=20 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=lam-minecraft-1-node-dev-$slurm_job_id \\n --tags lam minecraft 1-node \\n --entity instant-uv \\n --project jafar \\n --num_latents=100 \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n",shellscript,tab +30,423680,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1688,0,"",shellscript,selection_mouse +31,423686,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1687,0,"",shellscript,selection_command +32,424256,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1650,0,"",shellscript,selection_mouse +33,424290,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1649,0,"",shellscript,selection_command +34,424894,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1688,0,"",shellscript,selection_mouse +35,424895,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1687,0,"",shellscript,selection_command +36,425478,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1688,0,"",shellscript,selection_mouse +37,425490,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1687,0,"",shellscript,selection_command +38,426207,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1668,0,"",shellscript,selection_mouse +39,426213,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1667,0,"",shellscript,selection_command +40,442135,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",38,0,"",shellscript,selection_mouse +41,442145,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",37,0,"",shellscript,selection_command +42,479533,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1732,0,"",shellscript,selection_mouse +43,479538,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1731,0,"",shellscript,selection_command +44,480001,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1720,0,"",shellscript,selection_mouse +45,480012,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1719,0,"",shellscript,selection_command +46,481122,"slurm/jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",0,0,"",shellscript,tab +47,483951,"slurm/jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",2074,0,"",shellscript,selection_mouse +48,483986,"slurm/jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",2073,0,"",shellscript,selection_command +49,484763,"slurm/jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",1973,0,"",shellscript,selection_mouse +50,485330,"slurm/jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",1943,0,"",shellscript,selection_mouse +51,487847,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",0,0,"",shellscript,tab +52,490060,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1720,0,"\n --darkness_threshold=50 \",shellscript,content +53,490118,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1725,0,"",shellscript,selection_command +54,496206,"slurm/jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",0,0,"",shellscript,tab +55,504449,"slurm/jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes_filter_dark_req.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit/dynamics-cotraining/%x_%j.log\n#SBATCH --job-name=train_dynamics_maskgit_8_node\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=3423250\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/holiday/maskgit/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_1e-4/3412401\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --restore_ckpt \\n --wandb_id $slurm_job_id \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=160 \\n --init_lr=0 \\n --darkness_threshold=50 \\n --dyna_type=maskgit \\n --num_latent_actions=100 \\n --max_lr=8e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-maskgit-8-node-darkness-filter-$slurm_job_id \\n --tags dynamics maskgit 8-node post-launch-main darkness-filter \\n --entity instant-uv \\n --project jafar \\n --dyna_dim=1024 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=16 \\n --dyna_ffn_dim=4096 \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n",shellscript,tab +56,509893,"slurm/jobs/mihir/horeka/mask_prob_fix/train_dynamics_8_nodes.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit-maskprob-fix/dynamics-cotraining/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/maskgit-maskprob-fix/dynamics-cotraining/%x_%j.log\n#SBATCH --job-name=train_dynamics_maskprob_fix_8_node\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\n# CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/maskgit-maskprob-fix/$job_name/$slurm_job_id\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/maskgit-maskprob-fix/train_dynamics_maskprob_fix_8_node/3371237\nmkdir -p $CHECKPOINT_DIR\n\n# tokenizer with the new structure supporting larger ffn_dim\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/train_tokenizer_lr_sweep_1e-4_larger_ffn/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --restore_ckpt \\n --wandb_id=3371237 \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --init_lr=0 \\n --max_lr=8e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-maskprob-fix-8-node-3371237 \\n --tags dynamics maskprob-fix 8-node \\n --entity instant-uv \\n --project jafar \\n --dyna_dim=1024 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=16 \\n --dyna_ffn_dim=4096 \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n",shellscript,tab +57,518730,"slurm/jobs/mihir/horeka/atari/train_tokenizer_lr_3e-5.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/atari/tokenizer/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/atari/tokenizer/%x_%j.log\n#SBATCH --job-name=train_tokenizer_3e-5\n#SBATCH --reservation=llmtum\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n # optional: trigger checkpoint saving here\n # e.g., touch $checkpoint_dir/requeue_trigger\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_atari/array_records\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --save_ckpt \\n --image_height=84 \\n --image_width=64 \\n $restore_ckpt_flag \\n --wandb_id $SLURM_JOB_ID \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=48 \\n --init_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=atari-tokenizer-3e-5-$slurm_job_id \\n --tags tokenizer atari 3e-5 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n\n",shellscript,tab +58,520317,"slurm/jobs/mihir/horeka/atari/train_tokenizer_lr_3e-5.sbatch",456,0,"",shellscript,selection_mouse +59,522754,"slurm/jobs/mihir/horeka/maskgit_big_runs/train_dynamics_8_nodes_filter_dark.sbatch",0,0,"",shellscript,tab +60,524822,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",0,0,"",shellscript,tab +61,525775,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",483,0,"",shellscript,selection_mouse +62,525966,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",483,19,"ueue\n#SBATCH --sign",shellscript,selection_mouse +63,525967,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",483,56,"ueue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n",shellscript,selection_mouse +64,526729,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",436,0,"",shellscript,selection_mouse +65,527758,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",469,0,"\n#SBATCH --reservation=llmtum",shellscript,content +66,527777,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",470,0,"",shellscript,selection_command +67,541847,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",0,0,"",shellscript,tab +68,550189,"TERMINAL",0,0,"sbatch slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",,terminal_command +69,550221,"TERMINAL",0,0,"]633;E;2025-09-01 12:50:48 sbatch slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch;2bb8f881-ac5e-40fe-80c9-f1201978c519]633;C",,terminal_output +70,550956,"TERMINAL",0,0,"queue",,terminal_command +71,551044,"TERMINAL",0,0,"]633;E;2025-09-01 12:50:49 queue;2bb8f881-ac5e-40fe-80c9-f1201978c519]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Mon Sep 1 12:50:49 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3454890 accelerat train_la tum_cte0 R\t0:00\t 1 hkn0410",,terminal_output +72,552085,"TERMINAL",0,0,"501",,terminal_output +73,553128,"TERMINAL",0,0,"12",,terminal_output +74,554194,"TERMINAL",0,0,"23",,terminal_output +75,554790,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +76,555964,"TERMINAL",0,0,"idling",,terminal_command +77,556042,"TERMINAL",0,0,"]633;E;2025-09-01 12:50:54 idling;2bb8f881-ac5e-40fe-80c9-f1201978c519]633;C[?1049h(B[?7hEvery 1.0s: sinfo_t_idlehkn1993.localdomain: Mon Sep 1 12:50:54 2025Partition dev_cpuonly:\t 6 nodes idle\rPartition cpuonly: 131 nodes idle\rPartition dev_accelerated:\t 1 nodes idle\rPartition accelerated: 32 nodes idle\rPartition dev_accelerated-h100 :\t 0 nodes idle\rPartition accelerated-h100:\t 0 nodes idle\rPartition large:\t 0 nodes idle\rPartition accelerated-h200:\t 0 nodes idle",,terminal_output +78,557084,"TERMINAL",0,0,"5\t ",,terminal_output +79,557885,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +80,560084,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",0,0,"",shellscript,tab +81,595042,"TERMINAL",0,0,"queue",,terminal_command +82,595103,"TERMINAL",0,0,"]633;E;2025-09-01 12:51:33 queue;2bb8f881-ac5e-40fe-80c9-f1201978c519]633;C",,terminal_output +83,595167,"TERMINAL",0,0,"[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Mon Sep 1 12:51:33 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3454890 accelerat train_la tum_cte0 R\t0:44\t 1 hkn0410",,terminal_output +84,596173,"TERMINAL",0,0,"45",,terminal_output +85,597213,"TERMINAL",0,0,"56",,terminal_output +86,598095,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +87,606660,"TERMINAL",0,0,"",,terminal_focus +88,608790,"TERMINAL",0,0,"source /home/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/bin/activate",,terminal_command +89,608814,"TERMINAL",0,0,"]633;E;2025-09-01 12:51:47 source /home/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/bin/activate;494c8224-3c77-4dff-9ba2-7c9ac811f0d6]633;C]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0]633;P;Cwd=/home/hk-project-p0023960/tum_cte0515/Projects/jafar",,terminal_output +90,609423,"TERMINAL",0,0,"logs",,terminal_command +91,609460,"TERMINAL",0,0,"]633;E;2025-09-01 12:51:47 logs;494c8224-3c77-4dff-9ba2-7c9ac811f0d6]633;C]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output +92,610920,"TERMINAL",0,0,"ls",,terminal_command +93,610983,"TERMINAL",0,0,"]633;E;2025-09-01 12:51:49 ls;494c8224-3c77-4dff-9ba2-7c9ac811f0d6]633;C",,terminal_output +94,611300,"TERMINAL",0,0,"atari train_lam_action_space_scaling_50_3329804.log\r\nbig_run train_lam_action_space_scaling_50_3331286.log\r\nbig-runs train_lam_action_space_scaling_6_3318549.log\r\ncausal train_lam_action_space_scaling_6_3320178.log\r\ncoinrun train_lam_action_space_scaling_6_3321528.log\r\nlam train_lam_action_space_scaling_6_3329790.log\r\nmaskgit train_lam_action_space_scaling_6_3329805.log\r\nmaskgit-maskprob-fix train_lam_action_space_scaling_6_3331287.log\r\npreprocess train_lam_action_space_scaling_8_3318550.log\r\ntrain_dyn_causal_180M_3372931.log train_lam_action_space_scaling_8_3329791.log\r\ntrain_dyn_causal_180M_3372963.log train_lam_action_space_scaling_8_3329806.log\r\ntrain_dyn_causal_180M_3372969.log train_lam_action_space_scaling_8_3331288.log\r\ntrain_dyn_causal_180M_3373107.log train_lam_minecraft_overfit_sample_3309655.log\r\ntrain_dyn_causal_255M_3372932.log train_lam_model_size_scaling_38M_3317098.log\r\ntrain_dyn_causal_255M_3372970.log train_lam_model_size_scaling_38M_3317115.log\r\ntrain_dyn_causal_255M_3373108.log train_lam_model_size_scaling_38M_3317231.log\r\ntrain_dyn_causal_356M_3372934.log train_tokenizer_batch_size_scaling_16_node_3321526.log\r\ntrain_dyn_causal_356M_3372971.log train_tokenizer_batch_size_scaling_1_node_3318551.log\r\ntrain_dyn_causal_356M_3373109.log train_tokenizer_batch_size_scaling_2_node_3318552.log\r\ntrain_dyn_causal_500M_3372936.log train_tokenizer_batch_size_scaling_2_node_3330806.log\r\ntrain_dyn_causal_500M_3372972.log train_tokenizer_batch_size_scaling_2_node_3330848.log\r\ntrain_dyn_causal_500M_3373110.log train_tokenizer_batch_size_scaling_2_node_3331282.log\r\ntrain_dyn_new_arch-bugfixed-spatial-shift_3359343.log train_tokenizer_batch_size_scaling_4_node_3318553.log\r\ntrain_dyn_new_arch-bugfixed-temporal-shift_3359349.log train_tokenizer_batch_size_scaling_4_node_3320175.log\r\ntrain_dyn_yolorun_3333026.log train_tokenizer_batch_size_scaling_4_node_3321524.log\r\ntrain_dyn_yolorun_3333448.log train_tokenizer_batch_size_scaling_8_node_3320176.log\r\ntrain_dyn_yolorun_3335345.log train_tokenizer_batch_size_scaling_8_node_3321525.log\r\ntrain_dyn_yolorun_3335362.log train_tokenizer_minecraft_overfit_sample_3309656.log\r\ntrain_dyn_yolorun_3348592.log train_tokenizer_model_size_scaling_127M_3317233.log\r\ntrain_dyn_yolorun_new_arch_3351743.log train_tokenizer_model_size_scaling_127M_3318554.log\r\ntrain_dyn_yolorun_new_arch_3352103.log train_tokenizer_model_size_scaling_140M_3313562.log\r\ntrain_dyn_yolorun_new_arch_3352115.log train_tokenizer_model_size_scaling_140M_3316019.log\r\ntrain_dyn_yolorun_new_arch_3358457.log train_tokenizer_model_size_scaling_200M_3313563.log\r\ntrain_lam_action_space_scaling_10_3320179.log train_tokenizer_model_size_scaling_200M_3316020.log\r\ntrain_lam_action_space_scaling_10_3321529.log train_tokenizer_model_size_scaling_227M_3317234.log\r\ntrain_lam_action_space_scaling_10_3329786.log train_tokenizer_model_size_scaling_227M_3318555.log\r\ntrain_lam_action_space_scaling_10_3329801.log train_tokenizer_model_size_scaling_227M_3320173.log\r\ntrain_lam_action_space_scaling_10_3331283.log train_tokenizer_model_size_scaling_227M_3321523.log\r\ntrain_lam_action_space_scaling_12_3318546.log train_tokenizer_model_size_scaling_37M_3313565.log\r\ntrain_lam_action_space_scaling_12_3320177.log train_tokenizer_model_size_scaling_37M_3316022.log\r\ntrain_lam_action_space_scaling_12_3321527.log train_tokenizer_model_size_scaling_37M_3317232.log\r\ntrain_lam_action_space_scaling_12_3329787.log train_tokenizer_model_size_scaling_37M_3317239.log\r\ntrain_lam_action_space_scaling_12_3329802.log train_tokenizer_model_size_scaling_37M_3318556.log\r\ntrain_lam_action_space_scaling_12_3331284.log train_tokenizer_model_size_scaling_74M_3318557.log\r\ntrain_lam_action_space_scaling_20_3318547.log train_tokenizer_model_size_scaling_74M_3320174.log\r\ntrain_lam_action_space_scaling_20_3329788.log train_tokenizer_model_size_scaling_74M_3321522.log\r\ntrain_lam_action_space_scaling_20_3329803.log train_tokenizer_model_size_scaling_80M_3313564.log\r\ntrain_lam_action_space_scaling_20_3331285.log train_tokenizer_model_size_scaling_80M_3316026.log\r\ntrain_lam_action_space_scaling_50_3320180.log yoloruns\r\ntrain_lam_action_space_scaling_50_3329789.log\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output +95,620547,"TERMINAL",0,0,"cd lam/",,terminal_command +96,620817,"TERMINAL",0,0,"ls",,terminal_command +97,620889,"TERMINAL",0,0,"]633;E;2025-09-01 12:51:59 ls;494c8224-3c77-4dff-9ba2-7c9ac811f0d6]633;C",,terminal_output +98,621063,"TERMINAL",0,0,"train_lam_minecraft_8node_3431870.log train_lam_minecraft_8node_3431876.log train_lam_minecraft_8node_3431895.log\r\ntrain_lam_minecraft_8node_3431875.log train_lam_minecraft_8node_3431885.log train_lam_minecraft_8node_3454890.log\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam]633;D;0",,terminal_output +99,626411,"TERMINAL",0,0,"ls",,terminal_command +100,631974,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",0,0,"",shellscript,tab +101,650494,"TERMINAL",0,0,"queue",,terminal_command +102,650598,"TERMINAL",0,0,"]633;E;2025-09-01 12:52:28 queue;494c8224-3c77-4dff-9ba2-7c9ac811f0d6]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Mon Sep 1 12:52:28 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3454890 accelerat train_la tum_cte0 R\t1:39\t 1 hkn0410",,terminal_output +103,651626,"TERMINAL",0,0,"940",,terminal_output +104,652282,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam]633;D;0",,terminal_output +105,656326,"TERMINAL",0,0,"tail -f train_lam_minecraft_8node_3454890.log",,terminal_command +106,656368,"TERMINAL",0,0,"]633;E;2025-09-01 12:52:34 tail -f train_lam_minecraft_8node_3454890.log;494c8224-3c77-4dff-9ba2-7c9ac811f0d6]633;CSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0410\r\nGpuFreq=control_disabled\r\nwandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\nwandb: Tracking run with wandb version 0.19.11\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/wandb/run-20250901_125152-3454890\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run lam-minecraft-1-node-dev-3454890\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/3454890\r\n",,terminal_output +107,710344,"TERMINAL",0,0,"WARNING:absl:Dropping 2 examples of 89394 examples (shard 4).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 4).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 4).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 4).\r\nRunning on 4 devices.\r\nCounting all components: ['action_in', 'action_up', 'decoder', 'encoder', 'patch_up', 'vq']\r\nParameter counts:\r\n{'action_in': 768, 'action_up': 16896, 'decoder': 17474816, 'encoder': 17228832, 'patch_up': 393728, 'vq': 3200, 'total': 35118240}\r\nStarting training from step 0...\r\nRunning on 4 devices.\r\nCounting all components: ['action_in', 'action_up', 'decoder', 'encoder', 'patch_up', 'vq']\r\nParameter counts:\r\n{'action_in': 768, 'action_up': 16896, 'decoder': 17474816, 'encoder': 17228832, 'patch_up': 393728, 'vq': 3200, 'total': 35118240}\r\nStarting training from step 0...\r\nRunning on 4 devices.\r\nCounting all components: ['action_in', 'action_up', 'decoder', 'encoder', 'patch_up', 'vq']\r\nParameter counts:\r\n{'action_in': 768, 'action_up': 16896, 'decoder': 17474816, 'encoder': 17228832, 'patch_up': 393728, 'vq': 3200, 'total': 35118240}\r\nStarting training from step 0...\r\nRunning on 4 devices.\r\nCounting all components: ['action_in', 'action_up', 'decoder', 'encoder', 'patch_up', 'vq']\r\nParameter counts:\r\n{'action_in': 768, 'action_up': 16896, 'decoder': 17474816, 'encoder': 17228832, 'patch_up': 393728, 'vq': 3200, 'total': 35118240}\r\nStarting training from step 0...\r\n",,terminal_output +108,730357,"TERMINAL",0,0,"2025-09-01 12:53:47.644825: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +109,731365,"TERMINAL",0,0,"2025-09-01 12:53:49.181617: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-01 12:53:49.182147: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +110,737379,"TERMINAL",0,0,"2025-09-01 12:53:54.741874: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-01 12:53:54.741904: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +111,772400,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",0,0,"",shellscript,tab +112,785818,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1697,0,"",shellscript,selection_mouse +113,785851,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1696,0,"",shellscript,selection_command +114,786354,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1791,0,"",shellscript,selection_mouse +115,786362,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1790,0,"",shellscript,selection_command +116,786980,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1962,0,"",shellscript,selection_mouse +117,786997,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1961,0,"",shellscript,selection_command +118,811809,"slurm/utils/mihir/model_sizes.md",0,0,"# Genie 1 - Model Sizes and their configs\n\n## Tokenizer model: sizes\n\ndefault: \n| Model | model_dim | num_blocks | num_heads | latent_dim | num_latents | Est. Params |\n|-------|-----------|------------|-----------|------------|-------------|-------------|\n| default | 512 | 8 | 8 | 32 | 1024 | ~38M |\n\n### scaling up \n#### (not tested yet - TODO @mihir)\n\n| Model | model_dim | num_blocks | num_heads | latent_dim | num_latents | Est. Params |\n|-------|-----------|------------|-----------|------------|-------------|-------------|\n| L1 | 768 | 12 | 12 | 64 | 2048 | ~80M |\n| L2 | 1024 | 12 | 16 | 128 | 2048 | ~140M |\n| L3 | 1152 | 16 | 16 | 128 | 4096 | ~200M |\n| L4 | 896 | 16 | 14 | 96 | 4096 | ~120M |\n| L5 | 1536 | 12 | 24 | 256 | 2048 | ~190M |\n\n\n### tiny models\n| Model | model_dim | num_blocks | num_heads | latent_dim | num_latents | Est. Params |\n|-------|-----------|------------|-----------|------------|-------------|-------------|\n| S1 | 128 | 2 | 2 | 8 | 128 | ~0.6M |\n| S2 | 192 | 2 | 3 | 16 | 128 | ~1.3M |\n| S3 | 256 | 3 | 4 | 16 | 256 | ~3.6M |\n| S4 | 320 | 4 | 5 | 24 | 256 | ~7.4M |\n| S5 | 384 | 4 | 6 | 32 | 512 | ~10M |\n\n\n## Latent Action model: sizes\ndefault: \n| Model | model_dim | num_blocks | num_heads | latent_dim | num_latents | Est. Params |\n|-------|-----------|------------|-----------|------------|-------------|-------------|\n| default | 512 | 8 | 8 | 32 | 6 | ~39M |\n\n### scaling up \n#### (not tested yet - TODO @mihir)\n\n| Name | model_dim | num_blocks | num_heads | latent_dim | num_latents | Est. Params |\n|--------------|-----------|------------|-----------|------------|-------------|-------------|\n| XL | 1024 | 12 | 16 | 64 | 12 | ~200M |\n| L | 896 | 12 | 14 | 48 | 8 | ~150M |\n| M+ | 768 | 10 | 12 | 48 | 8 | ~100M |\n| M | 640 | 10 | 10 | 32 | 8 | ~70M |\n| Base+ | 512 | 12 | 8 | 32 | 8 | ~55M |\n\n\n### tiny models\n| Name | model_dim | num_blocks | num_heads | latent_dim | num_latents | Est. Params |\n|--------------|-----------|------------|-----------|------------|-------------|-------------|\n| XS | 128 | 2 | 2 | 8 | 4 | ~0.9M |\n| S | 160 | 2 | 2 | 8 | 4 | ~1.3M |\n| S+ | 192 | 3 | 3 | 8 | 4 | ~2.4M |\n| M- | 256 | 4 | 4 | 16 | 6 | ~5.4M |\n| M | 320 | 6 | 4 | 16 | 6 | ~12M |\n\n\n## Dynamics model: sizes \n\n| Config | dyna_dim | dyna_num_blocks | dyna_num_heads | dyna_ffn_dim | Approx. Params |\n|--------|----------|-----------------|---------------|----------------|----------------|\n| 1 | 512 | 12 | 8 || ~36M (77M total) |\n| 2 | 768 | 16 | 12 || ~110M (180M total) |\n| 3 | 1024 | 16 | 16 | 4096 | ~180M (255M total) |\n| 4 | 1024 | 24 | 16 | 4096 | ~270M (356M total) |\n| 5 | 1536 | 24 | 24 || ~500M |\n\n\n### tiny models\n| Config | dyna_dim | dyna_num_blocks | dyna_num_heads | Approx. Params |\n|--------|----------|-----------------|---------------|----------------|\n| A | 128 | 2 | 4 | ~1.5M |\n| B | 256 | 2 | 4 | ~3.5M |\n| C | 256 | 4 | 4 | ~6M |\n| D | 384 | 4 | 6 | ~12M |\n| E | 512 | 4 | 8 | ~18M |\n",markdown,tab +119,823405,"TERMINAL",0,0,"Filtering out sequence with average brightness 24.848534413194443, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.483316305121527, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 9.379828300347222, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 27.673830663194455, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.52555222526041, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 29.932063086371528, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.47289565581598, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.331307913628475, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 14.90293144574653, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.358255647135415, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.67461656380208, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.858823306857644, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.6497311453993, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.868902018663185, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 22.599402040364577, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 6.572883183159724, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.96038383506946, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 8.203265067708331, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 23.007096327256946, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.5221109392361, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.40983376866319, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.34761852473958, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.87565206553818, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.588127897569443, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.84445749262152, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.924731419270834, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 27.91030475651041, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.05798519618057, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.91061917274305, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.42965113845485, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.0773638298611, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.14640914453125, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.54003396831597, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.25781172048611, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 34.07691591536458, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.227902725260414, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.417728900173614, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 14.773970571614585, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 17.487862317708334, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.97858213107639, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.753541560329865, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.766726998697905, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 15.476055635850699, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.055581654947915, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 5.627149081163195, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 15.31705893880208, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.00060356206597, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.88665690364583, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 45.50409288498264, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 31.61641055902778, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.5580957096354, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 16.720037359809027, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 31.478678345052078, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.79012843446179, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.436763125434034, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.983562127604166, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.378709736979154, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.00539932725694, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.565465722656253, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.30490064756945, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.1476609592014, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.61714330729165, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 37.23104319097222, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 31.846979019965282, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.43747165147569, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.2896229765625, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.63308103428819, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.74481558463542, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 13.217998243055554, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 47.57220169444444, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.944998164062508, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 29.762338558159723, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 23.760039276041663, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 9.60974422829861, which is below the darkness threshold 50.0.\r\n",,terminal_output +120,833416,"TERMINAL",0,0,"Filtering out sequence with average brightness 24.139636642795132, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.84985505468751, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 8.405905884982642, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.82792859288194, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.425166213975697, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.74526238064237, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.82678295529514, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 15.942426434461805, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.580835393229165, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.77418353168403, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.001715821180554, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 29.403384335069443, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 19.892703191406248, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.41814324392361, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.46294229730904, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.23780273263889, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 16.99111554817708, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.177946543402776, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.17766344184027, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.860548177517362, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.05504833940973, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 29.932533608072916, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.527453131944444, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 7.137750802083334, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.58781443142361, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.79947780121527, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.68925760416666, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 30.60300879687501, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 33.95528398654513, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 15.448870614583333, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.355783140190965, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.60541607118055, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.53008347786458, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.785689658854174, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 49.518580784722225, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.513503248263895, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 46.004052375434014, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 26.30585684027778, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 7.372377047743056, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.12718165060765, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.04163297352432, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 17.51940406640625, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 27.589010426215275, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 48.519365423611106, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 35.50765792187501, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 39.19852556336807, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 24.427056894965272, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 44.802222088107634, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.85009925086805, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.442516197048604, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 5.21316979861111, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 14.449708920138889, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 32.485645563802095, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.516910422309028, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.553891733940965, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.65755801692708, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 13.071391086371525, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.324552201822904, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 19.327773061197917, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 18.491498563368054, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 42.80723423220486, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 40.19733232638888, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 43.33489234418402, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 7.683369060329861, which is below the darkness threshold 50.0.\r\nFiltering out episode with length 14, which is shorter than the requested sequence length 16.\r\nFiltering out sequence with average brightness 20.030340970920133, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 36.607144654947916, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 9.051733965277778, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 11.172398512586808, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 25.251918238715277, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 38.64909593532985, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 34.981573673611095, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 11.190590286892363, which is below the darkness threshold 50.0.\r\nFiltering out sequence with average brightness 41.483688669704854, which is below the darkness threshold 50.0.\r\n[Mon Sep 1 12:55:31 PM CEST 2025] caught sigusr1 (timeout warning), requeueing slurm job 3454890...\r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\nslurmstepd: error: *** STEP 3454890.0 ON hkn0410 CANCELLED AT 2025-09-01T12:55:31 DUE TO JOB REQUEUE ***\r\nslurmstepd: error: *** JOB 3454890 ON hkn0410 CANCELLED AT 2025-09-01T12:55:31 DUE TO JOB REQUEUE ***\r\n",,terminal_output +121,845130,"TERMINAL",0,0,"^C\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam]633;D;130",,terminal_output +122,846244,"TERMINAL",0,0,"queue",,terminal_command +123,846297,"TERMINAL",0,0,"]633;E;2025-09-01 12:55:44 queue;494c8224-3c77-4dff-9ba2-7c9ac811f0d6]633;C",,terminal_output +124,846361,"TERMINAL",0,0,"[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Mon Sep 1 12:55:44 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3454890 accelerat train_la tum_cte0 CG\t0:00\t 1 hkn0410",,terminal_output +125,847394,"TERMINAL",0,0,"5\t ",,terminal_output +126,848446,"TERMINAL",0,0,"6\t ",,terminal_output +127,849170,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam]633;D;0",,terminal_output +128,851432,"TERMINAL",0,0,"scancel 3454890",,terminal_command +129,851500,"TERMINAL",0,0,"]633;E;2025-09-01 12:55:49 scancel 3454890;494c8224-3c77-4dff-9ba2-7c9ac811f0d6]633;C]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam]633;D;0",,terminal_output +130,854904,"TERMINAL",0,0,"queue",,terminal_command +131,854966,"TERMINAL",0,0,"]633;E;2025-09-01 12:55:53 queue;494c8224-3c77-4dff-9ba2-7c9ac811f0d6]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Mon Sep 1 12:55:53 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3454890 accelerat train_la tum_cte0 CG\t4:42\t 1 hkn0410",,terminal_output +132,856016,"TERMINAL",0,0,"4\t ",,terminal_output +133,857060,"TERMINAL",0,0,"5\t ",,terminal_output +134,858106,"TERMINAL",0,0,"6\t ",,terminal_output +135,858412,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam]633;D;0",,terminal_output +136,863399,"TERMINAL",0,0,"bash",,terminal_focus +137,867312,"TERMINAL",0,0,"bash",,terminal_focus +138,869867,"TERMINAL",0,0,"queu",,terminal_command +139,869946,"TERMINAL",0,0,"]633;E;2025-09-01 12:56:08 queu;494c8224-3c77-4dff-9ba2-7c9ac811f0d6]633;Cbash: queu: command not found...\r\n",,terminal_output +140,871014,"TERMINAL",0,0,"^C\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam]633;D;130",,terminal_output +141,871712,"TERMINAL",0,0,"queue",,terminal_command +142,871788,"TERMINAL",0,0,"]633;E;2025-09-01 12:56:09 queue;494c8224-3c77-4dff-9ba2-7c9ac811f0d6]633;C",,terminal_output +143,871821,"TERMINAL",0,0,"[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Mon Sep 1 12:56:09 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3454890 accelerat train_la tum_cte0 CG\t4:42\t 1 hkn0410",,terminal_output +144,872787,"TERMINAL",0,0,"11\t ",,terminal_output +145,873869,"TERMINAL",0,0,"2\t ",,terminal_output +146,875093,"slurm/utils/mihir/model_sizes.md",0,0,"",markdown,tab +147,875153,"TERMINAL",0,0,"3\t ",,terminal_output +148,875970,"TERMINAL",0,0,"4\t ",,terminal_output +149,876969,"TERMINAL",0,0,"5\t ",,terminal_output +150,877198,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",0,0,"",shellscript,tab +151,878050,"slurm/utils/mihir/model_sizes.md",0,0,"",markdown,tab +152,878175,"TERMINAL",0,0,"6\t ",,terminal_output +153,879089,"TERMINAL",0,0,"\r7\t ",,terminal_output +154,880118,"TERMINAL",0,0,"8\t 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slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch;2bb8f881-ac5e-40fe-80c9-f1201978c519]633;CSubmitted batch job 3454917\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +674,1217695,"TERMINAL",0,0,"bash",,terminal_focus +675,1219797,"TERMINAL",0,0,"queue",,terminal_command +676,1219864,"TERMINAL",0,0,"]633;E;2025-09-01 13:01:58 queue;494c8224-3c77-4dff-9ba2-7c9ac811f0d6]633;C",,terminal_output +677,1219928,"TERMINAL",0,0,"[?1049h(B[?7hEvery 1.0s: squeu... hkn1993.localdomain: Mon Sep 1 13:01:58 2025JOBID PARTITION NAME USER ST\tTIME NODESNODELIST(REASON)3454917 accelerat train_la tum_cte0 R\t0:03\t 1hkn0410",,terminal_output +678,1220921,"TERMINAL",0,0,"94",,terminal_output +679,1221969,"TERMINAL",0,0,"2:005",,terminal_output +680,1222947,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Mon Sep 1 13:02:01 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3454917 accelerat train_la tum_cte0 R\t0:06\t 1 hkn0410",,terminal_output 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train_lam_minecraft_8node_3454890.log\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam]633;D;0]633;P;Cwd=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam",,terminal_output +690,1232852,"TERMINAL",0,0,"ls",,terminal_command +691,1234023,"TERMINAL",0,0,"queue",,terminal_command +692,1234097,"TERMINAL",0,0,"]633;E;2025-09-01 13:02:12 queue;494c8224-3c77-4dff-9ba2-7c9ac811f0d6]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Mon Sep 1 13:02:12 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3454917 accelerat train_la tum_cte0 R\t0:17\t 1 hkn0410",,terminal_output +693,1235165,"TERMINAL",0,0,"38",,terminal_output +694,1236204,"TERMINAL",0,0,"49",,terminal_output +695,1237252,"TERMINAL",0,0,"520",,terminal_output +696,1238301,"TERMINAL",0,0,"61",,terminal_output +697,1239353,"TERMINAL",0,0,"72",,terminal_output +698,1240394,"TERMINAL",0,0,"83",,terminal_output 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accelerated-h100:\t 0 nodes idle\rPartition large:\t 0 nodes idle\rPartition accelerated-h200:\t 0 nodes idle",,terminal_output +721,1261207,"TERMINAL",0,0,"9\t ",,terminal_output +722,1261671,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam]633;D;0",,terminal_output +723,1262775,"TERMINAL",0,0,"ls",,terminal_command +724,1262868,"TERMINAL",0,0,"]633;E;2025-09-01 13:02:41 ls;494c8224-3c77-4dff-9ba2-7c9ac811f0d6]633;Ctrain_lam_minecraft_8node_3431870.log train_lam_minecraft_8node_3431885.log train_lam_minecraft_8node_3454917.log\r\ntrain_lam_minecraft_8node_3431875.log train_lam_minecraft_8node_3431895.log\r\ntrain_lam_minecraft_8node_3431876.log train_lam_minecraft_8node_3454890.log\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam]633;D;0",,terminal_output +725,1268280,"TERMINAL",0,0,"tail -f train_lam_minecraft_8node_3454917.log",,terminal_command +726,1268352,"TERMINAL",0,0,"]633;E;2025-09-01 13:02:46 tail -f train_lam_minecraft_8node_3454917.log;494c8224-3c77-4dff-9ba2-7c9ac811f0d6]633;CSLURM_SUBMIT_HOST=hkn1993.localdomain\r\nSLURM_JOB_ID=3454917\r\nSLURM_NODEID=0\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=train_lam_minecraft_1node_dev\r\nSLURM_NTASKS_PER_NODE=4\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0410\r\nGpuFreq=control_disabled\r\nwandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +727,1269313,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.19.11\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/wandb/run-20250901_130246-3454917\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run lam-minecraft-1-node-dev-3454917\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/3454917\r\n",,terminal_output +728,1346905,"slurm/utils/mihir/model_sizes.md",0,0,"",markdown,tab +729,1384360,"TERMINAL",0,0,"WARNING:absl:Dropping 2 examples of 89394 examples (shard 4).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 4).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 4).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 4).\r\nRunning on 4 devices.\r\nCounting all components: ['action_in', 'action_up', 'decoder', 'encoder', 'patch_up', 'vq']\r\nParameter counts:\r\n{'action_in': 768, 'action_up': 66560, 'decoder': 203400960, 'encoder': 202416704, 'patch_up': 787456, 'vq': 6400, 'total': 406678848}\r\nStarting training from step 0...\r\nRunning on 4 devices.\r\nCounting all components: ['action_in', 'action_up', 'decoder', 'encoder', 'patch_up', 'vq']\r\nParameter counts:\r\n{'action_in': 768, 'action_up': 66560, 'decoder': 203400960, 'encoder': 202416704, 'patch_up': 787456, 'vq': 6400, 'total': 406678848}\r\nStarting training from step 0...\r\nRunning on 4 devices.\r\nCounting all components: ['action_in', 'action_up', 'decoder', 'encoder', 'patch_up', 'vq']\r\nParameter counts:\r\n{'action_in': 768, 'action_up': 66560, 'decoder': 203400960, 'encoder': 202416704, 'patch_up': 787456, 'vq': 6400, 'total': 406678848}\r\nStarting training from step 0...\r\nRunning on 4 devices.\r\nCounting all components: ['action_in', 'action_up', 'decoder', 'encoder', 'patch_up', 'vq']\r\nParameter counts:\r\n{'action_in': 768, 'action_up': 66560, 'decoder': 203400960, 'encoder': 202416704, 'patch_up': 787456, 'vq': 6400, 'total': 406678848}\r\nStarting training from step 0...\r\n",,terminal_output +730,1411368,"TERMINAL",0,0,"2025-09-01 13:05:09.574071: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +731,1413376,"TERMINAL",0,0,"2025-09-01 13:05:10.947126: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +732,1417388,"TERMINAL",0,0,"2025-09-01 13:05:14.805231: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-01 13:05:14.805270: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +733,1493412,"TERMINAL",0,0,"[Mon Sep 1 01:06:31 PM CEST 2025] caught sigusr1 (timeout warning), requeueing slurm job 3454917...\r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\nslurmstepd: error: *** JOB 3454917 ON hkn0410 CANCELLED AT 2025-09-01T13:06:31 DUE TO JOB REQUEUE ***\r\nslurmstepd: error: *** STEP 3454917.0 ON hkn0410 CANCELLED AT 2025-09-01T13:06:31 DUE TO JOB REQUEUE ***\r\nsrun: got SIGCONT\r\n",,terminal_output +734,1508166,"TERMINAL",0,0,"^C\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam]633;D;130",,terminal_output +735,1509752,"TERMINAL",0,0,"queue",,terminal_command +736,1509799,"TERMINAL",0,0,"]633;E;2025-09-01 13:06:48 queue;494c8224-3c77-4dff-9ba2-7c9ac811f0d6]633;C",,terminal_output 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+752,1528351,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",86,0,"",shellscript,selection_command +753,1529038,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",85,1,"",shellscript,content +754,1529126,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",85,0,"3",shellscript,content +755,1529127,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",86,0,"",shellscript,selection_keyboard +756,1536900,"TERMINAL",0,0,"bash",,terminal_focus +757,1565199,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/train_lam_minecraft_8node_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/train_lam_minecraft_8node_%j.log\n#SBATCH --job-name=train_lam_minecraft_8node\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/lam/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_lam.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $slurm_job_id \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=160 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=lam-minecraft-8-node-$slurm_job_id \\n --tags lam minecraft 8-node \\n --entity instant-uv \\n --project jafar \\n --num_latents=100 \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n",shellscript,tab +758,1569686,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",0,0,"",shellscript,tab +759,1575877,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node.sbatch",0,0,"",shellscript,tab +760,1577181,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node copy.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/train_lam_minecraft_8node_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/train_lam_minecraft_8node_%j.log\n#SBATCH --job-name=train_lam_minecraft_8node\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/lam/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_lam.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $slurm_job_id \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=160 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=lam-minecraft-8-node-$slurm_job_id \\n --tags lam minecraft 8-node \\n --entity instant-uv \\n --project jafar \\n --num_latents=100 \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n",shellscript,tab +761,1593683,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-base.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/train_lam_minecraft_8node_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/train_lam_minecraft_8node_%j.log\n#SBATCH --job-name=train_lam_minecraft_8node\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/lam/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_lam.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $slurm_job_id \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=160 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=lam-minecraft-8-node-$slurm_job_id \\n --tags lam minecraft 8-node \\n --entity instant-uv \\n --project jafar \\n --num_latents=100 \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n",shellscript,tab +762,1606125,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",0,0,"",shellscript,tab +763,1606972,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2031,0,"",shellscript,selection_mouse +764,1607476,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2074,0,"",shellscript,selection_mouse +765,1608761,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1791,0,"",shellscript,selection_mouse +766,1609230,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",1774,0,"",shellscript,selection_mouse 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+848,1771371,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-base copy.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/%x_%j.log\n#SBATCH --job-name=train_lam_minecraft_8node_darkness_filter_37M\n#SBATCH --reservation=llmtum\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/lam/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_lam.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $slurm_job_id \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=160 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --darkness_threshold=50 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=lam-minecraft-8-node-darkness-filter-37M-$slurm_job_id \\n --tags lam minecraft 8-node darkness-filter 37M \\n --entity instant-uv \\n --project jafar \\n --num_latents=100 \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n",shellscript,tab +849,1783059,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-base-400M.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/%x_%j.log\n#SBATCH --job-name=train_lam_minecraft_8node_darkness_filter_37M\n#SBATCH --reservation=llmtum\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/lam/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_lam.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $slurm_job_id \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=160 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --darkness_threshold=50 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=lam-minecraft-8-node-darkness-filter-37M-$slurm_job_id \\n --tags lam minecraft 8-node darkness-filter 37M \\n --entity instant-uv \\n --project jafar \\n --num_latents=100 \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n",shellscript,tab +850,1794461,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-400M.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/%x_%j.log\n#SBATCH --job-name=train_lam_minecraft_8node_darkness_filter_37M\n#SBATCH --reservation=llmtum\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/lam/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_lam.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $slurm_job_id \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=160 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --darkness_threshold=50 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=lam-minecraft-8-node-darkness-filter-37M-$slurm_job_id \\n --tags lam minecraft 8-node darkness-filter 37M \\n --entity instant-uv \\n --project jafar \\n --num_latents=100 \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n",shellscript,tab 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+927,1896591,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-400M.sbatch",1918,2,"400",shellscript,content +928,1899323,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-400M.sbatch",1992,0,"base",shellscript,content +929,1900300,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-400M.sbatch",1992,4,"",shellscript,content +930,1901489,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-400M.sbatch",1991,0,"",shellscript,selection_command +931,1902213,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-400M.sbatch",1996,0,"\n ",shellscript,content +932,1902790,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-400M.sbatch",2001,0," --model_dim=1024 \\n --num_blocks=12 \\n --num_heads=16 \\n --latent_dim=64 \\n --ffn_dim=4096 \",shellscript,content 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+940,1905972,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-400M.sbatch",2000,0,"",shellscript,selection_command +941,1914365,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-400M copy.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/%x_%j.log\n#SBATCH --job-name=train_lam_minecraft_8node_darkness_filter_400M\n#SBATCH --reservation=llmtum\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/lam/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_lam.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $slurm_job_id \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=160 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --darkness_threshold=50 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=lam-minecraft-8-node-darkness-filter-400M-$slurm_job_id \\n --tags lam minecraft 8-node darkness-filter 400M \\n --entity instant-uv \\n --project jafar \\n --num_latents=100 \\n --model_dim=1024 \\n --num_blocks=12 \\n --num_heads=16 \\n --latent_dim=64 \\n --ffn_dim=4096 \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n",shellscript,tab +942,1919891,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-200M.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/%x_%j.log\n#SBATCH --job-name=train_lam_minecraft_8node_darkness_filter_400M\n#SBATCH --reservation=llmtum\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/lam/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_lam.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $slurm_job_id \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=160 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --darkness_threshold=50 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=lam-minecraft-8-node-darkness-filter-400M-$slurm_job_id \\n --tags lam minecraft 8-node darkness-filter 400M \\n --entity instant-uv \\n --project jafar \\n --num_latents=100 \\n --model_dim=1024 \\n --num_blocks=12 \\n --num_heads=16 \\n --latent_dim=64 \\n --ffn_dim=4096 \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n",shellscript,tab +943,1923696,"slurm/utils/mihir/model_sizes.md",0,0,"",markdown,tab +944,1932775,"slurm/utils/mihir/model_sizes.md",1780,0,"",markdown,selection_mouse +945,1933510,"slurm/utils/mihir/model_sizes.md",1726,0,"",markdown,selection_mouse +946,1934062,"slurm/utils/mihir/model_sizes.md",1762,0,"",markdown,selection_mouse +947,1935035,"TERMINAL",0,0,"bash",,terminal_focus +948,1936446,"TERMINAL",0,0,"queue",,terminal_command +949,1936526,"TERMINAL",0,0,"]633;E;2025-09-01 13:13:54 queue;494c8224-3c77-4dff-9ba2-7c9ac811f0d6]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Mon Sep 1 13:13:54 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)",,terminal_output +950,1937565,"TERMINAL",0,0,"5",,terminal_output +951,1937734,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam]633;D;0",,terminal_output 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+985,1972304,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2069,0,"3",shellscript,content +986,1972305,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2070,0,"",shellscript,selection_keyboard +987,1972438,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2070,0,"2",shellscript,content +988,1972439,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2071,0,"",shellscript,selection_keyboard +989,1972650,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2070,0,"",shellscript,selection_command +990,1972796,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2092,0,"",shellscript,selection_command +991,1977306,"TERMINAL",0,0,"bash",,terminal_focus +992,1979036,"TERMINAL",0,0,"python",,terminal_command +993,1979069,"TERMINAL",0,0,"]633;E;2025-09-01 13:14:37 python;b7f1e619-a2b5-49a5-a127-74d220979cb5]633;C",,terminal_output +994,1979393,"TERMINAL",0,0,"Python 3.10.18 (main, Jun 4 2025, 17:36:27) [Clang 20.1.4 ] on linux\r\nType ""help"", ""copyright"", ""credits"" or ""license"" for more information.\r\n",,terminal_output +995,1979799,"TERMINAL",0,0,">>> ",,terminal_output +996,1981877,"TERMINAL",0,0,"6",,terminal_output +997,1982117,"TERMINAL",0,0,"[?25l4[?25h",,terminal_output +998,1982449,"TERMINAL",0,0,"[?25l0[?25h",,terminal_output +999,1983228,"TERMINAL",0,0,"[?25l*[?25h",,terminal_output +1000,1983792,"TERMINAL",0,0,"[?25l4[?25h",,terminal_output +1001,1983885,"TERMINAL",0,0,"\r\n2560\r\n>>> ",,terminal_output +1002,1987719,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2091,1,"",shellscript,content +1003,1987844,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2090,1,"",shellscript,content +1004,1987992,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2089,1,"",shellscript,content +1005,1989140,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2088,1,"",shellscript,content 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NODELIST(REASON)3454941 accelerat train_la tum_cte0 R\t0:371 hkn0813",,terminal_output +1061,2075686,"TERMINAL",0,0,"338 ",,terminal_output +1062,2076752,"TERMINAL",0,0,"440 ",,terminal_output +1063,2077758,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam]633;D;0",,terminal_output +1064,2081572,"TERMINAL",0,0,"tail -f train_lam_minecraft_8node_3454941.log",,terminal_command +1065,2081604,"TERMINAL",0,0,"]633;E;2025-09-01 13:16:19 tail -f train_lam_minecraft_8node_3454941.log;494c8224-3c77-4dff-9ba2-7c9ac811f0d6]633;CSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1993.localdomain\r\nSLURM_JOB_ID=3454941\r\nSLURM_NODEID=0\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=train_lam_minecraft_1node_dev\r\nSLURM_NTASKS_PER_NODE=4\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0813\r\nGpuFreq=control_disabled\r\n",,terminal_output +1066,2101567,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +1067,2102568,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.19.11\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/wandb/run-20250901_131639-3454941\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run lam-minecraft-1-node-dev-3454941\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/3454941\r\n",,terminal_output +1068,2171005,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-133M.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/%x_%j.log\n#SBATCH --job-name=train_lam_minecraft_8node_darkness_filter_400M\n#SBATCH --reservation=llmtum\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/lam/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_lam.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $slurm_job_id \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=160 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --darkness_threshold=50 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=lam-minecraft-8-node-darkness-filter-400M-$slurm_job_id \\n --tags lam minecraft 8-node darkness-filter 400M \\n --entity instant-uv \\n --project jafar \\n --num_latents=100 \\n --model_dim=640 \\n --num_blocks=10 \\n --num_heads=10 \\n --latent_dim=32 \\n --ffn_dim=2560 \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait 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+1146,2183506,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-133M.sbatch",439,0,"133",shellscript,content +1147,2183506,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-133M.sbatch",436,3,"",shellscript,content +1148,2209627,"TERMINAL",0,0,"WARNING:absl:Dropping 2 examples of 89394 examples (shard 4).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 4).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 4).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 4).\r\nRunning on 4 devices.\r\nCounting all components: ['action_in', 'action_up', 'decoder', 'encoder', 'patch_up', 'vq']\r\nParameter counts:\r\n{'action_in': 768, 'action_up': 21120, 'decoder': 66562688, 'encoder': 66173088, 'patch_up': 492160, 'vq': 3200, 'total': 133253024}\r\nStarting training from step 0...\r\nRunning on 4 devices.\r\nCounting all components: ['action_in', 'action_up', 'decoder', 'encoder', 'patch_up', 'vq']\r\nParameter counts:\r\n{'action_in': 768, 'action_up': 21120, 'decoder': 66562688, 'encoder': 66173088, 'patch_up': 492160, 'vq': 3200, 'total': 133253024}\r\nStarting training from step 0...\r\nRunning on 4 devices.\r\nCounting all components: ['action_in', 'action_up', 'decoder', 'encoder', 'patch_up', 'vq']\r\nParameter counts:\r\n{'action_in': 768, 'action_up': 21120, 'decoder': 66562688, 'encoder': 66173088, 'patch_up': 492160, 'vq': 3200, 'total': 133253024}\r\nStarting training from step 0...\r\nRunning on 4 devices.\r\nCounting all components: ['action_in', 'action_up', 'decoder', 'encoder', 'patch_up', 'vq']\r\nParameter counts:\r\n{'action_in': 768, 'action_up': 21120, 'decoder': 66562688, 'encoder': 66173088, 'patch_up': 492160, 'vq': 3200, 'total': 133253024}\r\nStarting training from step 0...\r\n",,terminal_output +1149,2229737,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-133M copy.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/%x_%j.log\n#SBATCH --job-name=train_lam_minecraft_8node_darkness_filter_133M\n#SBATCH --reservation=llmtum\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/lam/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_lam.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $slurm_job_id \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=160 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --darkness_threshold=50 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=lam-minecraft-8-node-darkness-filter-133M-$slurm_job_id \\n --tags lam minecraft 8-node darkness-filter 133M \\n --entity instant-uv \\n --project jafar \\n --num_latents=100 \\n --model_dim=640 \\n --num_blocks=10 \\n --num_heads=10 \\n --latent_dim=32 \\n --ffn_dim=2560 \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait $child_pid\n",shellscript,tab +1150,2231905,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-133M copy.sbatch",995,0,"",shellscript,selection_mouse +1151,2231905,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-133M copy.sbatch",994,0,"",shellscript,selection_command +1152,2235917,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",0,0,"",shellscript,tab +1153,2237400,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2072,0,"",shellscript,selection_mouse +1154,2237762,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2091,0,"",shellscript,selection_mouse +1155,2240214,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2004,0,"",shellscript,selection_mouse +1156,2240330,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2003,3,"640",shellscript,selection_mouse +1157,2242891,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2004,0,"",shellscript,selection_mouse +1158,2242892,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2003,3,"640",shellscript,selection_mouse +1159,2244624,"TERMINAL",0,0,"2025-09-01 13:19:02.407350: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1160,2245629,"TERMINAL",0,0,"2025-09-01 13:19:03.604053: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-01 13:19:03.604689: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1161,2248342,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2003,3,"7",shellscript,content +1162,2248344,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2004,0,"",shellscript,selection_keyboard +1163,2248644,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2004,0,"6",shellscript,content +1164,2248645,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2005,0,"",shellscript,selection_keyboard +1165,2248920,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2005,0,"8",shellscript,content +1166,2248921,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2006,0,"",shellscript,selection_keyboard +1167,2251636,"TERMINAL",0,0,"2025-09-01 13:19:09.018693: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-01 13:19:09.018731: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1168,2254503,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2049,0,"",shellscript,selection_mouse +1169,2255179,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2048,1,"",shellscript,content +1170,2255184,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2048,0,"2",shellscript,content +1171,2255184,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2049,0,"",shellscript,selection_keyboard +1172,2260607,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2070,0,"",shellscript,selection_mouse +1173,2260741,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2069,2,"32",shellscript,selection_mouse +1174,2262143,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2069,2,"4",shellscript,content +1175,2262144,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2070,0,"",shellscript,selection_keyboard +1176,2262185,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2070,0,"8",shellscript,content +1177,2262186,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2071,0,"",shellscript,selection_keyboard +1178,2263371,"TERMINAL",0,0,"python",,terminal_focus +1179,2266659,"TERMINAL",0,0,"7",,terminal_output +1180,2268083,"TERMINAL",0,0,"[?25l6[?25h",,terminal_output +1181,2268353,"TERMINAL",0,0,"[?25l8[?25h",,terminal_output +1182,2269050,"TERMINAL",0,0,"[?25l*[?25h",,terminal_output +1183,2269340,"TERMINAL",0,0,"[?25l4[?25h",,terminal_output +1184,2269428,"TERMINAL",0,0,"\r\n3072\r\n>>> ",,terminal_output +1185,2272902,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2113,0,"",shellscript,selection_mouse +1186,2273709,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2091,0,"",shellscript,selection_mouse +1187,2274582,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2092,0,"",shellscript,selection_command +1188,2274863,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2091,1,"",shellscript,content +1189,2275065,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2090,1,"",shellscript,content +1190,2275190,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2089,1,"",shellscript,content +1191,2275330,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2088,1,"",shellscript,content +1192,2275690,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",2088,0,"3072",shellscript,content +1193,2289186,"TERMINAL",0,0,"tail",,terminal_focus 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3454941;494c8224-3c77-4dff-9ba2-7c9ac811f0d6]633;C]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam]633;D;0",,terminal_output +1202,2393834,"TERMINAL",0,0,"queue",,terminal_command +1203,2393920,"TERMINAL",0,0,"]633;E;2025-09-01 13:21:32 queue;494c8224-3c77-4dff-9ba2-7c9ac811f0d6]633;C[?1049h(B[?7hEvery 1.0s: s... hkn1993.localdomain: Mon Sep 1 13:21:32 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3454941 accelerat train_la tum_cte0 CG\t5:491 hkn0813",,terminal_output +1204,2394969,"TERMINAL",0,0,"3",,terminal_output +1205,2395658,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam]633;D;0",,terminal_output +1206,2397287,"slurm/utils/mihir/model_sizes.md",0,0,"",markdown,tab +1207,2405382,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_1node_dev.sbatch",0,0,"",shellscript,tab +1208,2409303,"TERMINAL",0,0,"bash",,terminal_focus 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--output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/%x_%j.log\n#SBATCH --job-name=train_lam_minecraft_8node_darkness_filter_133M\n#SBATCH --reservation=llmtum\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/lam/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_lam.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $slurm_job_id \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=160 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --darkness_threshold=50 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=lam-minecraft-8-node-darkness-filter-133M-$slurm_job_id \\n --tags lam minecraft 8-node darkness-filter 133M \\n --entity instant-uv \\n --project jafar \\n --num_latents=100 \\n --model_dim=768 \\n --num_blocks=10 \\n --num_heads=12 \\n --latent_dim=48 \\n --ffn_dim=3072 \\n --data_dir $array_records_dir &\n\nchild_pid=$!\n\nwait 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+1334,2486393,"TERMINAL",0,0,"]633;E;2025-09-01 13:23:04 ls;494c8224-3c77-4dff-9ba2-7c9ac811f0d6]633;C",,terminal_output +1335,2486556,"TERMINAL",0,0,"train_lam_minecraft_8node_3431870.log\r\ntrain_lam_minecraft_8node_3431875.log\r\ntrain_lam_minecraft_8node_3431876.log\r\ntrain_lam_minecraft_8node_3431885.log\r\ntrain_lam_minecraft_8node_3431895.log\r\ntrain_lam_minecraft_8node_3454890.log\r\ntrain_lam_minecraft_8node_3454917.log\r\ntrain_lam_minecraft_8node_3454941.log\r\ntrain_lam_minecraft_8node_3454944.log\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam]633;D;0",,terminal_output +1336,2487009,"TERMINAL",0,0,"515 ",,terminal_output +1337,2488012,"TERMINAL",0,0,"616 ",,terminal_output +1338,2489056,"TERMINAL",0,0,"717 ",,terminal_output +1339,2489873,"TERMINAL",0,0,"tail -f train_lam_minecraft_8node_3454944.log",,terminal_command +1340,2489919,"TERMINAL",0,0,"]633;E;2025-09-01 13:23:08 tail -f 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Use `wandb login --relogin` to force relogin\r\nwandb: Tracking run with wandb version 0.19.11\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/wandb/run-20250901_132247-3454944\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run lam-minecraft-1-node-dev-3454944\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/3454944\r\n",,terminal_output +1341,2490140,"TERMINAL",0,0,"818 ",,terminal_output +1342,2491152,"TERMINAL",0,0,"919 ",,terminal_output +1343,2492212,"TERMINAL",0,0,"1020 ",,terminal_output +1344,2493265,"TERMINAL",0,0,"121 ",,terminal_output +1345,2494305,"TERMINAL",0,0,"222 ",,terminal_output +1346,2495362,"TERMINAL",0,0,"323 ",,terminal_output +1347,2496428,"TERMINAL",0,0,"424 ",,terminal_output +1348,2497468,"TERMINAL",0,0,"525 ",,terminal_output +1349,2498514,"TERMINAL",0,0,"626 ",,terminal_output 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\",shellscript,selection_mouse +1515,2618581,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +1516,2618632,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-132M.sbatch",2092,12,"n_dim=3072 \",shellscript,selection_mouse +1517,2618702,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-132M.sbatch",2091,13,"fn_dim=3072 \",shellscript,selection_mouse +1518,2618703,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-132M.sbatch",2090,14,"ffn_dim=3072 \",shellscript,selection_mouse +1519,2618738,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-132M.sbatch",2089,15,"-ffn_dim=3072 \",shellscript,selection_mouse +1520,2618739,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-132M.sbatch",2066,38,"--latent_dim=48 \\n --ffn_dim=3072 \",shellscript,selection_mouse +1521,2618776,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-132M.sbatch",2065,39," --latent_dim=48 \\n --ffn_dim=3072 \",shellscript,selection_mouse +1522,2618811,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-132M.sbatch",2064,40," --latent_dim=48 \\n --ffn_dim=3072 \",shellscript,selection_mouse +1523,2618812,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-132M.sbatch",2043,61," --num_heads=12 \\n --latent_dim=48 \\n --ffn_dim=3072 \",shellscript,selection_mouse +1524,2618846,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-132M.sbatch",2042,62," --num_heads=12 \\n --latent_dim=48 \\n --ffn_dim=3072 \",shellscript,selection_mouse +1525,2618881,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-132M.sbatch",2041,63," --num_heads=12 \\n --latent_dim=48 \\n --ffn_dim=3072 \",shellscript,selection_mouse +1526,2618882,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-132M.sbatch",2019,85," --num_blocks=10 \\n --num_heads=12 \\n --latent_dim=48 \\n --ffn_dim=3072 \",shellscript,selection_mouse +1527,2619074,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-132M.sbatch",1997,107," --model_dim=768 \\n --num_blocks=10 \\n --num_heads=12 \\n --latent_dim=48 \\n --ffn_dim=3072 \",shellscript,selection_mouse +1528,2619586,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.19.11\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/wandb/run-20250901_132516-3454948\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run lam-minecraft-1-node-dev-3454948\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/3454948\r\n",,terminal_output +1529,2619725,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-132M.sbatch",1997,107,"",shellscript,content +1530,2620227,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-132M.sbatch",1997,0," --model_dim=896 \\n --num_blocks=12 \\n --num_heads=14 \\n --latent_dim=48 \\n --ffn_dim=3584 \",shellscript,content +1531,2677387,"TERMINAL",0,0,"^C\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam]633;D;130",,terminal_output +1532,2679594,"TERMINAL",0,0,"queue",,terminal_command +1533,2679665,"TERMINAL",0,0,"]633;E;2025-09-01 13:26:17 queue;494c8224-3c77-4dff-9ba2-7c9ac811f0d6]633;C[?1049h(B[?7hEvery 1.0s: s... hkn1993.localdomain: Mon Sep 1 13:26:17 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3454948 accelerat train_la tum_cte0 R\t1:501 hkn0813",,terminal_output +1534,2680740,"TERMINAL",0,0,"851 ",,terminal_output +1535,2681758,"TERMINAL",0,0,"2053 ",,terminal_output +1536,2682356,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam]633;D;0",,terminal_output +1537,2684568,"TERMINAL",0,0,"scancel 3454948",,terminal_command +1538,2687410,"TERMINAL",0,0,"bash",,terminal_focus +1539,2691122,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-311M.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/lam/%x_%j.log\n#SBATCH --job-name=train_lam_minecraft_8node_darkness_filter_133M\n#SBATCH --reservation=llmtum\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# --- signal trap to requeue job before timeout ---\nrequeue_job() {\n echo ""[$(date)] caught sigusr1 (timeout warning), requeueing slurm job $SLURM_JOB_ID...""\n scontrol requeue $SLURM_JOB_ID\n exit 0\n}\n\ntrap requeue_job sigusr1\n\n# set checkpoint flag based on restart count\nrestart_count=$(scontrol show job $SLURM_JOB_ID | grep -o 'Restarts=[0-9]*' | cut -d'=' -f2)\n\nif [ $restart_count -eq 0 ]; then\n restore_ckpt_flag=""--no-restore-ckpt""\nelse\n restore_ckpt_flag=""--restore-ckpt""\nfi\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/lam/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_lam.py \\n --save_ckpt \\n $restore_ckpt_flag \\n --wandb_id $slurm_job_id \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=160 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --darkness_threshold=50 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=lam-minecraft-8-node-darkness-filter-133M-$slurm_job_id \\n --tags 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+1573,2710139,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-311M.sbatch",439,0,"11",shellscript,content +1574,2710139,"slurm/jobs/mihir/horeka/lam/train_lam_minecraft_8node-darkness-filter-311M.sbatch",436,2,"",shellscript,content +1575,2720174,"TERMINAL",0,0,"queue",,terminal_command +1576,2720233,"TERMINAL",0,0,"]633;E;2025-09-01 13:26:58 queue;2bb8f881-ac5e-40fe-80c9-f1201978c519]633;C[?1049h(B[?7hEvery 1.0s: s... hkn1993.localdomain: Mon Sep 1 13:26:58 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3454948 accelerat train_la tum_cte0 CG\t1:551 hkn0813",,terminal_output +1577,2720836,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +1578,2722514,"TERMINAL",0,0,"idöing",,terminal_command +1579,2722567,"TERMINAL",0,0,"]633;E;2025-09-01 13:27:00 idöing;2bb8f881-ac5e-40fe-80c9-f1201978c519]633;Cbash: idöing: command not found...\r\n",,terminal_output 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+3322,4725020,"TERMINAL",0,0,"3371",,terminal_output +3323,4726078,"TERMINAL",0,0,"4482",,terminal_output +3324,4727160,"TERMINAL",0,0,"5593",,terminal_output +3325,4728178,"TERMINAL",0,0,"66404",,terminal_output +3326,4729233,"TERMINAL",0,0,"7715",,terminal_output +3327,4730282,"TERMINAL",0,0,"8826",,terminal_output +3328,4731329,"TERMINAL",0,0,"9937",,terminal_output +3329,4732390,"TERMINAL",0,0,"304048",,terminal_output +3330,4733425,"TERMINAL",0,0,"1159",,terminal_output +3331,4734473,"TERMINAL",0,0,"22650",,terminal_output +3332,4735519,"TERMINAL",0,0,"3371",,terminal_output +3333,4736570,"TERMINAL",0,0,"4482",,terminal_output +3334,4737615,"TERMINAL",0,0,"5593",,terminal_output +3335,4738787,"TERMINAL",0,0,"66504",,terminal_output +3336,4739026,"train_tokenizer.py",0,0,"from dataclasses import dataclass, field\nimport os\nfrom typing import cast\n\nimport einops\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\nimport flax.nnx as nnx\n\nfrom models.tokenizer import TokenizerVQVAE\nfrom utils.dataloader import get_dataloader\nfrom utils.lr_utils import get_lr_schedule\nfrom utils.parameter_utils import count_parameters_by_component\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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n vq_beta: float = 0.25\n batch_size: int = 48\n init_lr: float = 0.0\n max_lr: float = 3e-4\n decay_end: float = 0.0\n wsd_decay_steps: int = (\n 20000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n )\n lr_schedule: str = ""wsd"" # supported options: wsd, cos\n warmup_steps: int = 10000\n # Tokenizer\n model_dim: int = 512\n ffn_dim: int = 2048\n latent_dim: int = 32\n num_latents: int = 1024\n patch_size: int = 4\n num_blocks: int = 4\n num_heads: int = 8\n dropout: float = 0.0\n codebook_dropout: float = 0.01\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\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_checkpoint_keep_period: int = 20000\n log_gradients: bool = False\n wandb_id: str = """"\n use_flash_attention: bool = True\n\n\nargs = tyro.cli(Args)\n\n\ndef tokenizer_loss_fn(\n model: TokenizerVQVAE, inputs: dict\n) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n # --- Compute loss ---\n # FIXME (f.srambical): Can we even do native int8 training without casting the video at all?\n # FIXME (f.srambical): If the tokenizer is the reason for the dynamics model being memory-bound,\n # should we at least train the tokenizer natively in int8?\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n model.train()\n outputs = model(inputs, training=True)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n mse = jnp.square(gt - 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 = gt.clip(0, 1).reshape(-1, *gt.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(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@nnx.jit\ndef train_step(\n tokenizer: TokenizerVQVAE, optimizer: nnx.Optimizer, inputs: dict\n) -> tuple[jax.Array, jax.Array, dict]:\n def loss_fn(model: TokenizerVQVAE) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n return tokenizer_loss_fn(model, inputs)\n\n (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(\n tokenizer\n )\n optimizer.update(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 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.key(args.seed)\n\n # --- Initialize model ---\n rng, _rng = jax.random.split(rng)\n rngs = nnx.Rngs(_rng)\n tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.model_dim,\n ffn_dim=args.ffn_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 param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs,\n )\n\n _, params, _ = nnx.split(tokenizer, nnx.Param, ...)\n param_counts = count_parameters_by_component(params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\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 if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n lr_schedule = get_lr_schedule(\n args.lr_schedule,\n args.init_lr,\n args.max_lr,\n args.decay_end,\n args.num_steps,\n args.warmup_steps,\n args.wsd_decay_steps,\n )\n tx = optax.adamw(\n learning_rate=lr_schedule,\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.dtype,\n )\n optimizer = nnx.Optimizer(tokenizer, 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 videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n\n model_state = nnx.state(optimizer.model)\n model_sharded_state = jax.lax.with_sharding_constraint(\n model_state, replicated_sharding\n )\n nnx.update(optimizer.model, model_sharded_state)\n optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)\n optimizer_sharded_state = jax.lax.with_sharding_constraint(\n optimizer_state, replicated_sharding\n )\n nnx.update(optimizer, optimizer_sharded_state)\n\n # --- Initialize checkpoint manager ---\n step = 0\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n\n # --- Create DataLoaderIterator from dataloader ---\n image_shape = (args.image_height, args.image_width, args.image_channels)\n array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n\n # --- Restore checkpoint ---\n if args.restore_ckpt:\n abstract_optimizer = nnx.eval_shape(lambda: optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator), # type: ignore\n ),\n )\n restored_optimizer_state = restored[""model_state""]\n nnx.update(optimizer, restored_optimizer_state)\n grain_iterator = restored[""dataloader_state""]\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored dataloader and model state from step {step}"")\n\n # --- TRAIN LOOP ---\n dataloader = (\n jax.make_array_from_process_local_data(videos_sharding, elem)\n for elem in grain_iterator\n )\n print(f""Starting training from step {step}..."")\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n inputs = dict(videos=videos)\n loss, recon, metrics = train_step(tokenizer, optimizer, inputs)\n metrics[""lr""] = lr_schedule(step)\n print(f""Step {step}, loss: {loss}"")\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 **metrics,\n }\n )\n if step % args.log_image_interval == 0:\n gt_seq = inputs[""videos""][0].astype(jnp.float32) / 255.0\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 # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n optimizer_state = nnx.state(optimizer)\n checkpoint_manager.save(\n step,\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n grain_iterator # type: ignore\n ),\n ),\n )\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n checkpoint_manager.close()\n",python,tab +3337,4739718,"TERMINAL",0,0,"7715",,terminal_output +3338,4740705,"train_tokenizer.py",597,0,"",python,selection_mouse +3339,4740723,"train_tokenizer.py",596,0,"",python,selection_command +3340,4740776,"TERMINAL",0,0,"8937",,terminal_output +3341,4741838,"TERMINAL",0,0,"405048",,terminal_output +3342,4742870,"TERMINAL",0,0,"1159",,terminal_output +3343,4743909,"TERMINAL",0,0,"2263:00",,terminal_output +3344,4744971,"TERMINAL",0,0,"3371",,terminal_output +3345,4746013,"TERMINAL",0,0,"4482",,terminal_output +3346,4747070,"TERMINAL",0,0,"5593",,terminal_output +3347,4748159,"TERMINAL",0,0,"663:004",,terminal_output +3348,4749191,"TERMINAL",0,0,"7715",,terminal_output +3349,4750197,"TERMINAL",0,0,"8826",,terminal_output +3350,4751240,"TERMINAL",0,0,"9937",,terminal_output +3351,4752292,"TERMINAL",0,0,"503:0048",,terminal_output +3352,4753390,"TERMINAL",0,0,"1159",,terminal_output +3353,4754414,"TERMINAL",0,0,"22610",,terminal_output +3354,4755437,"TERMINAL",0,0,"3371",,terminal_output +3355,4756483,"train_dynamics.py",0,0,"",python,tab +3356,4756985,"TERMINAL",0,0,"4593",,terminal_output +3357,4758116,"TERMINAL",0,0,"66104",,terminal_output +3358,4759056,"TERMINAL",0,0,"7715",,terminal_output +3359,4760088,"TERMINAL",0,0,"8826",,terminal_output +3360,4760834,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar_jobs]633;D;0",,terminal_output +3361,4762438,"TERMINAL",0,0,"idling",,terminal_command +3362,4762537,"TERMINAL",0,0,"]633;E;2025-09-01 14:01:00 idling;2bb8f881-ac5e-40fe-80c9-f1201978c519]633;C[?1049h(B[?7hEvery 1.0s: sinfo_t_idlehkn1993.localdomain: Mon Sep 1 14:01:00 2025Partition dev_cpuonly: 12 nodes idle\rPartition cpuonly: 107 nodes idle\rPartition dev_accelerated:\t 0 nodes idle\rPartition accelerated:\t 8 nodes idle\rPartition dev_accelerated-h100 :\t 0 nodes idle\rPartition accelerated-h100:\t 0 nodes idle\rPartition large:\t 0 nodes idle\rPartition accelerated-h200:\t 0 nodes idle",,terminal_output +3363,4763559,"TERMINAL",0,0,"1",,terminal_output +3364,4764602,"TERMINAL",0,0,"2",,terminal_output +3365,4765642,"TERMINAL",0,0,"3",,terminal_output +3366,4766687,"TERMINAL",0,0,"4",,terminal_output +3367,4767505,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar_jobs]633;D;0",,terminal_output +3368,4799115,"train_dynamics.py",0,0,"",python,tab +3369,4800180,"train_dynamics.py",1155,0,"",python,selection_mouse +3370,4800681,"train_dynamics.py",1213,0,"",python,selection_mouse +3371,4802891,"train_tokenizer.py",0,0,"",python,tab +3372,4804170,"train_tokenizer.py",1198,0,"",python,selection_mouse +3373,4805914,"train_tokenizer.py",1214,0,"\n darkness_threshold: float = 0.0",python,content +3374,4805917,"train_tokenizer.py",1219,0,"",python,selection_command +3375,4807447,"train_dynamics.py",0,0,"",python,tab +3376,4816990,"train_dynamics.py",10510,0,"",python,selection_mouse +3377,4820047,"train_tokenizer.py",0,0,"",python,tab +3378,4828118,"train_tokenizer.py",9320,0,"",python,selection_mouse +3379,4828824,"train_tokenizer.py",9326,0,"\n darkness_threshold=args.darkness_threshold,",python,content +3380,4828862,"train_tokenizer.py",9335,0,"",python,selection_command +3381,4831959,"train_tokenizer.py",9098,0,"",python,selection_mouse +3382,4832444,"utils/dataloader.py",0,0,"import jax\nimport numpy as np\nimport grain\nfrom typing import Any\nimport pickle\n\n\nclass EpisodeLengthFilter(grain.transforms.Filter):\n """"""\n A Grain Filter that keeps only episodes with sufficient length.\n """"""\n\n def __init__(self, seq_len: int, image_h: int, image_w: int, image_c: int):\n """"""Initializes the filter with sequence length requirements.""""""\n self.seq_len = seq_len\n self.image_h = image_h\n self.image_w = image_w\n self.image_c = image_c\n\n def filter(self, element: Any) -> bool:\n """"""\n Filters episodes based on length.\n\n Args:\n element: A dictionary representing one record from the DataSource.\n Expected to contain 'raw_video' (bytes) and 'sequence_length' (int)\n\n Returns:\n True if the episode has sufficient length, False otherwise.\n """"""\n assert isinstance(element, bytes)\n element = pickle.loads(element)\n\n current_episode_len = element[""sequence_length""]\n if current_episode_len < self.seq_len:\n print(\n f""Filtering out episode with length {current_episode_len}, which is ""\n f""shorter than the requested sequence length {self.seq_len}.""\n )\n return False\n\n return True\n\n\nclass ProcessEpisodeAndSlice(grain.transforms.RandomMap):\n """"""\n A Grain Transformation that combines parsing, slicing, and normalizing.\n """"""\n\n def __init__(self, seq_len: int, image_h: int, image_w: int, image_c: int):\n """"""Initializes the transformation with processing parameters.""""""\n self.seq_len = seq_len\n self.image_h = image_h\n self.image_w = image_w\n self.image_c = image_c\n\n def random_map(self, element: dict, rng: np.random.Generator) -> Any:\n """"""\n Processes a single raw episode from the data source.\n\n Args:\n element: A dictionary representing one record from the DataSource.\n Expected to contain 'raw_video' (bytes) and 'sequence_length' (int)\n rng: A per-record random number generator provided by the Grain sampler.\n\n Returns:\n A processed video sequence as a NumPy array with shape\n (seq_len, height, width, channels) and dtype float32.\n """"""\n assert isinstance(element, bytes)\n element = pickle.loads(element)\n\n video_shape = (\n element[""sequence_length""],\n self.image_h,\n self.image_w,\n self.image_c,\n )\n episode_tensor = np.frombuffer(element[""raw_video""], dtype=np.uint8)\n episode_tensor = episode_tensor.reshape(video_shape)\n\n current_episode_len = episode_tensor.shape[0]\n if current_episode_len < self.seq_len:\n raise ValueError(\n f""Episode length {current_episode_len} is shorter than ""\n f""requested sequence length {self.seq_len}. This should ""\n f""have been filtered out.""\n )\n\n max_start_idx = current_episode_len - self.seq_len\n\n start_idx = rng.integers(0, max_start_idx + 1)\n\n seq = episode_tensor[start_idx : start_idx + self.seq_len]\n\n return seq\n\n\nclass DarknessFilter(grain.transforms.Filter):\n """"""\n A Grain Filter that filters out sequences with images that are too dark.\n """"""\n\n def __init__(self, darkness_threshold: float):\n """"""Initializes the filter with darkness threshold.""""""\n self.darkness_threshold = darkness_threshold\n\n def filter(self, element: Any) -> bool:\n """"""\n Filters sequences based on darkness.\n\n Args:\n element: A NumPy array representing a processed video sequence.\n\n Returns:\n True if the sequence is not too dark, False otherwise.\n """"""\n # Convert the RGB image to grayscale using numpy\n element_greyscale = np.dot(element[...,:3], [0.2989, 0.5870, 0.1140])\n average_brightness = np.mean(element_greyscale)\n if average_brightness < self.darkness_threshold:\n print(\n f""Filtering out sequence with average brightness {average_brightness}, ""\n f""which is below the darkness threshold {self.darkness_threshold}.""\n )\n return False\n\n return True\n\n\ndef get_dataloader(\n array_record_paths: list[str],\n seq_len: int,\n global_batch_size: int,\n image_h: int,\n image_w: int,\n image_c: int,\n darkness_threshold: float = 0.,\n num_workers: int = 1,\n prefetch_buffer_size: int = 1,\n seed: int = 42,\n):\n """"""\n Creates a data loading pipeline using Grain.\n """"""\n if not array_record_paths:\n raise ValueError(""array_record_paths list cannot be empty."")\n\n num_processes = jax.process_count()\n\n if global_batch_size % num_processes != 0:\n raise ValueError(\n f""Global batch size {global_batch_size} must be divisible by ""\n f""the number of JAX processes {num_processes} for proper sharding.""\n )\n per_process_batch_size = global_batch_size // num_processes\n\n source = grain.sources.ArrayRecordDataSource(array_record_paths)\n\n sampler = grain.samplers.IndexSampler(\n num_records=len(source),\n shard_options=grain.sharding.ShardByJaxProcess(drop_remainder=True),\n shuffle=True,\n num_epochs=None,\n seed=seed,\n )\n\n operations = [\n EpisodeLengthFilter(\n seq_len=seq_len, image_h=image_h, image_w=image_w, image_c=image_c\n ),\n ProcessEpisodeAndSlice(\n seq_len=seq_len, image_h=image_h, image_w=image_w, image_c=image_c\n ),\n DarknessFilter(\n darkness_threshold=darkness_threshold\n ),\n grain.transforms.Batch(batch_size=per_process_batch_size, drop_remainder=True),\n ]\n\n read_options = grain.ReadOptions(\n prefetch_buffer_size=prefetch_buffer_size,\n num_threads=1,\n )\n dataloader = grain.DataLoader(\n data_source=source,\n sampler=sampler,\n operations=operations,\n worker_count=num_workers,\n worker_buffer_size=1,\n read_options=read_options,\n )\n\n return dataloader\n",python,tab 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PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3454956 accelerat train_la tum_cte0 R\t5:50\t 8 hkn[0410,0530-0533,0536,0604,0813]3454955 accelerat train_la tum_cte0 R41:48\t 8 hkn[0504,0520,0720,0722-0724,0728,0731]3454954 accelerat train_la tum_cte0 R41:52\t 8 hkn[0521-0528]3454953 accelerat train_la tum_cte0 R41:56\t 8 hkn[0703,0706-0707,0711-0715]",,terminal_output +3455,5280885,"TERMINAL",0,0,"91937",,terminal_output +3456,5281936,"TERMINAL",0,0,"4025048",,terminal_output +3457,5282983,"TERMINAL",0,0,"13159",,terminal_output +3458,5284037,"TERMINAL",0,0,"24262:00",,terminal_output +3459,5285087,"TERMINAL",0,0,"35371",,terminal_output +3460,5286135,"TERMINAL",0,0,"46482",,terminal_output +3461,5287192,"TERMINAL",0,0,"57593",,terminal_output +3462,5288114,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar_jobs]633;D;0",,terminal_output diff --git 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initialized successfully\n10:05:04 PM [info] Initial git state: [object Object]\n",Log,tab +3,5417,"TERMINAL",0,0,"bash",,terminal_focus +4,5417,"TERMINAL",0,0,"bash",,terminal_focus +5,6124,"TERMINAL",0,0,"cd $ws_dir",,terminal_command +6,6170,"TERMINAL",0,0,"]633;E;2025-07-09 22:05:09 cd $ws_dir;af8e09eb-6e10-4bc6-b809-4360d3d8a533]633;C]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared]633;D;0",,terminal_output +7,6750,"TERMINAL",0,0,"ls",,terminal_command +8,6793,"TERMINAL",0,0,"]633;E;2025-07-09 22:05:10 ls;af8e09eb-6e10-4bc6-b809-4360d3d8a533]633;C",,terminal_output +9,6877,"TERMINAL",0,0,"checkpoints count_items.sh data data_new huggingface logs scripts\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared]633;D;0",,terminal_output +10,9152,"TERMINAL",0,0,"/bin/python3 /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/printEnvVariablesToFile.py /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash/envVars.txt",,terminal_command +11,9197,"TERMINAL",0,0,"]633;E;2025-07-09 22:05:12 /bin/python3 /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/printEnvVariablesToFile.py /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash/envVars.txt;cf945349-79a2-42c6-b5b1-8428ad73779e]633;C]0;tum_cte0515@hkn1993:/hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash]633;D;0",,terminal_output +12,14140,"TERMINAL",0,0,"cd logs/logs_mihir/",,terminal_command +13,14654,"TERMINAL",0,0,"ls",,terminal_command +14,14676,"TERMINAL",0,0,"]633;E;2025-07-09 22:05:18 ls;af8e09eb-6e10-4bc6-b809-4360d3d8a533]633;Ctrain_dyn_yolorun_3333026.log\r\ntrain_dyn_yolorun_3333448.log\r\ntrain_lam_action_space_scaling_10_3320179.log\r\ntrain_lam_action_space_scaling_10_3321529.log\r\ntrain_lam_action_space_scaling_10_3329786.log\r\ntrain_lam_action_space_scaling_10_3329801.log\r\ntrain_lam_action_space_scaling_10_3331283.log\r\ntrain_lam_action_space_scaling_12_3318546.log\r\ntrain_lam_action_space_scaling_12_3320177.log\r\ntrain_lam_action_space_scaling_12_3321527.log\r\ntrain_lam_action_space_scaling_12_3329787.log\r\ntrain_lam_action_space_scaling_12_3329802.log\r\ntrain_lam_action_space_scaling_12_3331284.log\r\ntrain_lam_action_space_scaling_20_3318547.log\r\ntrain_lam_action_space_scaling_20_3329788.log\r\ntrain_lam_action_space_scaling_20_3329803.log\r\ntrain_lam_action_space_scaling_20_3331285.log\r\ntrain_lam_action_space_scaling_50_3320180.log\r\ntrain_lam_action_space_scaling_50_3329789.log\r\ntrain_lam_action_space_scaling_50_3329804.log\r\ntrain_lam_action_space_scaling_50_3331286.log\r\ntrain_lam_action_space_scaling_6_3318549.log\r\ntrain_lam_action_space_scaling_6_3320178.log\r\ntrain_lam_action_space_scaling_6_3321528.log\r\ntrain_lam_action_space_scaling_6_3329790.log\r\ntrain_lam_action_space_scaling_6_3329805.log\r\ntrain_lam_action_space_scaling_6_3331287.log\r\ntrain_lam_action_space_scaling_8_3318550.log\r\ntrain_lam_action_space_scaling_8_3329791.log\r\ntrain_lam_action_space_scaling_8_3329806.log\r\ntrain_lam_action_space_scaling_8_3331288.log\r\ntrain_lam_minecraft_overfit_sample_3309655.log\r\ntrain_lam_model_size_scaling_38M_3317098.log\r\ntrain_lam_model_size_scaling_38M_3317115.log\r\ntrain_lam_model_size_scaling_38M_3317231.log\r\ntrain_tokenizer_batch_size_scaling_16_node_3321526.log\r\ntrain_tokenizer_batch_size_scaling_1_node_3318551.log\r\ntrain_tokenizer_batch_size_scaling_2_node_3318552.log\r\ntrain_tokenizer_batch_size_scaling_2_node_3330806.log\r\ntrain_tokenizer_batch_size_scaling_2_node_3330848.log\r\ntrain_tokenizer_batch_size_scaling_2_node_3331282.log\r\ntrain_tokenizer_batch_size_scaling_4_node_3318553.log\r\ntrain_tokenizer_batch_size_scaling_4_node_3320175.log\r\ntrain_tokenizer_batch_size_scaling_4_node_3321524.log\r\ntrain_tokenizer_batch_size_scaling_8_node_3320176.log\r\ntrain_tokenizer_batch_size_scaling_8_node_3321525.log\r\ntrain_tokenizer_minecraft_overfit_sample_3309656.log\r\ntrain_tokenizer_model_size_scaling_127M_3317233.log\r\ntrain_tokenizer_model_size_scaling_127M_3318554.log\r\ntrain_tokenizer_model_size_scaling_140M_3313562.log\r\ntrain_tokenizer_model_size_scaling_140M_3316019.log\r\ntrain_tokenizer_model_size_scaling_200M_3313563.log\r\ntrain_tokenizer_model_size_scaling_200M_3316020.log\r\ntrain_tokenizer_model_size_scaling_227M_3317234.log\r\ntrain_tokenizer_model_size_scaling_227M_3318555.log\r\ntrain_tokenizer_model_size_scaling_227M_3320173.log\r\ntrain_tokenizer_model_size_scaling_227M_3321523.log\r\ntrain_tokenizer_model_size_scaling_37M_3313565.log\r\ntrain_tokenizer_model_size_scaling_37M_3316022.log\r\ntrain_tokenizer_model_size_scaling_37M_3317232.log\r\ntrain_tokenizer_model_size_scaling_37M_3317239.log\r\ntrain_tokenizer_model_size_scaling_37M_3318556.log\r\ntrain_tokenizer_model_size_scaling_74M_3318557.log\r\ntrain_tokenizer_model_size_scaling_74M_3320174.log\r\ntrain_tokenizer_model_size_scaling_74M_3321522.log\r\ntrain_tokenizer_model_size_scaling_80M_3313564.log\r\ntrain_tokenizer_model_size_scaling_80M_3316026.log\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output +15,19274,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=10:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_dyn_yolorun\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\n# array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\ntf_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_tfrecord\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/train_lam_action_space_scaling_20/3318547/lam_1751657975_200000/\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-tf-records-$slurm_job_id \\n --tags dynamics yolo-run tf_records \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $tf_records_dir \\n --lam_checkpoint=$lam_ckpt_dir\nSLURM_JOB_USER=tum_cte0515\nSLURM_TASKS_PER_NODE=4(x2)\nSLURM_JOB_UID=999226\nSLURM_TASK_PID=2954262\nSLURM_JOB_GPUS=0,1,2,3\nSLURM_LOCALID=0\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar\nSLURMD_NODENAME=hkn0423\nSLURM_JOB_START_TIME=1752084560\nSLURM_CLUSTER_NAME=hk\nSLURM_JOB_END_TIME=1752120560\nSLURM_CPUS_ON_NODE=24\nSLURM_JOB_CPUS_PER_NODE=24(x2)\nSLURM_GPUS_ON_NODE=4\nSLURM_GTIDS=0\nSLURM_JOB_PARTITION=accelerated\nSLURM_TRES_PER_TASK=cpu=5\nSLURM_OOM_KILL_STEP=0\nSLURM_JOB_NUM_NODES=2\nSLURM_JOBID=3333448\nSLURM_JOB_QOS=normal\nSLURM_PROCID=0\nSLURM_CPUS_PER_TASK=5\nSLURM_NTASKS=8\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e12.hkn0423\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\nSLURM_SCRIPT_CONTEXT=prolog_task\nSLURM_NODELIST=hkn[0423,0806]\nSLURM_JOB_ACCOUNT=hk-project-p0023960\nSLURM_PRIO_PROCESS=0\nSLURM_NPROCS=8\nSLURM_NNODES=2\nSLURM_SUBMIT_HOST=hkn1991.localdomain\nSLURM_JOB_ID=3333448\nSLURM_NODEID=0\nSLURM_CONF=/etc/slurm/slurm.conf\nSLURM_JOB_NAME=train_dyn_yolorun\nSLURM_NTASKS_PER_NODE=4\nSLURM_JOB_GID=502226\nSLURM_JOB_NODELIST=hkn[0423,0806]\nSLURM_JOB_USER=tum_cte0515\nSLURM_TASKS_PER_NODE=4(x2)\nSLURM_JOB_UID=999226\nSLURM_TASK_PID=2954262\nSLURM_JOB_GPUS=0,1,2,3\nSLURM_LOCALID=0\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar\nSLURMD_NODENAME=hkn0423\nSLURM_JOB_START_TIME=1752084560\nSLURM_CLUSTER_NAME=hk\nSLURM_JOB_END_TIME=1752120560\nSLURM_CPUS_ON_NODE=24\nSLURM_JOB_CPUS_PER_NODE=24(x2)\nSLURM_GPUS_ON_NODE=4\nSLURM_GTIDS=0\nSLURM_JOB_PARTITION=accelerated\nSLURM_TRES_PER_TASK=cpu=5\nSLURM_OOM_KILL_STEP=0\nSLURM_JOB_NUM_NODES=2\nSLURM_JOBID=3333448\nSLURM_JOB_QOS=normal\nSLURM_PROCID=0\nSLURM_CPUS_PER_TASK=5\nSLURM_NTASKS=8\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e12.hkn0423\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\nSLURM_SCRIPT_CONTEXT=prolog_task\nSLURM_NODELIST=hkn[0423,0806]\nSLURM_JOB_ACCOUNT=hk-project-p0023960\nSLURM_PRIO_PROCESS=0\nSLURM_NPROCS=8\nSLURM_NNODES=2\nSLURM_SUBMIT_HOST=hkn1991.localdomain\nSLURM_JOB_ID=3333448\nSLURM_NODEID=0\nSLURM_CONF=/etc/slurm/slurm.conf\nSLURM_JOB_NAME=train_dyn_yolorun\nSLURM_NTASKS_PER_NODE=4\nSLURM_JOB_GID=502226\nSLURM_JOB_NODELIST=hkn[0423,0806]\nGpuFreq=control_disabled\nGpuFreq=control_disabled\n2025-07-09 20:19:02.647121: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-07-09 20:19:02.647117: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-07-09 20:19:02.648691: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-07-09 20:19:02.648812: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1752085142.716728 2954324 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1752085142.716563 2954325 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1752085142.716532 2954326 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1752085142.716725 2954327 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nE0000 00:00:1752085142.737751 2954324 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1752085142.737711 2954325 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1752085142.737715 2954326 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1752085142.738027 2954327 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nW0000 00:00:1752085142.856993 2954324 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085142.857013 2954324 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085142.857016 2954324 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085142.857019 2954324 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085142.856933 2954325 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085142.856963 2954325 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085142.856965 2954325 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085142.856967 2954325 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085142.856931 2954326 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085142.856960 2954326 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085142.856962 2954326 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085142.856964 2954326 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085142.856992 2954327 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085142.857013 2954327 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085142.857016 2954327 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085142.857019 2954327 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n2025-07-09 20:22:41.392553: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-07-09 20:22:41.392553: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-07-09 20:22:41.392657: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-07-09 20:22:41.392551: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1752085361.421720 2486505 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1752085361.421857 2486506 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1752085361.421738 2486507 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1752085361.421746 2486508 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nE0000 00:00:1752085361.431849 2486505 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1752085361.431899 2486506 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1752085361.431838 2486507 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1752085361.431848 2486508 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nW0000 00:00:1752085440.068327 2486505 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085440.068381 2486505 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085440.068383 2486505 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085440.068385 2486505 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085440.068450 2486507 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085440.068482 2486507 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085440.068484 2486507 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085440.068486 2486507 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085440.068452 2486506 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085440.068517 2486506 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085440.068524 2486506 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085440.068529 2486506 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085440.068578 2486508 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085440.068647 2486508 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085440.068654 2486508 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752085440.068659 2486508 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1752086325.429163 2954325 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1752086325.429223 2954326 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1752086325.429137 2954327 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1752086325.429380 2954324 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1752086619.300899 2486505 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1752086619.300888 2486506 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1752086619.300918 2486507 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1752086619.300903 2486508 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\n2025-07-09 20:46:13.254855: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\n2025-07-09 20:46:13.628110: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\n2025-07-09 20:46:16.135680: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\n2025-07-09 20:46:16.561003: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\n2025-07-09 20:46:17.956943: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\n2025-07-09 20:46:19.857695: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\n2025-07-09 20:46:21.437934: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\n2025-07-09 20:46:23.111821: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\nwandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\nwandb: Tracking run with wandb version 0.19.11\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/wandb/run-20250709_204631-otadl0hf\nwandb: Run `wandb offline` to turn off syncing.\nwandb: Syncing run dynamics-yolorun-tf-records-3333448\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/otadl0hf\n2025-07-09 20:48:53.135539: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\n2025-07-09 20:48:53.716202: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\n2025-07-09 20:48:56.159316: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\n2025-07-09 20:48:56.602919: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\n2025-07-09 20:48:57.910995: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\n2025-07-09 20:48:58.401831: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\n2025-07-09 20:49:01.090595: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\n2025-07-09 20:49:01.653781: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\n2025-07-09 20:52:32.339391: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service.cc:1730] Use error polling to propagate the following error to all tasks: UNAVAILABLE: The following tasks are unhealthy (stopped sending heartbeats):\n/job:jax_worker/replica:0/task:3\nThe tasks have crashed. Check the task logs for an earlier error, or scheduler events (e.g. preemption, eviction) to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-09 20:52:32.339939: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: DEADLINE_EXCEEDED: Deadline Exceeded\n\nRPC: /tensorflow.CoordinationService/Heartbeat\n2025-07-09 20:52:32.355006: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-09 20:52:32.354928: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-09 20:52:32.354975: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-09 20:52:32.355098: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-09 20:52:32.355597: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: The following tasks are unhealthy (stopped sending heartbeats):\n/job:jax_worker/replica:0/task:3\nThe tasks have crashed. Check the task logs for an earlier error, or scheduler events (e.g. preemption, eviction) to debug further.\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-09 20:52:32.355511: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: The following tasks are unhealthy (stopped sending heartbeats):\n/job:jax_worker/replica:0/task:3\nThe tasks have crashed. Check the task logs for an earlier error, or scheduler events (e.g. preemption, eviction) to debug further.\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-09 20:52:32.355721: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: The following tasks are unhealthy (stopped sending heartbeats):\n/job:jax_worker/replica:0/task:3\nThe tasks have crashed. Check the task logs for an earlier error, or scheduler events (e.g. preemption, eviction) to debug further.\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-09 20:52:32.355778: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: The following tasks are unhealthy (stopped sending heartbeats):\n/job:jax_worker/replica:0/task:3\nThe tasks have crashed. Check the task logs for an earlier error, or scheduler events (e.g. preemption, eviction) to debug further.\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-09 20:53:06.248355: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-09 20:53:06.250291: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-09 20:53:06.251049: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-09 20:53:06.251219: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: The following tasks are unhealthy (stopped sending heartbeats):\n/job:jax_worker/replica:0/task:3\nThe tasks have crashed. Check the task logs for an earlier error, or scheduler events (e.g. preemption, eviction) to debug further.\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-09 20:53:06.251366: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: The following tasks are unhealthy (stopped sending heartbeats):\n/job:jax_worker/replica:0/task:3\nThe tasks have crashed. Check the task logs for an earlier error, or scheduler events (e.g. preemption, eviction) to debug further.\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-09 20:53:06.253667: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: The following tasks are unhealthy (stopped sending heartbeats):\n/job:jax_worker/replica:0/task:3\nThe tasks have crashed. Check the task logs for an earlier error, or scheduler events (e.g. preemption, eviction) to debug further.\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nsrun: error: hkn0423: task 3: Aborted (core dumped)\nsrun: error: hkn0423: task 1: Aborted (core dumped)\nsrun: error: hkn0423: task 0: Aborted (core dumped)\nsrun: error: hkn0423: task 2: Aborted (core dumped)\nsrun: error: hkn0806: task 6: Aborted (core dumped)\nsrun: error: hkn0806: task 7: Aborted (core dumped)\nsrun: error: hkn0806: task 4: Aborted (core dumped)\nsrun: error: hkn0806: task 5: Aborted (core dumped)\n\n============================= JOB FEEDBACK =============================\n\nJob ID: 3333448\nCluster: hk\nUser/Group: tum_cte0515/hk-project-p0023960\nAccount: hk-project-p0023960\nState: FAILED (exit code 134)\nPartition: accelerated\nNodes: 2\nCores per node: 24\nNodelist: hkn[0423,0806]\nCPU Utilized: 00:34:42\nCPU Efficiency: 1.58% of 1-12:40:00 core-walltime\nJob Wall-clock time: 00:45:50\nStarttime: Wed Jul 9 20:09:20 2025\nEndtime: Wed Jul 9 20:55:10 2025\nMemory Utilized: 39.02 GB (estimated maximum)\nMemory Efficiency: 0.00% of 0.00 MB (0.00 MB/node)\nEnergy Consumed: 2748325 Joule / 763.423611111111 Watthours\nAverage node power draw: 999.390909090909 Watt\n",log,tab +16,20276,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",2028,0,"",log,selection_mouse +17,20301,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",2027,0,"",log,selection_command +18,21932,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",31703,0,"",log,selection_command +19,43289,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",23977,0,"",log,selection_mouse +20,44225,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",24021,0,"",log,selection_mouse +21,44976,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",24081,0,"",log,selection_mouse +22,45779,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",24126,0,"",log,selection_mouse +23,46581,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",24162,0,"",log,selection_mouse +24,47228,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",24149,0,"",log,selection_mouse +25,47384,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",24138,17,"DEADLINE_EXCEEDED",log,selection_mouse +26,48169,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",23787,0,"",log,selection_mouse +27,48200,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",23786,0,"",log,selection_command +28,48313,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",23787,0,"",log,selection_mouse +29,48340,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",23786,0,"",log,selection_command +30,48554,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",23595,193,"The tasks have crashed. Check the task logs for an earlier error, or scheduler events (e.g. preemption, eviction) to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n",log,selection_mouse +31,48567,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",23596,192,"he tasks have crashed. Check the task logs for an earlier error, or scheduler events (e.g. preemption, eviction) to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n",log,selection_command +32,49181,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",24048,0,"",log,selection_mouse +33,49364,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",24045,6,"Python",log,selection_mouse +34,50492,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",24127,0,"",log,selection_mouse +35,51508,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",24149,0,"",log,selection_mouse +36,51683,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",24138,17,"DEADLINE_EXCEEDED",log,selection_mouse +37,52515,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",24146,0,"",log,selection_mouse +38,52516,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",24138,17,"DEADLINE_EXCEEDED",log,selection_mouse +39,69294,"TERMINAL",0,0,"bash",,terminal_focus +40,80583,"TERMINAL",0,0,"queue",,terminal_command +41,80635,"TERMINAL",0,0,"]633;E;2025-07-09 22:06:24 queue;16c08be6-3885-420f-ac53-f5272aed6e54]633;C",,terminal_output +42,80736,"TERMINAL",0,0,"[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:06:24 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON) 3331283 accelerat train_la tum_cte0 R 21:28:11\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:28:11\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:28:11\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:28:11\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:28:11\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:28:11\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:28:52\t 2 hkn[0513,0516]",,terminal_output +43,81868,"TERMINAL",0,0,"52222223",,terminal_output +44,82788,"TERMINAL",0,0,"63333334",,terminal_output +45,83881,"TERMINAL",0,0,"74444445",,terminal_output +46,84876,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:06:28 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON) 3331283 accelerat train_la tum_cte0 R 21:28:15\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:28:15\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:28:15\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:28:15\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:28:15\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:28:15\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:28:56\t 2 hkn[0513,0516]",,terminal_output +47,84985,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:06:28 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3331283 accelerat train_la tum_cte0 R 21:28:15\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:28:15\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:28:15\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:28:15\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:28:15\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:28:15\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:28:56\t 2 hkn[0513,0516]",,terminal_output +48,85932,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:06:29 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3331283 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:28:57\t 2 hkn[0513,0516]",,terminal_output +49,86060,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:06:29 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3331283 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:28:57\t 2 hkn[0513,0516]",,terminal_output +50,86129,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:06:29 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3331283 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:28:57\t 2 hkn[0513,0516]",,terminal_output +51,86248,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:06:29 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3331283 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:28:16\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:28:57\t 2 hkn[0513,0516]",,terminal_output +52,86693,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:06:30 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3331283 accelerat train_la tum_cte0 R 21:28:17\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:28:17\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:28:17\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:28:17\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:28:17\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:28:17\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:28:58\t 2 hkn[0513,0516]",,terminal_output +53,86823,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:06:30 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3331283 accelerat train_la tum_cte0 R 21:28:17\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:28:17\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:28:17\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:28:17\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:28:17\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:28:17\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:28:58\t 2 hkn[0513,0516]\t",,terminal_output +54,87156,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:06:30 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3331283 accelerat train_la tum_cte0 R 21:28:17\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:28:17\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:28:17\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:28:17\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:28:17\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:28:17\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:28:58\t 2 hkn[0513,0516]",,terminal_output +55,87417,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:06:31 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3331283 accelerat train_la tum_cte0 R 21:28:18\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:28:18\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:28:18\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:28:18\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:28:18\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:28:18\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:28:59\t 2 hkn[0513,0516]",,terminal_output +56,87520,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:06:31 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3331283 accelerat train_la tum_cte0 R 21:28:18\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:28:18\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:28:18\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:28:18\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:28:18\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:28:18\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:28:59\t 2 hkn[0513,0516]",,terminal_output +57,88247,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:06:31 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3331283 accelerat train_la tum_cte0 R 21:28:18\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:28:18\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:28:18\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:28:18\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:28:18\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:28:18\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:28:59\t 2 hkn[0513,0516] ",,terminal_output +58,89480,"TERMINAL",0,0,"22020202020209:01",,terminal_output +59,89604,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:06:33 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3331283 accelerat train_la tum_cte0 R 21:28:20\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:28:20\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:28:20\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:28:20\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:28:20\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:28:20\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:29:01\t 2 hkn[0513,0516] ",,terminal_output +60,90130,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:06:33 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3331283 accelerat train_la tum_cte0 R 21:28:20\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:28:20\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:28:20\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:28:20\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:28:20\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:28:20\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:29:01\t 2 hkn[0513,0516]",,terminal_output +61,90990,"TERMINAL",0,0,"41111112",,terminal_output +62,92227,"TERMINAL",0,0,"52222223",,terminal_output +63,93240,"TERMINAL",0,0,"63333334",,terminal_output +64,93295,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:06:36 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3331283 accelerat train_la tum_cte0 R 21:28:23\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:28:23\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:28:23\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:28:23\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:28:23\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:28:23\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:29:04\t 2 hkn[0513,0516] ",,terminal_output +65,94297,"TERMINAL",0,0,"75555556",,terminal_output +66,95475,"TERMINAL",0,0,"96666667",,terminal_output +67,96416,"TERMINAL",0,0,"407777778",,terminal_output +68,97444,"TERMINAL",0,0,"18888889",,terminal_output +69,98517,"TERMINAL",0,0,"299999910",,terminal_output +70,99545,"TERMINAL",0,0,"33030303030301",,terminal_output +71,100638,"TERMINAL",0,0,"41111112",,terminal_output +72,101870,"TERMINAL",0,0,"52222223",,terminal_output +73,102894,"TERMINAL",0,0,"63333334",,terminal_output +74,103743,"TERMINAL",0,0,"74444445",,terminal_output +75,104794,"TERMINAL",0,0,"85555556",,terminal_output +76,105845,"TERMINAL",0,0,"96666667",,terminal_output +77,106889,"TERMINAL",0,0,"507777778",,terminal_output +78,107927,"TERMINAL",0,0,"18888889",,terminal_output +79,108981,"TERMINAL",0,0,"299999920",,terminal_output +80,110032,"TERMINAL",0,0,"34040404040401",,terminal_output +81,111083,"TERMINAL",0,0,"41111112",,terminal_output +82,112123,"TERMINAL",0,0,"52222223",,terminal_output +83,113193,"TERMINAL",0,0,"63333334",,terminal_output +84,114244,"TERMINAL",0,0,"74444445",,terminal_output +85,115295,"TERMINAL",0,0,"86666667",,terminal_output +86,116353,"TERMINAL",0,0,"7:007777778",,terminal_output +87,117392,"TERMINAL",0,0,"18888889",,terminal_output +88,118447,"TERMINAL",0,0,"299999930",,terminal_output +89,119493,"TERMINAL",0,0,"35050505050501",,terminal_output +90,120568,"TERMINAL",0,0,"41111112",,terminal_output +91,121613,"TERMINAL",0,0,"52222223",,terminal_output +92,122873,"TERMINAL",0,0,"63333334",,terminal_output +93,123818,"TERMINAL",0,0,"74444445",,terminal_output +94,124776,"TERMINAL",0,0,"85555556",,terminal_output +95,125992,"TERMINAL",0,0,"96666667",,terminal_output +96,126858,"TERMINAL",0,0,"107777778",,terminal_output +97,127938,"TERMINAL",0,0,"18888889",,terminal_output +98,128975,"TERMINAL",0,0,"299999940",,terminal_output +99,130016,"TERMINAL",0,0,"39:009:009:009:009:009:001",,terminal_output +100,131074,"TERMINAL",0,0,"41111112",,terminal_output +101,132080,"TERMINAL",0,0,"52222223",,terminal_output +102,133179,"TERMINAL",0,0,"63333334",,terminal_output +103,134209,"TERMINAL",0,0,"74444445",,terminal_output +104,135221,"TERMINAL",0,0,"85555556",,terminal_output +105,136275,"TERMINAL",0,0,"97777778",,terminal_output +106,137407,"TERMINAL",0,0,"218888889",,terminal_output +107,138442,"TERMINAL",0,0,"299999950",,terminal_output +108,139490,"TERMINAL",0,0,"31010101010101",,terminal_output +109,140472,"TERMINAL",0,0,"41111112",,terminal_output +110,141535,"TERMINAL",0,0,"52222223",,terminal_output +111,142675,"TERMINAL",0,0,"63333334",,terminal_output +112,143722,"TERMINAL",0,0,"74444445",,terminal_output +113,144782,"TERMINAL",0,0,"85555556",,terminal_output +114,145860,"TERMINAL",0,0,"96666667",,terminal_output +115,146884,"TERMINAL",0,0,"307777778",,terminal_output +116,147938,"TERMINAL",0,0,"18888889",,terminal_output +117,148973,"TERMINAL",0,0,"299999930:00",,terminal_output +118,150041,"TERMINAL",0,0,"32020202020201",,terminal_output +119,151080,"TERMINAL",0,0,"41111112",,terminal_output +120,152148,"TERMINAL",0,0,"52222223",,terminal_output +121,153178,"TERMINAL",0,0,"63333334",,terminal_output +122,154235,"TERMINAL",0,0,"74444445",,terminal_output +123,155292,"TERMINAL",0,0,"86666667",,terminal_output +124,156366,"TERMINAL",0,0,"407777778",,terminal_output +125,157372,"TERMINAL",0,0,"18888889",,terminal_output +126,158494,"TERMINAL",0,0,"299999910",,terminal_output +127,159593,"TERMINAL",0,0,"33030303030301",,terminal_output +128,160535,"TERMINAL",0,0,"41111112",,terminal_output +129,161634,"TERMINAL",0,0,"52222223",,terminal_output +130,162706,"TERMINAL",0,0,"63333334",,terminal_output +131,163683,"TERMINAL",0,0,"74444445",,terminal_output +132,164835,"TERMINAL",0,0,"85555556",,terminal_output +133,165818,"TERMINAL",0,0,"96666667",,terminal_output +134,166868,"TERMINAL",0,0,"507777778",,terminal_output +135,167974,"TERMINAL",0,0,"18888889",,terminal_output +136,168978,"TERMINAL",0,0,"299999920",,terminal_output +137,170008,"TERMINAL",0,0,"34040404040401",,terminal_output +138,171057,"TERMINAL",0,0,"41111112",,terminal_output +139,172111,"TERMINAL",0,0,"52222223",,terminal_output +140,173164,"TERMINAL",0,0,"63333334",,terminal_output +141,174215,"TERMINAL",0,0,"74444445",,terminal_output +142,175286,"TERMINAL",0,0,"85555556",,terminal_output +143,176340,"TERMINAL",0,0,"97777778",,terminal_output +144,177390,"TERMINAL",0,0,"8:018888889",,terminal_output +145,178419,"TERMINAL",0,0,"299999930",,terminal_output +146,179464,"TERMINAL",0,0,"35050505050501",,terminal_output +147,180519,"TERMINAL",0,0,"41111112",,terminal_output +148,181541,"TERMINAL",0,0,"52222223",,terminal_output +149,182811,"TERMINAL",0,0,"63333334",,terminal_output +150,183724,"TERMINAL",0,0,"74444445",,terminal_output +151,184715,"TERMINAL",0,0,"85555556",,terminal_output +152,185745,"TERMINAL",0,0,"96666667",,terminal_output +153,186823,"TERMINAL",0,0,"107777778",,terminal_output +154,187920,"TERMINAL",0,0,"18888889",,terminal_output +155,188948,"TERMINAL",0,0,"299999940",,terminal_output +156,189949,"TERMINAL",0,0,"330:0030:0030:0030:0030:0030:001",,terminal_output +157,191020,"TERMINAL",0,0,"41111112",,terminal_output +158,192046,"TERMINAL",0,0,"52222223",,terminal_output +159,193195,"TERMINAL",0,0,"63333334",,terminal_output +160,194204,"TERMINAL",0,0,"74444445",,terminal_output +161,195229,"TERMINAL",0,0,"85555556",,terminal_output +162,196327,"TERMINAL",0,0,"96666667",,terminal_output +163,196483,"TERMINAL",0,0,"bash",,terminal_focus +164,197352,"TERMINAL",0,0,"207777778",,terminal_output +165,198501,"TERMINAL",0,0,"299999950",,terminal_output +166,199488,"TERMINAL",0,0,"31010101010101",,terminal_output +167,200538,"TERMINAL",0,0,"41111112",,terminal_output +168,201513,"TERMINAL",0,0,"52222223",,terminal_output +169,202621,"TERMINAL",0,0,"63333334",,terminal_output +170,203574,"TERMINAL",0,0,"74444445",,terminal_output +171,204616,"TERMINAL",0,0,"85555556",,terminal_output +172,205666,"TERMINAL",0,0,"96666667",,terminal_output +173,206726,"TERMINAL",0,0,"307777778",,terminal_output +174,207760,"TERMINAL",0,0,"18888889",,terminal_output +175,208813,"TERMINAL",0,0,"29999991:00",,terminal_output +176,209867,"TERMINAL",0,0,"32020202020201",,terminal_output +177,210928,"TERMINAL",0,0,"41111112",,terminal_output +178,211979,"TERMINAL",0,0,"52222223",,terminal_output +179,213023,"TERMINAL",0,0,"63333334",,terminal_output +180,214070,"TERMINAL",0,0,"74444445",,terminal_output +181,215122,"TERMINAL",0,0,"85555556",,terminal_output +182,216170,"TERMINAL",0,0,"96666667",,terminal_output +183,217223,"TERMINAL",0,0,"407777778",,terminal_output +184,218272,"TERMINAL",0,0,"18888889",,terminal_output +185,219337,"TERMINAL",0,0,"330303030303011",,terminal_output +186,220369,"TERMINAL",0,0,"41111112",,terminal_output +187,221420,"TERMINAL",0,0,"52222223",,terminal_output 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+505,568905,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",30553,13,"/PollForError",log,selection_mouse +506,568966,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",30554,111,"PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nsrun: error: hkn0423: task 3: Aborted",log,selection_mouse +507,568967,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",30554,103,"PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nsrun: error: hkn0423: task 3:",log,selection_mouse +508,568968,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",30554,101,"PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nsrun: error: hkn0423: task ",log,selection_mouse +509,568968,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",30554,100,"PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nsrun: error: hkn0423: task",log,selection_mouse +510,569006,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",30554,95,"PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nsrun: error: hkn0423:",log,selection_mouse +511,569028,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",30554,94,"PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nsrun: error: hkn0423",log,selection_mouse +512,569064,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",30534,32,"CoordinationService/PollForError",log,selection_mouse +513,569155,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",30533,33,".CoordinationService/PollForError",log,selection_mouse +514,569187,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",30523,43,"tensorflow.CoordinationService/PollForError",log,selection_mouse +515,570163,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",30528,0,"",log,selection_mouse +516,574272,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",23605,0,"",log,selection_mouse +517,574442,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",23605,4,"have",log,selection_mouse +518,574709,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",23605,5,"have ",log,selection_mouse +519,575079,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",23610,0,"",log,selection_mouse +520,575472,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",23586,0,"",log,selection_mouse +521,575665,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",23586,1,"0",log,selection_mouse +522,575840,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",23562,33,"/job:jax_worker/replica:0/task:3\n",log,selection_mouse +523,585293,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",25820,0,"",log,selection_mouse +524,585585,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",25709,111," [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-09 20:52:32.355511: F external/xla/xla/pj",log,selection_mouse +525,585634,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",25659,161,"\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-09 20:52:32.355511: F external/xla/xla/pj",log,selection_mouse +526,585634,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",25578,242,"earlier error, or scheduler events (e.g. preemption, eviction) to debug further.\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-09 20:52:32.355511: F external/xla/xla/pj",log,selection_mouse +527,585635,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",25526,294,"\nThe tasks have crashed. Check the task logs for an earlier error, or scheduler events (e.g. preemption, eviction) to debug further.\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-09 20:52:32.355511: F external/xla/xla/pj",log,selection_mouse +528,585716,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",25118,702,"/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: The following tasks are unhealthy (stopped sending heartbeats):\n/job:jax_worker/replica:0/task:3\nThe tasks have crashed. Check the task logs for an earlier error, or scheduler events (e.g. preemption, eviction) to debug further.\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-09 20:52:32.355511: F external/xla/xla/pj",log,selection_mouse +529,585719,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",24696,1124,"distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-09 20:52:32.355098: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-09 20:52:32.355597: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: The following tasks are unhealthy (stopped sending heartbeats):\n/job:jax_worker/replica:0/task:3\nThe tasks have crashed. Check the task logs for an earlier error, or scheduler events (e.g. preemption, eviction) to debug further.\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-09 20:52:32.355511: F external/xla/xla/pj",log,selection_mouse +530,585720,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",24273,1547,"/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-09 20:52:32.354928: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-09 20:52:32.354975: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-09 20:52:32.355098: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-09 20:52:32.355597: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: The following tasks are unhealthy (stopped sending heartbeats):\n/job:jax_worker/replica:0/task:3\nThe tasks have crashed. Check the task logs for an earlier error, or scheduler events (e.g. preemption, eviction) to debug further.\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-09 20:52:32.355511: F external/xla/xla/pj",log,selection_mouse +531,585757,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",24175,1645,"\nRPC: /tensorflow.CoordinationService/Heartbeat\n2025-07-09 20:52:32.355006: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-09 20:52:32.354928: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-09 20:52:32.354975: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-09 20:52:32.355098: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-09 20:52:32.355597: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: UNAVAILABLE: The following tasks are unhealthy (stopped sending heartbeats):\n/job:jax_worker/replica:0/task:3\nThe tasks have crashed. Check the task logs for an earlier error, or scheduler events (e.g. preemption, eviction) to debug further.\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-09 20:52:32.355511: F external/xla/xla/pj",log,selection_mouse +532,585938,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3333448.log",24175,0,"",log,selection_mouse +533,660171,"TERMINAL",0,0,"bash",,terminal_focus +534,668014,"TERMINAL",0,0,"queue",,terminal_command +535,668035,"TERMINAL",0,0,"]633;E;2025-07-09 22:16:11 queue;af8e09eb-6e10-4bc6-b809-4360d3d8a533]633;C",,terminal_output +536,668113,"TERMINAL",0,0,"[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:16:11 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3331283 accelerat train_la tum_cte0 R 21:37:58\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:37:58\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:37:58\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:37:58\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:37:58\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:37:58\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:38:39\t 2 hkn[0513,0516]",,terminal_output +537,669185,"TERMINAL",0,0,"299999940",,terminal_output +538,670175,"TERMINAL",0,0,"38:008:008:008:008:008:001",,terminal_output +539,670925,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:16:14 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3331283 accelerat train_la tum_cte0 R 21:38:01\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:38:01\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:38:01\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:38:01\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:38:01\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:38:01\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:38:42\t 2 hkn[0513,0516]",,terminal_output +540,671937,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:16:15 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3331283 accelerat train_la tum_cte0 R 21:38:02\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:38:02\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:38:02\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:38:02\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:38:02\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:38:02\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:38:43\t 2 hkn[0513,0516]",,terminal_output +541,672333,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:16:16 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3331283 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:38:44\t 2 hkn[0513,0516]",,terminal_output +542,672817,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:16:16 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3331283 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:38:44\t 2 hkn[0513,0516]",,terminal_output +543,673023,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:16:16 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3331283 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:38:44\t 2 hkn[0513,0516]",,terminal_output +544,673333,"TERMINAL",0,0,"Every 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:16:16 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3331283 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:38:03\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:38:44\t 2 hkn[0513,0516]",,terminal_output +545,674406,"TERMINAL",0,0,"75555556",,terminal_output +546,675389,"TERMINAL",0,0,"96666667",,terminal_output +547,676401,"TERMINAL",0,0,"207777778",,terminal_output +548,677455,"TERMINAL",0,0,"18888889",,terminal_output +549,678501,"TERMINAL",0,0,"299999950",,terminal_output +550,679553,"TERMINAL",0,0,"31010101010101",,terminal_output +551,680613,"TERMINAL",0,0,"41111112",,terminal_output +552,681656,"TERMINAL",0,0,"52222223",,terminal_output +553,682707,"TERMINAL",0,0,"63333334",,terminal_output +554,683770,"TERMINAL",0,0,"74444445",,terminal_output +555,684814,"TERMINAL",0,0,"85555556",,terminal_output +556,685909,"TERMINAL",0,0,"96666667",,terminal_output +557,686924,"TERMINAL",0,0,"307777778",,terminal_output +558,687963,"TERMINAL",0,0,"18888889",,terminal_output +559,689054,"TERMINAL",0,0,"29999999:00",,terminal_output +560,690100,"TERMINAL",0,0,"32020202020201",,terminal_output +561,691106,"TERMINAL",0,0,"41111112",,terminal_output +562,692160,"TERMINAL",0,0,"52222223",,terminal_output +563,693214,"TERMINAL",0,0,"63333334",,terminal_output +564,694256,"TERMINAL",0,0,"74444445",,terminal_output +565,694836,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output +566,697584,"TERMINAL",0,0,"idling",,terminal_command +567,697634,"TERMINAL",0,0,"]633;E;2025-07-09 22:16:41 idling;af8e09eb-6e10-4bc6-b809-4360d3d8a533]633;C",,terminal_output +568,697745,"TERMINAL",0,0,"[?1049h(B[?7hEvery 1.0s: sinfo_t_idlehkn1993.localdomain: Wed Jul 9 22:16:41 2025Partition dev_cpuonly: 11 nodes idle\rPartition cpuonly:\t 9 nodes idle\rPartition dev_accelerated:\t 2 nodes idle\rPartition accelerated:\t 1 nodes idle\rPartition dev_accelerated-h100 :\t 1 nodes idle\rPartition accelerated-h100:\t 0 nodes idle\rPartition large:\t 7 nodes idle",,terminal_output +569,698740,"TERMINAL",0,0,"2\t",,terminal_output +570,699788,"TERMINAL",0,0,"3\t",,terminal_output +571,700802,"TERMINAL",0,0,"4\t",,terminal_output +572,701881,"TERMINAL",0,0,"5\t",,terminal_output +573,702916,"TERMINAL",0,0,"6\t",,terminal_output +574,703934,"TERMINAL",0,0,"7\t",,terminal_output +575,704993,"TERMINAL",0,0,"8\t",,terminal_output +576,706012,"TERMINAL",0,0,"9\t",,terminal_output +577,706736,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output +578,863469,"TERMINAL",0,0,"salloc --time=24:00:00 --partition=accelerated --nodes=1 --ntasks-per-node=5 --gres=gpu:1 --cpus-per-task=5",,terminal_command +579,863544,"TERMINAL",0,0,"]633;E;2025-07-09 22:19:27 salloc --time=24:00:00 --partition=accelerated --nodes=1 --ntasks-per-node=5 --gres=gpu:1 --cpus-per-task=5;af8e09eb-6e10-4bc6-b809-4360d3d8a533]633;Csalloc: Pending job allocation 3333583\r\nsalloc: job 3333583 queued and waiting for resources\r\n",,terminal_output +580,869813,"TERMINAL",0,0,"^Csalloc: Job allocation 3333583 has been revoked.\r\nsalloc: Job aborted due to signal\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;1",,terminal_output +581,878939,"TERMINAL",0,0,"salloc --time=24:00:00 --partition=accelerated --nodes=1 --ntasks-per-node=5 --gres=gpu:1 --cpus-per-task=5^C",,terminal_command +582,878972,"TERMINAL",0,0,"^C[?2004l\r[?2004h[?2004l\r\r\n]633;E;;af8e09eb-6e10-4bc6-b809-4360d3d8a533]633;C]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D",,terminal_output +583,882335,"TERMINAL",0,0,"screen",,terminal_command +584,882414,"TERMINAL",0,0,"]633;E;2025-07-09 22:19:46 screen;af8e09eb-6e10-4bc6-b809-4360d3d8a533]633;Cbash: screen: command not found...\r\n",,terminal_output +585,883624,"TERMINAL",0,0,"^C\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;130",,terminal_output +586,885644,"TERMINAL",0,0,"tmux",,terminal_command +587,885758,"TERMINAL",0,0,"]633;E;2025-07-09 22:19:49 tmux;af8e09eb-6e10-4bc6-b809-4360d3d8a533]633;C[?1049h[?1h=[?12l[?25h[?1000l[?1002l[?1003l[?1006l[?1005l(B[?12l[?25h[?1006l[?1000l[?1002l[?1003l[?2004l[>c[>q[?25l\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n[0] 0:tmux* ""hkn1993.localdomain"" 22:19 09-Jul-25(B[?12l[?25h(B[?12l[?25h[?1006l[?1000l[?1002l[?1003l[?2004l[?25l\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n[0] 0:tmux* ""hkn1993.localdomain"" 22:19 09-Jul-25(B[?12l[?25h",,terminal_output +588,885836,"TERMINAL",0,0,"[?7727h",,terminal_output +589,888714,"TERMINAL",0,0,"jafar[tum_cte0515@hkn1993 logs_mihir]$ [?2004h[?25l[0] 0:bash* ""hkn1993.localdomain"" 22:19 09-Jul-25(B[?12l[?25h",,terminal_output +590,894603,"TERMINAL",0,0,"\r\nlogout\r\n[?2004l(B[?1l>[?12l[?25h[?1000l[?1002l[?1003l[?1006l[?1005l[?7727l[?1004l[?1049l[exited]\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output +591,895553,"TERMINAL",0,0,"bash",,terminal_focus +592,896773,"TERMINAL",0,0,"tmux",,terminal_command +593,896829,"TERMINAL",0,0,"]633;E;2025-07-09 22:20:00 tmux;249d0687-2136-49d1-ad97-c40acd813191]633;C[?1049h[?1h=[?12l[?25h[?1000l[?1002l[?1003l[?1006l[?1005l(B[?12l[?25h[?1006l[?1000l[?1002l[?1003l[?2004l[>c[>q[?25l\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n[0] 0:tmux* ""hkn1993.localdomain"" 22:20 09-Jul-25(B[?12l[?25h(B[?12l[?25h[?1006l[?1000l[?1002l[?1003l[?2004l[?25l\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n[0] 0:tmux* ""hkn1993.localdomain"" 22:20 09-Jul-25(B[?12l[?25h",,terminal_output +594,896957,"TERMINAL",0,0,"[?7727h",,terminal_output +595,898822,"TERMINAL",0,0,"jafar[tum_cte0515@hkn1993 jafar]$ [?2004h[?25l[0] 0:bash* ""hkn1993.localdomain"" 22:20 09-Jul-25(B[?12l[?25h",,terminal_output +596,899101,"TERMINAL",0,0,"\r\n[?2004ljafar[tum_cte0515@hkn1993 jafar]$ [?2004h",,terminal_output +597,900391,"TERMINAL",0,0,"salloc --time=24:00:00 --partition=accelerated --nodes=1 --ntasks-per-node=5 --gres=gpu:1 --cpus-per-task=5(B",,terminal_output +598,902613,"TERMINAL",0,0,"salloc --time=24:00:00 --partition=accelerated --nodes=1 --ntasks-per-node=5 --gres=gpu:1 --cpus-per-task=5\r\n[?2004l",,terminal_output +599,902669,"TERMINAL",0,0,"salloc: Granted job allocation 3333584\r\n",,terminal_output +600,902812,"TERMINAL",0,0,"salloc: Waiting for resource configuration\r\n",,terminal_output +601,906747,"TERMINAL",0,0,"bash",,terminal_focus +602,907970,"TERMINAL",0,0,"queue",,terminal_command +603,908100,"TERMINAL",0,0,"]633;E;2025-07-09 22:20:11 queue;af8e09eb-6e10-4bc6-b809-4360d3d8a533]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Wed Jul 9 22:20:11 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3333584 accelerat interact tum_cte0 R\t0:05\t 1 hkn05233331283 accelerat train_la tum_cte0 R 21:41:58\t 2 hkn[0533,0536]3331284 accelerat train_la tum_cte0 R 21:41:58\t 2 hkn[0601-0602]3331285 accelerat train_la tum_cte0 R 21:41:58\t 2 hkn[0701,0710]3331286 accelerat train_la tum_cte0 R 21:41:58\t 2 hkn[0425,0428]3331287 accelerat train_la tum_cte0 R 21:41:58\t 2 hkn[0430-0431]3331288 accelerat train_la tum_cte0 R 21:41:58\t 2 hkn[0809,0811]3331282 accelerat train_to tum_cte0 R 21:42:39\t 2 hkn[0513,0516]",,terminal_output +604,908544,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output +605,909517,"TERMINAL",0,0,"idling",,terminal_command +606,909598,"TERMINAL",0,0,"]633;E;2025-07-09 22:20:13 idling;af8e09eb-6e10-4bc6-b809-4360d3d8a533]633;C[?1049h(B[?7hEvery 1.0s: sinfo_t_idlehkn1993.localdomain: Wed Jul 9 22:20:13 2025Partition dev_cpuonly: 11 nodes idle\rPartition cpuonly:\t 9 nodes idle\rPartition dev_accelerated:\t 2 nodes idle\rPartition accelerated:\t 1 nodes idle\rPartition dev_accelerated-h100 :\t 1 nodes idle\rPartition accelerated-h100:\t 0 nodes idle\rPartition large:\t 7 nodes idle",,terminal_output +607,910626,"TERMINAL",0,0,"4",,terminal_output +608,911671,"TERMINAL",0,0,"5",,terminal_output +609,911825,"TERMINAL",0,0,"[?25l[0] 0:salloc* ""hkn1993.localdomain"" 22:20 09-Jul-25(B[?12l[?25h",,terminal_output +610,912718,"TERMINAL",0,0,"6",,terminal_output +611,913758,"TERMINAL",0,0,"7",,terminal_output +612,914799,"TERMINAL",0,0,"8",,terminal_output +613,915837,"TERMINAL",0,0,"9",,terminal_output +614,916893,"TERMINAL",0,0,"20",,terminal_output +615,917916,"TERMINAL",0,0,"1",,terminal_output +616,918965,"TERMINAL",0,0,"2",,terminal_output +617,920009,"TERMINAL",0,0,"3",,terminal_output +618,921051,"TERMINAL",0,0,"4",,terminal_output +619,922126,"TERMINAL",0,0,"5",,terminal_output +620,923137,"TERMINAL",0,0,"6",,terminal_output +621,924185,"TERMINAL",0,0,"7",,terminal_output +622,925224,"TERMINAL",0,0,"8",,terminal_output +623,925737,"TERMINAL",0,0,"tmux",,terminal_focus +624,926268,"TERMINAL",0,0,"9",,terminal_output +625,927321,"TERMINAL",0,0,"31",,terminal_output +626,928326,"TERMINAL",0,0,"(B[?1l>[?12l[?25h[?1000l[?1002l[?1003l[?1006l[?1005l[?7727l[?1004l[?1049l[detached (from session 0)]\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +627,928375,"TERMINAL",0,0,"2",,terminal_output +628,929396,"TERMINAL",0,0,"3",,terminal_output +629,930430,"TERMINAL",0,0,"4",,terminal_output +630,930598,"TERMINAL",0,0,"tmux",,terminal_command +631,930647,"TERMINAL",0,0,"]633;E;2025-07-09 22:20:34 tmux;249d0687-2136-49d1-ad97-c40acd813191]633;C[?1049h[?1h=[?12l[?25h[?1000l[?1002l[?1003l[?1006l[?1005l(B[?12l[?25h[?1006l[?1000l[?1002l[?1003l[?2004l[>c[>q[?25l\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n[1] 0:tmux* ""hkn1993.localdomain"" 22:20 09-Jul-25(B[?12l[?25h(B[?12l[?25h[?1006l[?1000l[?1002l[?1003l[?2004l[?25l\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n[1] 0:tmux* ""hkn1993.localdomain"" 22:20 09-Jul-25(B[?12l[?25h",,terminal_output +632,930731,"TERMINAL",0,0,"[?7727h",,terminal_output +633,931483,"TERMINAL",0,0,"5",,terminal_output +634,932500,"TERMINAL",0,0,"6",,terminal_output +635,932807,"TERMINAL",0,0,"jafar[tum_cte0515@hkn1993 jafar]$ [?2004h[?25l[1] 0:bash* ""hkn1993.localdomain"" 22:20 09-Jul-25(B[?12l[?25h",,terminal_output +636,933538,"TERMINAL",0,0,"7",,terminal_output +637,933956,"TERMINAL",0,0,"salloc --time=24:00:00 --partition=accelerated --nodes=1 --ntasks-per-node=5 --gres=gpu:1 --cpus-per-task=5(B",,terminal_output +638,934579,"TERMINAL",0,0,"8",,terminal_output +639,935622,"TERMINAL",0,0,"9",,terminal_output +640,936137,"TERMINAL",0,0,"salloc --time=24:00:00 --partition=accelerated --nodes=1 --ntasks-per-node=5 --gres=gpu:1 --cpus-per-task=5",,terminal_output +641,936670,"TERMINAL",0,0,"40",,terminal_output +642,936827,"TERMINAL",0,0,"",,terminal_output +643,937012,"TERMINAL",0,0,"",,terminal_output +644,937278,"TERMINAL",0,0,"",,terminal_output +645,937526,"TERMINAL",0,0,"",,terminal_output +646,937707,"TERMINAL",0,0,"1",,terminal_output +647,937989,"TERMINAL",0,0,"",,terminal_output +648,938268,"TERMINAL",0,0,"",,terminal_output +649,938634,"TERMINAL",0,0,"",,terminal_output +650,938759,"TERMINAL",0,0,"2",,terminal_output +651,938809,"TERMINAL",0,0,"",,terminal_output +652,939435,"TERMINAL",0,0," --cpus-per-task=5",,terminal_output +653,939577,"TERMINAL",0,0,"4 --cpus-per-task=5",,terminal_output +654,939788,"TERMINAL",0,0,"3",,terminal_output +655,940010,"TERMINAL",0,0,"",,terminal_output +656,940231,"TERMINAL",0,0,"",,terminal_output +657,940407,"TERMINAL",0,0,"",,terminal_output +658,940627,"TERMINAL",0,0,"",,terminal_output +659,940764,"TERMINAL",0,0,"",,terminal_output +660,940831,"TERMINAL",0,0,"4",,terminal_output +661,940899,"TERMINAL",0,0,"",,terminal_output +662,941165,"TERMINAL",0,0,"",,terminal_output +663,941624,"TERMINAL",0,0,"",,terminal_output +664,941879,"TERMINAL",0,0,"5",,terminal_output +665,942102,"TERMINAL",0,0,"",,terminal_output +666,942922,"TERMINAL",0,0,"6",,terminal_output +667,943103,"TERMINAL",0,0,"",,terminal_output +668,943207,"TERMINAL",0,0,"[1@2",,terminal_output +669,943958,"TERMINAL",0,0,"7",,terminal_output +670,944997,"TERMINAL",0,0,"8",,terminal_output +671,946031,"TERMINAL",0,0,"9",,terminal_output +672,946336,"TERMINAL",0,0,"",,terminal_output +673,946566,"TERMINAL",0,0,"",,terminal_output +674,946914,"TERMINAL",0,0,"",,terminal_output +675,947084,"TERMINAL",0,0,"50",,terminal_output +676,947391,"TERMINAL",0,0,"",,terminal_output +677,948125,"TERMINAL",0,0,"1",,terminal_output +678,948372,"TERMINAL",0,0,"",,terminal_output +679,948536,"TERMINAL",0,0,"[1@4",,terminal_output +680,949187,"TERMINAL",0,0,"2",,terminal_output +681,950202,"TERMINAL",0,0,"3",,terminal_output +682,951244,"TERMINAL",0,0,"4",,terminal_output +683,952285,"TERMINAL",0,0,"5",,terminal_output +684,953325,"TERMINAL",0,0,"7",,terminal_output +685,954366,"TERMINAL",0,0,"8",,terminal_output +686,955404,"TERMINAL",0,0,"9",,terminal_output +687,956448,"TERMINAL",0,0,"1:00",,terminal_output +688,956587,"TERMINAL",0,0,"\r\n[?2004lsalloc: Pending job allocation 3333586\r\nsalloc: job 3333586 queued and waiting for resources\r\n",,terminal_output +689,957491,"TERMINAL",0,0,"1",,terminal_output +690,958537,"TERMINAL",0,0,"2",,terminal_output +691,958852,"TERMINAL",0,0,"[?25l[1] 0:salloc* ""hkn1993.localdomain"" 22:21 09-Jul-25(B[?12l[?25h",,terminal_output +692,959576,"TERMINAL",0,0,"3",,terminal_output +693,960613,"TERMINAL",0,0,"4",,terminal_output +694,961662,"TERMINAL",0,0,"5",,terminal_output +695,962722,"TERMINAL",0,0,"6",,terminal_output +696,963747,"TERMINAL",0,0,"7",,terminal_output +697,964829,"TERMINAL",0,0,"8",,terminal_output +698,965630,"TERMINAL",0,0,"tmux --reattach",,terminal_command +699,965666,"TERMINAL",0,0,"]633;E;2025-07-09 22:21:09 tmux --reattach;249d0687-2136-49d1-ad97-c40acd813191]633;Cusage: tmux [-2CDlNuvV] [-c shell-command] [-f file] [-L socket-name]\r\n [-S socket-path] [-T features] [command [flags]]\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;1",,terminal_output +700,965831,"TERMINAL",0,0,"9",,terminal_output +701,966875,"TERMINAL",0,0,"10",,terminal_output +702,967928,"TERMINAL",0,0,"1",,terminal_output +703,968963,"TERMINAL",0,0,"2",,terminal_output +704,970013,"TERMINAL",0,0,"3",,terminal_output +705,971040,"TERMINAL",0,0,"4",,terminal_output +706,972132,"TERMINAL",0,0,"5",,terminal_output +707,972281,"TERMINAL",0,0,"tmux --help",,terminal_command +708,972326,"TERMINAL",0,0,"]633;E;2025-07-09 22:21:16 tmux --help;249d0687-2136-49d1-ad97-c40acd813191]633;Cusage: tmux [-2CDlNuvV] [-c shell-command] [-f file] [-L socket-name]\r\n [-S socket-path] [-T features] [command [flags]]\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;1",,terminal_output +709,973157,"TERMINAL",0,0,"6",,terminal_output +710,974218,"TERMINAL",0,0,"7",,terminal_output +711,975223,"TERMINAL",0,0,"8",,terminal_output +712,976284,"TERMINAL",0,0,"9",,terminal_output +713,976802,"TERMINAL",0,0,"tmux -h",,terminal_command +714,976841,"TERMINAL",0,0,"]633;E;2025-07-09 22:21:20 tmux -h;249d0687-2136-49d1-ad97-c40acd813191]633;Ctmux: unknown option -- h\r\nusage: tmux [-2CDlNuvV] [-c shell-command] [-f file] [-L socket-name]\r\n [-S socket-path] [-T features] [command [flags]]\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;1",,terminal_output +715,977310,"TERMINAL",0,0,"20",,terminal_output +716,978348,"TERMINAL",0,0,"2",,terminal_output +717,979400,"TERMINAL",0,0,"3",,terminal_output 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;249d0687-2136-49d1-ad97-c40acd813191]633;C[?1049h[?1h=[?12l[?25h[?1000l[?1002l[?1003l[?1006l[?1005l(B[?12l[?25h[?1006l[?1000l[?1002l[?1003l[?2004l[>c[>q[?25ljafar[tum_cte0515@hkn1993 jafar]$ salloc --time=24:00:00 --partition=accelerated --nodes=2 --ntasks-per-node=4 --gres=gpu:4 --cpus-per-task=5\r\nsalloc: Pending job allocation 3333586\r\nsalloc: job 3333586 queued and waiting for resources\r\n\r\n\r\n\r\n\r\n\r\n\r\n[1] 0:salloc* ""hkn1993.localdomain"" 22:21 09-Jul-25(B[?12l[?25h(B[?12l[?25h[?1006l[?1000l[?1002l[?1003l[?2004l[?25ljafar[tum_cte0515@hkn1993 jafar]$ salloc --time=24:00:00 --partition=accelerated --nodes=2 --ntasks-per-node=4 --gres=gpu:4 --cpus-per-task=5\r\nsalloc: Pending job allocation 3333586\r\nsalloc: job 3333586 queued and waiting for resources\r\n\r\n\r\n\r\n\r\n\r\n\r\n[1] 0:salloc* ""hkn1993.localdomain"" 22:21 09-Jul-25(B[?12l[?25h",,terminal_output +735,995364,"TERMINAL",0,0,"[?7727h",,terminal_output +736,996137,"TERMINAL",0,0,"9",,terminal_output 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+842,1107197,"TERMINAL",0,0,"3047777778",,terminal_output +843,1107898,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output +844,2647719,"genie.py",0,0,"from typing import Dict, Any\n\nimport optax\nimport jax\nimport jax.numpy as jnp\nimport flax.linen as nn\nfrom jax import NamedSharding\nfrom flax.training.train_state import TrainState\nfrom flax.training import orbax_utils\nfrom orbax.checkpoint import PyTreeCheckpointer\n\nfrom models.dynamics import DynamicsMaskGIT\nfrom models.lam import LatentActionModel\nfrom models.tokenizer import TokenizerVQVAE\n\n\nclass Genie(nn.Module):\n """"""Genie model""""""\n\n # --- Tokenizer ---\n in_dim: int\n tokenizer_dim: int\n latent_patch_dim: int\n num_patch_latents: int\n patch_size: int\n tokenizer_num_blocks: int\n tokenizer_num_heads: int\n # --- LAM ---\n lam_dim: int\n latent_action_dim: int\n num_latent_actions: int\n lam_patch_size: int\n lam_num_blocks: int\n lam_num_heads: int\n # --- Dynamics ---\n dyna_dim: int\n dyna_num_blocks: int\n dyna_num_heads: int\n dropout: float = 0.0\n mask_limit: float = 0.0\n\n def setup(self):\n self.tokenizer = TokenizerVQVAE(\n in_dim=self.in_dim,\n model_dim=self.tokenizer_dim,\n latent_dim=self.latent_patch_dim,\n num_latents=self.num_patch_latents,\n patch_size=self.patch_size,\n num_blocks=self.tokenizer_num_blocks,\n num_heads=self.tokenizer_num_heads,\n dropout=0.0,\n codebook_dropout=0.0,\n )\n self.lam = LatentActionModel(\n in_dim=self.in_dim,\n model_dim=self.lam_dim,\n latent_dim=self.latent_patch_dim,\n num_latents=self.num_latent_actions,\n patch_size=self.lam_patch_size,\n num_blocks=self.lam_num_blocks,\n num_heads=self.lam_num_heads,\n dropout=0.0,\n codebook_dropout=0.0,\n )\n self.dynamics = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n num_latents=self.num_patch_latents,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n mask_limit=self.mask_limit,\n )\n\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\n tokenizer_outputs = self.tokenizer.vq_encode(batch[""videos""], training=False)\n lam_outputs = self.lam.vq_encode(batch[""videos""], training=False)\n outputs = dict(\n video_tokens=jax.lax.stop_gradient(tokenizer_outputs[""indices""]),\n latent_actions=jax.lax.stop_gradient(lam_outputs[""z_q""]),\n )\n outputs[""mask_rng""] = batch[""mask_rng""]\n dyna_outputs = self.dynamics(outputs, training)\n outputs.update(dyna_outputs)\n mle_indices = jnp.argmax(outputs[""token_logits""], axis=-1)\n outputs[""recon""] = self.tokenizer.decode(\n mle_indices, batch[""videos""].shape[2:4]\n )\n return outputs\n\n @nn.compact\n def sample_mihir(\n self,\n batch: Dict[str, Any],\n seq_len: int,\n steps: int,\n temperature: float,\n sample_argmax: bool,\n ) -> Any:\n # B == batch_size\n # T == num_frames (input)\n # N == num_patches\n # S == seq_len\n # A == action_space\n # D == latent_dim\n # --- Encode videos and actions ---\n tokenizer_out = self.tokenizer.vq_encode(batch[""videos""], training=False)\n token_idxs = tokenizer_out[""indices""] # (B, T, N)\n B, T, N = token_idxs.shape\n S = seq_len\n print(""token_idxs shape:"", token_idxs.shape)\n pad_shape = (B, S - T, N)\n pad = jnp.zeros(pad_shape, dtype=token_idxs.dtype)\n token_idxs = jnp.concatenate([token_idxs, pad], axis=1) # shape (B, S, N)\n action_tokens = self.lam.vq.get_codes(batch[""latent_actions""])\n\n # begin potential forloop (from T to S)\n initial_T = T\n for T in range(initial_T, S):\n # Create a mask that is 1 (True) where we just padded\n # token_idxs shape: (B, S, N), T = original length, S = seq_len\n # mask is True for padded positions (i.e., t >= T)\n mask = (jnp.arange(S)[None, :, None] >= T) # shape (1, S, 1)\n mask = jnp.broadcast_to(mask, (B, S, N)) # shape (B, S, N)\n init_mask = mask.astype(bool)\n token_idxs *= ~init_mask\n #print(""token_idxs[0,:,0]:"", token_idxs[0,:,0])\n #print(""init_mask[0,:,0]:"", init_mask[0,:,0])\n\n assert init_mask.shape == (B, S, N), ""Wrong mask shape""\n\n # --- Initialize MaskGIT ---\n init_carry = (\n batch[""rng""],\n init_mask,\n token_idxs,\n action_tokens,\n )\n MaskGITLoop = nn.scan(\n MaskGITStepMihir,\n variable_broadcast=""params"",\n split_rngs={""params"": False},\n in_axes=0,\n out_axes=0,\n length=steps,\n )\n\n # --- Run MaskGIT loop ---\n loop_fn = MaskGITLoop(\n dynamics=self.dynamics,\n tokenizer=self.tokenizer,\n temperature=temperature,\n sample_argmax=sample_argmax,\n steps=steps,\n )\n final_carry, _ = loop_fn(init_carry, jnp.arange(steps))\n token_idxs = final_carry[2]\n\n new_frame_pixels = self.tokenizer.decode(\n token_idxs,\n video_hw=batch[""videos""].shape[2:4],\n )\n return new_frame_pixels\n\n @nn.compact\n def sample(\n self,\n batch: Dict[str, Any],\n steps: int = 25,\n temperature: int = 1,\n sample_argmax: bool = False,\n ) -> Any:\n # B == batch_size\n # T == num_frames (input)\n # N == num_patches\n # S == seq_len\n # A == action_space\n # D == latent_dim\n # --- Encode videos and actions ---\n tokenizer_out = self.tokenizer.vq_encode(batch[""videos""], training=False)\n token_idxs = tokenizer_out[""indices""]# (B, T, N)\n new_frame_idxs = jnp.zeros_like(token_idxs)[:, 0] # (B, N) \n action_tokens = self.lam.vq.get_codes(batch[""latent_actions""]) # (B, S, A, D)\n\n # --- Initialize MaskGIT ---\n init_mask = jnp.ones_like(token_idxs, dtype=bool)[:, 0] # (B, N)\n init_carry = (\n batch[""rng""],\n new_frame_idxs,\n init_mask,\n token_idxs,\n action_tokens,\n )\n MaskGITLoop = nn.scan(\n MaskGITStep,\n variable_broadcast=""params"",\n split_rngs={""params"": False},\n in_axes=0,\n out_axes=0,\n length=steps,\n )\n\n # --- Run MaskGIT loop ---\n loop_fn = MaskGITLoop(\n dynamics=self.dynamics,\n tokenizer=self.tokenizer,\n temperature=temperature,\n sample_argmax=sample_argmax,\n steps=steps,\n )\n final_carry, _ = loop_fn(init_carry, jnp.arange(steps))\n new_frame_idxs = final_carry[1]\n new_frame_pixels = self.tokenizer.decode(\n jnp.expand_dims(new_frame_idxs, 1),\n video_hw=batch[""videos""].shape[2:4],\n )\n return new_frame_pixels\n\n def vq_encode(self, batch, training) -> Dict[str, Any]:\n # --- Preprocess videos ---\n lam_output = self.lam.vq_encode(batch[""videos""], training=training)\n return lam_output[""indices""]\n\n\nclass MaskGITStepMihir(nn.Module):\n dynamics: nn.Module\n tokenizer: nn.Module\n temperature: float\n sample_argmax: bool\n steps: int\n\n @nn.compact\n def __call__(self, carry, x):\n rng, mask, token_idxs, action_tokens = carry\n step = x\n B, S, N = token_idxs.shape[:3]\n\n # --- Construct + encode video ---\n vid_embed = self.dynamics.patch_embed(token_idxs)\n # Mask vid_embed: set to mask_token where mask==1, else keep vid_embed\n # mask: (B, S, N), vid_embed: (B, S, N, D), mask_token: (D,)\n mask_token = self.dynamics.mask_token # (1,1, 1, D,)\n # Expand mask to (B, S, N, 1) for broadcasting\n mask_expanded = mask[..., None]\n vid_embed = jnp.where(mask_expanded, mask_token, vid_embed)\n\n # --- Predict transition ---\n act_embed = self.dynamics.action_up(action_tokens)\n vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\n unmasked_ratio = jnp.cos(jnp.pi * (step + 1) / (self.steps * 2))\n step_temp = self.temperature * (1.0 - unmasked_ratio)\n final_logits = self.dynamics.dynamics(vid_embed)[:, -1] / step_temp\n\n # --- Sample new tokens for final frame ---\n if self.sample_argmax:\n sampled_token_idxs = jnp.argmax(final_logits, axis=-1)\n else:\n rng, _rng = jax.random.split(rng)\n sampled_token_idxs = jnp.where(\n step == self.steps - 1,\n jnp.argmax(final_logits, axis=-1),\n jax.random.categorical(_rng, final_logits),\n )\n gather_fn = jax.vmap(jax.vmap(lambda x, y: x[y]))\n final_token_probs = gather_fn(jax.nn.softmax(final_logits), sampled_token_idxs)\n final_token_probs += ~mask\n # Update masked tokens only\n jax.debug.print(""maskgit-sampled-token_idxs[0,:,0]: {}"", token_idxs[0,:,0])\n token_idxs = jnp.where(mask, sampled_token_idxs, token_idxs)\n jax.debug.print(""maskgit-token_idxs[0,:,0]: {}"", token_idxs[0,:,0])\n\n # --- Update mask ---\n num_unmasked_tokens = jnp.round(N * (1.0 - unmasked_ratio)).astype(int)\n idx_mask = jnp.arange(final_token_probs.shape[-1]) > num_unmasked_tokens\n sorted_idxs = jnp.argsort(final_token_probs, axis=-1, descending=True)\n mask_update_fn = jax.vmap(lambda msk, ids: msk.at[ids].set(idx_mask))\n new_mask = mask_update_fn(mask, sorted_idxs)\n\n new_carry = (rng, new_mask, token_idxs, action_tokens)\n return new_carry, None\n\n\nclass MaskGITStep(nn.Module):\n dynamics: nn.Module\n tokenizer: nn.Module\n temperature: float\n sample_argmax: bool\n steps: int\n\n @nn.compact\n def __call__(self, carry, x):\n rng, final_token_idxs, mask, token_idxs, action_tokens = carry\n step = x\n B, T, N = token_idxs.shape[:3]\n\n # --- Construct + encode video ---\n vid_token_idxs = jnp.concatenate(\n (token_idxs, jnp.expand_dims(final_token_idxs, 1)), axis=1\n ) # (B, T+1, N)\n vid_embed = self.dynamics.patch_embed(vid_token_idxs) # (B, T+1, N, D)\n curr_masked_frame = jnp.where(\n jnp.expand_dims(mask, -1), # (B, N, 1)\n self.dynamics.mask_token[0], # (B, 1, D)\n vid_embed[:, -1], # (B, N, D)\n ) # (B, N, D)\n vid_embed = vid_embed.at[:, -1].set(curr_masked_frame)\n\n # --- Predict transition ---\n act_embed = self.dynamics.action_up(action_tokens)\n vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\n unmasked_ratio = jnp.cos(jnp.pi * (step + 1) / (self.steps * 2))\n step_temp = self.temperature * (1.0 - unmasked_ratio)\n final_logits = self.dynamics.dynamics(vid_embed)[:, -1] / step_temp\n\n # --- Sample new tokens for final frame ---\n if self.sample_argmax:\n sampled_token_idxs = jnp.argmax(final_logits, axis=-1)\n else:\n rng, _rng = jax.random.split(rng)\n sampled_token_idxs = jnp.where(\n step == self.steps - 1,\n jnp.argmax(final_logits, axis=-1),\n jax.random.categorical(_rng, final_logits),\n )\n gather_fn = jax.vmap(jax.vmap(lambda x, y: x[y]))\n final_token_probs = gather_fn(jax.nn.softmax(final_logits), sampled_token_idxs)\n final_token_probs += ~mask\n # Update masked tokens only\n new_token_idxs = jnp.where(mask, sampled_token_idxs, final_token_idxs)\n\n # --- Update mask ---\n num_unmasked_tokens = jnp.round(N * (1.0 - unmasked_ratio)).astype(int)\n idx_mask = jnp.arange(final_token_probs.shape[-1]) > num_unmasked_tokens\n sorted_idxs = jnp.argsort(final_token_probs, axis=-1, descending=True)\n mask_update_fn = jax.vmap(lambda msk, ids: msk.at[ids].set(idx_mask))\n new_mask = mask_update_fn(mask, sorted_idxs)\n\n new_carry = (rng, new_token_idxs, new_mask, token_idxs, action_tokens)\n return new_carry, None\n\n\ndef restore_genie_components(\n train_state: TrainState,\n sharding: NamedSharding,\n inputs: Dict[str, jax.Array],\n rng: jax.Array,\n args,\n):\n """"""Restore pre-trained Genie components""""""\n rng, _rng = jax.random.split(rng)\n\n dummy_tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.tokenizer_dim,\n latent_dim=args.latent_patch_dim,\n num_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n num_blocks=args.tokenizer_num_blocks,\n num_heads=args.tokenizer_num_heads,\n dropout=args.dropout,\n codebook_dropout=args.dropout,\n )\n dummy_lam = LatentActionModel(\n in_dim=args.image_channels,\n model_dim=args.lam_dim,\n latent_dim=args.latent_patch_dim,\n num_latents=args.num_latent_actions,\n patch_size=args.lam_patch_size,\n num_blocks=args.lam_num_blocks,\n num_heads=args.lam_num_heads,\n dropout=args.dropout,\n codebook_dropout=args.dropout,\n )\n tokenizer_init_params = dummy_tokenizer.init(_rng, inputs)\n lam_init_params = dummy_lam.init(_rng, inputs)\n\n # dummy values since we only use tx to initialize the dummy train states\n dummy_tx = optax.adamw(\n learning_rate=optax.constant_schedule(args.max_lr),\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n )\n\n dummy_tokenizer_train_state = TrainState.create(\n apply_fn=dummy_tokenizer.apply, params=tokenizer_init_params, tx=dummy_tx\n )\n dummy_lam_train_state = TrainState.create(\n apply_fn=dummy_lam.apply, params=lam_init_params, tx=dummy_tx\n )\n\n def create_abstract_sharded_pytree(pytree_template, sharding_spec):\n """"""Replaces arrays in a pytree with ShapeDtypeStructs having the given sharding.""""""\n\n def map_fn(leaf_template):\n if hasattr(leaf_template, ""shape"") and hasattr(leaf_template, ""dtype""):\n return jax.ShapeDtypeStruct(\n leaf_template.shape, leaf_template.dtype, sharding=sharding_spec\n )\n return leaf_template\n\n return jax.tree_util.tree_map(map_fn, pytree_template)\n\n abstract_sharded_tokenizer_state = create_abstract_sharded_pytree(\n dummy_tokenizer_train_state, sharding\n )\n abstract_sharded_lam_state = create_abstract_sharded_pytree(\n dummy_lam_train_state, sharding\n )\n\n tokenizer_restore_target = {""model"": abstract_sharded_tokenizer_state}\n lam_restore_target = {""model"": abstract_sharded_lam_state}\n\n tokenizer_restore_args = orbax_utils.restore_args_from_target(\n tokenizer_restore_target\n )\n lam_restore_args = orbax_utils.restore_args_from_target(lam_restore_target)\n\n restored_tokenizer_params = (\n PyTreeCheckpointer()\n .restore(\n args.tokenizer_checkpoint,\n item=tokenizer_restore_target,\n restore_args=tokenizer_restore_args,\n )[""model""]\n .params[""params""]\n )\n restored_lam_params = (\n PyTreeCheckpointer()\n .restore(\n args.lam_checkpoint, item=lam_restore_target, restore_args=lam_restore_args\n )[""model""]\n .params[""params""]\n )\n # Genie does not initialize all LAM modules, thus we omit those extra modules during restoration\n # (f.srambical) FIXME: Currently, this is a small HBM memory crunch since the LAM's decoder is loaded into HBM and immediately dicarded.\n # A workaround would be to restore to host memory first, and only move the weights to HBM after pruning the decoder\n restored_lam_params = {\n k: v\n for k, v in restored_lam_params.items()\n if k in train_state.params[""params""][""lam""]\n }\n\n train_state.params[""params""][""tokenizer""].update(restored_tokenizer_params)\n train_state.params[""params""][""lam""].update(restored_lam_params)\n\n return train_state\n",python,tab diff --git a/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-5612308a-14dd-4f05-ae65-c6cd496f68351752499707410-2025_07_14-15.29.02.547/source.csv b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-5612308a-14dd-4f05-ae65-c6cd496f68351752499707410-2025_07_14-15.29.02.547/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..2330b57218282aa5f45b812dfce9366c34fb4f9e --- /dev/null +++ b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-5612308a-14dd-4f05-ae65-c6cd496f68351752499707410-2025_07_14-15.29.02.547/source.csv @@ -0,0 +1,127 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +2,206,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"3:29:02 PM [info] Activating crowd-code\n3:29:02 PM [info] Recording started\n3:29:02 PM [info] Initializing git provider using file system watchers...\n",Log,tab +3,365,"extension-output-pdoom-org.crowd-code-#1-crowd-code",150,0,"3:29:02 PM [info] Git repository found\n3:29:02 PM [info] Git provider initialized successfully\n3:29:02 PM [info] Initial git state: [object Object]\n",Log,content +4,2862,"TERMINAL",0,0,"/bin/python3 /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/printEnvVariablesToFile.py /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash/envVars.txt",,terminal_command +5,2909,"TERMINAL",0,0,"]633;E;2025-07-14 15:29:05 /bin/python3 /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/printEnvVariablesToFile.py /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash/envVars.txt;96649176-7412-48ad-98ea-7639505dc671]633;C",,terminal_output +6,2952,"TERMINAL",0,0,"]0;tum_cte0515@hkn1990:/hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash]633;D;0",,terminal_output +7,7671,"TERMINAL",0,0,"watch",,terminal_focus +8,24040,"TERMINAL",0,0,"salloc --time=10:00:00 --partition=accelerated --nodes=1 --ntasks-per-node=1 --gres=gpu:1 --cpus-per-task=5",,terminal_command +9,24098,"TERMINAL",0,0,"]633;E;2025-07-14 15:29:26 salloc --time=10:00:00 --partition=accelerated --nodes=1 --ntasks-per-node=1 --gres=gpu:1 --cpus-per-task=5;ba61a590-3751-4127-93b1-5716be1c7bd0]633;Csalloc: Pending job allocation 3344502\r\nsalloc: job 3344502 queued and waiting for resources\r\n",,terminal_output +10,25688,"TERMINAL",0,0,"^Csalloc: Job allocation 3344502 has been revoked.\r\nsalloc: Job aborted due to signal\r\n]0;tum_cte0515@hkn1990:~/Projects/jafar]633;D;1]633;P;Cwd=/home/hk-project-p0023960/tum_cte0515/Projects/jafar",,terminal_output +11,30368,"TERMINAL",0,0,"salloc --time=01:00:00 --partition=accelerated --nodes=1 --ntasks-per-node=1 --gres=gpu:1 --cpus-per-task=5",,terminal_command +12,30400,"TERMINAL",0,0,"]633;E;2025-07-14 15:29:32 salloc --time=01:00:00 --partition=accelerated --nodes=1 --ntasks-per-node=1 --gres=gpu:1 --cpus-per-task=5;ba61a590-3751-4127-93b1-5716be1c7bd0]633;Csalloc: Pending job allocation 3344503\r\nsalloc: job 3344503 queued and waiting for resources\r\n",,terminal_output +13,31244,"TERMINAL",0,0,"^Csalloc: Job allocation 3344503 has been revoked.\r\nsalloc: Job aborted due to signal\r\n]0;tum_cte0515@hkn1990:~/Projects/jafar]633;D;1",,terminal_output +14,35141,"TERMINAL",0,0,"salloc --time=01:00:00 --partition=dev_accelerated --nodes=1 --ntasks-per-node=1 --gres=gpu:1 --cpus-per-task=5",,terminal_command +15,35153,"TERMINAL",0,0,"]633;E;2025-07-14 15:29:37 salloc --time=01:00:00 --partition=dev_accelerated --nodes=1 --ntasks-per-node=1 --gres=gpu:1 --cpus-per-task=5;ba61a590-3751-4127-93b1-5716be1c7bd0]633;Csalloc: Granted job allocation 3344505\r\n",,terminal_output +16,35274,"TERMINAL",0,0,"salloc: Waiting for resource configuration\r\n",,terminal_output +17,37228,"TERMINAL",0,0,"bash",,terminal_focus +18,38854,"TERMINAL",0,0,"queue",,terminal_command +19,38926,"TERMINAL",0,0,"]633;E;2025-07-14 15:29:41 queue;450fbccd-48d1-432b-b44b-06394616d158]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1990.localdomain: Mon Jul 14 15:29:41 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3341079 accelerat train_to tum_cte0 R 1-04:59:15\t 2 hkn[0628-0629]3341080 accelerat train_to tum_cte0 R 1-04:59:15\t 2 hkn[0631-0632]3344246 accelerat interact tum_cte0 R 1:13:32\t 2 hkn[0626,0630]3344505 dev_accel interact tum_cte0 R\t0:04\t 1 hkn0402",,terminal_output +20,39972,"TERMINAL",0,0,"26635",,terminal_output +21,41004,"TERMINAL",0,0,"37746",,terminal_output +22,42041,"TERMINAL",0,0,"48857",,terminal_output +23,43101,"TERMINAL",0,0,"59968",,terminal_output +24,44132,"TERMINAL",0,0,"6202079",,terminal_output +25,45165,"TERMINAL",0,0,"711810",,terminal_output +26,46236,"TERMINAL",0,0,"82291",,terminal_output +27,46520,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1990:~/Projects/jafar]633;D;0",,terminal_output +28,49617,"TERMINAL",0,0,"idling",,terminal_command +29,49671,"TERMINAL",0,0,"]633;E;2025-07-14 15:29:52 idling;450fbccd-48d1-432b-b44b-06394616d158]633;C[?1049h(B[?7hEvery 1.0s: sinfo_t_idlehkn1990.localdomain: Mon Jul 14 15:29:52 2025Partition dev_cpuonly: 10 nodes idle\rPartition cpuonly: 42 nodes idle\rPartition dev_accelerated:\t 1 nodes idle\rPartition accelerated: 40 nodes idle\rPartition dev_accelerated-h100 :\t 1 nodes idle\rPartition accelerated-h100:\t 0 nodes idle\rPartition large:\t 7 nodes idle",,terminal_output +30,50700,"TERMINAL",0,0,"3",,terminal_output +31,51749,"TERMINAL",0,0,"4",,terminal_output +32,52802,"TERMINAL",0,0,"5",,terminal_output +33,53446,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1990:~/Projects/jafar]633;D;0",,terminal_output +34,62317,"TERMINAL",0,0,"salloc: Nodes hkn0402 are ready for job\r\n",,terminal_output +35,63118,"TERMINAL",0,0,"]0;tum_cte0515@hkn0402:~/Projects/jafar[?2004h[tum_cte0515@hkn0402 jafar]$ ",,terminal_output +36,84352,"TERMINAL",0,0,"srun",,terminal_focus +37,85382,"TERMINAL",0,0,"[?25lso[?25h[?25lo[?25h",,terminal_output +38,85520,"TERMINAL",0,0,"[?25lu[?25h",,terminal_output +39,85615,"TERMINAL",0,0,"[?25lr[?25h",,terminal_output +40,85840,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +41,85947,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +42,86009,"TERMINAL",0,0,"[?25l [?25h",,terminal_output +43,86141,"TERMINAL",0,0,"[?25l.[?25h[?25lv[?25h",,terminal_output +44,86295,"TERMINAL",0,0,"env/",,terminal_output +45,86587,"TERMINAL",0,0,"",,terminal_output +46,86945,"TERMINAL",0,0,"[?25lb[?25h",,terminal_output +47,87054,"TERMINAL",0,0,"in/",,terminal_output +48,87522,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +49,87637,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +50,87784,"TERMINAL",0,0,"tivate",,terminal_output +51,88103,"TERMINAL",0,0,"\r\n[?2004l\r]0;tum_cte0515@hkn0402:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0402 jafar]$ ",,terminal_output +52,94820,"sample.py",0,0,"from dataclasses import dataclass\nimport time\nimport os\n\nimport dm_pix as pix\nimport einops\nimport jax\nimport jax.numpy as jnp\nimport flax.linen as nn\nimport numpy as np\nfrom orbax.checkpoint import PyTreeCheckpointer\nfrom flax.training.train_state import TrainState\nimport grain\nimport orbax.checkpoint as ocp\nimport optax\nfrom PIL import Image, ImageDraw\nimport tyro\n\nfrom genie import Genie\nfrom utils.dataloader import get_dataloader\n\n\n@dataclass\nclass Args:\n # Experiment\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/coinrun_episodes""\n checkpoint: str = """"\n # Sampling\n batch_size: int = 1\n maskgit_steps: int = 25\n temperature: float = 1.0\n sample_argmax: bool = True\n start_frame: int = 0\n # Tokenizer checkpoint\n tokenizer_dim: int = 512\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 8\n tokenizer_num_heads: int = 8\n # LAM checkpoint\n lam_dim: int = 512\n latent_action_dim: int = 32\n num_latent_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 8\n lam_num_heads: int = 8\n # Dynamics checkpoint\n dyna_dim: int = 512\n dyna_num_blocks: int = 12\n dyna_num_heads: int = 8\n\n\nargs = tyro.cli(Args)\nrng = jax.random.PRNGKey(args.seed)\n\n# --- Load Genie checkpoint ---\ngenie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n latent_action_dim=args.latent_action_dim,\n num_latent_actions=args.num_latent_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n lam_co_train=True,\n # Dynamics\n dyna_dim=args.dyna_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n)\nrng, _rng = jax.random.split(rng)\nimage_shape = (args.image_height, args.image_width, args.image_channels)\ndummy_inputs = dict(\n videos=jnp.zeros((args.batch_size, args.seq_len, *image_shape), dtype=jnp.float32),\n mask_rng=_rng,\n)\nrng, _rng = jax.random.split(rng)\nparams = genie.init(_rng, dummy_inputs)\n\n# ckpt = PyTreeCheckpointer().restore(args.checkpoint)[""model""][""params""][""params""]\n# Create a dummy TrainState for checkpoint structure\nlr_schedule = optax.warmup_cosine_decay_schedule(\n 0.0, 3e-4, 1000, 300000\n)\ntx = optax.adamw(learning_rate=lr_schedule, b1=0.9, b2=0.9, weight_decay=1e-4)\ndummy_train_state = TrainState.create(\n apply_fn=genie.apply,\n params=params, # or params if your model expects that\n tx=tx, # No optimizer needed for eval\n)\n\nhandler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\nhandler_registry.add('model_state', ocp.args.StandardSave, ocp.handlers.StandardCheckpointHandler)\nhandler_registry.add('model_state', ocp.args.StandardRestore, ocp.handlers.StandardCheckpointHandler)\n\n\n# Set up Orbax CheckpointManager\ncheckpoint_manager = ocp.CheckpointManager(\n args.checkpoint, # Directory containing the checkpoint\n options=ocp.CheckpointManagerOptions(max_to_keep=1, step_format_fixed_length=6),\n handler_registry=handler_registry\n)\n\n# Prepare abstract state for restore\nabstract_train_state = jax.tree_util.tree_map(\n ocp.utils.to_shape_dtype_struct, dummy_train_state\n)\n\n# Restore the checkpoint (only model_state needed for sampling)\nrestored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.StandardRestore(abstract_train_state),\n ),\n)\nrestored_train_state = restored[""model_state""]\n\n# Extract model parameters for inference\nparams = restored_train_state.params\n\n\n# params[""params""].update(ckpt)\n\n\ndef _sampling_wrapper(module, batch):\n return module.sample(batch, args.seq_len, args.maskgit_steps, args.temperature, args.sample_argmax)\n\n# --- Define autoregressive sampling loop ---\ndef _autoreg_sample(rng, video_batch, action_batch):\n vid = video_batch[:, : args.start_frame + 1]\n sampling_fn = jax.jit(nn.apply(_sampling_wrapper, genie)) \n rng, _rng = jax.random.split(rng)\n batch = dict(videos=vid, latent_actions=action_batch, rng=_rng)\n generated_vid = sampling_fn(\n params,\n batch\n )\n return generated_vid\n\n# # --- Get video + latent actions ---\narray_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n]\ngrain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n)\ninitial_state = grain_dataloader._create_initial_state()\ngrain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\njax.debug.breakpoint()\nvideo_batch = next(grain_iterator)\njax.debug.breakpoint()\n# video_batch = np.load(""overfit_dir/single_sample_corner.npy"")\n# Get latent actions for all videos in the batch\nbatch = dict(videos=video_batch)\naction_batch = genie.apply(params, batch, False, method=Genie.vq_encode)\naction_batch = action_batch.reshape(video_batch.shape[0], args.seq_len - 1, 1)\n\n# --- Sample + evaluate video ---\nvid = _autoreg_sample(rng, video_batch, action_batch)\ngt = video_batch[:, : vid.shape[1]].clip(0, 1).reshape(-1, *video_batch.shape[2:])\nrecon = vid.clip(0, 1).reshape(-1, *vid.shape[2:])\nssim = pix.ssim(gt[:, args.start_frame + 1 :], recon[:, args.start_frame + 1 :]).mean()\nprint(f""SSIM: {ssim}"")\n\n# --- Construct video ---\ntrue_videos = (video_batch * 255).astype(np.uint8)\npred_videos = (vid * 255).astype(np.uint8)\nvideo_comparison = np.zeros((2, *vid.shape), dtype=np.uint8)\nvideo_comparison[0] = true_videos[:, :args.seq_len]\nvideo_comparison[1] = pred_videos\nframes = einops.rearrange(video_comparison, ""n b t h w c -> t (b h) (n w) c"")\n\n# --- Save video --- \nimgs = [Image.fromarray(img) for img in frames]\n# Write actions on each frame, on each row (i.e., for each video in the batch, on the GT row)\nfor t, img in enumerate(imgs[1:]):\n d = ImageDraw.Draw(img)\n for row in range(action_batch.shape[0]):\n action = action_batch[row, t, 0]\n y_offset = row * video_batch.shape[2] + 2\n d.text((2, y_offset), f""{action}"", fill=255)\nimgs[0].save(\n f""generation_{time.time()}.gif"",\n save_all=True,\n append_images=imgs[1:],\n duration=250,\n loop=0,\n)\n",python,tab +53,94915,"TERMINAL",0,0,"\r(jafar) [tum_cte0515@hkn0402 jafar]$ \r(jafar) [tum_cte0515@hkn0402 jafar]$ ",,terminal_output +54,148636,"TERMINAL",0,0,"source .venv/bin/activate",,terminal_output +55,148874,"TERMINAL",0,0,"idling",,terminal_output +56,149041,"TERMINAL",0,0,"python sample.py --checkpoint /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/ --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +57,160599,"TERMINAL",0,0,"bash",,terminal_focus +58,164468,"TERMINAL",0,0,"tmux a",,terminal_command +59,164479,"TERMINAL",0,0,"]633;E;2025-07-14 15:31:46 tmux a;450fbccd-48d1-432b-b44b-06394616d158]633;Cno sessions\r\n]0;tum_cte0515@hkn1990:~/Projects/jafar]633;D;1",,terminal_output +60,176523,"TERMINAL",0,0,"srun",,terminal_focus +61,181253,"TERMINAL",0,0,"idling\r\n\r\r\n\r\r\n\r",,terminal_output +62,182032,"TERMINAL",0,0,"source .venv/bin/activate",,terminal_output +63,182304,"TERMINAL",0,0,"",,terminal_output +64,182514,"TERMINAL",0,0,"",,terminal_output +65,183774,"TERMINAL",0,0,"\r(reverse-i-search)`': ",,terminal_output +66,184082,"TERMINAL",0,0,"m': python sample.py --checkpoint /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/ --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +67,184283,"TERMINAL",0,0,"\ro': python sample.py --checkpoint /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/ --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +68,184445,"TERMINAL",0,0,"\rd': python sample.py --checkpoint /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/ --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +69,184620,"TERMINAL",0,0,"[?25lm\ru': module unload devel/cuda/12.4\r\n\r\r\n\r\r\n\r[?25h",,terminal_output +70,184813,"TERMINAL",0,0,"[1@l': modul",,terminal_output +71,184920,"TERMINAL",0,0,"[?25lm[1@e': module[?25h",,terminal_output +72,186275,"TERMINAL",0,0,"[?25l\r[9@jafar) [tum_cte0515@hkn0402 jafar]$ module\r\n[?2004l\r[?25h",,terminal_output +73,186368,"TERMINAL",0,0,"Lmod Warning: \r\n----------------------------------------------------------------------------------------------------\r\nThe following dependent module(s) are not currently loaded: devel/cuda/12.4 (required by:\r\nmpi/openmpi/5.0)\r\n----------------------------------------------------------------------------------------------------\r\n\r\n\r\n\r\n]0;tum_cte0515@hkn0402:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0402 jafar]$ ",,terminal_output +74,187458,"TERMINAL",0,0,"m",,terminal_output +75,188318,"TERMINAL",0,0,"\r(reverse-i-search)`': ",,terminal_output +76,188537,"TERMINAL",0,0,"[?25lm': module unload devel/cuda/12.4\r[?25h",,terminal_output +77,188712,"TERMINAL",0,0,"[?25lm[1@o': mo[?25h",,terminal_output +78,188827,"TERMINAL",0,0,"[?25lm[1@d': mod[?25h",,terminal_output +79,188885,"TERMINAL",0,0,"[?25lme': python sample.py --checkpoint /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/ --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked[?25h",,terminal_output +80,189124,"TERMINAL",0,0,"\rfailed reverse-i-search)`modeu': python sample.py --checkpoint /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsizescaling_36M_2_node/ --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +81,189331,"TERMINAL",0,0,"[?25lm\rl': python sample.py --checkpoint /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/ --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked[?25h",,terminal_output +82,190338,"TERMINAL",0,0,"^C\r\n\r\n\r[?2004l\r[?2004h[?2004l\r\r\n]0;tum_cte0515@hkn0402:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0402 jafar]$ ",,terminal_output +83,190778,"TERMINAL",0,0,"\r(reverse-i-search)`': ",,terminal_output +84,190999,"TERMINAL",0,0,"m': module unload devel/cuda/12.4\r",,terminal_output +85,191308,"TERMINAL",0,0,"[?25lm[1@o': mo[?25h",,terminal_output +86,192625,"TERMINAL",0,0,"python sample.py --checkpoint /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/ --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +87,193357,"TERMINAL",0,0,"modelsize-scaling/train_dynamics_modelsize_scaling_3",,terminal_output +88,193987,"TERMINAL",0,0,"modelsize-scaling/train_dynamics_modelsize_scaling_3087000 --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +89,194447,"TERMINAL",0,0,"modelsize-scaling/train_dynamics_modelsize_scaling_3",,terminal_output +90,194807,"TERMINAL",0,0,"\rmodule unload devel/cuda/12.4\r\n\r\r\n\r\r\n\r",,terminal_output +91,195611,"TERMINAL",0,0,"mpi/openmpi/5.0\r",,terminal_output +92,196151,"TERMINAL",0,0,"[?25l\r[13@jafar) [tum_cte0515@hkn0402 jafar]$ mo\r\n[?2004l\r[?25h",,terminal_output +93,196276,"TERMINAL",0,0,"]0;tum_cte0515@hkn0402:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0402 jafar]$ ",,terminal_output +94,197041,"TERMINAL",0,0,"module unload mpi/openmpi/5.0",,terminal_output +95,197244,"TERMINAL",0,0,"devel/cuda/12.4",,terminal_output +96,197809,"TERMINAL",0,0,"[?25l[?2004l\r[?25h\r\nNote: the module ""devel/cuda/12.4"" cannot be unloaded because it was not loaded.\r\n\r\n]0;tum_cte0515@hkn0402:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0402 jafar]$ ",,terminal_output +97,202765,"TERMINAL",0,0,"module unload devel/cuda/12.4",,terminal_output +98,202990,"TERMINAL",0,0,"mpi/openmpi/5.0",,terminal_output +99,203154,"TERMINAL",0,0,"devel/cuda/12.4",,terminal_output +100,203381,"TERMINAL",0,0,"source .venv/bin/activate",,terminal_output +101,203782,"TERMINAL",0,0,"idling",,terminal_output +102,204328,"TERMINAL",0,0,"python sample.py --checkpoint /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/ --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +103,205187,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +104,206994,"sample.py",0,0,"",python,tab +105,206997,"sample.py",5254,0,"",python,selection_mouse +106,207025,"sample.py",5253,0,"",python,selection_command +107,218430,"TERMINAL",0,0,"2025-07-14 15:32:40.851942: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +108,231780,"TERMINAL",0,0,"2025-07-14 15:32:54.216693: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +109,248304,"TERMINAL",0,0,"2025-07-14 15:33:10.775327: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +110,252736,"TERMINAL",0,0,"WARNING:absl:Missing metrics for step 91000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/091000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 89000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/089000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 60000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/060000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 40000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/040000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 90000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/090000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 80000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_36M_2_node/080000/metrics/metrics not found.\r\n",,terminal_output +111,273123,"sample.py",5019,0,"",python,selection_mouse +112,273126,"sample.py",5018,0,"",python,selection_command +113,273749,"sample.py",5254,0,"",python,selection_mouse +114,273751,"sample.py",5253,0,"",python,selection_command +115,277876,"TERMINAL",0,0,"bash",,terminal_focus +116,283470,"TERMINAL",0,0,"cd $ws_dir",,terminal_command +117,284396,"TERMINAL",0,0,"cd logs/",,terminal_command +118,284816,"TERMINAL",0,0,"ls",,terminal_command +119,284828,"TERMINAL",0,0,"]633;E;2025-07-14 15:33:47 ls;450fbccd-48d1-432b-b44b-06394616d158]633;C3306965 logs_alfred logs_franz logs_mihir\r\n]0;tum_cte0515@hkn1990:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs]633;D;0",,terminal_output +120,287852,"TERMINAL",0,0,"Entering jdb:\r\n(jdb) ",,terminal_output +121,287863,"TERMINAL",0,0,"cd logs_mihir/",,terminal_command +122,287879,"TERMINAL",0,0,"]633;E;2025-07-14 15:33:50 cd logs_mihir/;450fbccd-48d1-432b-b44b-06394616d158]633;C]0;tum_cte0515@hkn1990:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output +123,288184,"TERMINAL",0,0,"ls",,terminal_command +124,288235,"TERMINAL",0,0,"]633;E;2025-07-14 15:33:50 ls;450fbccd-48d1-432b-b44b-06394616d158]633;C",,terminal_output +125,288365,"TERMINAL",0,0,"big_run train_lam_action_space_scaling_20_3329788.log train_lam_model_size_scaling_38M_3317098.log train_tokenizer_model_size_scaling_140M_3316019.log\r\nbig-runs train_lam_action_space_scaling_20_3329803.log train_lam_model_size_scaling_38M_3317115.log train_tokenizer_model_size_scaling_200M_3313563.log\r\ntrain_dyn_yolorun_3333026.log train_lam_action_space_scaling_20_3331285.log train_lam_model_size_scaling_38M_3317231.log train_tokenizer_model_size_scaling_200M_3316020.log\r\ntrain_dyn_yolorun_3333448.log train_lam_action_space_scaling_50_3320180.log train_tokenizer_batch_size_scaling_16_node_3321526.log train_tokenizer_model_size_scaling_227M_3317234.log\r\ntrain_dyn_yolorun_3335345.log train_lam_action_space_scaling_50_3329789.log train_tokenizer_batch_size_scaling_1_node_3318551.log train_tokenizer_model_size_scaling_227M_3318555.log\r\ntrain_dyn_yolorun_3335362.log train_lam_action_space_scaling_50_3329804.log train_tokenizer_batch_size_scaling_2_node_3318552.log train_tokenizer_model_size_scaling_227M_3320173.log\r\ntrain_lam_action_space_scaling_10_3320179.log train_lam_action_space_scaling_50_3331286.log train_tokenizer_batch_size_scaling_2_node_3330806.log train_tokenizer_model_size_scaling_227M_3321523.log\r\ntrain_lam_action_space_scaling_10_3321529.log train_lam_action_space_scaling_6_3318549.log train_tokenizer_batch_size_scaling_2_node_3330848.log train_tokenizer_model_size_scaling_37M_3313565.log\r\ntrain_lam_action_space_scaling_10_3329786.log train_lam_action_space_scaling_6_3320178.log train_tokenizer_batch_size_scaling_2_node_3331282.log train_tokenizer_model_size_scaling_37M_3316022.log\r\ntrain_lam_action_space_scaling_10_3329801.log train_lam_action_space_scaling_6_3321528.log train_tokenizer_batch_size_scaling_4_node_3318553.log train_tokenizer_model_size_scaling_37M_3317232.log\r\ntrain_lam_action_space_scaling_10_3331283.log train_lam_action_space_scaling_6_3329790.log train_tokenizer_batch_size_scaling_4_node_3320175.log train_tokenizer_model_size_scaling_37M_3317239.log\r\ntrain_lam_action_space_scaling_12_3318546.log train_lam_action_space_scaling_6_3329805.log train_tokenizer_batch_size_scaling_4_node_3321524.log train_tokenizer_model_size_scaling_37M_3318556.log\r\ntrain_lam_action_space_scaling_12_3320177.log train_lam_action_space_scaling_6_3331287.log train_tokenizer_batch_size_scaling_8_node_3320176.log train_tokenizer_model_size_scaling_74M_3318557.log\r\ntrain_lam_action_space_scaling_12_3321527.log train_lam_action_space_scaling_8_3318550.log train_tokenizer_batch_size_scaling_8_node_3321525.log train_tokenizer_model_size_scaling_74M_3320174.log\r\ntrain_lam_action_space_scaling_12_3329787.log train_lam_action_space_scaling_8_3329791.log train_tokenizer_minecraft_overfit_sample_3309656.log train_tokenizer_model_size_scaling_74M_3321522.log\r\ntrain_lam_action_space_scaling_12_3329802.log train_lam_action_space_scaling_8_3329806.log train_tokenizer_model_size_scaling_127M_3317233.log train_tokenizer_model_size_scaling_80M_3313564.log\r\ntrain_lam_action_space_scaling_12_3331284.log train_lam_action_space_scaling_8_3331288.log train_tokenizer_model_size_scaling_127M_3318554.log train_tokenizer_model_size_scaling_80M_3316026.log\r\ntrain_lam_action_space_scaling_20_3318547.log train_lam_minecraft_overfit_sample_3309655.log train_tokenizer_model_size_scaling_140M_3313562.log\r\n]0;tum_cte0515@hkn1990:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output +126,291553,"TERMINAL",0,0,"cd big-runs/",,terminal_command +127,292418,"TERMINAL",0,0,"ls",,terminal_command diff --git a/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-619817ef-f0fa-4ff3-8f61-fe4646f7e2971752667688154-2025_07_16-14.09.00.461/source.csv b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-619817ef-f0fa-4ff3-8f61-fe4646f7e2971752667688154-2025_07_16-14.09.00.461/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..4dc8c4b5a63aa986b76dd3e7edb6716c0072fe53 --- /dev/null +++ b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-619817ef-f0fa-4ff3-8f61-fe4646f7e2971752667688154-2025_07_16-14.09.00.461/source.csv @@ -0,0 +1,2382 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +2,93,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"",Log,tab +3,329,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"2:09:00 PM [info] Activating crowd-code\n2:09:00 PM [info] Recording started\n2:09:00 PM [info] Initializing git provider using file system watchers...\n2:09:00 PM [info] Git repository found\n2:09:00 PM [info] Git provider initialized successfully\n",Log,content +4,380,"extension-output-pdoom-org.crowd-code-#1-crowd-code",245,0,"2:09:00 PM [info] Initial git state: [object Object]\n",Log,content +5,2739,"TERMINAL",0,0,"",,terminal_focus +6,3206,"TERMINAL",0,0,"/bin/python3 /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/printEnvVariablesToFile.py /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash/envVars.txt",,terminal_command +7,3235,"TERMINAL",0,0,"]633;E;2025-07-16 14:09:03 /bin/python3 /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/printEnvVariablesToFile.py /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash/envVars.txt;f275eb64-c8e0-41e9-a028-3428eb3f91db]633;C]0;tum_cte0515@hkn1990:/hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash]633;D;0",,terminal_output +8,6981,"TERMINAL",0,0,"bash",,terminal_focus +9,9682,"TERMINAL",0,0,"queue",,terminal_command +10,9732,"TERMINAL",0,0,"]633;E;2025-07-16 14:09:10 queue;97c203bb-2de3-4bf0-b19e-fa122ab0b933]633;C",,terminal_output +11,9800,"TERMINAL",0,0,"[?1049h(B[?7hEvery 1.0s: squeue --mehkn1990.localdomain: Wed Jul 16 14:09:10 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3348592 accelerat train_dy tum_cte0 R 14:08:48\t 2 hkn[0416,0421]3348397 accelerat train_dy tum_cte0 R 19:30:30\t 2 hkn[0810,0815]3348399 accelerat train_dy tum_cte0 R 19:30:30\t 2 hkn[0601,0603]3348400 accelerat train_dy tum_cte0 R 19:30:30\t 2 hkn[0604,0608]3350245 accelerat interact tum_cte0 R17:42\t 2 hkn[0423-0424]3345116 accelerat train_dy tum_cte0 R 1-20:14:15\t 2 hkn[0503,0506]",,terminal_output +12,10526,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1990:~/Projects/jafar]633;D;0",,terminal_output +13,11309,"TERMINAL",0,0,"idling",,terminal_command +14,11376,"TERMINAL",0,0,"]633;E;2025-07-16 14:09:11 idling;97c203bb-2de3-4bf0-b19e-fa122ab0b933]633;C[?1049h(B[?7hEvery 1.0s: sinfo_t_idlehkn1990.localdomain: Wed Jul 16 14:09:11 2025Partition dev_cpuonly:\t 9 nodes idle\rPartition cpuonly:\t 4 nodes idle\rPartition dev_accelerated:\t 1 nodes idle\rPartition accelerated: 10 nodes idle\rPartition dev_accelerated-h100 :\t 0 nodes idle\rPartition accelerated-h100:\t 0 nodes idle\rPartition large:\t 7 nodes idle",,terminal_output +15,12159,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1990:~/Projects/jafar]633;D;0",,terminal_output 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found...\r\n",,terminal_output +24,20011,"TERMINAL",0,0,"^C\r\n]0;tum_cte0515@hkn1990:~/Projects/jafar]633;D;130",,terminal_output +25,21463,"TERMINAL",0,0,"queue",,terminal_command +26,21514,"TERMINAL",0,0,"]633;E;2025-07-16 14:09:21 queue;dea9d5fc-91fd-447c-886d-4b0240ae057d]633;C",,terminal_output +27,21579,"TERMINAL",0,0,"[?1049h(B[?7hEvery 1.0s: squeue --mehkn1990.localdomain: Wed Jul 16 14:09:21 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3348592 accelerat train_dy tum_cte0 R 14:08:59\t 2 hkn[0416,0421]3348397 accelerat train_dy tum_cte0 R 19:30:41\t 2 hkn[0810,0815]3348399 accelerat train_dy tum_cte0 R 19:30:41\t 2 hkn[0601,0603]3348400 accelerat train_dy tum_cte0 R 19:30:41\t 2 hkn[0604,0608]3350302 accelerat interact tum_cte0 R\t0:04\t 2 hkn[0509,0511]3350245 accelerat interact tum_cte0 R17:53\t 2 hkn[0423-0424]3345116 accelerat train_dy tum_cte0 R 1-20:14:26\t 2 hkn[0503,0506]",,terminal_output +28,22616,"TERMINAL",0,0,"29:01333658",,terminal_output 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.venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=""debug""\nslurm_job_id=""debug-mihir""\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/train_tokenizer_lr_sweep_1e-4\n\nenv | grep SLURM\n\nXLA_FLAGS=--xla_gpu_autotune_level=0 srun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --grad_clip_threshold=10 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-debug-run-$slurm_job_id \\n --tags dynamics debug \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n ",shellscript,tab +35,34735,"utils/nn.py",0,0,"import math\nfrom typing import Dict, Tuple\n\nfrom flax import linen as nn\nimport jax\nimport jax.numpy as jnp\n\n\nclass PositionalEncoding(nn.Module):\n """"""https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/JAX/tutorial6/Transformers_and_MHAttention.html""""""\n\n d_model: int # Hidden dimensionality of the input.\n max_len: int = 5000 # Maximum length of a sequence to expect.\n\n def setup(self):\n # Create matrix of [SeqLen, HiddenDim] representing the positional encoding for max_len inputs\n self.pe = jnp.zeros((self.max_len, self.d_model))\n position = jnp.arange(0, self.max_len, dtype=jnp.float32)[:, None]\n div_term = jnp.exp(\n jnp.arange(0, self.d_model, 2) * (-math.log(10000.0) / self.d_model)\n )\n self.pe = self.pe.at[:, 0::2].set(jnp.sin(position * div_term))\n self.pe = self.pe.at[:, 1::2].set(jnp.cos(position * div_term))\n\n def __call__(self, x):\n x = x + self.pe[: x.shape[2]]\n return x\n\nclass CausalTransformerBlock(nn.Module):\n model_dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x\n\nclass CausalTransformer(nn.Module):\n model_dim: int\n out_dim: int\n num_blocks: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # Input projection and normalization\n x = nn.Sequential(\n [\n nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n ),\n nn.Dense(self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n ),\n nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n ),\n ]\n )(x)\n # Causal transformer blocks\n for _ in range(self.num_blocks):\n x = CausalTransformerBlock(\n model_dim=self.model_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # Output projection\n x = nn.Dense(\n self.out_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n return x # (B, T, E)\n\n\nclass STBlock(nn.Module):\n dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x\n\n\nclass STTransformer(nn.Module):\n model_dim: int\n out_dim: int\n num_blocks: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n x = nn.Sequential(\n [\n nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n ),\n nn.Dense(self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n ),\n nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n ),\n ]\n )(x)\n for _ in range(self.num_blocks):\n x = STBlock(\n dim=self.model_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n x = nn.Dense(\n self.out_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n return x # (B, T, E)\n\n\ndef normalize(x):\n return x / (jnp.linalg.norm(x, ord=2, axis=-1, keepdims=True) + 1e-8)\n\n\nclass VectorQuantizer(nn.Module):\n latent_dim: int\n num_latents: int\n dropout: float\n\n def setup(self):\n self.codebook = normalize(\n self.param(\n ""codebook"",\n nn.initializers.lecun_uniform(),\n (self.num_latents, self.latent_dim),\n )\n )\n self.drop = nn.Dropout(self.dropout, deterministic=False)\n\n def __call__(\n self, x: jax.Array, training: bool\n ) -> Tuple[jax.Array, jax.Array, jax.Array, jax.Array]:\n # --- Compute distances ---\n x = normalize(x)\n codebook = normalize(self.codebook)\n distance = -jnp.matmul(x, codebook.T)\n if training:\n dropout_key = self.make_rng(""dropout"")\n distance = self.drop(distance, rng=dropout_key)\n\n # --- Get indices and embeddings ---\n indices = jnp.argmin(distance, axis=-1)\n z = self.codebook[indices]\n\n # --- Straight through estimator ---\n z_q = x + jax.lax.stop_gradient(z - x)\n return z_q, z, x, indices\n\n def get_codes(self, indices: jax.Array):\n return self.codebook[indices]\n",python,tab +36,44575,"TERMINAL",0,0,"salloc: Nodes hkn[0509,0511] are ready for job\r\n",,terminal_output +37,45793,"TERMINAL",0,0,"]0;tum_cte0515@hkn0509:~/Projects/jafar[?2004h[tum_cte0515@hkn0509 jafar]$ ",,terminal_output +38,61665,"utils/nn.py",1286,0,"",python,selection_mouse +39,61679,"utils/nn.py",1285,0,"",python,selection_command +40,64584,"TERMINAL",0,0,"s",,terminal_output +41,64687,"TERMINAL",0,0,"[?25lo[?25h",,terminal_output +42,64795,"TERMINAL",0,0,"[?25lu[?25h",,terminal_output +43,64980,"TERMINAL",0,0,"[?25lr[?25h",,terminal_output +44,65173,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +45,65291,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +46,65353,"TERMINAL",0,0,"[?25l [?25h",,terminal_output +47,65416,"TERMINAL",0,0,"[?25l.[?25h",,terminal_output +48,65504,"TERMINAL",0,0,"[?25lv[?25h",,terminal_output +49,65713,"TERMINAL",0,0,"env/",,terminal_output +50,65938,"TERMINAL",0,0,"[?25lb[?25h",,terminal_output +51,66001,"TERMINAL",0,0,"in/",,terminal_output +52,66262,"TERMINAL",0,0,"[?25la[?25h[?25lc[?25h",,terminal_output +53,66474,"TERMINAL",0,0,"tivate",,terminal_output +54,66740,"TERMINAL",0,0,"[?25l[?2004l\r[?25h]0;tum_cte0515@hkn0509:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0509 jafar]$ ",,terminal_output +55,68208,"genie.py",0,0,"from typing import Dict, Any\n\nimport optax\nimport jax\nimport jax.numpy as jnp\nimport flax.linen as nn\nfrom flax.training.train_state import TrainState\nimport orbax.checkpoint as ocp\n\nfrom models.dynamics import DynamicsMaskGIT\nfrom models.lam import LatentActionModel\nfrom models.tokenizer import TokenizerVQVAE\n\nimport os\nimport grain\n\n\nclass Genie(nn.Module):\n """"""Genie model""""""\n\n # --- Tokenizer ---\n in_dim: int\n tokenizer_dim: int\n latent_patch_dim: int\n num_patch_latents: int\n patch_size: int\n tokenizer_num_blocks: int\n tokenizer_num_heads: int\n # --- LAM ---\n lam_dim: int\n latent_action_dim: int\n num_latent_actions: int\n lam_patch_size: int\n lam_num_blocks: int\n lam_num_heads: int\n lam_co_train: bool\n # --- Dynamics ---\n dyna_dim: int\n dyna_num_blocks: int\n dyna_num_heads: int\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n dropout: float = 0.0\n mask_limit: float = 0.0\n\n def setup(self):\n self.tokenizer = TokenizerVQVAE(\n in_dim=self.in_dim,\n model_dim=self.tokenizer_dim,\n latent_dim=self.latent_patch_dim,\n num_latents=self.num_patch_latents,\n patch_size=self.patch_size,\n num_blocks=self.tokenizer_num_blocks,\n num_heads=self.tokenizer_num_heads,\n dropout=0.0,\n codebook_dropout=0.0,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )\n self.lam = LatentActionModel(\n in_dim=self.in_dim,\n model_dim=self.lam_dim,\n latent_dim=self.latent_patch_dim,\n num_latents=self.num_latent_actions,\n patch_size=self.lam_patch_size,\n num_blocks=self.lam_num_blocks,\n num_heads=self.lam_num_heads,\n dropout=0.0,\n codebook_dropout=0.0,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )\n self.dynamics = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n num_latents=self.num_patch_latents,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n mask_limit=self.mask_limit,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )\n\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\n tokenizer_outputs = self.tokenizer.vq_encode(batch[""videos""], training=False)\n lam_outputs = self.lam.vq_encode(batch[""videos""], training=False)\n latent_actions = jax.lax.cond(\n self.lam_co_train,\n lambda: lam_outputs[""z_q""],\n lambda: jax.lax.stop_gradient(lam_outputs[""z_q""])\n )\n outputs = dict(\n video_tokens=jax.lax.stop_gradient(tokenizer_outputs[""indices""]),\n latent_actions=latent_actions,\n )\n outputs[""mask_rng""] = batch[""mask_rng""]\n dyna_outputs = self.dynamics(outputs, training)\n outputs.update(dyna_outputs)\n mle_indices = jnp.argmax(outputs[""token_logits""], axis=-1)\n outputs[""recon""] = self.tokenizer.decode(\n mle_indices, batch[""videos""].shape[2:4]\n )\n outputs[""lam_indices""] = lam_outputs[""indices""]\n return outputs\n\n @nn.compact\n def sample(\n self,\n batch: Dict[str, Any],\n seq_len: int,\n steps: int = 25,\n temperature: float = 1,\n sample_argmax: bool = False,\n ) -> Any:\n """"""\n Autoregressively samples up to `seq_len` future frames, following Figure 8 of the paper.\n\n - Input frames are tokenized once.\n - Future frames are generated autoregressively in token space.\n - All frames are detokenized in a single pass.\n\n Note:\n - For interactive or step-wise sampling, detokenization should occur after each action.\n - To maintain consistent tensor shapes across timesteps, all current and future frames are decoded at every step.\n - Temporal causal structure is preserved by \n a) reapplying the mask before each decoding step.\n b) a temporal causal mask is applied within each ST-transformer block.\n\n Dimension keys:\n B: batch size \n T: number of input (conditioning) frames \n N: patches per frame \n S: sequence length \n A: action space \n D: model latent dimension\n """"""\n # --- Encode videos and actions ---\n tokenizer_out = self.tokenizer.vq_encode(batch[""videos""], training=False)\n token_idxs = tokenizer_out[""indices""] # (B, T, N)\n B, T, N = token_idxs.shape\n pad_shape = (B, seq_len - T, N)\n pad = jnp.zeros(pad_shape, dtype=token_idxs.dtype)\n token_idxs = jnp.concatenate([token_idxs, pad], axis=1) # (B, S, N)\n action_tokens = self.lam.vq.get_codes(batch[""latent_actions""])\n\n MaskGITLoop = nn.scan(\n MaskGITStep,\n variable_broadcast=""params"",\n split_rngs={""params"": False},\n in_axes=0,\n out_axes=0,\n length=steps,\n )\n \n loop_fn = MaskGITLoop(\n dynamics=self.dynamics,\n tokenizer=self.tokenizer,\n temperature=temperature,\n sample_argmax=sample_argmax,\n steps=steps,\n )\n\n def generation_step_fn(carry, step_t):\n rng, current_token_idxs = carry\n rng, step_rng = jax.random.split(rng)\n\n # Mask current and future frames (i.e., t >= step_t)\n mask = jnp.arange(seq_len) >= step_t # (S,)\n mask = jnp.broadcast_to(mask[None, :, None], (B, seq_len, N)) # (B, S, N)\n mask = mask.astype(bool)\n masked_token_idxs = current_token_idxs * ~mask\n\n # --- Initialize and run MaskGIT loop ---\n init_carry_maskgit = (\n step_rng,\n masked_token_idxs,\n mask,\n action_tokens,\n )\n final_carry_maskgit, _ = loop_fn(init_carry_maskgit, jnp.arange(steps))\n updated_token_idxs = final_carry_maskgit[1]\n new_carry = (rng, updated_token_idxs)\n return new_carry, None\n\n # --- Run the autoregressive generation using scan ---\n initial_carry = (batch[""rng""], token_idxs)\n timesteps_to_scan = jnp.arange(T, seq_len)\n final_carry, _ = jax.lax.scan(\n generation_step_fn,\n initial_carry,\n timesteps_to_scan\n )\n final_token_idxs = final_carry[1]\n\n # --- Decode all tokens at once at the end ---\n final_frames = self.tokenizer.decode(\n final_token_idxs,\n video_hw=batch[""videos""].shape[2:4],\n )\n return final_frames\n\n def vq_encode(self, batch, training) -> Dict[str, Any]:\n # --- Preprocess videos ---\n lam_output = self.lam.vq_encode(batch[""videos""], training=training)\n return lam_output[""indices""]\n\n\nclass MaskGITStep(nn.Module):\n dynamics: nn.Module\n tokenizer: nn.Module\n temperature: float\n sample_argmax: bool\n steps: int\n\n @nn.compact\n def __call__(self, carry, x):\n rng, token_idxs, mask, action_tokens = carry\n step = x\n N = token_idxs.shape[2]\n\n # --- Construct + encode video ---\n vid_embed = self.dynamics.patch_embed(token_idxs) # (B, S, N, D)\n mask_token = self.dynamics.mask_token # (1, 1, 1, D,)\n mask_expanded = mask[..., None] # (B, S, N, 1) \n vid_embed = jnp.where(mask_expanded, mask_token, vid_embed)\n\n # --- Predict transition ---\n act_embed = self.dynamics.action_up(action_tokens)\n vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\n unmasked_ratio = jnp.cos(jnp.pi * (step + 1) / (self.steps * 2))\n step_temp = self.temperature * (1.0 - unmasked_ratio)\n final_logits = self.dynamics.dynamics(vid_embed) / step_temp\n\n # --- Sample new tokens for final frame ---\n if self.sample_argmax:\n sampled_token_idxs = jnp.argmax(final_logits, axis=-1)\n else:\n rng, _rng = jax.random.split(rng)\n sampled_token_idxs = jax.random.categorical(_rng, final_logits)\n gather_fn = jax.vmap(jax.vmap(jax.vmap(lambda x, y: x[y])))\n final_token_probs = gather_fn(jax.nn.softmax(final_logits), sampled_token_idxs)\n final_token_probs += ~mask\n # Update masked tokens only\n token_idxs = jnp.where(mask, sampled_token_idxs, token_idxs)\n\n # --- Update mask ---\n num_unmasked_tokens = jnp.round(N * (1.0 - unmasked_ratio)).astype(int)\n idx_mask = jnp.arange(final_token_probs.shape[-1]) > num_unmasked_tokens\n sorted_idxs = jnp.argsort(final_token_probs, axis=-1, descending=True)\n mask_update_fn = jax.vmap(lambda msk, ids: msk.at[ids].set(idx_mask))\n new_mask = mask_update_fn(mask, sorted_idxs)\n\n new_carry = (rng, token_idxs, new_mask, action_tokens)\n return new_carry, None\n\ndef restore_genie_components(\n train_state: TrainState,\n sharding: jax.sharding.NamedSharding,\n grain_iterator: grain.DataLoaderIterator,\n inputs: Dict[str, jax.Array],\n rng: jax.Array,\n args,\n):\n """"""Restore pre-trained Genie components""""""\n rng, _rng = jax.random.split(rng)\n\n # dummy values since we only use tx to initialize the dummy train states\n dummy_tx = optax.adamw(\n learning_rate=optax.constant_schedule(args.max_lr),\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n )\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add('model_state', ocp.args.StandardRestore, ocp.handlers.StandardCheckpointHandler)\n handler_registry.add('dataloader_state', grain.checkpoint.CheckpointRestore, grain.checkpoint.CheckpointHandler)\n \n\n checkpoint_options = ocp.CheckpointManagerOptions(\n step_format_fixed_length=6,\n )\n tokenizer_checkpoint_manager = ocp.CheckpointManager(\n directory=args.tokenizer_checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n dummy_tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.tokenizer_dim,\n latent_dim=args.latent_patch_dim,\n num_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n num_blocks=args.tokenizer_num_blocks,\n num_heads=args.tokenizer_num_heads,\n dropout=args.dropout,\n codebook_dropout=args.dropout,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n )\n tokenizer_init_params = dummy_tokenizer.init(_rng, inputs)\n dummy_tokenizer_train_state = TrainState.create(\n apply_fn=dummy_tokenizer.apply, params=tokenizer_init_params, tx=dummy_tx\n )\n abstract_sharded_tokenizer_state = _create_abstract_sharded_pytree(\n dummy_tokenizer_train_state, sharding\n )\n restored_tokenizer = tokenizer_checkpoint_manager.restore(\n step=tokenizer_checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.StandardRestore(abstract_sharded_tokenizer_state),\n dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator),\n ),\n )[""model_state""]\n restored_tokenizer_params = restored_tokenizer.params[""params""]\n train_state.params[""params""][""tokenizer""].update(restored_tokenizer_params)\n tokenizer_checkpoint_manager.close()\n\n if args.lam_checkpoint:\n lam_checkpoint_manager = ocp.CheckpointManager(\n directory=args.lam_checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n dummy_lam = LatentActionModel(\n in_dim=args.image_channels,\n model_dim=args.lam_dim,\n latent_dim=args.latent_patch_dim,\n num_latents=args.num_latent_actions,\n patch_size=args.lam_patch_size,\n num_blocks=args.lam_num_blocks,\n num_heads=args.lam_num_heads,\n dropout=args.dropout,\n codebook_dropout=args.dropout,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n )\n lam_init_params = dummy_lam.init(_rng, inputs)\n dummy_lam_train_state = TrainState.create(\n apply_fn=dummy_lam.apply, params=lam_init_params, tx=dummy_tx\n )\n abstract_sharded_lam_state = _create_abstract_sharded_pytree(\n dummy_lam_train_state, sharding\n )\n restored_lam = lam_checkpoint_manager.restore(\n step=lam_checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.StandardRestore(abstract_sharded_lam_state),\n dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator),\n ),\n )[""model_state""]\n restored_lam_params = restored_lam.params[""params""]\n # Genie does not initialize all LAM modules, thus we omit those extra modules during restoration\n # (f.srambical) FIXME: Currently, this is a small HBM memory crunch since the LAM's decoder is loaded into HBM and immediately dicarded.\n # A workaround would be to restore to host memory first, and only move the weights to HBM after pruning the decoder\n restored_lam_params = {\n k: v\n for k, v in restored_lam_params.items()\n if k in train_state.params[""params""][""lam""]\n }\n train_state.params[""params""][""lam""].update(restored_lam_params)\n lam_checkpoint_manager.close()\n\n return train_state\n\ndef _create_abstract_sharded_pytree(pytree_template, sharding_spec):\n """"""Replaces arrays in a pytree with ShapeDtypeStructs having the given sharding.""""""\n\n def map_fn(leaf_template):\n if hasattr(leaf_template, ""shape"") and hasattr(leaf_template, ""dtype""):\n return jax.ShapeDtypeStruct(\n leaf_template.shape, leaf_template.dtype, sharding=sharding_spec\n )\n return leaf_template\n\n return jax.tree_util.tree_map(map_fn, pytree_template)",python,tab +56,71445,"genie.py",1936,0,"",python,selection_mouse +57,71450,"genie.py",1935,0,"",python,selection_command +58,72209,"genie.py",1962,0,"",python,selection_mouse +59,72748,"genie.py",1952,0,"",python,selection_mouse +60,73513,"genie.py",1937,40," self.dynamics = DynamicsMaskGIT(",python,selection_command +61,73697,"genie.py",1937,77," self.dynamics = DynamicsMaskGIT(\n model_dim=self.dyna_dim,",python,selection_command +62,73850,"genie.py",1937,125," self.dynamics = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n num_latents=self.num_patch_latents,",python,selection_command +63,73999,"genie.py",1937,170," self.dynamics = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n num_latents=self.num_patch_latents,\n num_blocks=self.dyna_num_blocks,",python,selection_command +64,74154,"genie.py",1937,213," self.dynamics = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n num_latents=self.num_patch_latents,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,",python,selection_command +65,74333,"genie.py",1937,247," self.dynamics = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n num_latents=self.num_patch_latents,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,",python,selection_command +66,74493,"genie.py",1937,287," self.dynamics = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n num_latents=self.num_patch_latents,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n mask_limit=self.mask_limit,",python,selection_command +67,74650,"genie.py",1937,329," self.dynamics = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n num_latents=self.num_patch_latents,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n mask_limit=self.mask_limit,\n param_dtype=self.param_dtype,",python,selection_command +68,74782,"genie.py",1937,359," self.dynamics = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n num_latents=self.num_patch_latents,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n mask_limit=self.mask_limit,\n param_dtype=self.param_dtype,\n dtype=self.dtype,",python,selection_command +69,74929,"genie.py",1937,369," self.dynamics = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n num_latents=self.num_patch_latents,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n mask_limit=self.mask_limit,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )",python,selection_command +70,75176,"genie.py",1945,0,"",python,selection_command +71,76155,"genie.py",2305,0,"#",python,content +72,76156,"genie.py",2279,0,"#",python,content +73,76156,"genie.py",2237,0,"#",python,content +74,76156,"genie.py",2197,0,"#",python,content +75,76156,"genie.py",2163,0,"#",python,content +76,76156,"genie.py",2120,0,"#",python,content +77,76156,"genie.py",2075,0,"#",python,content +78,76156,"genie.py",2027,0,"#",python,content +79,76156,"genie.py",1990,0,"#",python,content +80,76157,"genie.py",1945,0,"#",python,content +81,76160,"genie.py",1946,0,"",python,selection_keyboard +82,76331,"genie.py",2315,0," ",python,content +83,76332,"genie.py",2288,0," ",python,content +84,76332,"genie.py",2245,0," ",python,content +85,76332,"genie.py",2204,0," ",python,content +86,76332,"genie.py",2169,0," ",python,content 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",python,content +125,81964,"genie.py",1959,0,"",python,selection_keyboard +126,82183,"genie.py",1959,0,"=",python,content +127,82184,"genie.py",1960,0,"",python,selection_keyboard +128,82283,"genie.py",1960,0," ",python,content +129,82284,"genie.py",1961,0,"",python,selection_keyboard +130,85672,"genie.py",1961,0,"N",python,content +131,85674,"genie.py",1962,0,"",python,selection_keyboard +132,85874,"genie.py",1962,0,"o",python,content +133,85876,"genie.py",1963,0,"",python,selection_keyboard +134,85997,"genie.py",1963,0,"n",python,content +135,85999,"genie.py",1964,0,"",python,selection_keyboard +136,86109,"genie.py",1964,0,"e",python,content +137,86110,"genie.py",1965,0,"",python,selection_keyboard +138,86438,"genie.py",1964,0,"",python,selection_command +139,88763,"genie.py",267,0,"",python,selection_mouse +140,88765,"genie.py",266,0,"",python,selection_command +141,89247,"genie.py",226,0,"",python,selection_mouse +142,89259,"genie.py",225,0,"",python,selection_command +143,90226,"genie.py",226,0,"",python,selection_command +144,90410,"genie.py",226,0,",",python,content +145,90412,"genie.py",227,0,"",python,selection_keyboard +146,90505,"genie.py",227,0," ",python,content +147,90506,"genie.py",228,0,"",python,selection_keyboard +148,90719,"genie.py",228,0,"C",python,content +149,90720,"genie.py",229,0,"",python,selection_keyboard +150,91429,"genie.py",228,1,"CausalTransformer",python,content +151,94577,"genie.py",1955,0,"",python,selection_mouse +152,95205,"genie.py",1984,0,"",python,selection_mouse +153,95939,"genie.py",1980,4,"",python,content +154,96353,"genie.py",1980,0,"C",python,content +155,96354,"genie.py",1981,0,"",python,selection_keyboard +156,96571,"genie.py",1981,0,"a",python,content +157,96573,"genie.py",1982,0,"",python,selection_keyboard +158,97070,"genie.py",1980,2,"CausalTransformer",python,content +159,98850,"models/dynamics.py",0,0,"from typing import Dict, Any\n\nimport jax\nimport jax.numpy as jnp\nimport flax.linen as nn\n\nfrom utils.nn import STTransformer, CausalTransformer\n\n\nclass DynamicsMaskGIT(nn.Module):\n """"""MaskGIT dynamics model""""""\n\n model_dim: int\n num_latents: int\n num_blocks: int\n num_heads: int\n dropout: float\n mask_limit: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n def setup(self):\n self.dynamics = STTransformer(\n self.model_dim,\n self.num_latents,\n self.num_blocks,\n self.num_heads,\n self.dropout,\n self.param_dtype,\n self.dtype,\n )\n self.patch_embed = nn.Embed(self.num_latents, self.model_dim)\n self.mask_token = self.param(\n ""mask_token"",\n nn.initializers.lecun_uniform(),\n (1, 1, 1, self.model_dim),\n )\n self.action_up = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )\n\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\n # --- Mask videos ---\n vid_embed = self.patch_embed(batch[""video_tokens""])\n if training:\n rng1, rng2 = jax.random.split(batch[""mask_rng""])\n mask_prob = jax.random.uniform(rng1, minval=self.mask_limit)\n mask = jax.random.bernoulli(rng2, mask_prob, vid_embed.shape[:-1])\n mask = mask.at[:, 0].set(False)\n vid_embed = jnp.where(jnp.expand_dims(mask, -1), self.mask_token, vid_embed)\n else:\n mask = None\n\n # --- Predict transition ---\n act_embed = self.action_up(batch[""latent_actions""])\n vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\n logits = self.dynamics(vid_embed)\n return dict(token_logits=logits, mask=mask)\n\n\nclass DynamicsAutoregressive(nn.Module):\n """"""Autoregressive (causal) dynamics model""""""\n\n model_dim: int\n num_latents: int\n num_blocks: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n def setup(self):\n # Use a causal transformer instead of STTransformer\n self.dynamics = CausalTransformer(\n self.model_dim,\n self.num_latents,\n self.num_blocks,\n self.num_heads,\n self.dropout,\n self.param_dtype,\n self.dtype,\n )\n self.patch_embed = nn.Embed(self.num_latents, self.model_dim)\n self.action_up = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )\n\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\n # No random masking, just embed the tokens\n vid_embed = self.patch_embed(batch[""video_tokens""])\n act_embed = self.action_up(batch[""latent_actions""])\n vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\n logits = self.dynamics(vid_embed, causal=True) # Pass causal=True if needed\n return dict(token_logits=logits, mask=None)\n",python,tab +160,102366,"models/dynamics.py",0,0,"",python,tab +161,104695,"genie.py",0,0,"",python,tab +162,104696,"genie.py",1955,0,"",python,selection_mouse +163,105080,"genie.py",1997,0,"",python,selection_mouse +164,106023,"genie.py",1997,0,"()",python,content +165,106025,"genie.py",1998,0,"",python,selection_keyboard +166,106251,"genie.py",1998,0,"\n \n ",python,content 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\\r\n --batch_size=96 \\r\n --min_lr=0 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=1000 \\r\n --log \\r\n --log_checkpoint_interval=1000 \\r\n --name=dynamics-debug-run-$slurm_job_id \\r\n --tags dynamics debug \\r\n --entity instant-uv \\r\n --project jafar \\r\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\r\n --data_dir $array_records_dir\r\n ",,terminal_output 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We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n╭─ Unrecognized options ──────────────────────────╮\r\n│ Unrecognized options: --min-lr=0 │\r\n│ ─────────────────────────────────────────────── │\r\n│ For full helptext, run train_dynamics.py --help │\r\n╰─────────────────────────────────────────────────╯\r\n╭─ Unrecognized options ──────────────────────────╮\r\n│ Unrecognized options: --min-lr=0 │\r\n│ ─────────────────────────────────────────────── │\r\n│ For full helptext, run train_dynamics.py --help │\r\n╰─────────────────────────────────────────────────╯\r\n╭─ Unrecognized options ──────────────────────────╮\r\n│ Unrecognized options: --min-lr=0 │\r\n│ ─────────────────────────────────────────────── │\r\n│ For full helptext, run train_dynamics.py --help │\r\n╰─────────────────────────────────────────────────╯\r\n╭─ Unrecognized options ──────────────────────────╮\r\n│ Unrecognized options: --min-lr=0 │\r\n│ ─────────────────────────────────────────────── │\r\n│ For full helptext, run train_dynamics.py --help │\r\n╰─────────────────────────────────────────────────╯\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n╭─ Unrecognized options ──────────────────────────╮\r\n│ Unrecognized options: --min-lr=0 │\r\n│ ─────────────────────────────────────────────── │\r\n│ For full helptext, run train_dynamics.py --help │\r\n╰─────────────────────────────────────────────────╯\r\n╭─ Unrecognized options ──────────────────────────╮\r\n│ Unrecognized options: --min-lr=0 │\r\n│ ─────────────────────────────────────────────── │\r\n│ For full helptext, run train_dynamics.py --help │\r\n╰─────────────────────────────────────────────────╯\r\n╭─ Unrecognized options ──────────────────────────╮\r\n│ Unrecognized options: --min-lr=0 │\r\n│ ─────────────────────────────────────────────── │\r\n│ For full helptext, run train_dynamics.py --help │\r\n╰─────────────────────────────────────────────────╯\r\n╭─ Unrecognized options ──────────────────────────╮\r\n│ Unrecognized options: --min-lr=0 │\r\n│ ─────────────────────────────────────────────── │\r\n│ For full helptext, run train_dynamics.py --help │\r\n╰─────────────────────────────────────────────────╯\r\n",,terminal_output +534,534244,"TERMINAL",0,0,"srun: error: hkn0511: tasks 4-7: Exited with exit code 2\r\n",,terminal_output +535,534318,"TERMINAL",0,0,"srun: error: hkn0509: tasks 0-3: Exited with exit code 2\r\n]0;tum_cte0515@hkn0509:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0509 jafar]$ ",,terminal_output +536,548393,"slurm/jobs/mihir/horeka/yolo-runs/tester.sh",0,0,"",shellscript,tab +537,549773,"slurm/jobs/mihir/horeka/yolo-runs/tester.sh",733,0,"",shellscript,selection_command +538,550689,"slurm/jobs/mihir/horeka/yolo-runs/tester.sh",729,17,"",shellscript,content +539,550747,"slurm/jobs/mihir/horeka/yolo-runs/tester.sh",733,0,"",shellscript,selection_command +540,551731,"slurm/jobs/mihir/horeka/yolo-runs/tester.sh",734,0,"",shellscript,selection_command 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.venv/bin/activate\r\n\r\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\r\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\r\n\r\njob_name=""debug""\r\nslurm_job_id=""debug-mihir""\r\n\r\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/train_tokenizer_lr_sweep_1e-4\r\n\r\nenv | grep SLURM\r\n\r\nXLA_FLAGS=--xla_gpu_autotune_level=0 srun python train_dynamics.py \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=96 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=1000 \\r\n --log \\r\n --log_checkpoint_interval=1000 \\r\n --name=dynamics-debug-run-$slurm_job_id \\r\n --tags dynamics debug \\r\n --entity instant-uv \\r\n --project jafar \\r\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\r\n --data_dir $array_records_dir\r\n ",,terminal_output +560,561603,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=4(x2)\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=692048\r\nSLURM_JOB_GPUS=0,1,2,3\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar\r\nSLURMD_NODENAME=hkn0509\r\nSLURM_JOB_START_TIME=1752667757\r\nSLURM_STEP_NODELIST=hkn0509\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1752703757\r\nSLURM_PMI2_SRUN_PORT=40311\r\nSLURM_CPUS_ON_NODE=24\r\nSLURM_JOB_CPUS_PER_NODE=24(x2)\r\nSLURM_GPUS_ON_NODE=4\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=accelerated\r\nSLURM_TRES_PER_TASK=cpu=5\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=2\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3350302\r\nSLURM_PTY_PORT=34583\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.198\r\nSLURM_PTY_WIN_ROW=43\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=5\r\nSLURM_NTASKS=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e10.hkn0509\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.198\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=300\r\nSLURM_NODELIST=hkn[0509,0511]\r\nSLURM_SRUN_COMM_PORT=35053\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NPROCS=8\r\nSLURM_NNODES=2\r\nSLURM_SUBMIT_HOST=hkn1990.localdomain\r\nSLURM_JOB_ID=3350302\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0509\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_NTASKS_PER_NODE=4\r\nSLURM_STEP_LAUNCHER_PORT=35053\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn[0509,0511]\r\n",,terminal_output +561,561725,"TERMINAL",0,0,"GpuFreq=control_disabled\r\nGpuFreq=control_disabled\r\n",,terminal_output +562,563734,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +563,563821,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +564,567415,"TERMINAL",0,0,"Running on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\n",,terminal_output +565,589805,"genie.py",0,0,"",python,tab +566,599111,"genie.py",0,0,"",python,tab +567,599698,"genie.py",1372,0,"",python,selection_mouse +568,599701,"genie.py",1371,0,"",python,selection_command +569,602582,"genie.py",1372,0,"",python,selection_mouse +570,602584,"genie.py",1371,0,"",python,selection_command +571,604660,"models/dynamics.py",0,0,"",python,tab +572,608559,"train_dynamics.py",0,0,"from dataclasses import dataclass, field\nimport os\n\nimport einops\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.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\n\nfrom genie import Genie, restore_genie_components\nfrom utils.dataloader import get_dataloader\nfrom utils.lr_utils import get_lr_schedule\nfrom utils.parameter_utils import count_parameters_by_component\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n batch_size: int = 36\n init_lr: float = 0.0\n max_lr: float = 3e-5\n decay_end: float = 0.0\n wsd_decay_steps: int = 10000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n warmup_steps: int = 5000\n lr_schedule : str = ""wsd"" # supported options: wsd, cos\n # Tokenizer\n tokenizer_dim: int = 512\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 8\n tokenizer_num_heads: int = 8\n tokenizer_checkpoint: str = """"\n # LAM\n lam_dim: int = 512\n latent_action_dim: int = 32\n num_latent_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 8\n lam_num_heads: int = 8\n lam_checkpoint: str = """"\n # Dynamics\n dyna_dim: int = 512\n dyna_num_blocks: int = 12\n dyna_num_heads: int = 8\n dropout: float = 0.0\n mask_limit: float = 0.5\n param_dtype: jnp.dtype = jnp.float32\n dtype: jnp.dtype = jnp.bfloat16\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n name: str = ""train_dynamics""\n tags: list[str] = field(default_factory=lambda: [""dynamics""])\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 25000\n log_checkpoint_keep_period: int = 20000\n log_gradients: bool = False\n wandb_id: str = """"\n\n\nargs = tyro.cli(Args)\n\n\ndef dynamics_loss_fn(params, state, inputs):\n """"""Compute masked dynamics loss""""""\n inputs[""videos""] = inputs[""videos""].astype(args.dtype) / 255.0\n outputs = state.apply_fn(\n params,\n inputs,\n training=True,\n rngs={""params"": inputs[""rng""], ""dropout"": inputs[""dropout_rng""]},\n )\n mask = outputs[""mask""]\n outputs[""token_logits""] = outputs[""token_logits""].astype(jnp.float32)\n ce_loss = optax.softmax_cross_entropy_with_integer_labels(\n outputs[""token_logits""], outputs[""video_tokens""]\n )\n ce_loss = (mask * ce_loss).sum() / mask.sum()\n acc = outputs[""token_logits""].argmax(-1) == outputs[""video_tokens""]\n acc = (mask * acc).sum() / mask.sum()\n select_probs = jax.nn.softmax(outputs[""token_logits""])\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() # type: ignore\n ssim = pix.ssim(gt, recon).mean() # type: ignore\n _, index_counts_lam = jnp.unique_counts(\n jnp.ravel(outputs[""lam_indices""]), size=args.num_latent_actions, fill_value=0\n )\n _, index_counts_tokenizer = jnp.unique_counts(\n jnp.ravel(outputs[""video_tokens""]), size=args.num_patch_latents, fill_value=0\n )\n codebook_usage_lam = (index_counts_lam != 0).mean()\n codebook_usage_tokenizer = (index_counts_tokenizer != 0).mean()\n metrics = dict(\n cross_entropy_loss=ce_loss,\n masked_token_accuracy=acc,\n select_logit=outputs[""token_logits""].max(-1).mean(),\n select_p=select_probs.max(-1).mean(),\n entropy=jax.scipy.special.entr(select_probs).sum(-1).mean(),\n psnr=psnr,\n ssim=ssim,\n codebook_usage_lam=codebook_usage_lam,\n codebook_usage_tokenizer=codebook_usage_tokenizer,\n )\n return ce_loss, (outputs[""recon""], metrics)\n\n\n@jax.jit\ndef train_step(state, inputs):\n """"""Update state and compute metrics""""""\n grad_fn = jax.value_and_grad(dynamics_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[""gradients_std/""] = jax.tree.map(\n lambda x: x.std(), grads[""params""][""dynamics""]\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\n # --- Initialize model ---\n genie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n latent_action_dim=args.latent_action_dim,\n num_latent_actions=args.num_latent_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n lam_co_train=not args.lam_checkpoint,\n # Dynamics\n dyna_dim=args.dyna_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n dropout=args.dropout,\n mask_limit=args.mask_limit,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n )\n rng, _rng = jax.random.split(rng)\n image_shape = (args.image_height, args.image_width, args.image_channels)\n dummy_inputs = dict(\n videos=jnp.zeros(\n (per_device_batch_size_for_init, args.seq_len, *image_shape),\n dtype=args.dtype,\n ),\n action=jnp.zeros(\n (per_device_batch_size_for_init, args.seq_len), dtype=args.dtype\n ),\n mask_rng=_rng,\n )\n rng, _rng = jax.random.split(rng)\n init_params = genie.init(_rng, dummy_inputs)\n\n param_counts = count_parameters_by_component(init_params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\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 if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n lr_schedule = get_lr_schedule(args.lr_schedule, \n args.init_lr, \n args.max_lr, \n args.decay_end, \n args.num_steps, \n args.warmup_steps, \n args.wsd_decay_steps)\n tx = optax.adamw(learning_rate=lr_schedule, b1=0.9, b2=0.9, weight_decay=1e-4, mu_dtype=args.dtype)\n train_state = TrainState.create(apply_fn=genie.apply, params=init_params, tx=tx)\n\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 videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n train_state = jax.device_put(train_state, replicated_sharding)\n\n # --- Initialize checkpoint manager ---\n step = 0\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.StandardSave, ocp.handlers.StandardCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.StandardRestore, ocp.handlers.StandardCheckpointHandler\n )\n handler_registry.add(""dataloader_state"", grain.checkpoint.CheckpointSave, grain.checkpoint.CheckpointHandler) # type: ignore\n handler_registry.add(""dataloader_state"", grain.checkpoint.CheckpointRestore, grain.checkpoint.CheckpointHandler) # type: ignore\n\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n\n # --- Create DataLoaderIterator from dataloader ---\n array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n\n # --- Restore checkpoint ---\n if args.restore_ckpt:\n # Restore full dynamics model\n abstract_train_state = jax.tree_util.tree_map(\n ocp.utils.to_shape_dtype_struct, train_state\n )\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.StandardRestore(abstract_train_state),\n dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator),\n ),\n )\n train_state = restored[""model_state""]\n grain_iterator = restored[""dataloader_state""]\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored dataloader and model state from step {step}"")\n else:\n # Restore from pre-trained tokenizer (and LAM)\n train_state = restore_genie_components(\n train_state, replicated_sharding, grain_iterator, dummy_inputs, rng, args\n )\n\n # --- TRAIN LOOP ---\n dataloader = (jax.make_array_from_process_local_data(videos_sharding, elem) for elem in grain_iterator) # type: ignore\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n rng, _rng, _rng_dropout, _rng_mask = jax.random.split(rng, 4)\n\n inputs = dict(\n videos=videos,\n rng=_rng,\n dropout_rng=_rng_dropout,\n mask_rng=_rng_mask,\n )\n train_state, loss, recon, metrics = train_step(train_state, inputs)\n metrics[""lr""] = lr_schedule(step)\n print(f""Step {step}, loss: {loss}"")\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 **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 if jax.process_index() == 0:\n log_images = dict(\n image=wandb.Image(np.asarray(gt_seq[args.seq_len - 1])),\n recon=wandb.Image(np.asarray(recon_seq[args.seq_len - 1])),\n true_vs_recon=wandb.Image(\n np.asarray(comparison_seq.astype(np.uint8))\n ),\n )\n wandb.log(log_images)\n # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n checkpoint_manager.save(\n step,\n args=ocp.args.Composite(\n model_state=ocp.args.StandardSave(train_state),\n dataloader_state=grain.checkpoint.CheckpointSave(\n grain_iterator\n ),\n ),\n )\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n checkpoint_manager.close()\n",python,tab +573,611943,"train_dynamics.py",1962,0,"",python,selection_mouse +574,611965,"train_dynamics.py",1961,0,"",python,selection_command +575,612803,"train_dynamics.py",2275,0,"",python,selection_mouse +576,612815,"train_dynamics.py",2274,0,"",python,selection_command +577,613417,"train_dynamics.py",2251,0,"",python,selection_mouse +578,613432,"train_dynamics.py",2250,0,"",python,selection_command +579,664891,"genie.py",0,0,"",python,tab +580,665641,"slurm/jobs/mihir/horeka/yolo-runs/tester.sh",0,0,"",shellscript,tab +581,669567,"TERMINAL",0,0,"l",,terminal_output +582,669634,"TERMINAL",0,0,"\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py(94)\r\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\r\n-> jax.debug.breakpoint()\r\n tokenizer_outputs = self.tokenizer.vq_encode(batch[""videos""], training=False)\r\n lam_outputs = self.lam.vq_encode(batch[""videos""], training=False)\r\n latent_actions = jax.lax.cond(\r\n self.lam_co_train,\r\n lambda: lam_outputs[""z_q""],\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py(94)\r\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\r\n-> jax.debug.breakpoint()\r\n tokenizer_outputs = self.tokenizer.vq_encode(batch[""videos""], training=False)\r\n lam_outputs = self.lam.vq_encode(batch[""videos""], training=False)\r\n latent_actions = jax.lax.cond(\r\n self.lam_co_train,\r\n lambda: lam_outputs[""z_q""],\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py(94)\r\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\r\n-> jax.debug.breakpoint()\r\n tokenizer_outputs = self.tokenizer.vq_encode(batch[""videos""], training=False)\r\n lam_outputs = self.lam.vq_encode(batch[""videos""], training=False)\r\n latent_actions = jax.lax.cond(\r\n self.lam_co_train,\r\n lambda: lam_outputs[""z_q""],\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py(94)\r\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\r\n-> jax.debug.breakpoint()\r\n tokenizer_outputs = self.tokenizer.vq_encode(batch[""videos""], training=False)\r\n lam_outputs = self.lam.vq_encode(batch[""videos""], training=False)\r\n latent_actions = jax.lax.cond(\r\n self.lam_co_train,\r\n lambda: lam_outputs[""z_q""],\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py(94)\r\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\r\n-> jax.debug.breakpoint()\r\n tokenizer_outputs = self.tokenizer.vq_encode(batch[""videos""], training=False)\r\n lam_outputs = self.lam.vq_encode(batch[""videos""], training=False)\r\n latent_actions = jax.lax.cond(\r\n self.lam_co_train,\r\n lambda: lam_outputs[""z_q""],\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py(94)\r\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\r\n-> jax.debug.breakpoint()\r\n tokenizer_outputs = self.tokenizer.vq_encode(batch[""videos""], training=False)\r\n lam_outputs = self.lam.vq_encode(batch[""videos""], training=False)\r\n latent_actions = jax.lax.cond(\r\n self.lam_co_train,\r\n lambda: lam_outputs[""z_q""],\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py(94)\r\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\r\n-> jax.debug.breakpoint()\r\n tokenizer_outputs = self.tokenizer.vq_encode(batch[""videos""], training=False)\r\n lam_outputs = self.lam.vq_encode(batch[""videos""], training=False)\r\n latent_actions = jax.lax.cond(\r\n self.lam_co_train,\r\n lambda: lam_outputs[""z_q""],\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py(94)\r\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\r\n-> jax.debug.breakpoint()\r\n tokenizer_outputs = self.tokenizer.vq_encode(batch[""videos""], training=False)\r\n lam_outputs = self.lam.vq_encode(batch[""videos""], training=False)\r\n latent_actions = jax.lax.cond(\r\n self.lam_co_train,\r\n lambda: lam_outputs[""z_q""],\r\n",,terminal_output +583,676493,"genie.py",0,0,"",python,tab +584,677388,"genie.py",0,0,"",python,tab +585,678421,"utils/nn.py",0,0,"",python,tab +586,679025,"utils/nn.py",0,0,"",python,tab +587,681584,"models/dynamics.py",0,0,"",python,tab +588,683073,"models/dynamics.py",2310,0,"",python,selection_mouse +589,685956,"models/dynamics.py",2202,0,"",python,selection_mouse +590,685958,"models/dynamics.py",2201,0,"",python,selection_command +591,688934,"models/dynamics.py",2110,0,"",python,selection_mouse +592,694241,"genie.py",0,0,"",python,tab +593,696704,"genie.py",2795,0,"",python,selection_command +594,705850,"TERMINAL",0,0,"b",,terminal_output +595,705996,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +596,706058,"TERMINAL",0,0,"[?25lt[?25h",,terminal_output +597,706273,"TERMINAL",0,0,"[?25lc[?25h[?25lh[?25h",,terminal_output +598,707023,"TERMINAL",0,0,"[?25l[[?25h",,terminal_output +599,707373,"TERMINAL",0,0,"[?25l""[?25h",,terminal_output +600,707633,"TERMINAL",0,0,"[?25lv[?25h",,terminal_output +601,707695,"TERMINAL",0,0,"[?25li[?25h",,terminal_output +602,707876,"TERMINAL",0,0,"[?25ld[?25h",,terminal_output +603,708003,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +604,708080,"TERMINAL",0,0,"[?25lo[?25h",,terminal_output +605,708266,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +606,710169,"TERMINAL",0,0,"[?25l""[?25h",,terminal_output +607,711021,"TERMINAL",0,0,"[?25l][?25h",,terminal_output +608,711757,"TERMINAL",0,0,"[?25l.[?25h",,terminal_output +609,712001,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +610,712119,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +611,712235,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +612,712296,"TERMINAL",0,0,"[?25lp[?25h",,terminal_output +613,712472,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +614,712663,"TERMINAL",0,0,"\r\n(jdb) (12, 16, 90, 160, 3)\r\n(jdb) (12, 16, 90, 160, 3)\r\n(jdb) (12, 16, 90, 160, 3)\r\n(jdb) (12, 16, 90, 160, 3)\r\n(jdb) (12, 16, 90, 160, 3)\r\n(jdb) (12, 16, 90, 160, 3)\r\n(jdb) (12, 16, 90, 160, 3)\r\n(jdb) (12, 16, 90, 160, 3)\r\n",,terminal_output +615,718913,"TERMINAL",0,0,"c",,terminal_output +616,719773,"TERMINAL",0,0,"\r\n",,terminal_output +617,720917,"TERMINAL",0,0,"2025-07-16 14:21:01.263644: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:21:01.274352: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:21:01.282749: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:21:01.304237: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +618,720971,"TERMINAL",0,0,"2025-07-16 14:21:01.332260: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:21:01.332261: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +619,721072,"TERMINAL",0,0,"2025-07-16 14:21:01.381194: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:21:01.394754: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +620,730283,"genie.py",0,0,"",python,tab +621,730283,"genie.py",2818,0,"",python,selection_mouse +622,730545,"genie.py",2816,17,"tokenizer_outputs",python,selection_mouse +623,734720,"TERMINAL",0,0,"(jdb) Entering jdb:\r\n",,terminal_output +624,734841,"TERMINAL",0,0,"(jdb) Entering jdb:\r\n",,terminal_output +625,735141,"TERMINAL",0,0,"(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n",,terminal_output +626,736058,"TERMINAL",0,0,"(jdb) Entering jdb:\r\n",,terminal_output +627,736233,"TERMINAL",0,0,"(jdb) Entering jdb:\r\n",,terminal_output +628,737074,"TERMINAL",0,0,"(jdb) Entering jdb:\r\n",,terminal_output +629,737384,"TERMINAL",0,0,"(jdb) Entering jdb:\r\n",,terminal_output +630,739373,"genie.py",3394,0,"",python,selection_mouse +631,740393,"genie.py",3384,0,"",python,selection_mouse +632,743693,"genie.py",1994,0,"",python,selection_mouse +633,744090,"models/dynamics.py",0,0,"",python,tab +634,749659,"TERMINAL",0,0,"l",,terminal_output +635,749837,"TERMINAL",0,0,"\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py(92)\r\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\r\n-> jax.debug.breakpoint()\r\n vid_embed = self.patch_embed(batch[""video_tokens""])\r\n act_embed = self.action_up(batch[""latent_actions""])\r\n vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\r\n jax.debug.breakpoint()\r\n logits = self.dynamics(vid_embed, causal=True)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py(92)\r\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\r\n-> jax.debug.breakpoint()\r\n vid_embed = self.patch_embed(batch[""video_tokens""])\r\n act_embed = self.action_up(batch[""latent_actions""])\r\n vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\r\n jax.debug.breakpoint()\r\n logits = self.dynamics(vid_embed, causal=True)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py(92)\r\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\r\n-> jax.debug.breakpoint()\r\n vid_embed = self.patch_embed(batch[""video_tokens""])\r\n act_embed = self.action_up(batch[""latent_actions""])\r\n vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\r\n jax.debug.breakpoint()\r\n logits = self.dynamics(vid_embed, causal=True)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py(92)\r\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\r\n-> jax.debug.breakpoint()\r\n vid_embed = self.patch_embed(batch[""video_tokens""])\r\n act_embed = self.action_up(batch[""latent_actions""])\r\n vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\r\n jax.debug.breakpoint()\r\n logits = self.dynamics(vid_embed, causal=True)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py(92)\r\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\r\n-> jax.debug.breakpoint()\r\n vid_embed = self.patch_embed(batch[""video_tokens""])\r\n act_embed = self.action_up(batch[""latent_actions""])\r\n vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\r\n jax.debug.breakpoint()\r\n logits = self.dynamics(vid_embed, causal=True)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py(92)\r\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\r\n-> jax.debug.breakpoint()\r\n vid_embed = self.patch_embed(batch[""video_tokens""])\r\n act_embed = self.action_up(batch[""latent_actions""])\r\n vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\r\n jax.debug.breakpoint()\r\n logits = self.dynamics(vid_embed, causal=True)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py(92)\r\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\r\n-> jax.debug.breakpoint()\r\n vid_embed = self.patch_embed(batch[""video_tokens""])\r\n act_embed = self.action_up(batch[""latent_actions""])\r\n vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\r\n jax.debug.breakpoint()\r\n logits = self.dynamics(vid_embed, causal=True)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py(92)\r\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\r\n-> jax.debug.breakpoint()\r\n vid_embed = self.patch_embed(batch[""video_tokens""])\r\n act_embed = self.action_up(batch[""latent_actions""])\r\n vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\r\n jax.debug.breakpoint()\r\n logits = self.dynamics(vid_embed, causal=True)\r\n",,terminal_output +636,755438,"TERMINAL",0,0,"^[[A",,terminal_output +637,755935,"TERMINAL",0,0,"^[[A",,terminal_output +638,756888,"TERMINAL",0,0,"[?25l [?25h",,terminal_output +639,757282,"TERMINAL",0,0,"[?25l [?25h",,terminal_output +640,757918,"TERMINAL",0,0,"b",,terminal_output +641,757980,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +642,758124,"TERMINAL",0,0,"[?25lt[?25h",,terminal_output +643,758263,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +644,758319,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +645,758772,"TERMINAL",0,0,"[?25l[[?25h",,terminal_output +646,759142,"TERMINAL",0,0,"[?25l""[?25h",,terminal_output +647,759688,"TERMINAL",0,0,"[?25lv[?25h",,terminal_output +648,759825,"TERMINAL",0,0,"[?25li[?25h",,terminal_output +649,759965,"TERMINAL",0,0,"[?25ld[?25h",,terminal_output +650,760124,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +651,760187,"TERMINAL",0,0,"[?25lo[?25h",,terminal_output +652,760399,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +653,760812,"TERMINAL",0,0,"[?25l""[?25h",,terminal_output +654,761198,"TERMINAL",0,0,"[?25l][?25h",,terminal_output +655,761546,"TERMINAL",0,0,"[?25l.[?25h",,terminal_output +656,761721,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +657,761782,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +658,761945,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +659,762060,"TERMINAL",0,0,"[?25lp[?25h",,terminal_output +660,762122,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +661,762249,"TERMINAL",0,0,"\r\n(jdb) *** KeyError: 'videos'\r\n(jdb) *** KeyError: 'videos'\r\n(jdb) *** KeyError: 'videos'\r\n(jdb) *** KeyError: 'videos'\r\n(jdb) *** KeyError: 'videos'\r\n(jdb) *** KeyError: 'videos'\r\n(jdb) *** KeyError: 'videos'\r\n(jdb) *** KeyError: 'videos'\r\n",,terminal_output +662,765928,"genie.py",0,0,"",python,tab +663,770635,"genie.py",3433,0,"",python,selection_mouse +664,773618,"genie.py",3193,0,"",python,selection_mouse +665,773754,"genie.py",3186,12,"video_tokens",python,selection_mouse +666,776878,"TERMINAL",0,0,"b",,terminal_output +667,776985,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +668,777133,"TERMINAL",0,0,"[?25lt[?25h",,terminal_output +669,777237,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +670,777299,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +671,778208,"TERMINAL",0,0,"[?25l[[?25h",,terminal_output +672,778774,"TERMINAL",0,0,"[?25l""[?25h",,terminal_output +673,779741,"TERMINAL",0,0,"[?25lvideo_tokens[?25h",,terminal_output +674,780347,"TERMINAL",0,0,"[?25l""[?25h",,terminal_output +675,780795,"TERMINAL",0,0,"[?25l][?25h",,terminal_output +676,781091,"TERMINAL",0,0,"[?25l.[?25h",,terminal_output +677,781268,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +678,781405,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +679,781467,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +680,781618,"TERMINAL",0,0,"[?25lp[?25h",,terminal_output +681,781679,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +682,781918,"TERMINAL",0,0,"\r\n(jdb) (12, 16, 920)\r\n(jdb) (12, 16, 920)\r\n(jdb) (12, 16, 920)\r\n(jdb) (12, 16, 920)\r\n(jdb) (12, 16, 920)\r\n(jdb) (12, 16, 920)\r\n(jdb) (12, 16, 920)\r\n(jdb) (12, 16, 920)\r\n",,terminal_output +683,809411,"genie.py",0,0,"",python,tab +684,809412,"genie.py",3680,0,"",python,selection_mouse +685,809515,"genie.py",3679,0,"",python,selection_command +686,810951,"utils/nn.py",0,0,"",python,tab +687,826122,"models/dynamics.py",0,0,"",python,tab +688,827981,"genie.py",0,0,"",python,tab +689,828926,"models/dynamics.py",0,0,"",python,tab +690,832268,"TERMINAL",0,0,"c",,terminal_output +691,832847,"TERMINAL",0,0,"\r\n",,terminal_output +692,833527,"TERMINAL",0,0,"2025-07-16 14:22:53.868052: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:22:53.876120: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:22:53.886693: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:22:53.887821: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:22:53.891457: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:22:53.915671: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:22:53.915639: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:22:53.917118: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +693,833662,"TERMINAL",0,0,"(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n",,terminal_output +694,833732,"TERMINAL",0,0,"(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n",,terminal_output +695,841177,"TERMINAL",0,0,"b",,terminal_output +696,841322,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +697,841378,"TERMINAL",0,0,"[?25lt[?25h",,terminal_output +698,841586,"TERMINAL",0,0,"[?25lch[?25h",,terminal_output +699,842445,"TERMINAL",0,0,"[?25l[[?25h",,terminal_output +700,842819,"TERMINAL",0,0,"[?25l""[?25h",,terminal_output +701,843341,"TERMINAL",0,0,"[?25lv[?25h",,terminal_output +702,843399,"TERMINAL",0,0,"[?25li[?25h",,terminal_output +703,843581,"TERMINAL",0,0,"[?25ld[?25h",,terminal_output +704,844106,"TERMINAL",0,0,"[?25l_[?25h",,terminal_output +705,844402,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +706,844464,"TERMINAL",0,0,"[?25lm[?25h",,terminal_output +707,844778,"TERMINAL",0,0,"[?25lb[?25h",,terminal_output +708,844840,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +709,844963,"TERMINAL",0,0,"[?25ld[?25h",,terminal_output +710,845250,"TERMINAL",0,0,"[?25l""[?25h",,terminal_output +711,845639,"TERMINAL",0,0,"[?25l][?25h",,terminal_output +712,845974,"TERMINAL",0,0,"[?25l.[?25h",,terminal_output +713,846480,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +714,846733,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +715,847276,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +716,847337,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +717,847518,"TERMINAL",0,0,"[?25lp[?25h",,terminal_output +718,847579,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +719,847787,"TERMINAL",0,0,"\r\n(jdb) *** KeyError: 'vid_embed'\r\n(jdb) *** KeyError: 'vid_embed'\r\n(jdb) *** KeyError: 'vid_embed'\r\n(jdb) *** KeyError: 'vid_embed'\r\n(jdb) *** KeyError: 'vid_embed'\r\n(jdb) *** KeyError: 'vid_embed'\r\n(jdb) *** KeyError: 'vid_embed'\r\n(jdb) *** KeyError: 'vid_embed'\r\n",,terminal_output +720,852375,"TERMINAL",0,0,"[?25lvi[?25h[?25li[?25h",,terminal_output +721,852609,"TERMINAL",0,0,"[?25ld[?25h",,terminal_output +722,852790,"TERMINAL",0,0,"[?25l_[?25h",,terminal_output +723,853032,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +724,853137,"TERMINAL",0,0,"[?25lm[?25h",,terminal_output +725,853409,"TERMINAL",0,0,"[?25lb[?25h",,terminal_output +726,853472,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +727,853574,"TERMINAL",0,0,"[?25ld[?25h",,terminal_output +728,853635,"TERMINAL",0,0,"[?25l.[?25h",,terminal_output +729,853869,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +730,853958,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +731,854071,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +732,854133,"TERMINAL",0,0,"[?25lp[?25h",,terminal_output +733,854388,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +734,854408,"TERMINAL",0,0,"\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n",,terminal_output +735,858933,"models/dynamics.py",0,0,"",python,tab +736,858934,"models/dynamics.py",2952,0,"",python,selection_mouse +737,859456,"models/dynamics.py",2928,0,"",python,selection_mouse +738,859970,"models/dynamics.py",2917,0,"",python,selection_mouse +739,860482,"models/dynamics.py",2922,0,"",python,selection_mouse +740,860621,"models/dynamics.py",2921,5,"debug",python,selection_mouse +741,860826,"models/dynamics.py",2921,6,"debug.",python,selection_mouse +742,860826,"models/dynamics.py",2921,16,"debug.breakpoint",python,selection_mouse +743,860902,"models/dynamics.py",2921,18,"debug.breakpoint()",python,selection_mouse +744,861258,"models/dynamics.py",2939,0,"",python,selection_mouse +745,861297,"models/dynamics.py",2938,0,"",python,selection_command +746,861598,"models/dynamics.py",2939,0,"",python,selection_mouse +747,861599,"models/dynamics.py",2938,0,"",python,selection_command +748,861747,"models/dynamics.py",2938,1,")",python,selection_mouse +749,861749,"models/dynamics.py",2939,0,"",python,selection_command +750,861788,"models/dynamics.py",2927,12,"breakpoint()",python,selection_mouse +751,861855,"models/dynamics.py",2852,87," += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\n jax.debug.breakpoint()",python,selection_mouse +752,861856,"models/dynamics.py",2843,96,"vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\n jax.debug.breakpoint()",python,selection_mouse +753,862155,"models/dynamics.py",2917,22,"jax.debug.breakpoint()",python,selection_mouse +754,862620,"models/dynamics.py",2918,0,"",python,selection_mouse +755,862621,"models/dynamics.py",2917,3,"jax",python,selection_mouse +756,862792,"models/dynamics.py",2917,9,"jax.debug",python,selection_mouse +757,862827,"models/dynamics.py",2917,20,"jax.debug.breakpoint",python,selection_mouse +758,862892,"models/dynamics.py",2917,21,"jax.debug.breakpoint(",python,selection_mouse +759,862912,"models/dynamics.py",2917,22,"jax.debug.breakpoint()",python,selection_mouse +760,863217,"models/dynamics.py",2939,0,"",python,selection_mouse +761,863234,"models/dynamics.py",2938,0,"",python,selection_command +762,863364,"models/dynamics.py",2939,0,"",python,selection_mouse +763,863378,"models/dynamics.py",2938,0,"",python,selection_command +764,863519,"models/dynamics.py",2938,1,")",python,selection_mouse +765,863519,"models/dynamics.py",2939,0,"",python,selection_command +766,863584,"models/dynamics.py",2937,2,"()",python,selection_mouse +767,863585,"models/dynamics.py",2927,12,"breakpoint()",python,selection_mouse +768,863685,"models/dynamics.py",2926,13,".breakpoint()",python,selection_mouse +769,863705,"models/dynamics.py",2921,18,"debug.breakpoint()",python,selection_mouse +770,863784,"models/dynamics.py",2920,19,".debug.breakpoint()",python,selection_mouse +771,863818,"models/dynamics.py",2917,22,"jax.debug.breakpoint()",python,selection_mouse +772,864279,"models/dynamics.py",2919,0,"",python,selection_mouse +773,864280,"models/dynamics.py",2917,3,"jax",python,selection_mouse +774,864455,"models/dynamics.py",2917,9,"jax.debug",python,selection_mouse +775,864531,"models/dynamics.py",2853,67,"+= jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\n jax",python,selection_mouse +776,864531,"models/dynamics.py",2856,64,"jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\n jax",python,selection_mouse +777,864532,"models/dynamics.py",2860,60,"pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\n jax",python,selection_mouse +778,864532,"models/dynamics.py",2863,57,"(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\n jax",python,selection_mouse +779,864575,"models/dynamics.py",2864,56,"act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\n jax",python,selection_mouse +780,864989,"models/dynamics.py",2869,0,"",python,selection_mouse +781,865488,"models/dynamics.py",2967,0,"",python,selection_mouse +782,869712,"models/dynamics.py",2167,0,"",python,selection_mouse +783,870038,"utils/nn.py",0,0,"",python,tab +784,871380,"utils/nn.py",1190,0,"",python,selection_mouse +785,872380,"genie.py",0,0,"",python,tab +786,873999,"models/dynamics.py",0,0,"",python,tab +787,875464,"models/dynamics.py",2980,0,"",python,selection_mouse +788,875639,"models/dynamics.py",2980,2,", ",python,selection_mouse +789,875640,"models/dynamics.py",2980,3,", c",python,selection_mouse +790,875641,"models/dynamics.py",2980,5,", cau",python,selection_mouse +791,875706,"models/dynamics.py",2980,7,", causa",python,selection_mouse +792,875706,"models/dynamics.py",2980,8,", causal",python,selection_mouse +793,875738,"models/dynamics.py",2980,9,", causal=",python,selection_mouse +794,875738,"models/dynamics.py",2980,10,", causal=T",python,selection_mouse +795,875792,"models/dynamics.py",2980,11,", causal=Tr",python,selection_mouse +796,875804,"models/dynamics.py",2980,12,", causal=Tru",python,selection_mouse +797,875828,"models/dynamics.py",2980,13,", causal=True",python,selection_mouse +798,876863,"models/dynamics.py",2980,13,"",python,content +799,878124,"utils/nn.py",0,0,"",python,tab +800,879364,"utils/nn.py",1193,0,"",python,selection_mouse +801,880683,"utils/nn.py",1307,0,"",python,selection_mouse +802,881583,"utils/nn.py",1608,0,"",python,selection_mouse +803,882377,"utils/nn.py",1273,0,"",python,selection_mouse +804,883265,"utils/nn.py",1816,0,"",python,selection_mouse +805,884763,"TERMINAL",0,0,"c",,terminal_output +806,886111,"TERMINAL",0,0,"\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 107, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 107, in __call__\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 107, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 107, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 107, in __call__\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 107, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 107, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 107, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n logits = self.dynamics(vid_embed)\r\nTypeError: CausalTransformer.__call__() got an unexpected keyword argument 'causal'\r\n logits = self.dynamics(vid_embed)\r\nTypeError: CausalTransformer.__call__() got an unexpected keyword argument 'causal'\r\n logits = self.dynamics(vid_embed)\r\nTypeError: CausalTransformer.__call__() got an unexpected keyword argument 'causal'\r\n logits = self.dynamics(vid_embed)\r\nTypeError: CausalTransformer.__call__() got an unexpected keyword argument 'causal'\r\n logits = self.dynamics(vid_embed)\r\nTypeError: CausalTransformer.__call__() got an unexpected keyword argument 'causal'\r\n logits = self.dynamics(vid_embed)\r\nTypeError: CausalTransformer.__call__() got an unexpected keyword argument 'causal'\r\n logits = self.dynamics(vid_embed)\r\nTypeError: CausalTransformer.__call__() got an unexpected keyword argument 'causal'\r\n logits = self.dynamics(vid_embed)\r\nTypeError: CausalTransformer.__call__() got an unexpected keyword argument 'causal'\r\n",,terminal_output +807,887417,"TERMINAL",0,0,"(jdb) (jdb) (jdb) (jdb) (jdb) (jdb) (jdb) (jdb) ",,terminal_output +808,887814,"TERMINAL",0,0,"srun: error: hkn0511: tasks 4-5: Exited with exit code 1\r\nsrun: error: hkn0509: tasks 1-2: Exited with exit code 1\r\n",,terminal_output +809,887921,"TERMINAL",0,0,"srun: error: hkn0509: task 0: Exited with exit code 1\r\nsrun: error: hkn0511: task 7: Exited with exit code 1\r\n",,terminal_output +810,888156,"TERMINAL",0,0,"srun: error: hkn0511: task 6: Exited with exit code 1\r\nsrun: error: hkn0509: task 3: Exited with exit code 1\r\n]0;tum_cte0515@hkn0509:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0509 jafar]$ ",,terminal_output +811,888241,"TERMINAL",0,0,"^C[?2004l\r[?2004h[?2004l\r\r\n]0;tum_cte0515@hkn0509:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0509 jafar]$ ",,terminal_output +812,890190,"TERMINAL",0,0,"[?25lls[?25h[?25ls[?25h",,terminal_output +813,891194,"TERMINAL",0,0,"[?25ls[?25h[?25lm[?25h",,terminal_output +814,891314,"TERMINAL",0,0,"[?25li[?25h",,terminal_output +815,891504,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +816,893039,"TERMINAL",0,0,"[?1049h(B[?7hEvery 1.0s: nvidia-smihkn0509.localdomain: Wed Jul 16 14:23:51 2025Wed Jul 16 14:23:51 2025\r+-----------------------------------------------------------------------------------------+\r| NVIDIA-SMI 570.133.20Driver Version: 570.133.20 CUDA Version: 12.8 |\r|-----------------------------------------+------------------------+----------------------+\r| GPU NamePersistence-M | Bus-IdDisp.A | Volatile Uncorr. ECC |\r| Fan Temp PerfPwr:Usage/Cap |Memory-Usage | GPU-Util Compute M. |\r|||MIG M. |\r|=========================================+========================+======================|\r| 0 NVIDIA A100-SXM4-40GBOn | 00000000:31:00.0 Off |0 |\r| N/A 46C P057W / 300W |\t 27MiB / 40960MiB |\t 0%\t Default |\r|||Disabled |\r+-----------------------------------------+------------------------+----------------------+\r| 1 NVIDIA A100-SXM4-40GBOn | 00000000:4B:00.0 Off |0 |\r| N/A 46C P063W / 300W |\t 27MiB / 40960MiB |\t 0%\t Default |\r|||Disabled |\r+-----------------------------------------+------------------------+----------------------+\r| 2 NVIDIA A100-SXM4-40GBOn | 00000000:CA:00.0 Off |0 |\r| N/A 45C P053W / 300W |\t 27MiB / 40960MiB |\t 0%\t Default |\r|||Disabled |\r+-----------------------------------------+------------------------+----------------------+\r| 3 NVIDIA A100-SXM4-40GBOn | 00000000:E3:00.0 Off |0 |\r| N/A 43C P054W / 300W |\t 27MiB / 40960MiB |\t 0%\t Default |\r|||Disabled |\r+-----------------------------------------+------------------------+----------------------+\r+-----------------------------------------------------------------------------------------+\r| Processes:|\r| GPU GI CIPID Type Process nameGPU Memory |\r|ID IDUsage\t |\r|=========================================================================================|\r| 0 N/A N/A2668G /usr/libexec/Xorg17MiB |\r| 1 N/A N/A2668G /usr/libexec/Xorg17MiB |\r| 2 N/A N/A2668G /usr/libexec/Xorg17MiB |\r| 3 N/A N/A2668G /usr/libexec/Xorg17MiB |\r+-----------------------------------------------------------------------------------------+",,terminal_output +817,894058,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn0509:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0509 jafar]$ ",,terminal_output +818,895173,"genie.py",0,0,"",python,tab +819,897158,"genie.py",2797,0,"",python,selection_mouse +820,897846,"genie.py",2777,31,"",python,content +821,897883,"genie.py",2785,0,"",python,selection_command +822,899449,"models/dynamics.py",0,0,"",python,tab +823,900816,"models/dynamics.py",2707,0,"",python,selection_mouse +824,901357,"models/dynamics.py",2684,31,"",python,content +825,901419,"models/dynamics.py",2692,0,"",python,selection_command +826,901942,"models/dynamics.py",2752,0,"",python,selection_command +827,902134,"models/dynamics.py",2812,0,"",python,selection_command +828,902300,"models/dynamics.py",2886,0,"",python,selection_command +829,902451,"models/dynamics.py",2917,0,"",python,selection_command +830,902688,"models/dynamics.py",2886,0,"",python,selection_command +831,905085,"models/dynamics.py",2878,31,"",python,content +832,905185,"models/dynamics.py",2886,0,"",python,selection_command +833,906299,"models/dynamics.py",2904,0,"",python,selection_mouse +834,907614,"models/dynamics.py",2164,0,"",python,selection_mouse +835,907857,"utils/nn.py",0,0,"",python,tab +836,908726,"utils/nn.py",1186,0,"",python,selection_mouse +837,910292,"utils/nn.py",1216,0,"\n jax.debug.breakpoint()",python,content +838,910324,"utils/nn.py",1225,0,"",python,selection_command +839,911049,"utils/nn.py",1256,0,"",python,selection_command +840,911286,"utils/nn.py",1225,0,"",python,selection_command +841,911802,"utils/nn.py",1256,0,"",python,selection_command +842,911923,"utils/nn.py",1300,0,"",python,selection_command +843,912093,"utils/nn.py",1326,0,"",python,selection_command +844,912234,"utils/nn.py",1368,0,"",python,selection_command +845,912357,"utils/nn.py",1398,0,"",python,selection_command +846,912972,"utils/nn.py",1411,0,"",python,selection_command +847,913385,"utils/nn.py",1431,0,"\n jax.debug.breakpoint()",python,content +848,913423,"utils/nn.py",1440,0,"",python,selection_command +849,913719,"utils/nn.py",1471,0,"",python,selection_command +850,913932,"utils/nn.py",1519,0,"",python,selection_command +851,914028,"utils/nn.py",1592,0,"",python,selection_command +852,914166,"utils/nn.py",1627,0,"",python,selection_command +853,914351,"utils/nn.py",1665,0,"",python,selection_command +854,914458,"utils/nn.py",1706,0,"",python,selection_command +855,914662,"utils/nn.py",1745,0,"",python,selection_command +856,914820,"utils/nn.py",1787,0,"",python,selection_command +857,914942,"utils/nn.py",1817,0,"",python,selection_command +858,915134,"utils/nn.py",1848,0,"",python,selection_command +859,915859,"utils/nn.py",1857,0,"\n jax.debug.breakpoint()",python,content +860,915910,"utils/nn.py",1866,0,"",python,selection_command +861,916296,"utils/nn.py",1848,0,"",python,selection_command +862,916708,"utils/nn.py",1817,0,"",python,selection_command +863,916898,"utils/nn.py",1839,0,"\n jax.debug.breakpoint()",python,content +864,916909,"utils/nn.py",1848,0,"",python,selection_command +865,917137,"utils/nn.py",1879,0,"",python,selection_command +866,917301,"utils/nn.py",1897,0,"",python,selection_command +867,917437,"utils/nn.py",1920,0,"",python,selection_command +868,917587,"utils/nn.py",1929,0,"",python,selection_command +869,917767,"utils/nn.py",1951,0,"",python,selection_command +870,918042,"utils/nn.py",1977,0,"",python,selection_command +871,918398,"utils/nn.py",2019,0,"",python,selection_command +872,918709,"utils/nn.py",2049,0,"",python,selection_command +873,919178,"utils/nn.py",2053,0,"\n jax.debug.breakpoint()",python,content +874,919214,"utils/nn.py",2062,0,"",python,selection_command +875,919416,"utils/nn.py",2093,0,"",python,selection_command +876,919590,"utils/nn.py",2115,0,"",python,selection_command +877,919746,"utils/nn.py",2143,0,"",python,selection_command +878,919880,"utils/nn.py",2185,0,"",python,selection_command +879,920086,"utils/nn.py",2215,0,"",python,selection_command +880,920258,"utils/nn.py",2228,0,"",python,selection_command +881,920597,"utils/nn.py",2215,0,"",python,selection_command +882,920798,"utils/nn.py",2219,0,"\n jax.debug.breakpoint()",python,content +883,920849,"utils/nn.py",2228,0,"",python,selection_command +884,920992,"utils/nn.py",2259,0,"",python,selection_command +885,921179,"utils/nn.py",2282,0,"",python,selection_command +886,921526,"utils/nn.py",2259,0,"",python,selection_command +887,921726,"utils/nn.py",2273,0,"\n jax.debug.breakpoint()",python,content +888,921755,"utils/nn.py",2282,0,"",python,selection_command +889,921877,"utils/nn.py",2313,0,"",python,selection_command +890,922299,"utils/nn.py",2322,0,"\n jax.debug.breakpoint()",python,content +891,922306,"utils/nn.py",2331,0,"",python,selection_command +892,925613,"TERMINAL",0,0,"smi",,terminal_output +893,925993,"TERMINAL",0,0,"h slurm/jobs/mihir/horeka/yolo-runs/tester.sh",,terminal_output +894,926808,"TERMINAL",0,0,"\r\n[?2004l\r\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\r\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\r\n\r\njob_name=""debug""\r\nslurm_job_id=""debug-mihir""\r\n\r\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/train_tokenizer_lr_sweep_1e-4\r\n\r\nenv | grep SLURM\r\n\r\nXLA_FLAGS=--xla_gpu_autotune_level=0 srun python train_dynamics.py \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=96 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=1000 \\r\n --log \\r\n --log_checkpoint_interval=1000 \\r\n --name=dynamics-debug-run-$slurm_job_id \\r\n --tags dynamics debug \\r\n --entity instant-uv \\r\n --project jafar \\r\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\r\n --data_dir $array_records_dir\r\n ",,terminal_output +895,926922,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=4(x2)\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=692048\r\nSLURM_JOB_GPUS=0,1,2,3\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar\r\nSLURMD_NODENAME=hkn0509\r\nSLURM_JOB_START_TIME=1752667757\r\nSLURM_STEP_NODELIST=hkn0509\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1752703757\r\nSLURM_PMI2_SRUN_PORT=40311\r\nSLURM_CPUS_ON_NODE=24\r\nSLURM_JOB_CPUS_PER_NODE=24(x2)\r\nSLURM_GPUS_ON_NODE=4\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=accelerated\r\nSLURM_TRES_PER_TASK=cpu=5\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=2\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3350302\r\nSLURM_PTY_PORT=34583\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.198\r\nSLURM_PTY_WIN_ROW=43\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=5\r\nSLURM_NTASKS=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e10.hkn0509\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.198\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=300\r\nSLURM_NODELIST=hkn[0509,0511]\r\nSLURM_SRUN_COMM_PORT=35053\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NPROCS=8\r\nSLURM_NNODES=2\r\nSLURM_SUBMIT_HOST=hkn1990.localdomain\r\nSLURM_JOB_ID=3350302\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0509\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_NTASKS_PER_NODE=4\r\nSLURM_STEP_LAUNCHER_PORT=35053\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn[0509,0511]\r\n",,terminal_output +896,927072,"TERMINAL",0,0,"GpuFreq=control_disabled\r\nGpuFreq=control_disabled\r\n",,terminal_output +897,929214,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +898,932881,"TERMINAL",0,0,"2025-07-16 14:24:33.189282: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:24:33.191967: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:24:33.192346: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:24:33.203020: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:24:33.220334: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:24:33.225770: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:24:33.225768: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:24:33.229109: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +899,947412,"TERMINAL",0,0,"2025-07-16 14:24:47.774012: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +900,947586,"TERMINAL",0,0,"2025-07-16 14:24:47.947507: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:24:47.975268: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +901,947643,"TERMINAL",0,0,"2025-07-16 14:24:47.982137: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\nRunning on 8 devices.\r\nEntering jdb:\r\n",,terminal_output +902,947857,"TERMINAL",0,0,"2025-07-16 14:24:48.115747: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:24:48.119287: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\nRunning on 8 devices.\r\nEntering jdb:\r\n2025-07-16 14:24:48.180127: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:24:48.180058: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\nRunning on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\n",,terminal_output +903,947962,"TERMINAL",0,0,"Running on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\n",,terminal_output +904,948022,"TERMINAL",0,0,"Running on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\n",,terminal_output +905,1029957,"TERMINAL",0,0,"x",,terminal_output +906,1030122,"TERMINAL",0,0,"[?25l.[?25h",,terminal_output +907,1030346,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +908,1030482,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +909,1030645,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +910,1030781,"TERMINAL",0,0,"[?25lp[?25h",,terminal_output +911,1030886,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +912,1031103,"TERMINAL",0,0,"\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n",,terminal_output +913,1034920,"TERMINAL",0,0,"c",,terminal_output +914,1035143,"TERMINAL",0,0,"\r\n",,terminal_output +915,1035607,"TERMINAL",0,0,"(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n",,terminal_output +916,1041147,"TERMINAL",0,0,"s",,terminal_output +917,1041980,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +918,1042152,"TERMINAL",0,0,"[?25lq[?25h",,terminal_output +919,1042429,"TERMINAL",0,0,"[?25l_[?25h",,terminal_output +920,1042679,"TERMINAL",0,0,"[?25ll[?25h",,terminal_output +921,1042819,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +922,1042880,"TERMINAL",0,0,"[?25ln[?25h",,terminal_output +923,1043103,"TERMINAL",0,0,"\r\n(jdb) *** NameError: name 'seq_len' is not defined\r\n(jdb) *** NameError: name 'seq_len' is not defined\r\n(jdb) *** NameError: name 'seq_len' is not defined\r\n(jdb) *** NameError: name 'seq_len' is not defined\r\n(jdb) *** NameError: name 'seq_len' is not defined\r\n(jdb) *** NameError: name 'seq_len' is not defined\r\n(jdb) *** NameError: name 'seq_len' is not defined\r\n(jdb) *** NameError: name 'seq_len' is not defined\r\n",,terminal_output +924,1044906,"TERMINAL",0,0,"l",,terminal_output +925,1045087,"TERMINAL",0,0,"\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(108)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n ),\r\n ]\r\n )(x)\r\n # Causal transformer blocks\r\n-> jax.debug.breakpoint()\r\n for _ in range(self.num_blocks):\r\n x = CausalTransformerBlock(\r\n model_dim=self.model_dim,\r\n num_heads=self.num_heads,\r\n dropout=self.dropout,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(108)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n ),\r\n ]\r\n )(x)\r\n # Causal transformer blocks\r\n-> jax.debug.breakpoint()\r\n for _ in range(self.num_blocks):\r\n x = CausalTransformerBlock(\r\n model_dim=self.model_dim,\r\n num_heads=self.num_heads,\r\n dropout=self.dropout,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(108)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n ),\r\n ]\r\n )(x)\r\n # Causal transformer blocks\r\n-> jax.debug.breakpoint()\r\n for _ in range(self.num_blocks):\r\n x = CausalTransformerBlock(\r\n model_dim=self.model_dim,\r\n num_heads=self.num_heads,\r\n dropout=self.dropout,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(108)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n ),\r\n ]\r\n )(x)\r\n # Causal transformer blocks\r\n-> jax.debug.breakpoint()\r\n for _ in range(self.num_blocks):\r\n x = CausalTransformerBlock(\r\n model_dim=self.model_dim,\r\n num_heads=self.num_heads,\r\n dropout=self.dropout,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(108)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n ),\r\n ]\r\n )(x)\r\n # Causal transformer blocks\r\n-> jax.debug.breakpoint()\r\n for _ in range(self.num_blocks):\r\n x = CausalTransformerBlock(\r\n model_dim=self.model_dim,\r\n num_heads=self.num_heads,\r\n dropout=self.dropout,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(108)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n ),\r\n ]\r\n )(x)\r\n # Causal transformer blocks\r\n-> jax.debug.breakpoint()\r\n for _ in range(self.num_blocks):\r\n x = CausalTransformerBlock(\r\n model_dim=self.model_dim,\r\n num_heads=self.num_heads,\r\n dropout=self.dropout,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(108)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n ),\r\n ]\r\n )(x)\r\n # Causal transformer blocks\r\n-> jax.debug.breakpoint()\r\n for _ in range(self.num_blocks):\r\n x = CausalTransformerBlock(\r\n model_dim=self.model_dim,\r\n num_heads=self.num_heads,\r\n dropout=self.dropout,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(108)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n ),\r\n ]\r\n )(x)\r\n # Causal transformer blocks\r\n-> jax.debug.breakpoint()\r\n for _ in range(self.num_blocks):\r\n x = CausalTransformerBlock(\r\n model_dim=self.model_dim,\r\n num_heads=self.num_heads,\r\n dropout=self.dropout,\r\n",,terminal_output +926,1051890,"utils/nn.py",0,0,"",python,tab +927,1051891,"utils/nn.py",1517,0,"",python,selection_mouse +928,1052606,"utils/nn.py",1853,0,"",python,selection_mouse +929,1060259,"utils/nn.py",1235,0,"",python,selection_mouse +930,1060431,"utils/nn.py",1235,10,"breakpoint",python,selection_mouse +931,1060566,"utils/nn.py",1217,31," jax.debug.breakpoint()\n",python,selection_mouse +932,1081669,"TERMINAL",0,0,"x",,terminal_output +933,1081836,"TERMINAL",0,0,"[?25l.[?25h",,terminal_output +934,1082058,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +935,1082218,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +936,1082405,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +937,1083331,"TERMINAL",0,0,"[?25lp[?25h",,terminal_output +938,1083441,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +939,1083578,"TERMINAL",0,0,"\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n",,terminal_output +940,1092010,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +941,1093839,"TERMINAL",0,0,"\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n",,terminal_output +942,1096510,"utils/nn.py",0,0,"",python,tab +943,1096511,"utils/nn.py",1630,0,"",python,selection_mouse +944,1098665,"TERMINAL",0,0,"[?25ll[?25h",,terminal_output +945,1098855,"TERMINAL",0,0,"\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(38)\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n-> jax.debug.breakpoint()\r\n # LayerNorm + Causal Self-Attention\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(38)\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n-> jax.debug.breakpoint()\r\n # LayerNorm + Causal Self-Attention\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(38)\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n-> jax.debug.breakpoint()\r\n # LayerNorm + Causal Self-Attention\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(38)\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n-> jax.debug.breakpoint()\r\n # LayerNorm + Causal Self-Attention\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(38)\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n-> jax.debug.breakpoint()\r\n # LayerNorm + Causal Self-Attention\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(38)\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n-> jax.debug.breakpoint()\r\n # LayerNorm + Causal Self-Attention\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(38)\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n-> jax.debug.breakpoint()\r\n # LayerNorm + Causal Self-Attention\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(38)\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n-> jax.debug.breakpoint()\r\n # LayerNorm + Causal Self-Attention\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n",,terminal_output +946,1107685,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +947,1107810,"TERMINAL",0,0,"\r\n",,terminal_output +948,1107862,"TERMINAL",0,0,"(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n",,terminal_output +949,1109529,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +950,1109757,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +951,1110631,"TERMINAL",0,0,"[?25lq[?25h",,terminal_output +952,1110916,"TERMINAL",0,0,"[?25l_[?25h",,terminal_output +953,1111192,"TERMINAL",0,0,"[?25ll[?25h",,terminal_output +954,1111435,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +955,1111489,"TERMINAL",0,0,"[?25ln[?25h",,terminal_output +956,1112032,"TERMINAL",0,0,"\r\n(jdb) 16\r\n(jdb) 16\r\n(jdb) 16\r\n(jdb) 16\r\n(jdb) 16\r\n(jdb) 16\r\n(jdb) 16\r\n(jdb) 16\r\n",,terminal_output +957,1116819,"TERMINAL",0,0,"[?25ll[?25h",,terminal_output +958,1116963,"TERMINAL",0,0,"\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(45)\r\n # LayerNorm + Causal Self-Attention\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n-> jax.debug.breakpoint()\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(45)\r\n # LayerNorm + Causal Self-Attention\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n-> jax.debug.breakpoint()\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(45)\r\n # LayerNorm + Causal Self-Attention\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n-> jax.debug.breakpoint()\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(45)\r\n # LayerNorm + Causal Self-Attention\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n-> jax.debug.breakpoint()\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(45)\r\n # LayerNorm + Causal Self-Attention\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n-> jax.debug.breakpoint()\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(45)\r\n # LayerNorm + Causal Self-Attention\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n-> jax.debug.breakpoint()\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(45)\r\n # LayerNorm + Causal Self-Attention\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n-> jax.debug.breakpoint()\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(45)\r\n # LayerNorm + Causal Self-Attention\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n-> jax.debug.breakpoint()\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n",,terminal_output +959,1119212,"TERMINAL",0,0,"[?25lz[?25h",,terminal_output +960,1119599,"TERMINAL",0,0,"[?25l.[?25h",,terminal_output +961,1119932,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +962,1119993,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +963,1120262,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +964,1121427,"TERMINAL",0,0,"[?25lp[?25h",,terminal_output +965,1121490,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +966,1121585,"TERMINAL",0,0,"\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n",,terminal_output +967,1145764,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +968,1145939,"TERMINAL",0,0,"\r\n",,terminal_output +969,1146524,"TERMINAL",0,0,"Traceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (1, 1, 1, 16, 16), (12, 16, 8, 920, 920), (1, 1, 1, 1, 1).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 197, in broadcast_shapes\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _broadcast_shapes_cached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 299, in wrapper\r\n return cached(config.trace_context() if trace_context_in_key else _ignore(),\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 293, in cached\r\n return f(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 203, in _broadcast_shapes_cached\r\n return _broadcast_shapes_uncached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 222, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (1, 1, 1, 16, 16), (12, 16, 8, 920, 920), (1, 1, 1, 1, 1).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 197, in broadcast_shapes\r\n raise ValueError(f""Incompatible shapes for broadcasting: shapes={list(shapes)}"") from err\r\nValueError: Incompatible shapes for broadcasting: shapes=[(16, 16), (12, 16, 8, 920, 920), ()]\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n return _broadcast_shapes_cached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 299, in wrapper\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (1, 1, 1, 16, 16), (12, 16, 8, 920, 920), (1, 1, 1, 1, 1).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (1, 1, 1, 16, 16), (12, 16, 8, 920, 920), (1, 1, 1, 1, 1).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 197, in broadcast_shapes\r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n return cached(config.trace_context() if trace_context_in_key else _ignore(),\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 293, in cached\r\n return _broadcast_shapes_cached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 299, in wrapper\r\n return f(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 203, in _broadcast_shapes_cached\r\n return _broadcast_shapes_uncached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 222, in _broadcast_shapes_uncached\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 95, in __call__\r\n logits = self.dynamics(vid_embed)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 110, in __call__\r\n x = CausalTransformerBlock(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 48, in __call__\r\n return cached(config.trace_context() if trace_context_in_key else _ignore(),\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 293, in cached\r\n return f(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 203, in _broadcast_shapes_cached\r\n raise ValueError(f""Incompatible shapes for broadcasting: shapes={list(shapes)}"") from err\r\nValueError: Incompatible shapes for broadcasting: shapes=[(16, 16), (12, 16, 8, 920, 920), ()]\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\n z = nn.MultiHeadAttention(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 674, in __call__\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _broadcast_shapes_uncached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 222, in _broadcast_shapes_uncached\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (1, 1, 1, 16, 16), (12, 16, 8, 920, 920), (1, 1, 1, 1, 1).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n raise ValueError(f""Incompatible shapes for broadcasting: shapes={list(shapes)}"") from err\r\nValueError: Incompatible shapes for broadcasting: shapes=[(16, 16), (12, 16, 8, 920, 920), ()]\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (1, 1, 1, 16, 16), (12, 16, 8, 920, 920), (1, 1, 1, 1, 1).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 95, in __call__\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (1, 1, 1, 16, 16), (12, 16, 8, 920, 920), (1, 1, 1, 1, 1).\r\n x = self.attention_fn(*attn_args, **attn_kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 266, in dot_product_attention\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 197, in broadcast_shapes\r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n logits = self.dynamics(vid_embed)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 110, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 95, in __call__\r\n return _broadcast_shapes_cached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 299, in wrapper\r\n x = CausalTransformerBlock(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 48, in __call__\r\n logits = self.dynamics(vid_embed)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 110, in __call__\r\n z = nn.MultiHeadAttention(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 674, in __call__\r\n return cached(config.trace_context() if trace_context_in_key else _ignore(),\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 293, in cached\r\n x = CausalTransformerBlock(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 48, in __call__\r\n return f(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 203, in _broadcast_shapes_cached\r\n return _broadcast_shapes_uncached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 222, in _broadcast_shapes_uncached\r\n raise ValueError(f""Incompatible shapes for broadcasting: shapes={list(shapes)}"") from err\r\nValueError: Incompatible shapes for broadcasting: shapes=[(16, 16), (12, 16, 8, 920, 920), ()]\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n x = self.attention_fn(*attn_args, **attn_kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 266, in dot_product_attention\r\n attn_weights = dot_product_attention_weights(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 140, in dot_product_attention_weights\r\n z = nn.MultiHeadAttention(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 674, in __call__\r\n x = self.attention_fn(*attn_args, **attn_kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 266, in dot_product_attention\r\n attn_weights = dot_product_attention_weights(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 140, in dot_product_attention_weights\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (1, 1, 1, 16, 16), (12, 16, 8, 920, 920), (1, 1, 1, 1, 1).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n attn_weights = jnp.where(mask, attn_weights, big_neg)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/lax_numpy.py"", line 2821, in where\r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n attn_weights = jnp.where(mask, attn_weights, big_neg)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/lax_numpy.py"", line 2821, in where\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 95, in __call__\r\n logits = self.dynamics(vid_embed)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 110, in __call__\r\n x = CausalTransformerBlock(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 48, in __call__\r\n attn_weights = dot_product_attention_weights(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 140, in dot_product_attention_weights\r\n z = nn.MultiHeadAttention(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 674, in __call__\r\n x = self.attention_fn(*attn_args, **attn_kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 266, in dot_product_attention\r\n attn_weights = jnp.where(mask, attn_weights, big_neg)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/lax_numpy.py"", line 2821, in where\r\n return util._where(condition, x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 311, in _where\r\n condition, x_arr, y_arr = _broadcast_arrays(condition, x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 264, in _broadcast_arrays\r\n result_shape = lax.broadcast_shapes(*shapes)\r\nValueError: Incompatible shapes for broadcasting: shapes=[(16, 16), (12, 16, 8, 920, 920), ()]\r\n return util._where(condition, x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 311, in _where\r\n condition, x_arr, y_arr = _broadcast_arrays(condition, x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 264, in _broadcast_arrays\r\n result_shape = lax.broadcast_shapes(*shapes)\r\nValueError: Incompatible shapes for broadcasting: shapes=[(16, 16), (12, 16, 8, 920, 920), ()]\r\n attn_weights = dot_product_attention_weights(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 140, in dot_product_attention_weights\r\n return util._where(condition, x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 311, in _where\r\n condition, x_arr, y_arr = _broadcast_arrays(condition, x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 264, in _broadcast_arrays\r\n result_shape = lax.broadcast_shapes(*shapes)\r\nValueError: Incompatible shapes for broadcasting: shapes=[(16, 16), (12, 16, 8, 920, 920), ()]\r\n attn_weights = jnp.where(mask, attn_weights, big_neg)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/lax_numpy.py"", line 2821, in where\r\n return util._where(condition, x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 311, in _where\r\n condition, x_arr, y_arr = _broadcast_arrays(condition, x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 264, in _broadcast_arrays\r\n result_shape = lax.broadcast_shapes(*shapes)\r\nValueError: Incompatible shapes for broadcasting: shapes=[(16, 16), (12, 16, 8, 920, 920), ()]\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (1, 1, 1, 16, 16), (12, 16, 8, 920, 920), (1, 1, 1, 1, 1).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 197, in broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (1, 1, 1, 16, 16), (12, 16, 8, 920, 920), (1, 1, 1, 1, 1).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 197, in broadcast_shapes\r\n return _broadcast_shapes_cached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 299, in wrapper\r\n return _broadcast_shapes_cached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 299, in wrapper\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (1, 1, 1, 16, 16), (12, 16, 8, 920, 920), (1, 1, 1, 1, 1).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 197, in broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (1, 1, 1, 16, 16), (12, 16, 8, 920, 920), (1, 1, 1, 1, 1).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 197, in broadcast_shapes\r\n return _broadcast_shapes_cached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 299, in wrapper\r\n return _broadcast_shapes_cached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 299, in wrapper\r\n return cached(config.trace_context() if trace_context_in_key else _ignore(),\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 293, in cached\r\n return f(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 203, in _broadcast_shapes_cached\r\n return cached(config.trace_context() if trace_context_in_key else _ignore(),\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 293, in cached\r\n return cached(config.trace_context() if trace_context_in_key else _ignore(),\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 293, in cached\r\n return f(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 203, in _broadcast_shapes_cached\r\n return cached(config.trace_context() if trace_context_in_key else _ignore(),\r\n return _broadcast_shapes_uncached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 222, in _broadcast_shapes_uncached\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 293, in cached\r\n raise ValueError(f""Incompatible shapes for broadcasting: shapes={list(shapes)}"") from err\r\nValueError: Incompatible shapes for broadcasting: shapes=[(16, 16), (12, 16, 8, 920, 920), ()]\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (1, 1, 1, 16, 16), (12, 16, 8, 920, 920), (1, 1, 1, 1, 1).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n return f(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 203, in _broadcast_shapes_cached\r\n return _broadcast_shapes_uncached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 222, in _broadcast_shapes_uncached\r\n raise ValueError(f""Incompatible shapes for broadcasting: shapes={list(shapes)}"") from err\r\nValueError: Incompatible shapes for broadcasting: shapes=[(16, 16), (12, 16, 8, 920, 920), ()]\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (1, 1, 1, 16, 16), (12, 16, 8, 920, 920), (1, 1, 1, 1, 1).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n return f(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 203, in _broadcast_shapes_cached\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 95, in __call__\r\n logits = self.dynamics(vid_embed)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 110, in __call__\r\n x = CausalTransformerBlock(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 48, in __call__\r\n z = nn.MultiHeadAttention(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 674, in __call__\r\n return _broadcast_shapes_uncached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 222, in _broadcast_shapes_uncached\r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 95, in __call__\r\n logits = self.dynamics(vid_embed)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 110, in __call__\r\n x = CausalTransformerBlock(\r\n return _broadcast_shapes_uncached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 48, in __call__\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 222, in _broadcast_shapes_uncached\r\n raise ValueError(f""Incompatible shapes for broadcasting: shapes={list(shapes)}"") from err\r\nValueError: Incompatible shapes for broadcasting: shapes=[(16, 16), (12, 16, 8, 920, 920), ()]\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise ValueError(f""Incompatible shapes for broadcasting: shapes={list(shapes)}"") from err\r\nValueError: Incompatible shapes for broadcasting: shapes=[(16, 16), (12, 16, 8, 920, 920), ()]\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (1, 1, 1, 16, 16), (12, 16, 8, 920, 920), (1, 1, 1, 1, 1).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n z = nn.MultiHeadAttention(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 674, in __call__\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (1, 1, 1, 16, 16), (12, 16, 8, 920, 920), (1, 1, 1, 1, 1).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n x = self.attention_fn(*attn_args, **attn_kwargs)\r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 266, in dot_product_attention\r\n x = self.attention_fn(*attn_args, **attn_kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 266, in dot_product_attention\r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 95, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 95, in __call__\r\n logits = self.dynamics(vid_embed)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 110, in __call__\r\n logits = self.dynamics(vid_embed)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 110, in __call__\r\n x = CausalTransformerBlock(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 48, in __call__\r\n x = CausalTransformerBlock(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 48, in __call__\r\n z = nn.MultiHeadAttention(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 674, in __call__\r\n z = nn.MultiHeadAttention(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 674, in __call__\r\n attn_weights = dot_product_attention_weights(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 140, in dot_product_attention_weights\r\n attn_weights = dot_product_attention_weights(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 140, in dot_product_attention_weights\r\n x = self.attention_fn(*attn_args, **attn_kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 266, in dot_product_attention\r\n x = self.attention_fn(*attn_args, **attn_kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 266, in dot_product_attention\r\n attn_weights = jnp.where(mask, attn_weights, big_neg)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/lax_numpy.py"", line 2821, in where\r\n attn_weights = dot_product_attention_weights(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 140, in dot_product_attention_weights\r\n attn_weights = jnp.where(mask, attn_weights, big_neg)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/lax_numpy.py"", line 2821, in where\r\n attn_weights = dot_product_attention_weights(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 140, in dot_product_attention_weights\r\n attn_weights = jnp.where(mask, attn_weights, big_neg)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/lax_numpy.py"", line 2821, in where\r\n return util._where(condition, x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 311, in _where\r\n return util._where(condition, x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 311, in _where\r\n attn_weights = jnp.where(mask, attn_weights, big_neg)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/lax_numpy.py"", line 2821, in where\r\n condition, x_arr, y_arr = _broadcast_arrays(condition, x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 264, in _broadcast_arrays\r\n condition, x_arr, y_arr = _broadcast_arrays(condition, x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 264, in _broadcast_arrays\r\n result_shape = lax.broadcast_shapes(*shapes)\r\nValueError: Incompatible shapes for broadcasting: shapes=[(16, 16), (12, 16, 8, 920, 920), ()]\r\n result_shape = lax.broadcast_shapes(*shapes)\r\nValueError: Incompatible shapes for broadcasting: shapes=[(16, 16), (12, 16, 8, 920, 920), ()]\r\n return util._where(condition, x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 311, in _where\r\n condition, x_arr, y_arr = _broadcast_arrays(condition, x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 264, in _broadcast_arrays\r\n return util._where(condition, x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 311, in _where\r\n condition, x_arr, y_arr = _broadcast_arrays(condition, x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 264, in _broadcast_arrays\r\n result_shape = lax.broadcast_shapes(*shapes)\r\nValueError: Incompatible shapes for broadcasting: shapes=[(16, 16), (12, 16, 8, 920, 920), ()]\r\n result_shape = lax.broadcast_shapes(*shapes)\r\nValueError: Incompatible shapes for broadcasting: shapes=[(16, 16), (12, 16, 8, 920, 920), ()]\r\n",,terminal_output +970,1147501,"TERMINAL",0,0,"(jdb) (jdb) (jdb) (jdb) (jdb) ",,terminal_output +971,1147575,"TERMINAL",0,0,"(jdb) (jdb) (jdb) ",,terminal_output +972,1147867,"TERMINAL",0,0,"srun: error: hkn0509: tasks 0,2: Exited with exit code 1\r\n",,terminal_output +973,1147973,"TERMINAL",0,0,"srun: error: hkn0511: tasks 5,7: Exited with exit code 1\r\n",,terminal_output +974,1148080,"TERMINAL",0,0,"srun: error: hkn0509: task 1: Exited with exit code 1\r\nsrun: error: hkn0511: task 6: Exited with exit code 1\r\n",,terminal_output +975,1148206,"TERMINAL",0,0,"srun: error: hkn0509: task 3: Exited with exit code 1\r\n",,terminal_output +976,1148332,"TERMINAL",0,0,"srun: error: hkn0511: task 4: Exited with exit code 1\r\n]0;tum_cte0515@hkn0509:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0509 jafar]$ ",,terminal_output +977,1164752,"utils/nn.py",0,0,"",python,tab +978,1164753,"utils/nn.py",1616,0,"",python,selection_mouse +979,1165465,"utils/nn.py",1538,0,"",python,selection_mouse +980,1166883,"utils/nn.py",1446,0,"",python,selection_mouse +981,1167387,"utils/nn.py",1477,0,"",python,selection_command +982,1167546,"utils/nn.py",1525,0,"",python,selection_command +983,1167879,"utils/nn.py",1583,0,"\n jax.debug.breakpoint()",python,content +984,1167921,"utils/nn.py",1592,0,"",python,selection_command +985,1199064,"utils/nn.py",1870,0,"",python,selection_mouse +986,1199094,"utils/nn.py",1869,0,"",python,selection_command +987,1204515,"utils/nn.py",0,0,"",python,tab +988,1204515,"utils/nn.py",1242,0,"",python,selection_mouse +989,1206179,"utils/nn.py",1532,0,"",python,selection_mouse +990,1206888,"utils/nn.py",1605,0,"",python,selection_mouse +991,1207859,"utils/nn.py",1553,0,"",python,selection_mouse +992,1208677,"utils/nn.py",1548,0,"",python,selection_mouse +993,1209455,"utils/nn.py",1647,0,"",python,selection_mouse +994,1247122,"utils/nn.py",1606,0,"",python,selection_mouse +995,1247798,"utils/nn.py",1455,0,"",python,selection_mouse +996,1248712,"utils/nn.py",1432,31,"",python,content +997,1248786,"utils/nn.py",1440,0,"",python,selection_command +998,1248825,"utils/nn.py",1411,0,"",python,selection_command +999,1248975,"utils/nn.py",1398,0,"",python,selection_command +1000,1249106,"utils/nn.py",1368,0,"",python,selection_command +1001,1249261,"utils/nn.py",1326,0,"",python,selection_command +1002,1249372,"utils/nn.py",1300,0,"",python,selection_command +1003,1249506,"utils/nn.py",1256,0,"",python,selection_command +1004,1249671,"utils/nn.py",1225,0,"",python,selection_command +1005,1250082,"utils/nn.py",1217,31,"",python,content +1006,1250095,"utils/nn.py",1225,0,"",python,selection_command +1007,1250206,"utils/nn.py",1269,0,"",python,selection_command +1008,1250406,"utils/nn.py",1295,0,"",python,selection_command +1009,1250547,"utils/nn.py",1337,0,"",python,selection_command +1010,1250707,"utils/nn.py",1367,0,"",python,selection_command +1011,1250859,"utils/nn.py",1380,0,"",python,selection_command +1012,1251024,"utils/nn.py",1409,0,"",python,selection_command +1013,1251159,"utils/nn.py",1457,0,"",python,selection_command +1014,1251330,"utils/nn.py",1530,0,"",python,selection_command +1015,1251833,"utils/nn.py",1561,0,"",python,selection_command +1016,1251879,"utils/nn.py",1596,0,"",python,selection_command +1017,1253976,"utils/nn.py",2683,0,"",python,selection_mouse +1018,1254668,"utils/nn.py",2652,0,"",python,selection_command +1019,1254913,"utils/nn.py",2633,31,"",python,content +1020,1255002,"utils/nn.py",2641,0,"",python,selection_command +1021,1255037,"utils/nn.py",2668,0,"",python,selection_command +1022,1255565,"utils/nn.py",2682,0,"",python,selection_command +1023,1255567,"utils/nn.py",2712,0,"",python,selection_command +1024,1255582,"utils/nn.py",2762,0,"",python,selection_command +1025,1255587,"utils/nn.py",2800,0,"",python,selection_command +1026,1255621,"utils/nn.py",2819,0,"",python,selection_command +1027,1255648,"utils/nn.py",2860,0,"",python,selection_command +1028,1255674,"utils/nn.py",2906,0,"",python,selection_command +1029,1255764,"utils/nn.py",2940,0,"",python,selection_command +1030,1255764,"utils/nn.py",2959,0,"",python,selection_command +1031,1255775,"utils/nn.py",2989,0,"",python,selection_command +1032,1255859,"utils/nn.py",3039,0,"",python,selection_command +1033,1256058,"utils/nn.py",3077,0,"",python,selection_command +1034,1256221,"utils/nn.py",3096,0,"",python,selection_command +1035,1256344,"utils/nn.py",3110,0,"",python,selection_command +1036,1256467,"utils/nn.py",3123,0,"",python,selection_command +1037,1256605,"utils/nn.py",3159,0,"",python,selection_command +1038,1257006,"utils/nn.py",3151,31,"",python,content +1039,1257060,"utils/nn.py",3159,0,"",python,selection_command +1040,1257108,"utils/nn.py",3123,0,"",python,selection_command +1041,1258411,"utils/nn.py",3496,0,"",python,selection_mouse +1042,1258700,"utils/nn.py",3479,31,"",python,content +1043,1258758,"utils/nn.py",3487,0,"",python,selection_command +1044,1259777,"utils/nn.py",3626,0,"",python,selection_mouse +1045,1260049,"utils/nn.py",3612,31,"",python,content +1046,1260090,"utils/nn.py",3620,0,"",python,selection_command +1047,1261689,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/yolo-runs/tester.sh",,terminal_output +1048,1262287,"TERMINAL",0,0,"\r\n[?2004l\r\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\r\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\r\n\r\njob_name=""debug""\r\nslurm_job_id=""debug-mihir""\r\n\r\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/train_tokenizer_lr_sweep_1e-4\r\n\r\nenv | grep SLURM\r\n\r\nXLA_FLAGS=--xla_gpu_autotune_level=0 srun python train_dynamics.py \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=96 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=1000 \\r\n --log \\r\n --log_checkpoint_interval=1000 \\r\n --name=dynamics-debug-run-$slurm_job_id \\r\n --tags dynamics debug \\r\n --entity instant-uv \\r\n --project jafar \\r\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\r\n --data_dir $array_records_dir\r\n ",,terminal_output +1049,1262405,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=4(x2)\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=692048\r\nSLURM_JOB_GPUS=0,1,2,3\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar\r\nSLURMD_NODENAME=hkn0509\r\nSLURM_JOB_START_TIME=1752667757\r\nSLURM_STEP_NODELIST=hkn0509\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1752703757\r\nSLURM_PMI2_SRUN_PORT=40311\r\nSLURM_CPUS_ON_NODE=24\r\nSLURM_JOB_CPUS_PER_NODE=24(x2)\r\nSLURM_GPUS_ON_NODE=4\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=accelerated\r\nSLURM_TRES_PER_TASK=cpu=5\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=2\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3350302\r\nSLURM_PTY_PORT=34583\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.198\r\nSLURM_PTY_WIN_ROW=43\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=5\r\nSLURM_NTASKS=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e10.hkn0509\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.198\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=300\r\nSLURM_NODELIST=hkn[0509,0511]\r\nSLURM_SRUN_COMM_PORT=35053\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NPROCS=8\r\nSLURM_NNODES=2\r\nSLURM_SUBMIT_HOST=hkn1990.localdomain\r\nSLURM_JOB_ID=3350302\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0509\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_NTASKS_PER_NODE=4\r\nSLURM_STEP_LAUNCHER_PORT=35053\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn[0509,0511]\r\n",,terminal_output +1050,1262530,"TERMINAL",0,0,"GpuFreq=control_disabled\r\nGpuFreq=control_disabled\r\n",,terminal_output +1051,1264838,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +1052,1269211,"TERMINAL",0,0,"2025-07-16 14:30:09.517407: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:30:09.526133: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:30:09.549864: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:30:09.553191: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:30:09.554347: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:30:09.581597: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:30:09.596495: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1053,1269267,"TERMINAL",0,0,"2025-07-16 14:30:09.641337: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1054,1283700,"TERMINAL",0,0,"2025-07-16 14:30:24.076516: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1055,1283946,"TERMINAL",0,0,"2025-07-16 14:30:24.334901: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:30:24.339024: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1056,1284008,"TERMINAL",0,0,"2025-07-16 14:30:24.393498: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1057,1284063,"TERMINAL",0,0,"2025-07-16 14:30:24.440972: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1058,1284158,"TERMINAL",0,0,"2025-07-16 14:30:24.524450: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1059,1284515,"TERMINAL",0,0,"Running on 8 devices.\r\nEntering jdb:\r\n",,terminal_output +1060,1284934,"TERMINAL",0,0,"Running on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\n",,terminal_output +1061,1284981,"TERMINAL",0,0,"Running on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\n",,terminal_output +1062,1285435,"TERMINAL",0,0,"2025-07-16 14:30:25.720006: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1063,1285678,"TERMINAL",0,0,"2025-07-16 14:30:26.068169: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1064,1286256,"TERMINAL",0,0,"Running on 8 devices.\r\nEntering jdb:\r\n",,terminal_output +1065,1286549,"TERMINAL",0,0,"Running on 8 devices.\r\nEntering jdb:\r\n",,terminal_output +1066,1287738,"genie.py",0,0,"",python,tab +1067,1292259,"genie.py",3408,0,"",python,selection_mouse +1068,1293013,"genie.py",3353,0,"",python,selection_mouse +1069,1293996,"genie.py",3409,0,"",python,selection_mouse +1070,1296433,"genie.py",3353,0,"",python,selection_mouse +1071,1297449,"genie.py",1997,0,"",python,selection_mouse +1072,1297715,"models/dynamics.py",0,0,"",python,tab +1073,1301836,"TERMINAL",0,0,"[?25ll[?25h",,terminal_output +1074,1301971,"TERMINAL",0,0,"\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n",,terminal_output +1075,1313467,"models/dynamics.py",0,0,"",python,tab +1076,1313468,"models/dynamics.py",2916,0,"",python,selection_mouse +1077,1315188,"genie.py",0,0,"",python,tab +1078,1321025,"utils/nn.py",0,0,"",python,tab 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jnp.tril(jnp.ones((",python,selection_mouse +1118,1337719,"utils/nn.py",1418,79,"mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len",python,selection_mouse +1119,1337877,"utils/nn.py",1418,30,"mask: (1, 1, seq_len, seq_len)",python,selection_mouse +1120,1338512,"utils/nn.py",1448,0,"",python,selection_mouse +1121,1338555,"utils/nn.py",1447,0,"",python,selection_command +1122,1338881,"utils/nn.py",1448,0,"",python,selection_mouse +1123,1338896,"utils/nn.py",1447,0,"",python,selection_command +1124,1339055,"utils/nn.py",1447,1,")",python,selection_mouse +1125,1339110,"utils/nn.py",1448,0,"",python,selection_command +1126,1339111,"utils/nn.py",1440,8,"seq_len)",python,selection_mouse +1127,1339111,"utils/nn.py",1400,48,"\n # Causal mask: (1, 1, seq_len, seq_len)",python,selection_mouse +1128,1339288,"utils/nn.py",1428,20,"1, seq_len, seq_len)",python,selection_mouse +1129,1339361,"utils/nn.py",1427,21," 1, seq_len, seq_len)",python,selection_mouse 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+1143,1341017,"utils/nn.py",1447,0,"",python,selection_command +1144,1341530,"utils/nn.py",1448,0,"",python,selection_mouse +1145,1341537,"utils/nn.py",1447,0,"",python,selection_command +1146,1341780,"utils/nn.py",1447,1,")",python,selection_mouse +1147,1341780,"utils/nn.py",1400,47,"\n # Causal mask: (1, 1, seq_len, seq_len",python,selection_mouse +1148,1341785,"utils/nn.py",1448,0,"",python,selection_command +1149,1341866,"utils/nn.py",1400,48,"\n # Causal mask: (1, 1, seq_len, seq_len)",python,selection_mouse +1150,1341867,"utils/nn.py",1371,77,"\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)",python,selection_mouse +1151,1341924,"utils/nn.py",1400,48,"\n # Causal mask: (1, 1, seq_len, seq_len)",python,selection_mouse +1152,1341978,"utils/nn.py",1399,49,"]\n # Causal mask: (1, 1, seq_len, seq_len)",python,selection_mouse +1153,1342035,"utils/nn.py",1398,50,"1]\n # Causal mask: (1, 1, seq_len, seq_len)",python,selection_mouse +1154,1342093,"utils/nn.py",1397,51,"[1]\n # Causal mask: (1, 1, seq_len, seq_len)",python,selection_mouse +1155,1342141,"utils/nn.py",1425,23,"1, 1, seq_len, seq_len)",python,selection_mouse +1156,1342208,"utils/nn.py",1424,24,"(1, 1, seq_len, seq_len)",python,selection_mouse +1157,1342228,"utils/nn.py",1423,25," (1, 1, seq_len, seq_len)",python,selection_mouse +1158,1342650,"utils/nn.py",1423,0,"",python,selection_mouse +1159,1344477,"TERMINAL",0,0,"[?25lz[?25h",,terminal_output +1160,1344826,"TERMINAL",0,0,"[?25l.[?25h",,terminal_output +1161,1344998,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +1162,1345063,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +1163,1345167,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +1164,1345375,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +1165,1345929,"TERMINAL",0,0,"[?25lp[?25h",,terminal_output +1166,1345991,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +1167,1346242,"TERMINAL",0,0,"\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n",,terminal_output +1168,1358227,"genie.py",0,0,"",python,tab +1169,1361413,"genie.py",3400,0,"",python,selection_mouse +1170,1362034,"genie.py",3436,0,"",python,selection_mouse +1171,1363250,"genie.py",3411,0,"",python,selection_mouse +1172,1363794,"genie.py",3407,0,"",python,selection_mouse +1173,1363962,"genie.py",3401,12,"dyna_outputs",python,selection_mouse +1174,1364525,"genie.py",3353,0,"",python,selection_mouse +1175,1365679,"models/dynamics.py",0,0,"",python,tab +1176,1367759,"models/dynamics.py",2765,0,"",python,selection_mouse +1177,1368270,"models/dynamics.py",2826,0,"",python,selection_mouse +1178,1369042,"models/dynamics.py",2877,0,"\n ",python,content +1179,1380860,"models/dynamics.py",2886,0,"o",python,content +1180,1380862,"models/dynamics.py",2887,0,"",python,selection_keyboard 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+1301,1443717,"models/dynamics.py",2964,0," ",python,content +1302,1443718,"models/dynamics.py",2965,0,"",python,selection_keyboard +1303,1444547,"models/dynamics.py",2965,0,"-",python,content +1304,1444548,"models/dynamics.py",2966,0,"",python,selection_keyboard +1305,1444603,"models/dynamics.py",2966,0,"1",python,content +1306,1444603,"models/dynamics.py",2967,0,"",python,selection_keyboard +1307,1446153,"models/dynamics.py",2954,0,"",python,selection_mouse +1308,1446899,"models/dynamics.py",2958,0,"",python,selection_mouse +1309,1447880,"models/dynamics.py",2958,1,"2",python,selection_mouse +1310,1447955,"models/dynamics.py",2958,2,"2,",python,selection_mouse +1311,1447955,"models/dynamics.py",2958,3,"2, ",python,selection_mouse +1312,1447955,"models/dynamics.py",2958,4,"2, 1",python,selection_mouse +1313,1447980,"models/dynamics.py",2958,5,"2, 16",python,selection_mouse +1314,1448035,"models/dynamics.py",2958,6,"2, 16,",python,selection_mouse +1315,1448043,"models/dynamics.py",2958,7,"2, 16, ",python,selection_mouse +1316,1448095,"models/dynamics.py",2958,8,"2, 16, -",python,selection_mouse +1317,1448152,"models/dynamics.py",2958,9,"2, 16, -1",python,selection_mouse +1318,1448208,"models/dynamics.py",2958,10,"2, 16, -1)",python,selection_mouse +1319,1448545,"models/dynamics.py",2968,0,"",python,selection_mouse +1320,1460497,"models/dynamics.py",2952,0,"",python,selection_mouse +1321,1467152,"TERMINAL",0,0,"^Csrun: interrupt (one more within 1 sec to abort)\r\nsrun: StepId=3350302.3 tasks 0-7: running\r\n",,terminal_output +1322,1469263,"TERMINAL",0,0,"^Csrun: interrupt (one more within 1 sec to abort)\r\nsrun: StepId=3350302.3 tasks 0-7: running\r\n",,terminal_output +1323,1469474,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3350302.3\r\n(jdb) --KeyboardInterrupt--\r\nEntering jdb:\r\n(jdb) --KeyboardInterrupt--\r\nEntering jdb:\r\n(jdb) --KeyboardInterrupt--\r\nEntering jdb:\r\n(jdb) --KeyboardInterrupt--\r\nEntering jdb:\r\n(jdb) --KeyboardInterrupt--\r\nEntering jdb:\r\n(jdb) --KeyboardInterrupt--\r\nEntering jdb:\r\n(jdb) --KeyboardInterrupt--\r\nEntering jdb:\r\nsrun: forcing job termination\r\n(jdb) --KeyboardInterrupt--\r\nEntering jdb:\r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\nslurmstepd: error: *** STEP 3350302.3 ON hkn0509 CANCELLED AT 2025-07-16T14:33:29 ***\r\n",,terminal_output +1324,1469628,"TERMINAL",0,0,"(jdb) (jdb) ",,terminal_output +1325,1469706,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3350302.3\r\n(jdb) srun: job abort in progress\r\n(jdb) (jdb) (jdb) (jdb) (jdb) ",,terminal_output +1326,1470594,"TERMINAL",0,0,"]0;tum_cte0515@hkn0509:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0509 jafar]$ ",,terminal_output +1327,1471659,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/yolo-runs/tester.sh",,terminal_output +1328,1472310,"TERMINAL",0,0,"\r\n[?2004l\r\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\r\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\r\n\r\njob_name=""debug""\r\nslurm_job_id=""debug-mihir""\r\n\r\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/train_tokenizer_lr_sweep_1e-4\r\n\r\nenv | grep SLURM\r\n\r\nXLA_FLAGS=--xla_gpu_autotune_level=0 srun python train_dynamics.py \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=96 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=1000 \\r\n --log \\r\n --log_checkpoint_interval=1000 \\r\n --name=dynamics-debug-run-$slurm_job_id \\r\n --tags dynamics debug \\r\n --entity instant-uv \\r\n --project jafar \\r\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\r\n --data_dir $array_records_dir\r\n ",,terminal_output +1329,1472424,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=4(x2)\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=692048\r\nSLURM_JOB_GPUS=0,1,2,3\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar\r\nSLURMD_NODENAME=hkn0509\r\nSLURM_JOB_START_TIME=1752667757\r\nSLURM_STEP_NODELIST=hkn0509\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1752703757\r\nSLURM_PMI2_SRUN_PORT=40311\r\nSLURM_CPUS_ON_NODE=24\r\nSLURM_JOB_CPUS_PER_NODE=24(x2)\r\nSLURM_GPUS_ON_NODE=4\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=accelerated\r\nSLURM_TRES_PER_TASK=cpu=5\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=2\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3350302\r\nSLURM_PTY_PORT=34583\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.198\r\nSLURM_PTY_WIN_ROW=43\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=5\r\nSLURM_NTASKS=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e10.hkn0509\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.198\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=300\r\nSLURM_NODELIST=hkn[0509,0511]\r\nSLURM_SRUN_COMM_PORT=35053\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NPROCS=8\r\nSLURM_NNODES=2\r\nSLURM_SUBMIT_HOST=hkn1990.localdomain\r\nSLURM_JOB_ID=3350302\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0509\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_NTASKS_PER_NODE=4\r\nSLURM_STEP_LAUNCHER_PORT=35053\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn[0509,0511]\r\n",,terminal_output +1330,1472554,"TERMINAL",0,0,"GpuFreq=control_disabled\r\nGpuFreq=control_disabled\r\n",,terminal_output +1331,1474493,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +1332,1474551,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +1333,1474614,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +1334,1478289,"TERMINAL",0,0,"2025-07-16 14:33:38.584675: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:33:38.585308: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:33:38.606283: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:33:38.606536: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:33:38.606615: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:33:38.606614: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:33:38.612271: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:33:38.613650: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1335,1493113,"TERMINAL",0,0,"2025-07-16 14:33:53.406000: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:33:53.474327: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:33:53.480254: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:33:53.484968: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:33:53.487862: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:33:53.491163: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:33:53.492678: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1336,1493219,"TERMINAL",0,0,"2025-07-16 14:33:53.600193: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1337,1494656,"TERMINAL",0,0,"Running on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\n",,terminal_output +1338,1494760,"TERMINAL",0,0,"Running on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\n",,terminal_output +1339,1515105,"TERMINAL",0,0,"[?25ll[?25h",,terminal_output +1340,1515354,"TERMINAL",0,0,"\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n",,terminal_output +1341,1518209,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +1342,1518315,"TERMINAL",0,0,"[?25lu[?25h",,terminal_output +1343,1518444,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +1344,1518507,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +1345,1518758,"TERMINAL",0,0,"[?25la[?25h[?25ll[?25h",,terminal_output +1346,1519987,"TERMINAL",0,0,"[?25la[?25h",,terminal_output 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+1464,1570817,"models/dynamics.py",77,0,"s",python,content +1465,1570818,"models/dynamics.py",78,0,"",python,selection_keyboard +1466,1571126,"models/dynamics.py",77,0,"",python,selection_command +1467,1571863,"models/dynamics.py",65,14,"",python,content +1468,1572028,"models/dynamics.py",88,0,"\nimport einops",python,content +1469,1572086,"models/dynamics.py",89,0,"",python,selection_command +1470,1573365,"genie.py",0,0,"",python,tab +1471,1573709,"genie.py",2499,0,"",python,selection_mouse +1472,1577792,"utils/nn.py",0,0,"",python,tab +1473,1578289,"utils/nn.py",1200,0,"",python,selection_mouse +1474,1586013,"genie.py",0,0,"",python,tab +1475,1589204,"genie.py",8091,0,"",python,selection_mouse +1476,1590184,"genie.py",7920,0,"",python,selection_mouse +1477,1590764,"genie.py",7925,0,"",python,selection_mouse +1478,1591699,"genie.py",2499,0,"",python,selection_command +1479,1615997,"utils/nn.py",0,0,"",python,tab +1480,1619657,"models/dynamics.py",0,0,"",python,tab +1481,1620145,"models/dynamics.py",889,0,"",python,selection_mouse +1482,1620163,"models/dynamics.py",888,0,"",python,selection_command +1483,1632052,"sample.py",0,0,"from dataclasses import dataclass\nimport time\nimport os\n\nimport dm_pix as pix\nimport einops\nimport jax\nimport jax.numpy as jnp\nimport flax.linen as nn\nimport numpy as np\nfrom orbax.checkpoint import PyTreeCheckpointer\nfrom PIL import Image, ImageDraw\nimport tyro\n\nfrom genie import Genie\nfrom utils.dataloader import get_dataloader\n\n\n@dataclass\nclass Args:\n # Experiment\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/coinrun_episodes""\n checkpoint: str = """"\n # Sampling\n batch_size: int = 1\n maskgit_steps: int = 25\n temperature: float = 1.0\n sample_argmax: bool = True\n start_frame: int = 0\n # Tokenizer checkpoint\n tokenizer_dim: int = 512\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 8\n tokenizer_num_heads: int = 8\n # LAM checkpoint\n lam_dim: int = 512\n latent_action_dim: int = 32\n num_latent_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 8\n lam_num_heads: int = 8\n # Dynamics checkpoint\n dyna_dim: int = 512\n dyna_num_blocks: int = 12\n dyna_num_heads: int = 8\n param_dtype: jnp.dtype = jnp.float32\n dtype: jnp.dtype = jnp.bfloat16\n\n\nargs = tyro.cli(Args)\nrng = jax.random.PRNGKey(args.seed)\n\n# --- Load Genie checkpoint ---\ngenie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n latent_action_dim=args.latent_action_dim,\n num_latent_actions=args.num_latent_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n lam_co_train=False,\n # Dynamics\n dyna_dim=args.dyna_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n)\nrng, _rng = jax.random.split(rng)\nimage_shape = (args.image_height, args.image_width, args.image_channels)\ndummy_inputs = dict(\n videos=jnp.zeros((args.batch_size, args.seq_len, *image_shape), dtype=jnp.float32),\n mask_rng=_rng,\n)\nrng, _rng = jax.random.split(rng)\nparams = genie.init(_rng, dummy_inputs)\nckpt = PyTreeCheckpointer().restore(args.checkpoint)[""model""][""params""][""params""]\nparams[""params""].update(ckpt)\n\n\ndef _sampling_wrapper(module, batch):\n return module.sample(batch, args.seq_len, args.maskgit_steps, args.temperature, args.sample_argmax)\n\n# --- Define autoregressive sampling loop ---\ndef _autoreg_sample(rng, video_batch, action_batch):\n vid = video_batch[:, : args.start_frame + 1]\n sampling_fn = jax.jit(nn.apply(_sampling_wrapper, genie)) \n rng, _rng = jax.random.split(rng)\n batch = dict(videos=vid, latent_actions=action_batch, rng=_rng)\n generated_vid = sampling_fn(\n params,\n batch\n )\n return generated_vid\n\n# --- Get video + latent actions ---\narray_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n]\ndataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n args.batch_size,\n args.image_height,\n args.image_width,\n args.image_channels,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n)\nvideo_batch = next(iter(dataloader))\n# Get latent actions for all videos in the batch\nbatch = dict(videos=video_batch)\naction_batch = genie.apply(params, batch, False, method=Genie.vq_encode)\naction_batch = action_batch.reshape(video_batch.shape[0], args.seq_len - 1, 1)\n\n# --- Sample + evaluate video ---\nvid = _autoreg_sample(rng, video_batch, action_batch)\ngt = video_batch[:, : vid.shape[1]].clip(0, 1).reshape(-1, *video_batch.shape[2:])\nrecon = vid.clip(0, 1).reshape(-1, *vid.shape[2:])\nssim = pix.ssim(gt[:, args.start_frame + 1 :], recon[:, args.start_frame + 1 :]).mean()\nprint(f""SSIM: {ssim}"")\n\n# --- Construct video ---\ntrue_videos = (video_batch * 255).astype(np.uint8)\npred_videos = (vid * 255).astype(np.uint8)\nvideo_comparison = np.zeros((2, *vid.shape), dtype=np.uint8)\nvideo_comparison[0] = true_videos[:, :args.seq_len]\nvideo_comparison[1] = pred_videos\nframes = einops.rearrange(video_comparison, ""n b t h w c -> t (b h) (n w) c"")\n\n# --- Save video --- \nimgs = [Image.fromarray(img) for img in frames]\n# Write actions on each frame, on each row (i.e., for each video in the batch, on the GT row)\nfor t, img in enumerate(imgs[1:]):\n d = ImageDraw.Draw(img)\n for row in range(action_batch.shape[0]):\n action = action_batch[row, t, 0]\n y_offset = row * video_batch.shape[2] + 2\n d.text((2, y_offset), f""{action}"", fill=255)\nimgs[0].save(\n f""generation_{time.time()}.gif"",\n save_all=True,\n append_images=imgs[1:],\n duration=250,\n loop=0,\n)\n",python,tab +1484,1636581,"sample.py",4478,0,"",python,selection_mouse +1485,1636728,"sample.py",4478,1,"""",python,selection_mouse +1486,1636728,"sample.py",4478,3,"""n ",python,selection_mouse +1487,1636803,"sample.py",4478,6,"""n b t",python,selection_mouse +1488,1636806,"sample.py",4478,10,"""n b t h w",python,selection_mouse +1489,1636806,"sample.py",4433,45,"\nframes = einops.rearrange(video_comparison, ",python,selection_mouse +1490,1637035,"sample.py",4478,29,"""n b t h w c -> t (b h) (n w)",python,selection_mouse +1491,1637036,"sample.py",4478,31,"""n b t h w c -> t (b h) (n w) c",python,selection_mouse +1492,1637087,"sample.py",4478,32,"""n b t h w c -> t (b h) (n w) c""",python,selection_mouse +1493,1637087,"sample.py",4478,33,"""n b t h w c -> t (b h) (n w) 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--KeyboardInterrupt--\r\nEntering jdb:\r\n(jdb) --KeyboardInterrupt--\r\nEntering jdb:\r\n(jdb) --KeyboardInterrupt--\r\nEntering jdb:\r\n(jdb) --KeyboardInterrupt--\r\nEntering jdb:\r\n(jdb) --KeyboardInterrupt--\r\nEntering jdb:\r\n(jdb) --KeyboardInterrupt--\r\nEntering jdb:\r\n(jdb) --KeyboardInterrupt--\r\nEntering jdb:\r\n(jdb) --KeyboardInterrupt--\r\nEntering jdb:\r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\nslurmstepd: error: *** STEP 3350302.4 ON hkn0509 CANCELLED AT 2025-07-16T14:38:01 ***\r\n",,terminal_output +1678,1741535,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3350302.4\r\n(jdb) (jdb) srun: job abort in progress\r\n(jdb) (jdb) (jdb) (jdb) (jdb) (jdb) ",,terminal_output +1679,1741700,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3350302.4\r\nsrun: job abort in progress\r\n",,terminal_output +1680,1742456,"TERMINAL",0,0,"]0;tum_cte0515@hkn0509:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0509 jafar]$ ",,terminal_output +1681,1742973,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/yolo-runs/tester.sh",,terminal_output +1682,1743663,"TERMINAL",0,0,"\r\n[?2004l\r\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\r\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\r\n\r\njob_name=""debug""\r\nslurm_job_id=""debug-mihir""\r\n\r\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/train_tokenizer_lr_sweep_1e-4\r\n\r\nenv | grep SLURM\r\n\r\nXLA_FLAGS=--xla_gpu_autotune_level=0 srun python train_dynamics.py \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=96 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=1000 \\r\n --log \\r\n --log_checkpoint_interval=1000 \\r\n --name=dynamics-debug-run-$slurm_job_id \\r\n --tags dynamics debug \\r\n --entity instant-uv \\r\n --project jafar \\r\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\r\n --data_dir $array_records_dir\r\n ",,terminal_output +1683,1743777,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=4(x2)\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=692048\r\nSLURM_JOB_GPUS=0,1,2,3\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar\r\nSLURMD_NODENAME=hkn0509\r\nSLURM_JOB_START_TIME=1752667757\r\nSLURM_STEP_NODELIST=hkn0509\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1752703757\r\nSLURM_PMI2_SRUN_PORT=40311\r\nSLURM_CPUS_ON_NODE=24\r\nSLURM_JOB_CPUS_PER_NODE=24(x2)\r\nSLURM_GPUS_ON_NODE=4\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=accelerated\r\nSLURM_TRES_PER_TASK=cpu=5\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=2\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3350302\r\nSLURM_PTY_PORT=34583\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.198\r\nSLURM_PTY_WIN_ROW=43\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=5\r\nSLURM_NTASKS=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e10.hkn0509\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.198\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=300\r\nSLURM_NODELIST=hkn[0509,0511]\r\nSLURM_SRUN_COMM_PORT=35053\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NPROCS=8\r\nSLURM_NNODES=2\r\nSLURM_SUBMIT_HOST=hkn1990.localdomain\r\nSLURM_JOB_ID=3350302\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0509\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_NTASKS_PER_NODE=4\r\nSLURM_STEP_LAUNCHER_PORT=35053\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn[0509,0511]\r\n",,terminal_output 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We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +1686,1746068,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +1687,1748782,"genie.py",0,0,"",python,tab +1688,1750436,"utils/nn.py",0,0,"",python,tab +1689,1750623,"TERMINAL",0,0,"2025-07-16 14:38:10.896775: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:38:10.896774: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:38:10.909196: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:38:10.909198: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:38:10.919130: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:38:10.921034: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:38:10.933402: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:38:10.935568: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1690,1765187,"TERMINAL",0,0,"2025-07-16 14:38:25.545533: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1691,1765323,"TERMINAL",0,0,"2025-07-16 14:38:25.702584: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1692,1765401,"TERMINAL",0,0,"2025-07-16 14:38:25.770092: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:38:25.770093: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:38:25.783238: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1693,1765463,"TERMINAL",0,0,"2025-07-16 14:38:25.813381: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:38:25.837029: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:38:25.842421: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1694,1766516,"TERMINAL",0,0,"Running on 8 devices.\r\nEntering jdb:\r\n",,terminal_output +1695,1766569,"TERMINAL",0,0,"Running on 8 devices.\r\nEntering jdb:\r\n",,terminal_output +1696,1766686,"TERMINAL",0,0,"Running on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\n",,terminal_output +1697,1766744,"TERMINAL",0,0,"Running on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\n",,terminal_output +1698,1788212,"TERMINAL",0,0,"[?25ll[?25h",,terminal_output +1699,1788368,"TERMINAL",0,0,"\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(46)\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n seq_len = z.shape[1]\r\n # Causal mask: (1, 1, seq_len, seq_len)\r\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.model_dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n",,terminal_output +1700,1791774,"TERMINAL",0,0,"[?25lz[?25h",,terminal_output +1701,1792175,"TERMINAL",0,0,"[?25lz[?25h",,terminal_output +1702,1793638,"TERMINAL",0,0,"[?25l.[?25h",,terminal_output +1703,1793827,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +1704,1793923,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +1705,1794101,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +1706,1794163,"TERMINAL",0,0,"[?25lp[?25h",,terminal_output +1707,1794284,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +1708,1794431,"TERMINAL",0,0,"\r\n(jdb) (12, 14720, 512)\r\n(jdb) (12, 14720, 512)\r\n(jdb) (12, 14720, 512)\r\n(jdb) (12, 14720, 512)\r\n(jdb) (12, 14720, 512)\r\n(jdb) (12, 14720, 512)\r\n(jdb) (12, 14720, 512)\r\n(jdb) (12, 14720, 512)\r\n",,terminal_output +1709,1795281,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +1710,1795512,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +1711,1795734,"TERMINAL",0,0,"[?25lq[?25h",,terminal_output +1712,1796155,"TERMINAL",0,0,"[?25l_[?25h",,terminal_output +1713,1796491,"TERMINAL",0,0,"[?25ll[?25h",,terminal_output +1714,1796644,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +1715,1796780,"TERMINAL",0,0,"[?25ln[?25h",,terminal_output +1716,1797017,"TERMINAL",0,0,"\r\n(jdb) 14720\r\n(jdb) 14720\r\n(jdb) 14720\r\n(jdb) 14720\r\n(jdb) 14720\r\n(jdb) 14720\r\n(jdb) 14720\r\n(jdb) 14720\r\n",,terminal_output +1717,1806341,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +1718,1806833,"TERMINAL",0,0,"[?25lu[?25h",,terminal_output +1719,1807427,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +1720,1807486,"TERMINAL",0,0,"[?25lu[?25h",,terminal_output +1721,1807674,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +1722,1807814,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +1723,1807875,"TERMINAL",0,0,"[?25ll[?25h",,terminal_output +1724,1808477,"TERMINAL",0,0,"[?25l_[?25h",,terminal_output +1725,1808789,"TERMINAL",0,0,"[?25l,[?25h",,terminal_output +1726,1809287,"TERMINAL",0,0,"[?25lm[?25h",,terminal_output +1727,1809347,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +1728,1809413,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +1729,1809517,"TERMINAL",0,0,"[?25lk[?25h",,terminal_output +1730,1809779,"TERMINAL",0,0,"[?25l.[?25h",,terminal_output +1731,1809963,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +1732,1810109,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +1733,1810214,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +1734,1810277,"TERMINAL",0,0,"[?25lp[?25h",,terminal_output +1735,1810424,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +1736,1810567,"TERMINAL",0,0,"\r\n(jdb) (14720, 14720)\r\n(jdb) (14720, 14720)\r\n(jdb) (14720, 14720)\r\n(jdb) (14720, 14720)\r\n(jdb) (14720, 14720)\r\n(jdb) (14720, 14720)\r\n(jdb) (14720, 14720)\r\n(jdb) (14720, 14720)\r\n",,terminal_output +1737,1811211,"TERMINAL",0,0,"[?25ln[?25h",,terminal_output +1738,1812290,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +1739,1812497,"TERMINAL",0,0,"\r\n",,terminal_output +1740,1812807,"TERMINAL",0,0,"2025-07-16 14:39:13.194682: E external/xla/xla/service/gpu/gpu_hlo_schedule.cc:795] The byte size of input/output arguments (41964011520) exceeds the base limit (31805177856). This indicates an error in the calculation!\r\n2025-07-16 14:39:13.195190: W external/xla/xla/hlo/transforms/simplifiers/hlo_rematerialization.cc:3023] Can't reduce memory use below 0B (0 bytes) by rematerialization; only reduced to 39.08GiB (41964011520 bytes), down from 39.08GiB (41964011520 bytes) originally\r\n2025-07-16 14:39:13.195250: E external/xla/xla/service/gpu/gpu_hlo_schedule.cc:795] The byte size of input/output arguments (41964011520) exceeds the base limit (31805177856). This indicates an error in the calculation!\r\n2025-07-16 14:39:13.195419: E external/xla/xla/service/gpu/gpu_hlo_schedule.cc:795] The byte size of input/output arguments (41964011520) exceeds the base limit (31805177856). This indicates an error in the calculation!\r\n2025-07-16 14:39:13.195597: W external/xla/xla/hlo/transforms/simplifiers/hlo_rematerialization.cc:3023] Can't reduce memory use below 0B (0 bytes) by rematerialization; only reduced to 39.08GiB (41964011520 bytes), down from 39.08GiB (41964011520 bytes) originally\r\n2025-07-16 14:39:13.195662: E external/xla/xla/service/gpu/gpu_hlo_schedule.cc:795] The byte size of input/output arguments (41964011520) exceeds the base limit (31805177856). This indicates an error in the calculation!\r\n2025-07-16 14:39:13.195795: W external/xla/xla/hlo/transforms/simplifiers/hlo_rematerialization.cc:3023] Can't reduce memory use below 0B (0 bytes) by rematerialization; only reduced to 39.08GiB (41964011520 bytes), down from 39.08GiB (41964011520 bytes) originally\r\n2025-07-16 14:39:13.196040: W external/xla/xla/hlo/transforms/simplifiers/hlo_rematerialization.cc:3023] Can't reduce memory use below 0B (0 bytes) by rematerialization; only reduced to 39.08GiB (41964011520 bytes), down from 39.08GiB (41964011520 bytes) originally\r\n2025-07-16 14:39:13.197041: E external/xla/xla/service/gpu/gpu_hlo_schedule.cc:795] The byte size of input/output arguments (41964011520) exceeds the base limit (31805177856). This indicates an error in the calculation!\r\n2025-07-16 14:39:13.196941: E external/xla/xla/service/gpu/gpu_hlo_schedule.cc:795] The byte size of input/output arguments (41964011520) exceeds the base limit (31805177856). This indicates an error in the calculation!\r\n2025-07-16 14:39:13.197387: W external/xla/xla/hlo/transforms/simplifiers/hlo_rematerialization.cc:3023] Can't reduce memory use below 0B (0 bytes) by rematerialization; only reduced to 39.08GiB (41964011520 bytes), down from 39.08GiB (41964011520 bytes) originally\r\n2025-07-16 14:39:13.197372: W external/xla/xla/hlo/transforms/simplifiers/hlo_rematerialization.cc:3023] Can't reduce memory use below 0B (0 bytes) by rematerialization; only reduced to 39.08GiB (41964011520 bytes), down from 39.08GiB (41964011520 bytes) originally\r\n2025-07-16 14:39:13.197471: E external/xla/xla/service/gpu/gpu_hlo_schedule.cc:795] The byte size of input/output arguments (41964011520) exceeds the base limit (31805177856). This indicates an error in the calculation!\r\n2025-07-16 14:39:13.197862: E external/xla/xla/service/gpu/gpu_hlo_schedule.cc:795] The byte size of input/output arguments (41964011520) exceeds the base limit (31805177856). This indicates an error in the calculation!\r\n2025-07-16 14:39:13.197846: W external/xla/xla/hlo/transforms/simplifiers/hlo_rematerialization.cc:3023] Can't reduce memory use below 0B (0 bytes) by rematerialization; only reduced to 39.08GiB (41964011520 bytes), down from 39.08GiB (41964011520 bytes) originally\r\n2025-07-16 14:39:13.198228: W external/xla/xla/hlo/transforms/simplifiers/hlo_rematerialization.cc:3023] Can't reduce memory use below 0B (0 bytes) by rematerialization; only reduced to 39.08GiB (41964011520 bytes), down from 39.08GiB (41964011520 bytes) originally\r\n",,terminal_output +1741,1822942,"TERMINAL",0,0,"2025-07-16 14:39:23.304107: W external/xla/xla/tsl/framework/bfc_allocator.cc:501] Allocator (GPU_0_bfc) ran out of memory trying to allocate 38.75GiB (rounded to 41602252800)requested by op \r\nIf the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. \r\nCurrent allocation summary follows.\r\nCurrent allocation summary follows.\r\n2025-07-16 14:39:23.304100: W external/xla/xla/tsl/framework/bfc_allocator.cc:501] Allocator (GPU_1_bfc) ran out of memory trying to allocate 38.75GiB (rounded to 41602252800)requested by op \r\nIf the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. \r\nCurrent allocation summary follows.\r\nCurrent allocation summary follows.\r\n2025-07-16 14:39:23.304124: W external/xla/xla/tsl/framework/bfc_allocator.cc:501] Allocator (GPU_2_bfc) ran out of memory trying to allocate 38.75GiB (rounded to 41602252800)requested by op \r\nIf the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. \r\nCurrent allocation summary follows.\r\nCurrent allocation summary follows.\r\n2025-07-16 14:39:23.304039: W external/xla/xla/tsl/framework/bfc_allocator.cc:501] Allocator (GPU_3_bfc) ran out of memory trying to allocate 38.75GiB (rounded to 41602252800)requested by op \r\nIf the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. \r\nCurrent allocation summary follows.\r\nCurrent allocation summary follows.\r\n2025-07-16 14:39:23.304416: W external/xla/xla/tsl/framework/bfc_allocator.cc:512] *********___________________________________________________________________________________________\r\n2025-07-16 14:39:23.304421: W external/xla/xla/tsl/framework/bfc_allocator.cc:512] *********___________________________________________________________________________________________\r\nE0716 14:39:23.304428 705378 pjrt_stream_executor_client.cc:2916] Execution of replica 0 failed: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 41602252800 bytes. [tf-allocator-allocation-error='']\r\nE0716 14:39:23.304432 705380 pjrt_stream_executor_client.cc:2916] Execution of replica 0 failed: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 41602252800 bytes. [tf-allocator-allocation-error='']\r\n2025-07-16 14:39:23.304940: W external/xla/xla/tsl/framework/bfc_allocator.cc:512] *********___________________________________________________________________________________________\r\nE0716 14:39:23.304951 705379 pjrt_stream_executor_client.cc:2916] Execution of replica 0 failed: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 41602252800 bytes. [tf-allocator-allocation-error='']\r\n2025-07-16 14:39:23.304871: W external/xla/xla/tsl/framework/bfc_allocator.cc:512] *********___________________________________________________________________________________________\r\nE0716 14:39:23.304882 705381 pjrt_stream_executor_client.cc:2916] Execution of replica 0 failed: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 41602252800 bytes. [tf-allocator-allocation-error='']\r\n2025-07-16 14:39:23.304939: W external/xla/xla/tsl/framework/bfc_allocator.cc:501] Allocator (GPU_1_bfc) ran out of memory trying to allocate 38.75GiB (rounded to 41602252800)requested by op \r\nIf the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. \r\nCurrent allocation summary follows.\r\nCurrent allocation summary follows.\r\n2025-07-16 14:39:23.304938: W external/xla/xla/tsl/framework/bfc_allocator.cc:501] Allocator (GPU_3_bfc) ran out of memory trying to allocate 38.75GiB (rounded to 41602252800)requested by op \r\nIf the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. \r\nCurrent allocation summary follows.\r\nCurrent allocation summary follows.\r\n2025-07-16 14:39:23.305755: W external/xla/xla/tsl/framework/bfc_allocator.cc:512] *********___________________________________________________________________________________________\r\nE0716 14:39:23.305768 1058581 pjrt_stream_executor_client.cc:2916] Execution of replica 0 failed: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 41602252800 bytes. [tf-allocator-allocation-error='']\r\n2025-07-16 14:39:23.305758: W external/xla/xla/tsl/framework/bfc_allocator.cc:512] *********___________________________________________________________________________________________\r\n2025-07-16 14:39:23.306035: W external/xla/xla/tsl/framework/bfc_allocator.cc:501] Allocator (GPU_0_bfc) ran out of memory trying to allocate 38.75GiB (rounded to 41602252800)requested by op \r\nIf the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. \r\nCurrent allocation summary follows.\r\nCurrent allocation summary follows.\r\n2025-07-16 14:39:23.306050: W external/xla/xla/tsl/framework/bfc_allocator.cc:501] Allocator (GPU_2_bfc) ran out of memory trying to allocate 38.75GiB (rounded to 41602252800)requested by op \r\nIf the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. \r\nCurrent allocation summary follows.\r\nCurrent allocation summary follows.\r\nE0716 14:39:23.305769 1058583 pjrt_stream_executor_client.cc:2916] Execution of replica 0 failed: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 41602252800 bytes. [tf-allocator-allocation-error='']\r\n2025-07-16 14:39:23.306728: W external/xla/xla/tsl/framework/bfc_allocator.cc:512] *********___________________________________________________________________________________________\r\nE0716 14:39:23.306743 1058580 pjrt_stream_executor_client.cc:2916] Execution of replica 0 failed: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 41602252800 bytes. [tf-allocator-allocation-error='']\r\n2025-07-16 14:39:23.306729: W external/xla/xla/tsl/framework/bfc_allocator.cc:512] *********___________________________________________________________________________________________\r\nE0716 14:39:23.306743 1058582 pjrt_stream_executor_client.cc:2916] Execution of replica 0 failed: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 41602252800 bytes. [tf-allocator-allocation-error='']\r\n",,terminal_output +1742,1823009,"TERMINAL",0,0,"jax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n logits = self.dynamics(vid_embed_flat)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 107, in __call__\r\n x = CausalTransformerBlock(\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n logits = self.dynamics(vid_embed_flat)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 107, in __call__\r\n x = CausalTransformerBlock(\r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n logits = self.dynamics(vid_embed_flat)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 107, in __call__\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 47, in __call__\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 47, in __call__\r\n z = nn.MultiHeadAttention(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 674, in __call__\r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n x = CausalTransformerBlock(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 47, in __call__\r\n z = nn.MultiHeadAttention(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 674, in __call__\r\n z = nn.MultiHeadAttention(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 674, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n logits = self.dynamics(vid_embed_flat)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 107, in __call__\r\n x = CausalTransformerBlock(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 47, in __call__\r\n z = nn.MultiHeadAttention(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 674, in __call__\r\n x = self.attention_fn(*attn_args, **attn_kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 266, in dot_product_attention\r\n x = self.attention_fn(*attn_args, **attn_kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 266, in dot_product_attention\r\n attn_weights = dot_product_attention_weights(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 132, in dot_product_attention_weights\r\n x = self.attention_fn(*attn_args, **attn_kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 266, in dot_product_attention\r\n x = self.attention_fn(*attn_args, **attn_kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 266, in dot_product_attention\r\n attn_weights = dot_product_attention_weights(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 132, in dot_product_attention_weights\r\n attn_weights = dot_product_attention_weights(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 132, in dot_product_attention_weights\r\n attn_weights = einsum('...qhd,...khd->...hqk', query, key)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/einsum.py"", line 316, in einsum\r\n attn_weights = dot_product_attention_weights(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 132, in dot_product_attention_weights\r\n return jit_einsum(operand_arrays, contractions, precision,\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/contextlib.py"", line 79, in inner\r\n attn_weights = einsum('...qhd,...khd->...hqk', query, key)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/einsum.py"", line 316, in einsum\r\n return jit_einsum(operand_arrays, contractions, precision,\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/contextlib.py"", line 79, in inner\r\n attn_weights = einsum('...qhd,...khd->...hqk', query, key)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/einsum.py"", line 316, in einsum\r\n return jit_einsum(operand_arrays, contractions, precision,\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/contextlib.py"", line 79, in inner\r\n attn_weights = einsum('...qhd,...khd->...hqk', query, key)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/einsum.py"", line 316, in einsum\r\n return jit_einsum(operand_arrays, contractions, precision,\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/contextlib.py"", line 79, in inner\r\n return func(*args, **kwds)\r\njaxlib._jax.XlaRuntimeError: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 41602252800 bytes.\r\n return func(*args, **kwds)\r\njaxlib._jax.XlaRuntimeError: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 41602252800 bytes.\r\n return func(*args, **kwds)\r\njaxlib._jax.XlaRuntimeError: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 41602252800 bytes.\r\n return func(*args, **kwds)\r\njaxlib._jax.XlaRuntimeError: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 41602252800 bytes.\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n logits = self.dynamics(vid_embed_flat)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 107, in __call__\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n x = CausalTransformerBlock(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 47, in __call__\r\n z = nn.MultiHeadAttention(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 674, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n logits = self.dynamics(vid_embed_flat)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 107, in __call__\r\n x = CausalTransformerBlock(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 47, in __call__\r\n z = nn.MultiHeadAttention(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 674, in __call__\r\n x = self.attention_fn(*attn_args, **attn_kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 266, in dot_product_attention\r\n x = self.attention_fn(*attn_args, **attn_kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 266, in dot_product_attention\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n attn_weights = dot_product_attention_weights(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 132, in dot_product_attention_weights\r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n attn_weights = dot_product_attention_weights(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 132, in dot_product_attention_weights\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n logits = self.dynamics(vid_embed_flat)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 107, in __call__\r\n x = CausalTransformerBlock(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 47, in __call__\r\n logits = self.dynamics(vid_embed_flat)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 107, in __call__\r\n z = nn.MultiHeadAttention(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 674, in __call__\r\n x = CausalTransformerBlock(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 47, in __call__\r\n attn_weights = einsum('...qhd,...khd->...hqk', query, key)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/einsum.py"", line 316, in einsum\r\n z = nn.MultiHeadAttention(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 674, in __call__\r\n return jit_einsum(operand_arrays, contractions, precision,\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/contextlib.py"", line 79, in inner\r\n return func(*args, **kwds)\r\njaxlib._jax.XlaRuntimeError: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 41602252800 bytes.\r\n attn_weights = einsum('...qhd,...khd->...hqk', query, key)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/einsum.py"", line 316, in einsum\r\n return jit_einsum(operand_arrays, contractions, precision,\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/contextlib.py"", line 79, in inner\r\n x = self.attention_fn(*attn_args, **attn_kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 266, in dot_product_attention\r\n x = self.attention_fn(*attn_args, **attn_kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 266, in dot_product_attention\r\n attn_weights = dot_product_attention_weights(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 132, in dot_product_attention_weights\r\n attn_weights = dot_product_attention_weights(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 132, in dot_product_attention_weights\r\n attn_weights = einsum('...qhd,...khd->...hqk', query, key)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/einsum.py"", line 316, in einsum\r\n return jit_einsum(operand_arrays, contractions, precision,\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/contextlib.py"", line 79, in inner\r\n return func(*args, **kwds)\r\njaxlib._jax.XlaRuntimeError: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 41602252800 bytes.\r\n attn_weights = einsum('...qhd,...khd->...hqk', query, key)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/einsum.py"", line 316, in einsum\r\n return jit_einsum(operand_arrays, contractions, precision,\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/contextlib.py"", line 79, in inner\r\n return func(*args, **kwds)\r\njaxlib._jax.XlaRuntimeError: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 41602252800 bytes.\r\n return func(*args, **kwds)\r\njaxlib._jax.XlaRuntimeError: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 41602252800 bytes.\r\n",,terminal_output +1743,1823961,"TERMINAL",0,0,"(jdb) ",,terminal_output +1744,1824043,"TERMINAL",0,0,"(jdb) (jdb) (jdb) (jdb) (jdb) (jdb) (jdb) ",,terminal_output +1745,1824476,"TERMINAL",0,0,"srun: error: hkn0509: tasks 0,2: Exited with exit code 1\r\nsrun: error: hkn0511: tasks 5-6: Exited with exit code 1\r\n",,terminal_output +1746,1824529,"TERMINAL",0,0,"srun: error: hkn0509: task 1: Exited with exit code 1\r\n",,terminal_output +1747,1824589,"TERMINAL",0,0,"srun: error: hkn0511: task 7: Exited with exit code 1\r\n",,terminal_output +1748,1824742,"TERMINAL",0,0,"srun: error: hkn0509: task 3: Exited with exit code 1\r\n",,terminal_output +1749,1824806,"TERMINAL",0,0,"srun: error: hkn0511: task 4: Exited with exit code 1\r\n]0;tum_cte0515@hkn0509:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0509 jafar]$ ",,terminal_output +1750,1844976,"genie.py",0,0,"",python,tab +1751,1850719,"utils/nn.py",0,0,"",python,tab +1752,1891642,"utils/nn.py",5323,0,"",python,selection_mouse +1753,1891645,"utils/nn.py",5322,0,"",python,selection_command +1754,1892264,"utils/nn.py",5323,0,"",python,selection_mouse +1755,1892265,"utils/nn.py",5322,0,"",python,selection_command +1756,1892479,"utils/nn.py",5322,1,"x",python,selection_mouse +1757,1892481,"utils/nn.py",5323,0,"",python,selection_command +1758,1892687,"utils/nn.py",5300,23,"x + z\n\n return x",python,selection_mouse +1759,1892690,"utils/nn.py",5136,187," z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1760,1892691,"utils/nn.py",4776,547," )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1761,1892744,"utils/nn.py",4630,693," qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1762,1892786,"utils/nn.py",4514,809," causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1763,1892837,"utils/nn.py",4471,852," dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1764,1892873,"utils/nn.py",4429,894," param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1765,1892901,"utils/nn.py",4403,920," z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1766,1893058,"utils/nn.py",4429,894," param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1767,1893098,"utils/nn.py",4471,852," dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1768,1893110,"utils/nn.py",4501,822," )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1769,1893140,"utils/nn.py",4557,766," z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1770,1893168,"utils/nn.py",4704,619," param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1771,1893203,"utils/nn.py",4807,516," x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1772,1893239,"utils/nn.py",4885,438," z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1773,1893267,"utils/nn.py",4954,369," dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1774,1893320,"utils/nn.py",4998,325," # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1775,1893336,"utils/nn.py",4999,324," # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1776,1893356,"utils/nn.py",5000,323," # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1777,1893411,"utils/nn.py",4958,365," dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1778,1893452,"utils/nn.py",4854,469,"\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1779,1893489,"utils/nn.py",4632,691," qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1780,1893520,"utils/nn.py",4429,894," param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1781,1893563,"utils/nn.py",4359,964," z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1782,1893591,"utils/nn.py",4330,993," x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1783,1893691,"utils/nn.py",4293,1030," # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1784,1894002,"utils/nn.py",4274,1049," x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1785,1894003,"utils/nn.py",4261,1062," )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1786,1894004,"utils/nn.py",4150,1173," dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1787,1894041,"utils/nn.py",4115,1208," qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1788,1894213,"utils/nn.py",4029,1294," )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1789,1894214,"utils/nn.py",3999,1324," dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1790,1894214,"utils/nn.py",3931,1392," z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1791,1894215,"utils/nn.py",3851,1472," # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1792,1894284,"utils/nn.py",3770,1553," @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1793,1894335,"utils/nn.py",3851,1472," # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1794,1894560,"utils/nn.py",3800,1523," def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1795,1894663,"utils/nn.py",3784,1539," @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1796,1895435,"utils/nn.py",3770,1553," @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1797,1895436,"utils/nn.py",3769,1554,"\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1798,1895437,"utils/nn.py",3721,1602," param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1799,1895437,"utils/nn.py",3683,1640," num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1800,1895437,"utils/nn.py",3644,1679,"class STBlock(nn.Module):\n dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1801,1895438,"utils/nn.py",3642,1681,"\n\nclass STBlock(nn.Module):\n dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1802,1895438,"utils/nn.py",3501,1822," self.out_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n return x # (B, T, E)\n\n\nclass STBlock(nn.Module):\n dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1803,1895438,"utils/nn.py",3400,1923," dtype=self.dtype,\n )(x)\n # Output projection\n x = nn.Dense(\n self.out_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n return x # (B, T, E)\n\n\nclass STBlock(nn.Module):\n dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1804,1895480,"utils/nn.py",3232,2091," model_dim=self.model_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # Output projection\n x = nn.Dense(\n self.out_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n return x # (B, T, E)\n\n\nclass STBlock(nn.Module):\n dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1805,1895525,"utils/nn.py",3088,2235," ]\n )(x)\n # Causal transformer blocks\n for _ in range(self.num_blocks):\n x = CausalTransformerBlock(\n model_dim=self.model_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # Output projection\n x = nn.Dense(\n self.out_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n return x # (B, T, E)\n\n\nclass STBlock(nn.Module):\n dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1806,1895570,"utils/nn.py",2951,2372," nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n ),\n ]\n )(x)\n # Causal transformer blocks\n for _ in range(self.num_blocks):\n x = CausalTransformerBlock(\n model_dim=self.model_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # Output projection\n x = nn.Dense(\n self.out_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n return x # (B, T, E)\n\n\nclass STBlock(nn.Module):\n dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1807,1895617,"utils/nn.py",2811,2512," nn.Dense(self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n ),\n nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n ),\n ]\n )(x)\n # Causal transformer blocks\n for _ in range(self.num_blocks):\n x = CausalTransformerBlock(\n model_dim=self.model_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # Output projection\n x = nn.Dense(\n self.out_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n return x # (B, T, E)\n\n\nclass STBlock(nn.Module):\n dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1808,1895669,"utils/nn.py",2674,2649," nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n ),\n nn.Dense(self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n ),\n nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n ),\n ]\n )(x)\n # Causal transformer blocks\n for _ in range(self.num_blocks):\n x = CausalTransformerBlock(\n model_dim=self.model_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # Output projection\n x = nn.Dense(\n self.out_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n return x # (B, T, E)\n\n\nclass STBlock(nn.Module):\n dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1809,1895703,"utils/nn.py",3354,1969," param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # Output projection\n x = nn.Dense(\n self.out_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n return x # (B, T, E)\n\n\nclass STBlock(nn.Module):\n dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1810,1895732,"utils/nn.py",3599,1724," )(x)\n return x # (B, T, E)\n\n\nclass STBlock(nn.Module):\n dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1811,1895757,"utils/nn.py",3643,1680,"\nclass STBlock(nn.Module):\n dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1812,1895794,"utils/nn.py",3670,1653," dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1813,1895816,"utils/nn.py",3702,1621," dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1814,1895838,"utils/nn.py",3721,1602," param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1815,1895859,"utils/nn.py",3769,1554,"\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1816,1895881,"utils/nn.py",3770,1553," @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1817,1895902,"utils/nn.py",3784,1539," @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1818,1895929,"utils/nn.py",3800,1523," def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1819,1895952,"utils/nn.py",3851,1472," # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1820,1895974,"utils/nn.py",3887,1436," z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1821,1896015,"utils/nn.py",3931,1392," z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1822,1896126,"utils/nn.py",3887,1436," z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1823,1896168,"utils/nn.py",3851,1472," # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1824,1896260,"utils/nn.py",3800,1523," def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1825,1896338,"utils/nn.py",3784,1539," @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1826,1896339,"utils/nn.py",3769,1554,"\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1827,1896339,"utils/nn.py",3721,1602," param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1828,1896369,"utils/nn.py",3683,1640," num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1829,1896396,"utils/nn.py",3670,1653," dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1830,1896575,"utils/nn.py",3644,1679,"class STBlock(nn.Module):\n dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,selection_mouse +1831,1904810,"utils/nn.py",2340,0,"",python,selection_mouse +1832,1904829,"utils/nn.py",2339,0,"",python,selection_command +1833,1904965,"utils/nn.py",2339,1,"x",python,selection_mouse +1834,1904967,"utils/nn.py",2340,0,"",python,selection_command +1835,1905825,"utils/nn.py",2323,17,"\n return x",python,selection_mouse +1836,1905826,"utils/nn.py",2308,32,"g.breakpoint()\n\n return x",python,selection_mouse +1837,1905826,"utils/nn.py",2202,138,"ebug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1838,1905827,"utils/nn.py",1947,393," param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1839,1905928,"utils/nn.py",1528,812," jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1840,1905929,"utils/nn.py",1133,1207,"type: jnp.dtype\n\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1841,1905929,"utils/nn.py",1101,1239," param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1842,1905964,"utils/nn.py",1063,1277," num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1843,1906018,"utils/nn.py",1002,1338,"\nclass CausalTransformerBlock(nn.Module):\n model_dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1844,1906065,"utils/nn.py",919,1421,"\n def __call__(self, x):\n x = x + self.pe[: x.shape[2]]\n return x\n\nclass CausalTransformerBlock(nn.Module):\n model_dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1845,1906103,"utils/nn.py",765,1575," )\n self.pe = self.pe.at[:, 0::2].set(jnp.sin(position * div_term))\n self.pe = self.pe.at[:, 1::2].set(jnp.cos(position * div_term))\n\n def __call__(self, x):\n x = x + self.pe[: x.shape[2]]\n return x\n\nclass CausalTransformerBlock(nn.Module):\n model_dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1846,1906131,"utils/nn.py",919,1421,"\n def __call__(self, x):\n x = x + self.pe[: x.shape[2]]\n return x\n\nclass CausalTransformerBlock(nn.Module):\n model_dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1847,1906157,"utils/nn.py",948,1392," x = x + self.pe[: x.shape[2]]\n return x\n\nclass CausalTransformerBlock(nn.Module):\n model_dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1848,1906206,"utils/nn.py",1003,1337,"class CausalTransformerBlock(nn.Module):\n model_dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1849,1906244,"utils/nn.py",1101,1239," param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1850,1906272,"utils/nn.py",1150,1190," @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1851,1906317,"utils/nn.py",1166,1174," def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1852,1906361,"utils/nn.py",1261,1079," z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1853,1906384,"utils/nn.py",1329,1011," dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1854,1906460,"utils/nn.py",1359,981," )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1855,1906538,"utils/nn.py",1329,1011," dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1856,1906563,"utils/nn.py",1287,1053," param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1857,1906586,"utils/nn.py",1261,1079," z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1858,1906625,"utils/nn.py",1217,1123," # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1859,1906650,"utils/nn.py",1166,1174," def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1860,1906691,"utils/nn.py",1150,1190," @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1861,1906784,"utils/nn.py",1149,1191,"\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1862,1906801,"utils/nn.py",1128,1212," dtype: jnp.dtype\n\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1863,1906843,"utils/nn.py",1101,1239," param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1864,1906896,"utils/nn.py",1063,1277," num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1865,1906957,"utils/nn.py",1044,1296," model_dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1866,1907003,"utils/nn.py",1003,1337,"class CausalTransformerBlock(nn.Module):\n model_dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1867,1907105,"utils/nn.py",1002,1338,"\nclass CausalTransformerBlock(nn.Module):\n model_dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x",python,selection_mouse +1868,1907717,"utils/nn.py",1002,0,"",python,selection_mouse +1869,1908604,"utils/nn.py",1002,0,"\n",python,content +1870,1908816,"utils/nn.py",1003,0,"\n",python,content +1871,1909221,"utils/nn.py",1003,0,"",python,selection_command +1872,1909791,"utils/nn.py",1003,0,"class STBlock(nn.Module):\n dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x",python,content +1873,1911802,"utils/nn.py",1015,0,"",python,selection_mouse +1874,1912549,"utils/nn.py",1016,0,"",python,selection_command +1875,1912694,"utils/nn.py",1016,0,"2",python,content +1876,1912695,"utils/nn.py",1017,0,"",python,selection_keyboard +1877,1916643,"utils/nn.py",2183,0,"",python,selection_mouse +1878,1917720,"utils/nn.py",1892,0,"",python,selection_mouse +1879,1918104,"utils/nn.py",1891,0,"",python,selection_command +1880,1919774,"utils/nn.py",1872,0,"",python,selection_command +1881,1920304,"utils/nn.py",1848,0,"",python,selection_command +1882,1920316,"utils/nn.py",1806,0,"",python,selection_command +1883,1920398,"utils/nn.py",1780,0,"",python,selection_command +1884,1920399,"utils/nn.py",1736,0,"",python,selection_command +1885,1920400,"utils/nn.py",1707,0,"",python,selection_command +1886,1920438,"utils/nn.py",1670,0,"",python,selection_command +1887,1920461,"utils/nn.py",1652,0,"",python,selection_command +1888,1920516,"utils/nn.py",1650,0,"",python,selection_command +1889,1920527,"utils/nn.py",1632,0,"",python,selection_command +1890,1920572,"utils/nn.py",1608,0,"",python,selection_command +1891,1920573,"utils/nn.py",1566,0,"",python,selection_command +1892,1920955,"utils/nn.py",1608,0,"",python,selection_command +1893,1921151,"utils/nn.py",1632,0,"",python,selection_command +1894,1921303,"utils/nn.py",1650,0,"",python,selection_command +1895,1921452,"utils/nn.py",1652,0,"",python,selection_command +1896,1921614,"utils/nn.py",1670,0,"",python,selection_command +1897,1921769,"utils/nn.py",1707,0,"",python,selection_command +1898,1921921,"utils/nn.py",1736,0,"",python,selection_command +1899,1922054,"utils/nn.py",1780,0,"",python,selection_command +1900,1922259,"utils/nn.py",1806,0,"",python,selection_command +1901,1922429,"utils/nn.py",1848,0,"",python,selection_command +1902,1922537,"utils/nn.py",1872,0,"",python,selection_command +1903,1922704,"utils/nn.py",1891,0,"",python,selection_command +1904,1925720,"utils/nn.py",1910,0,"",python,selection_mouse +1905,1927484,"utils/nn.py",1401,0,"",python,selection_mouse +1906,1927486,"utils/nn.py",1400,0,"",python,selection_command +1907,1928278,"utils/nn.py",1401,0,"\n causal_mask = jnp.tri(z.shape[-2])",python,content +1908,1928339,"utils/nn.py",1410,0,"",python,selection_command +1909,1930498,"utils/nn.py",1663,0,"",python,selection_mouse +1910,1930509,"utils/nn.py",1662,0,"",python,selection_command +1911,1934017,"utils/nn.py",1675,0,"",python,selection_mouse +1912,1935938,"utils/nn.py",1675,0,",",python,content +1913,1935939,"utils/nn.py",1676,0,"",python,selection_keyboard +1914,1936040,"utils/nn.py",1676,0," ",python,content +1915,1936041,"utils/nn.py",1677,0,"",python,selection_keyboard +1916,1936239,"utils/nn.py",1677,0,"m",python,content +1917,1936240,"utils/nn.py",1678,0,"",python,selection_keyboard +1918,1936366,"utils/nn.py",1678,0,"a",python,content +1919,1936368,"utils/nn.py",1679,0,"",python,selection_keyboard +1920,1936479,"utils/nn.py",1679,0,"s",python,content +1921,1936481,"utils/nn.py",1680,0,"",python,selection_keyboard +1922,1936603,"utils/nn.py",1680,0,"k",python,content +1923,1936605,"utils/nn.py",1681,0,"",python,selection_keyboard +1924,1937128,"utils/nn.py",1681,0,"=",python,content +1925,1937130,"utils/nn.py",1682,0,"",python,selection_keyboard +1926,1937489,"utils/nn.py",1682,0,"c",python,content +1927,1937491,"utils/nn.py",1683,0,"",python,selection_keyboard +1928,1937705,"utils/nn.py",1683,0,"a",python,content +1929,1937707,"utils/nn.py",1684,0,"",python,selection_keyboard +1930,1937953,"utils/nn.py",1684,0,"u",python,content +1931,1937957,"utils/nn.py",1685,0,"",python,selection_keyboard +1932,1938117,"utils/nn.py",1685,0,"s",python,content 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num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,",python,selection_mouse +1972,2030098,"utils/nn.py",7845,195," dim=self.model_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,",python,selection_mouse +1973,2034096,"utils/nn.py",5989,0,"",python,selection_mouse +1974,2035069,"utils/nn.py",4951,8,"ST",python,content +1975,2035991,"utils/nn.py",4951,2,"CausalTransformerBlock",python,content +1976,2037948,"utils/nn.py",4972,0,"",python,selection_command +1977,2038334,"utils/nn.py",5217,0,"",python,selection_mouse +1978,2038801,"utils/nn.py",5193,0,"",python,selection_mouse +1979,2038805,"utils/nn.py",5192,0,"",python,selection_command +1980,2039487,"utils/nn.py",5177,16," )(x)",python,selection_command +1981,2039681,"utils/nn.py",5143,50," dtype=self.dtype,\n )(x)",python,selection_command +1982,2039845,"utils/nn.py",5097,96," param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)",python,selection_command +1983,2040009,"utils/nn.py",5059,134," dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)",python,selection_command +1984,2040120,"utils/nn.py",5017,176," num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)",python,selection_command +1985,2040262,"utils/nn.py",4975,218," model_dim=self.model_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)",python,selection_command +1986,2040377,"utils/nn.py",4935,258," x = CausalTransformerBlock(\n model_dim=self.model_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)",python,selection_command +1987,2040682,"utils/nn.py",4947,0,"",python,selection_command +1988,2041505,"utils/nn.py",5189,0,"#",python,content +1989,2041506,"utils/nn.py",5159,0,"#",python,content 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+2007,2043510,"utils/nn.py",4943,0,"",python,selection_command +2008,2045145,"utils/nn.py",4943,43,"",python,content +2009,2045156,"utils/nn.py",4907,0,"",python,selection_command +2010,2045884,"utils/nn.py",4934,0,"\n ",python,content +2011,2046130,"utils/nn.py",4947,0," dim=self.model_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,",python,content +2012,2047602,"utils/nn.py",5141,0,"",python,selection_command +2013,2047762,"utils/nn.py",5095,0,"",python,selection_command +2014,2047968,"utils/nn.py",5057,0,"",python,selection_command +2015,2048246,"utils/nn.py",5015,0,"",python,selection_command +2016,2048446,"utils/nn.py",4967,0,"",python,selection_command +2017,2049308,"utils/nn.py",4947,208,"",python,content +2018,2049351,"utils/nn.py",4907,0,"",python,selection_command +2019,2052681,"utils/nn.py",8068,0,"",python,selection_mouse +2020,2052685,"utils/nn.py",8067,0,"",python,selection_command 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dtype=self.dtype,\n )(x)",python,selection_mouse +2033,2053662,"utils/nn.py",7951,134," dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)",python,selection_mouse +2034,2053741,"utils/nn.py",7909,176," num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)",python,selection_mouse +2035,2053816,"utils/nn.py",7873,212," dim=self.model_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)",python,selection_mouse +2036,2053918,"utils/nn.py",7848,237," x = STBlock(\n dim=self.model_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)",python,selection_mouse +2037,2059600,"utils/nn.py",4914,0,"",python,selection_mouse +2038,2060709,"utils/nn.py",4934,0,"\n ",python,content +2039,2060979,"utils/nn.py",4947,0," x = STBlock(\n dim=self.model_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)",python,content +2040,2062258,"utils/nn.py",4959,0,"",python,selection_mouse +2041,2062716,"utils/nn.py",4955,4,"",python,content +2042,2062912,"utils/nn.py",4951,4,"",python,content +2043,2063306,"utils/nn.py",4947,4,"",python,content +2044,2064087,"utils/nn.py",4946,0,"",python,selection_command +2045,2070221,"utils/nn.py",4956,0,"",python,selection_mouse +2046,2071225,"utils/nn.py",4958,0,"",python,selection_mouse +2047,2072560,"utils/nn.py",4958,0,"2",python,content +2048,2072561,"utils/nn.py",4959,0,"",python,selection_keyboard +2049,2073772,"utils/nn.py",4980,0,"",python,selection_mouse +2050,2074174,"utils/nn.py",4957,0,"",python,selection_mouse +2051,2145248,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/yolo-runs/tester.sh",,terminal_output +2052,2146974,"utils/nn.py",0,0,"",python,tab +2053,2146975,"utils/nn.py",1246,0,"",python,selection_mouse +2054,2149184,"utils/nn.py",1246,0,"\n ",python,content 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+2161,2171742,"utils/nn.py",2911,0,"",python,selection_command +2162,2172107,"utils/nn.py",2934,0,"",python,selection_command +2163,2172651,"utils/nn.py",2943,0,"\n jax.debug.breakpoint()",python,content +2164,2172677,"utils/nn.py",2952,0,"",python,selection_command +2165,2174376,"TERMINAL",0,0,"\r\n[?2004l\r\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\r\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\r\n\r\njob_name=""debug""\r\nslurm_job_id=""debug-mihir""\r\n\r\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/train_tokenizer_lr_sweep_1e-4\r\n\r\nenv | grep SLURM\r\n\r\nXLA_FLAGS=--xla_gpu_autotune_level=0 srun python train_dynamics.py \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=96 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=1000 \\r\n --log \\r\n --log_checkpoint_interval=1000 \\r\n --name=dynamics-debug-run-$slurm_job_id \\r\n --tags dynamics debug \\r\n --entity instant-uv \\r\n --project jafar \\r\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\r\n --data_dir $array_records_dir\r\n ",,terminal_output +2166,2174497,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=4(x2)\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=692048\r\nSLURM_JOB_GPUS=0,1,2,3\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar\r\nSLURMD_NODENAME=hkn0509\r\nSLURM_JOB_START_TIME=1752667757\r\nSLURM_STEP_NODELIST=hkn0509\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1752703757\r\nSLURM_PMI2_SRUN_PORT=40311\r\nSLURM_CPUS_ON_NODE=24\r\nSLURM_JOB_CPUS_PER_NODE=24(x2)\r\nSLURM_GPUS_ON_NODE=4\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=accelerated\r\nSLURM_TRES_PER_TASK=cpu=5\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=2\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3350302\r\nSLURM_PTY_PORT=34583\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.198\r\nSLURM_PTY_WIN_ROW=43\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=5\r\nSLURM_NTASKS=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e10.hkn0509\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.198\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=300\r\nSLURM_NODELIST=hkn[0509,0511]\r\nSLURM_SRUN_COMM_PORT=35053\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NPROCS=8\r\nSLURM_NNODES=2\r\nSLURM_SUBMIT_HOST=hkn1990.localdomain\r\nSLURM_JOB_ID=3350302\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0509\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_NTASKS_PER_NODE=4\r\nSLURM_STEP_LAUNCHER_PORT=35053\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn[0509,0511]\r\n",,terminal_output +2167,2174648,"TERMINAL",0,0,"GpuFreq=control_disabled\r\nGpuFreq=control_disabled\r\n",,terminal_output +2168,2176703,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +2169,2176762,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +2170,2181258,"TERMINAL",0,0,"2025-07-16 14:45:21.647062: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2171,2181358,"TERMINAL",0,0,"2025-07-16 14:45:21.698199: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:45:21.706095: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:45:21.706095: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:45:21.707809: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:45:21.724705: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2172,2181462,"TERMINAL",0,0,"2025-07-16 14:45:21.848567: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2173,2181569,"TERMINAL",0,0,"2025-07-16 14:45:21.917413: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2174,2195681,"TERMINAL",0,0,"2025-07-16 14:45:36.071119: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2175,2196103,"TERMINAL",0,0,"2025-07-16 14:45:36.370808: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2176,2196148,"TERMINAL",0,0,"2025-07-16 14:45:36.509725: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2177,2196233,"TERMINAL",0,0,"2025-07-16 14:45:36.561480: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:45:36.609478: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:45:36.617283: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2178,2196945,"TERMINAL",0,0,"Running on 8 devices.\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (12, 14720, 512), (1, 512, 512).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 197, in broadcast_shapes\r\n return _broadcast_shapes_cached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 299, in wrapper\r\n return cached(config.trace_context() if trace_context_in_key else _ignore(),\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 293, in cached\r\n return f(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 203, in _broadcast_shapes_cached\r\n return _broadcast_shapes_uncached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 222, in _broadcast_shapes_uncached\r\n raise ValueError(f""Incompatible shapes for broadcasting: shapes={list(shapes)}"") from err\r\nValueError: Incompatible shapes for broadcasting: shapes=[(12, 14720, 512), (512, 512)]\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (12, 14720, 512), (1, 512, 512).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n logits = self.dynamics(vid_embed_flat)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 175, in __call__\r\n x = STBlock2(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 41, in __call__\r\n z = PositionalEncoding(self.dim)(x)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 26, in __call__\r\n x = x + self.pe[: x.shape[2]]\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 1083, in op\r\n return getattr(self.aval, f""_{name}"")(self, *args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 583, in deferring_binary_op\r\n return binary_op(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/ufunc_api.py"", line 182, in __call__\r\n return call(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/ufuncs.py"", line 1234, in add\r\n x, y = promote_args(""add"", x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 228, in promote_args\r\n return promote_shapes(fun_name, *promote_dtypes(*args))\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 64, in promote_shapes\r\n result_rank = len(lax.broadcast_shapes(*shapes))\r\nValueError: Incompatible shapes for broadcasting: shapes=[(12, 14720, 512), (512, 512)]\r\n",,terminal_output +2179,2197239,"TERMINAL",0,0,"Running on 8 devices.\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (12, 14720, 512), (1, 512, 512).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 197, in broadcast_shapes\r\n return _broadcast_shapes_cached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 299, in wrapper\r\n return cached(config.trace_context() if trace_context_in_key else _ignore(),\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 293, in cached\r\n return f(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 203, in _broadcast_shapes_cached\r\n return _broadcast_shapes_uncached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 222, in _broadcast_shapes_uncached\r\n raise ValueError(f""Incompatible shapes for broadcasting: shapes={list(shapes)}"") from err\r\nValueError: Incompatible shapes for broadcasting: shapes=[(12, 14720, 512), (512, 512)]\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (12, 14720, 512), (1, 512, 512).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n logits = self.dynamics(vid_embed_flat)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 175, in __call__\r\n x = STBlock2(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 41, in __call__\r\n z = PositionalEncoding(self.dim)(x)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 26, in __call__\r\n x = x + self.pe[: x.shape[2]]\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 1083, in op\r\n return getattr(self.aval, f""_{name}"")(self, *args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 583, in deferring_binary_op\r\n return binary_op(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/ufunc_api.py"", line 182, in __call__\r\n return call(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/ufuncs.py"", line 1234, in add\r\n x, y = promote_args(""add"", x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 228, in promote_args\r\n return promote_shapes(fun_name, *promote_dtypes(*args))\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 64, in promote_shapes\r\n result_rank = len(lax.broadcast_shapes(*shapes))\r\nValueError: Incompatible shapes for broadcasting: shapes=[(12, 14720, 512), (512, 512)]\r\n",,terminal_output +2180,2197344,"TERMINAL",0,0,"Running on 8 devices.\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (12, 14720, 512), (1, 512, 512).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 197, in broadcast_shapes\r\n return _broadcast_shapes_cached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 299, in wrapper\r\n return cached(config.trace_context() if trace_context_in_key else _ignore(),\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 293, in cached\r\n return f(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 203, in _broadcast_shapes_cached\r\n return _broadcast_shapes_uncached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 222, in _broadcast_shapes_uncached\r\n raise ValueError(f""Incompatible shapes for broadcasting: shapes={list(shapes)}"") from err\r\nValueError: Incompatible shapes for broadcasting: shapes=[(12, 14720, 512), (512, 512)]\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (12, 14720, 512), (1, 512, 512).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n logits = self.dynamics(vid_embed_flat)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 175, in __call__\r\n x = STBlock2(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 41, in __call__\r\n z = PositionalEncoding(self.dim)(x)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 26, in __call__\r\n x = x + self.pe[: x.shape[2]]\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 1083, in op\r\n return getattr(self.aval, f""_{name}"")(self, *args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 583, in deferring_binary_op\r\n return binary_op(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/ufunc_api.py"", line 182, in __call__\r\n return call(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/ufuncs.py"", line 1234, in add\r\n x, y = promote_args(""add"", x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 228, in promote_args\r\n return promote_shapes(fun_name, *promote_dtypes(*args))\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 64, in promote_shapes\r\n result_rank = len(lax.broadcast_shapes(*shapes))\r\nValueError: Incompatible shapes for broadcasting: shapes=[(12, 14720, 512), (512, 512)]\r\n",,terminal_output +2181,2197405,"TERMINAL",0,0,"Running on 8 devices.\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (12, 14720, 512), (1, 512, 512).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 197, in broadcast_shapes\r\n return _broadcast_shapes_cached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 299, in wrapper\r\n return cached(config.trace_context() if trace_context_in_key else _ignore(),\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 293, in cached\r\n return f(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 203, in _broadcast_shapes_cached\r\n return _broadcast_shapes_uncached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 222, in _broadcast_shapes_uncached\r\n raise ValueError(f""Incompatible shapes for broadcasting: shapes={list(shapes)}"") from err\r\nValueError: Incompatible shapes for broadcasting: shapes=[(12, 14720, 512), (512, 512)]\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (12, 14720, 512), (1, 512, 512).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n logits = self.dynamics(vid_embed_flat)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 175, in __call__\r\n x = STBlock2(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 41, in __call__\r\n z = PositionalEncoding(self.dim)(x)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 26, in __call__\r\n x = x + self.pe[: x.shape[2]]\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 1083, in op\r\n return getattr(self.aval, f""_{name}"")(self, *args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 583, in deferring_binary_op\r\n return binary_op(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/ufunc_api.py"", line 182, in __call__\r\n return call(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/ufuncs.py"", line 1234, in add\r\n x, y = promote_args(""add"", x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 228, in promote_args\r\n return promote_shapes(fun_name, *promote_dtypes(*args))\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 64, in promote_shapes\r\n result_rank = len(lax.broadcast_shapes(*shapes))\r\nValueError: Incompatible shapes for broadcasting: shapes=[(12, 14720, 512), (512, 512)]\r\n",,terminal_output +2182,2197479,"TERMINAL",0,0,"Running on 8 devices.\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (12, 14720, 512), (1, 512, 512).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 197, in broadcast_shapes\r\n return _broadcast_shapes_cached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 299, in wrapper\r\n return cached(config.trace_context() if trace_context_in_key else _ignore(),\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 293, in cached\r\n return f(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 203, in _broadcast_shapes_cached\r\n return _broadcast_shapes_uncached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 222, in _broadcast_shapes_uncached\r\n raise ValueError(f""Incompatible shapes for broadcasting: shapes={list(shapes)}"") from err\r\nValueError: Incompatible shapes for broadcasting: shapes=[(12, 14720, 512), (512, 512)]\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (12, 14720, 512), (1, 512, 512).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n logits = self.dynamics(vid_embed_flat)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 175, in __call__\r\n x = STBlock2(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 41, in __call__\r\n z = PositionalEncoding(self.dim)(x)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 26, in __call__\r\n x = x + self.pe[: x.shape[2]]\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 1083, in op\r\n return getattr(self.aval, f""_{name}"")(self, *args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 583, in deferring_binary_op\r\n return binary_op(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/ufunc_api.py"", line 182, in __call__\r\n return call(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/ufuncs.py"", line 1234, in add\r\n x, y = promote_args(""add"", x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 228, in promote_args\r\n return promote_shapes(fun_name, *promote_dtypes(*args))\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 64, in promote_shapes\r\n result_rank = len(lax.broadcast_shapes(*shapes))\r\nValueError: Incompatible shapes for broadcasting: shapes=[(12, 14720, 512), (512, 512)]\r\nRunning on 8 devices.\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (12, 14720, 512), (1, 512, 512).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 197, in broadcast_shapes\r\n return _broadcast_shapes_cached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 299, in wrapper\r\n return cached(config.trace_context() if trace_context_in_key else _ignore(),\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 293, in cached\r\n return f(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 203, in _broadcast_shapes_cached\r\n return _broadcast_shapes_uncached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 222, in _broadcast_shapes_uncached\r\n raise ValueError(f""Incompatible shapes for broadcasting: shapes={list(shapes)}"") from err\r\nValueError: Incompatible shapes for broadcasting: shapes=[(12, 14720, 512), (512, 512)]\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (12, 14720, 512), (1, 512, 512).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n logits = self.dynamics(vid_embed_flat)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 175, in __call__\r\n x = STBlock2(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 41, in __call__\r\n z = PositionalEncoding(self.dim)(x)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 26, in __call__\r\n x = x + self.pe[: x.shape[2]]\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 1083, in op\r\n return getattr(self.aval, f""_{name}"")(self, *args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 583, in deferring_binary_op\r\n return binary_op(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/ufunc_api.py"", line 182, in __call__\r\n return call(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/ufuncs.py"", line 1234, in add\r\n x, y = promote_args(""add"", x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 228, in promote_args\r\n return promote_shapes(fun_name, *promote_dtypes(*args))\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 64, in promote_shapes\r\n result_rank = len(lax.broadcast_shapes(*shapes))\r\nValueError: Incompatible shapes for broadcasting: shapes=[(12, 14720, 512), (512, 512)]\r\n",,terminal_output +2183,2197738,"TERMINAL",0,0,"2025-07-16 14:45:38.128552: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2184,2198254,"TERMINAL",0,0,"2025-07-16 14:45:38.641253: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2185,2199025,"TERMINAL",0,0,"Running on 8 devices.\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (12, 14720, 512), (1, 512, 512).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 197, in broadcast_shapes\r\n return _broadcast_shapes_cached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 299, in wrapper\r\n return cached(config.trace_context() if trace_context_in_key else _ignore(),\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 293, in cached\r\n return f(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 203, in _broadcast_shapes_cached\r\n return _broadcast_shapes_uncached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 222, in _broadcast_shapes_uncached\r\n raise ValueError(f""Incompatible shapes for broadcasting: shapes={list(shapes)}"") from err\r\nValueError: Incompatible shapes for broadcasting: shapes=[(12, 14720, 512), (512, 512)]\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (12, 14720, 512), (1, 512, 512).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n logits = self.dynamics(vid_embed_flat)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 175, in __call__\r\n x = STBlock2(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 41, in __call__\r\n z = PositionalEncoding(self.dim)(x)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 26, in __call__\r\n x = x + self.pe[: x.shape[2]]\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 1083, in op\r\n return getattr(self.aval, f""_{name}"")(self, *args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 583, in deferring_binary_op\r\n return binary_op(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/ufunc_api.py"", line 182, in __call__\r\n return call(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/ufuncs.py"", line 1234, in add\r\n x, y = promote_args(""add"", x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 228, in promote_args\r\n return promote_shapes(fun_name, *promote_dtypes(*args))\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 64, in promote_shapes\r\n result_rank = len(lax.broadcast_shapes(*shapes))\r\nValueError: Incompatible shapes for broadcasting: shapes=[(12, 14720, 512), (512, 512)]\r\n",,terminal_output +2186,2199573,"TERMINAL",0,0,"Running on 8 devices.\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (12, 14720, 512), (1, 512, 512).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 197, in broadcast_shapes\r\n return _broadcast_shapes_cached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 299, in wrapper\r\n return cached(config.trace_context() if trace_context_in_key else _ignore(),\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 293, in cached\r\n return f(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 203, in _broadcast_shapes_cached\r\n return _broadcast_shapes_uncached(*shapes)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 222, in _broadcast_shapes_uncached\r\n raise ValueError(f""Incompatible shapes for broadcasting: shapes={list(shapes)}"") from err\r\nValueError: Incompatible shapes for broadcasting: shapes=[(12, 14720, 512), (512, 512)]\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 219, in _broadcast_shapes_uncached\r\n return _try_broadcast_shapes(*rank_promoted_shapes, name='broadcast_shapes')\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/lax/lax.py"", line 136, in _try_broadcast_shapes\r\n raise TypeError(f'{name} got incompatible shapes for broadcasting: '\r\nTypeError: broadcast_shapes got incompatible shapes for broadcasting: (12, 14720, 512), (1, 512, 512).\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 199, in \r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 106, in __call__\r\n dyna_outputs = self.dynamics(outputs, training)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n logits = self.dynamics(vid_embed_flat)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 175, in __call__\r\n x = STBlock2(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 41, in __call__\r\n z = PositionalEncoding(self.dim)(x)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 26, in __call__\r\n x = x + self.pe[: x.shape[2]]\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 1083, in op\r\n return getattr(self.aval, f""_{name}"")(self, *args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 583, in deferring_binary_op\r\n return binary_op(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/ufunc_api.py"", line 182, in __call__\r\n return call(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/ufuncs.py"", line 1234, in add\r\n x, y = promote_args(""add"", x, y)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 228, in promote_args\r\n return promote_shapes(fun_name, *promote_dtypes(*args))\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/util.py"", line 64, in promote_shapes\r\n result_rank = len(lax.broadcast_shapes(*shapes))\r\nValueError: Incompatible shapes for broadcasting: shapes=[(12, 14720, 512), (512, 512)]\r\n",,terminal_output +2187,2200907,"TERMINAL",0,0,"srun: error: hkn0511: task 5: Exited with exit code 1\r\n",,terminal_output +2188,2200960,"TERMINAL",0,0,"srun: error: hkn0509: tasks 1-2: Exited with exit code 1\r\n",,terminal_output +2189,2201020,"TERMINAL",0,0,"srun: error: hkn0511: tasks 6-7: Exited with exit code 1\r\n",,terminal_output +2190,2201077,"TERMINAL",0,0,"srun: error: hkn0509: task 0: Exited with exit code 1\r\n",,terminal_output +2191,2201261,"TERMINAL",0,0,"srun: error: hkn0511: task 4: Exited with exit code 1\r\n",,terminal_output +2192,2201326,"TERMINAL",0,0,"srun: error: hkn0509: task 3: Exited with exit code 1\r\n]0;tum_cte0515@hkn0509:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0509 jafar]$ ",,terminal_output +2193,2220229,"models/dynamics.py",0,0,"",python,tab +2194,2222682,"models/dynamics.py",2861,0,"",python,selection_mouse +2195,2223164,"models/dynamics.py",2933,0,"",python,selection_mouse +2196,2224148,"models/dynamics.py",2932,0,"",python,selection_command +2197,2224830,"models/dynamics.py",2892,77,"",python,content +2198,2224913,"models/dynamics.py",2900,0,"",python,selection_command +2199,2224932,"models/dynamics.py",2826,0,"",python,selection_command +2200,2225996,"models/dynamics.py",2900,0,"vid_embed_flat = einops.rearrange(vid_embed, ""b t n e -> b (t n) e"")\n ",python,content +2201,2226031,"models/dynamics.py",2932,0,"",python,selection_command +2202,2227260,"models/dynamics.py",2892,77,"",python,content +2203,2227357,"models/dynamics.py",2900,0,"",python,selection_command +2204,2227588,"models/dynamics.py",2901,0,"",python,selection_command +2205,2228075,"models/dynamics.py",2902,0,"",python,selection_command +2206,2228106,"models/dynamics.py",2903,0,"",python,selection_command +2207,2228142,"models/dynamics.py",2904,0,"",python,selection_command +2208,2228227,"models/dynamics.py",2905,0,"",python,selection_command +2209,2228227,"models/dynamics.py",2906,0,"",python,selection_command +2210,2228245,"models/dynamics.py",2907,0,"",python,selection_command +2211,2228297,"models/dynamics.py",2908,0,"",python,selection_command +2212,2228327,"models/dynamics.py",2909,0,"",python,selection_command +2213,2228343,"models/dynamics.py",2910,0,"",python,selection_command +2214,2228360,"models/dynamics.py",2911,0,"",python,selection_command +2215,2228377,"models/dynamics.py",2912,0,"",python,selection_command +2216,2228427,"models/dynamics.py",2913,0,"",python,selection_command +2217,2228447,"models/dynamics.py",2914,0,"",python,selection_command +2218,2228505,"models/dynamics.py",2915,0,"",python,selection_command +2219,2228514,"models/dynamics.py",2916,0,"",python,selection_command +2220,2228544,"models/dynamics.py",2917,0,"",python,selection_command +2221,2228575,"models/dynamics.py",2918,0,"",python,selection_command +2222,2228603,"models/dynamics.py",2919,0,"",python,selection_command +2223,2228640,"models/dynamics.py",2920,0,"",python,selection_command +2224,2228665,"models/dynamics.py",2921,0,"",python,selection_command +2225,2228701,"models/dynamics.py",2922,0,"",python,selection_command +2226,2228751,"models/dynamics.py",2923,0,"",python,selection_command +2227,2228752,"models/dynamics.py",2924,0,"",python,selection_command +2228,2228803,"models/dynamics.py",2925,0,"",python,selection_command +2229,2228813,"models/dynamics.py",2926,0,"",python,selection_command +2230,2228845,"models/dynamics.py",2927,0,"",python,selection_command +2231,2228878,"models/dynamics.py",2928,0,"",python,selection_command +2232,2228893,"models/dynamics.py",2929,0,"",python,selection_command +2233,2229117,"models/dynamics.py",2930,0,"",python,selection_command +2234,2229363,"models/dynamics.py",2931,0,"",python,selection_command +2235,2229545,"models/dynamics.py",2932,0,"",python,selection_command +2236,2229813,"models/dynamics.py",2932,5,"",python,content +2237,2231561,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/yolo-runs/tester.sh",,terminal_output +2238,2231768,"TERMINAL",0,0,"\r\n[?2004l\r\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\r\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\r\n\r\njob_name=""debug""\r\nslurm_job_id=""debug-mihir""\r\n\r\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/train_tokenizer_lr_sweep_1e-4\r\n\r\nenv | grep SLURM\r\n\r\nXLA_FLAGS=--xla_gpu_autotune_level=0 srun python train_dynamics.py \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=96 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=1000 \\r\n --log \\r\n --log_checkpoint_interval=1000 \\r\n --name=dynamics-debug-run-$slurm_job_id \\r\n --tags dynamics debug \\r\n --entity instant-uv \\r\n --project jafar \\r\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\r\n --data_dir $array_records_dir\r\n ",,terminal_output +2239,2231883,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=4(x2)\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=692048\r\nSLURM_JOB_GPUS=0,1,2,3\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar\r\nSLURMD_NODENAME=hkn0509\r\nSLURM_JOB_START_TIME=1752667757\r\nSLURM_STEP_NODELIST=hkn0509\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1752703757\r\nSLURM_PMI2_SRUN_PORT=40311\r\nSLURM_CPUS_ON_NODE=24\r\nSLURM_JOB_CPUS_PER_NODE=24(x2)\r\nSLURM_GPUS_ON_NODE=4\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=accelerated\r\nSLURM_TRES_PER_TASK=cpu=5\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=2\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3350302\r\nSLURM_PTY_PORT=34583\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.198\r\nSLURM_PTY_WIN_ROW=43\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=5\r\nSLURM_NTASKS=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e10.hkn0509\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.198\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=300\r\nSLURM_NODELIST=hkn[0509,0511]\r\nSLURM_SRUN_COMM_PORT=35053\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NPROCS=8\r\nSLURM_NNODES=2\r\nSLURM_SUBMIT_HOST=hkn1990.localdomain\r\nSLURM_JOB_ID=3350302\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0509\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_NTASKS_PER_NODE=4\r\nSLURM_STEP_LAUNCHER_PORT=35053\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn[0509,0511]\r\n",,terminal_output +2240,2232009,"TERMINAL",0,0,"GpuFreq=control_disabled\r\nGpuFreq=control_disabled\r\n",,terminal_output +2241,2233969,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +2242,2234029,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +2243,2234233,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +2244,2238593,"TERMINAL",0,0,"2025-07-16 14:46:18.928428: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:46:18.929688: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:46:18.929703: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:46:18.934923: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:46:18.943388: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:46:18.953337: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:46:18.953496: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:46:18.984253: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2245,2253081,"TERMINAL",0,0,"2025-07-16 14:46:33.470669: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2246,2253305,"TERMINAL",0,0,"2025-07-16 14:46:33.673322: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:46:33.687927: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2247,2253394,"TERMINAL",0,0,"2025-07-16 14:46:33.726612: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:46:33.735623: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2248,2253458,"TERMINAL",0,0,"2025-07-16 14:46:33.798415: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:46:33.841145: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-16 14:46:33.841169: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2249,2254051,"TERMINAL",0,0,"Running on 8 devices.\r\nEntering jdb:\r\n",,terminal_output +2250,2254308,"TERMINAL",0,0,"Running on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\n",,terminal_output +2251,2254369,"TERMINAL",0,0,"Running on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\n",,terminal_output +2252,2254427,"TERMINAL",0,0,"Running on 8 devices.\r\nEntering jdb:\r\n",,terminal_output +2253,2254521,"TERMINAL",0,0,"Running on 8 devices.\r\nEntering jdb:\r\nRunning on 8 devices.\r\nEntering jdb:\r\n",,terminal_output +2254,2273292,"TERMINAL",0,0,"[?25ll[?25h",,terminal_output +2255,2273395,"TERMINAL",0,0,"\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(40)\r\n @nn.remat\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n # --- Spatial attention ---\r\n-> jax.debug.breakpoint()\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(40)\r\n @nn.remat\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n # --- Spatial attention ---\r\n-> jax.debug.breakpoint()\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(40)\r\n @nn.remat\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n # --- Spatial attention ---\r\n-> jax.debug.breakpoint()\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(40)\r\n @nn.remat\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n # --- Spatial attention ---\r\n-> jax.debug.breakpoint()\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(40)\r\n @nn.remat\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n # --- Spatial attention ---\r\n-> jax.debug.breakpoint()\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(40)\r\n @nn.remat\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n # --- Spatial attention ---\r\n-> jax.debug.breakpoint()\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(40)\r\n @nn.remat\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n # --- Spatial attention ---\r\n-> jax.debug.breakpoint()\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(40)\r\n @nn.remat\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n # --- Spatial attention ---\r\n-> jax.debug.breakpoint()\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n",,terminal_output +2256,2280654,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",0,0,"import math\nfrom typing import Dict, Tuple\n\nfrom flax import linen as nn\nimport jax\nimport jax.numpy as jnp\n\n\nclass PositionalEncoding(nn.Module):\n """"""https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/JAX/tutorial6/Transformers_and_MHAttention.html""""""\n\n d_model: int # Hidden dimensionality of the input.\n max_len: int = 5000 # Maximum length of a sequence to expect.\n\n def setup(self):\n # Create matrix of [SeqLen, HiddenDim] representing the positional encoding for max_len inputs\n self.pe = jnp.zeros((self.max_len, self.d_model))\n position = jnp.arange(0, self.max_len, dtype=jnp.float32)[:, None]\n div_term = jnp.exp(\n jnp.arange(0, self.d_model, 2) * (-math.log(10000.0) / self.d_model)\n )\n self.pe = self.pe.at[:, 0::2].set(jnp.sin(position * div_term))\n self.pe = self.pe.at[:, 1::2].set(jnp.cos(position * div_term))\n\n def __call__(self, x):\n x = x + self.pe[: x.shape[2]]\n return x\n\nclass STBlock2(nn.Module):\n dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n jax.debug.breakpoint()\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n jax.debug.breakpoint()\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n jax.debug.breakpoint()\n x = x.swapaxes(1, 2)\n jax.debug.breakpoint()\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n jax.debug.breakpoint()\n\n return x\n\nclass CausalTransformerBlock(nn.Module):\n model_dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # LayerNorm + Causal Self-Attention\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n seq_len = z.shape[1]\n # Causal mask: (1, 1, seq_len, seq_len)\n causal_mask = jnp.tril(jnp.ones((seq_len, seq_len), dtype=bool))\n jax.debug.breakpoint()\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n # Feedforward\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n jax.debug.breakpoint()\n z = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n jax.debug.breakpoint()\n z = nn.gelu(z)\n jax.debug.breakpoint()\n x = x + z\n jax.debug.breakpoint()\n\n return x\n\nclass CausalTransformer(nn.Module):\n model_dim: int\n out_dim: int\n num_blocks: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # Input projection and normalization\n x = nn.Sequential(\n [\n nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n ),\n nn.Dense(self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n ),\n nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n ),\n ]\n )(x)\n # Causal transformer blocks\n for _ in range(self.num_blocks):\n x = STBlock2(\n dim=self.model_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # x = CausalTransformerBlock(\n # model_dim=self.model_dim,\n # num_heads=self.num_heads,\n # dropout=self.dropout,\n # param_dtype=self.param_dtype,\n # dtype=self.dtype,\n # )(x)\n # Output projection\n x = nn.Dense(\n self.out_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n return x # (B, T, E)\n\n\nclass STBlock(nn.Module):\n dim: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.remat\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n x = x + z\n\n # --- Temporal attention ---\n x = x.swapaxes(1, 2)\n z = PositionalEncoding(self.dim)(x)\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n causal_mask = jnp.tri(z.shape[-2])\n z = nn.MultiHeadAttention(\n num_heads=self.num_heads,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z, mask=causal_mask)\n x = x + z\n x = x.swapaxes(1, 2)\n\n # --- Feedforward ---\n z = nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\n z = nn.Dense(\n self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(z)\n z = nn.gelu(z)\n x = x + z\n\n return x\n\n\nclass STTransformer(nn.Module):\n model_dim: int\n out_dim: int\n num_blocks: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n @nn.compact\n def __call__(self, x: jax.Array) -> jax.Array:\n x = nn.Sequential(\n [\n nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n ),\n nn.Dense(self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n ),\n nn.LayerNorm(\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n ),\n ]\n )(x)\n for _ in range(self.num_blocks):\n x = STBlock(\n dim=self.model_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n x = nn.Dense(\n self.out_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )(x)\n return x # (B, T, E)\n\n\ndef normalize(x):\n return x / (jnp.linalg.norm(x, ord=2, axis=-1, keepdims=True) + 1e-8)\n\n\nclass VectorQuantizer(nn.Module):\n latent_dim: int\n num_latents: int\n dropout: float\n\n def setup(self):\n self.codebook = normalize(\n self.param(\n ""codebook"",\n nn.initializers.lecun_uniform(),\n (self.num_latents, self.latent_dim),\n )\n )\n self.drop = nn.Dropout(self.dropout, deterministic=False)\n\n def __call__(\n self, x: jax.Array, training: bool\n ) -> Tuple[jax.Array, jax.Array, jax.Array, jax.Array]:\n # --- Compute distances ---\n x = normalize(x)\n codebook = normalize(self.codebook)\n distance = -jnp.matmul(x, codebook.T)\n if training:\n dropout_key = self.make_rng(""dropout"")\n distance = self.drop(distance, rng=dropout_key)\n\n # --- Get indices and embeddings ---\n indices = jnp.argmin(distance, axis=-1)\n z = self.codebook[indices]\n\n # --- Straight through estimator ---\n z_q = x + jax.lax.stop_gradient(z - x)\n return z_q, z, x, indices\n\n def get_codes(self, indices: jax.Array):\n return self.codebook[indices]\n",python,tab +2257,2283028,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",0,0,"",python,tab +2258,2288828,"TERMINAL",0,0,"[?25lx[?25h",,terminal_output +2259,2289162,"TERMINAL",0,0,"[?25l.[?25h",,terminal_output +2260,2289385,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +2261,2289477,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +2262,2289602,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +2263,2289666,"TERMINAL",0,0,"[?25lp[?25h",,terminal_output +2264,2289770,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +2265,2289955,"TERMINAL",0,0,"\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n",,terminal_output +2266,2295165,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +2267,2295995,"TERMINAL",0,0,"\r\n",,terminal_output +2268,2296227,"TERMINAL",0,0,"(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n",,terminal_output +2269,2297913,"TERMINAL",0,0,"[?25ll[?25h",,terminal_output +2270,2298083,"TERMINAL",0,0,"\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(47)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n causal_mask = jnp.tri(z.shape[-2])\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(47)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n causal_mask = jnp.tri(z.shape[-2])\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(47)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n causal_mask = jnp.tri(z.shape[-2])\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(47)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n causal_mask = jnp.tri(z.shape[-2])\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(47)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n causal_mask = jnp.tri(z.shape[-2])\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(47)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n causal_mask = jnp.tri(z.shape[-2])\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(47)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n causal_mask = jnp.tri(z.shape[-2])\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(47)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n causal_mask = jnp.tri(z.shape[-2])\r\n-> jax.debug.breakpoint()\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n",,terminal_output +2271,2301022,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +2272,2301529,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +2273,2301759,"TERMINAL",0,0,"[?25lu[?25h",,terminal_output +2274,2301895,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +2275,2302187,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +2276,2302292,"TERMINAL",0,0,"[?25ll[?25h",,terminal_output +2277,2302749,"TERMINAL",0,0,"[?25l_[?25h",,terminal_output +2278,2303507,"TERMINAL",0,0,"[?25lm[?25h",,terminal_output +2279,2303575,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +2280,2303636,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +2281,2303698,"TERMINAL",0,0,"[?25lk[?25h",,terminal_output +2282,2303946,"TERMINAL",0,0,"[?25l.[?25h",,terminal_output +2283,2304138,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +2284,2304338,"TERMINAL",0,0,"[?25lh[?25h[?25la[?25h",,terminal_output +2285,2304558,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +2286,2304698,"TERMINAL",0,0,"[?25lp[?25h",,terminal_output +2287,2305207,"TERMINAL",0,0,"[?25lp[?25h[?25le[?25h",,terminal_output +2288,2305408,"TERMINAL",0,0,"\r\n(jdb) (920, 920)\r\n(jdb) (920, 920)\r\n(jdb) (920, 920)\r\n(jdb) (920, 920)\r\n(jdb) (920, 920)\r\n(jdb) (920, 920)\r\n(jdb) (920, 920)\r\n(jdb) (920, 920)\r\n",,terminal_output +2289,2311543,"TERMINAL",0,0,"[?25lz[?25h",,terminal_output +2290,2311883,"TERMINAL",0,0,"[?25l.[?25h",,terminal_output +2291,2312086,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +2292,2312146,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +2293,2312297,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +2294,2312403,"TERMINAL",0,0,"[?25lp[?25h",,terminal_output +2295,2312465,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +2296,2312661,"TERMINAL",0,0,"\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n",,terminal_output +2297,2325447,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +2298,2325594,"TERMINAL",0,0,"\r\n",,terminal_output +2299,2325841,"TERMINAL",0,0,"(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n",,terminal_output +2300,2327092,"TERMINAL",0,0,"[?25ll[?25h",,terminal_output +2301,2327281,"TERMINAL",0,0,"\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(56)\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z, mask=causal_mask)\r\n x = x + z\r\n-> jax.debug.breakpoint()\r\n \r\n # --- Temporal attention ---\r\n x = x.swapaxes(1, 2)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(56)\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z, mask=causal_mask)\r\n x = x + z\r\n-> jax.debug.breakpoint()\r\n \r\n # --- Temporal attention ---\r\n x = x.swapaxes(1, 2)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(56)\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z, mask=causal_mask)\r\n x = x + z\r\n-> jax.debug.breakpoint()\r\n \r\n # --- Temporal attention ---\r\n x = x.swapaxes(1, 2)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(56)\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z, mask=causal_mask)\r\n x = x + z\r\n-> jax.debug.breakpoint()\r\n \r\n # --- Temporal attention ---\r\n x = x.swapaxes(1, 2)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(56)\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z, mask=causal_mask)\r\n x = x + z\r\n-> jax.debug.breakpoint()\r\n \r\n # --- Temporal attention ---\r\n x = x.swapaxes(1, 2)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(56)\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z, mask=causal_mask)\r\n x = x + z\r\n-> jax.debug.breakpoint()\r\n \r\n # --- Temporal attention ---\r\n x = x.swapaxes(1, 2)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(56)\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z, mask=causal_mask)\r\n x = x + z\r\n-> jax.debug.breakpoint()\r\n \r\n # --- Temporal attention ---\r\n x = x.swapaxes(1, 2)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(56)\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z, mask=causal_mask)\r\n x = x + z\r\n-> jax.debug.breakpoint()\r\n \r\n # --- Temporal attention ---\r\n x = x.swapaxes(1, 2)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n",,terminal_output +2302,2329562,"TERMINAL",0,0,"[?25lx[?25h",,terminal_output +2303,2329774,"TERMINAL",0,0,"[?25l.[?25h",,terminal_output +2304,2329879,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +2305,2330014,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +2306,2330198,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +2307,2330406,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +2308,2330875,"TERMINAL",0,0,"[?25lp[?25h",,terminal_output +2309,2330936,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +2310,2331101,"TERMINAL",0,0,"\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n(jdb) (12, 16, 920, 512)\r\n",,terminal_output +2311,2332440,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +2312,2332500,"TERMINAL",0,0,"\r\n",,terminal_output +2313,2332627,"TERMINAL",0,0,"(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n",,terminal_output +2314,2332688,"TERMINAL",0,0,"(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n",,terminal_output +2315,2334391,"TERMINAL",0,0,"[?25ll[?25h",,terminal_output +2316,2334754,"TERMINAL",0,0,"\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(65)\r\n x = x.swapaxes(1, 2)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n-> jax.debug.breakpoint()\r\n causal_mask = jnp.tri(z.shape[-2])\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(65)\r\n x = x.swapaxes(1, 2)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n-> jax.debug.breakpoint()\r\n causal_mask = jnp.tri(z.shape[-2])\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(65)\r\n x = x.swapaxes(1, 2)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n-> jax.debug.breakpoint()\r\n causal_mask = jnp.tri(z.shape[-2])\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(65)\r\n x = x.swapaxes(1, 2)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n-> jax.debug.breakpoint()\r\n causal_mask = jnp.tri(z.shape[-2])\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(65)\r\n x = x.swapaxes(1, 2)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n-> jax.debug.breakpoint()\r\n causal_mask = jnp.tri(z.shape[-2])\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(65)\r\n x = x.swapaxes(1, 2)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n-> jax.debug.breakpoint()\r\n causal_mask = jnp.tri(z.shape[-2])\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(65)\r\n x = x.swapaxes(1, 2)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n-> jax.debug.breakpoint()\r\n causal_mask = jnp.tri(z.shape[-2])\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(65)\r\n x = x.swapaxes(1, 2)\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n-> jax.debug.breakpoint()\r\n causal_mask = jnp.tri(z.shape[-2])\r\n z = nn.MultiHeadAttention(\r\n num_heads=self.num_heads,\r\n qkv_features=self.dim,\r\n dropout_rate=self.dropout,\r\n",,terminal_output +2317,2347408,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +2318,2347549,"TERMINAL",0,0,"\r\n",,terminal_output +2319,2347714,"TERMINAL",0,0,"(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n",,terminal_output +2320,2350908,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +2321,2351225,"TERMINAL",0,0,"\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n",,terminal_output +2322,2352452,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +2323,2352635,"TERMINAL",0,0,"\r\n",,terminal_output +2324,2352696,"TERMINAL",0,0,"(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n",,terminal_output +2325,2353602,"TERMINAL",0,0,"[?25ll[?25h",,terminal_output +2326,2353739,"TERMINAL",0,0,"\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(84)\r\n \r\n # --- Feedforward ---\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n-> jax.debug.breakpoint()\r\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\r\n z = nn.Dense(\r\n self.dim,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(84)\r\n \r\n # --- Feedforward ---\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n-> jax.debug.breakpoint()\r\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\r\n z = nn.Dense(\r\n self.dim,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(84)\r\n \r\n # --- Feedforward ---\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n-> jax.debug.breakpoint()\r\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\r\n z = nn.Dense(\r\n self.dim,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(84)\r\n \r\n # --- Feedforward ---\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n-> jax.debug.breakpoint()\r\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\r\n z = nn.Dense(\r\n self.dim,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(84)\r\n \r\n # --- Feedforward ---\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n-> jax.debug.breakpoint()\r\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\r\n z = nn.Dense(\r\n self.dim,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(84)\r\n \r\n # --- Feedforward ---\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n-> jax.debug.breakpoint()\r\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\r\n z = nn.Dense(\r\n self.dim,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(84)\r\n \r\n # --- Feedforward ---\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n-> jax.debug.breakpoint()\r\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\r\n z = nn.Dense(\r\n self.dim,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(84)\r\n \r\n # --- Feedforward ---\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(x)\r\n-> jax.debug.breakpoint()\r\n # FIXME (f.srambical): Here, the attention hidden dimension is the same as the FFN's. Usually, FFN hidden dimension is 4x model_dim\r\n z = nn.Dense(\r\n self.dim,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n",,terminal_output +2327,2354972,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +2328,2355128,"TERMINAL",0,0,"\r\n",,terminal_output +2329,2355243,"TERMINAL",0,0,"(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n",,terminal_output +2330,2355799,"TERMINAL",0,0,"[?25ll[?25h",,terminal_output +2331,2355976,"TERMINAL",0,0,"\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(93)\r\n self.dim,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n z = nn.gelu(z)\r\n x = x + z\r\n-> jax.debug.breakpoint()\r\n \r\n return x\r\n \r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(93)\r\n self.dim,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n z = nn.gelu(z)\r\n x = x + z\r\n-> jax.debug.breakpoint()\r\n \r\n return x\r\n \r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(93)\r\n self.dim,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n z = nn.gelu(z)\r\n x = x + z\r\n-> jax.debug.breakpoint()\r\n \r\n return x\r\n \r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(93)\r\n self.dim,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n z = nn.gelu(z)\r\n x = x + z\r\n-> jax.debug.breakpoint()\r\n \r\n return x\r\n \r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(93)\r\n self.dim,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n z = nn.gelu(z)\r\n x = x + z\r\n-> jax.debug.breakpoint()\r\n \r\n return x\r\n \r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(93)\r\n self.dim,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n z = nn.gelu(z)\r\n x = x + z\r\n-> jax.debug.breakpoint()\r\n \r\n return x\r\n \r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(93)\r\n self.dim,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n z = nn.gelu(z)\r\n x = x + z\r\n-> jax.debug.breakpoint()\r\n \r\n return x\r\n \r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(93)\r\n self.dim,\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n z = nn.gelu(z)\r\n x = x + z\r\n-> jax.debug.breakpoint()\r\n \r\n return x\r\n \r\n",,terminal_output +2332,2356805,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +2333,2357075,"TERMINAL",0,0,"\r\n",,terminal_output +2334,2357443,"TERMINAL",0,0,"(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n(jdb) Entering jdb:\r\n",,terminal_output +2335,2358741,"TERMINAL",0,0,"[?25ll[?25h",,terminal_output +2336,2358918,"TERMINAL",0,0,"\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(40)\r\n @nn.remat\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n # --- Spatial attention ---\r\n-> jax.debug.breakpoint()\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(40)\r\n @nn.remat\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n # --- Spatial attention ---\r\n-> jax.debug.breakpoint()\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(40)\r\n @nn.remat\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n # --- Spatial attention ---\r\n-> jax.debug.breakpoint()\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(40)\r\n @nn.remat\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n # --- Spatial attention ---\r\n-> jax.debug.breakpoint()\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(40)\r\n @nn.remat\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n # --- Spatial attention ---\r\n-> jax.debug.breakpoint()\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(40)\r\n @nn.remat\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n # --- Spatial attention ---\r\n-> jax.debug.breakpoint()\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(40)\r\n @nn.remat\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n # --- Spatial attention ---\r\n-> jax.debug.breakpoint()\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n(jdb) > /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py(40)\r\n @nn.remat\r\n @nn.compact\r\n def __call__(self, x: jax.Array) -> jax.Array:\r\n # --- Spatial attention ---\r\n-> jax.debug.breakpoint()\r\n z = PositionalEncoding(self.dim)(x)\r\n z = nn.LayerNorm(\r\n param_dtype=self.param_dtype,\r\n dtype=self.dtype,\r\n )(z)\r\n",,terminal_output +2337,2362132,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",0,0,"",python,tab +2338,2362133,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2962,0,"",python,selection_mouse +2339,2362858,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2944,31,"",python,content +2340,2362937,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2926,0,"",python,selection_command +2341,2363108,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2903,0,"",python,selection_command +2342,2363239,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2890,0,"",python,selection_command +2343,2363407,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2860,0,"",python,selection_command +2344,2363537,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2818,0,"",python,selection_command +2345,2363671,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2796,0,"",python,selection_command +2346,2363914,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2774,0,"",python,selection_command +2347,2364155,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2634,0,"",python,selection_command +2348,2364297,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2603,0,"",python,selection_command +2349,2364427,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2590,0,"",python,selection_command +2350,2364739,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2603,0,"",python,selection_command +2351,2365112,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2603,31,"",python,content +2352,2365155,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2611,0,"",python,selection_command +2353,2365156,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2598,0,"",python,selection_command +2354,2365335,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2568,0,"",python,selection_command +2355,2365458,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2526,0,"",python,selection_command +2356,2365598,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2500,0,"",python,selection_command +2357,2365743,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2470,0,"",python,selection_command +2358,2366065,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2461,0,"",python,selection_command +2359,2366209,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2438,0,"",python,selection_command +2360,2366604,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2430,31,"",python,content +2361,2366711,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2401,0,"",python,selection_command +2362,2366865,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2370,0,"",python,selection_command +2363,2367359,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2370,31,"",python,content +2364,2367451,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2378,0,"",python,selection_command +2365,2367452,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2360,0,"",python,selection_command +2366,2367568,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2329,0,"",python,selection_command +2367,2367748,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2299,0,"",python,selection_command +2368,2367908,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2257,0,"",python,selection_command +2369,2368057,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2218,0,"",python,selection_command +2370,2368188,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2183,0,"",python,selection_command +2371,2368333,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2145,0,"",python,selection_command +2372,2368540,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2110,0,"",python,selection_command +2373,2368634,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2067,0,"",python,selection_command +2374,2368805,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2036,0,"",python,selection_command +2375,2369184,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2028,31,"",python,content +2376,2369250,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",2036,0,"",python,selection_command +2377,2371149,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",1793,0,"",python,selection_mouse +2378,2371637,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",1775,31,"",python,content +2379,2372617,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",1493,0,"",python,selection_mouse +2380,2373099,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",1476,31,"",python,content +2381,2373123,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",1484,0,"",python,selection_command +2382,2374847,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py",1268,0,"",python,selection_mouse diff --git a/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-69e8c2c1-3349-4fa0-950c-3777ceb9c3751758621585749-2025_09_23-12.01.04.60/source.csv b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-69e8c2c1-3349-4fa0-950c-3777ceb9c3751758621585749-2025_09_23-12.01.04.60/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..e12104f1135996a6ad84990a74c9985daf2a8baf --- /dev/null +++ b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-69e8c2c1-3349-4fa0-950c-3777ceb9c3751758621585749-2025_09_23-12.01.04.60/source.csv @@ -0,0 +1,714 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +2,416,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"12:01:04 PM [info] Activating crowd-code\n12:01:04 PM [info] Recording started\n12:01:04 PM [info] Initializing git provider using file system watchers...\n12:01:04 PM [info] Git repository found\n12:01:04 PM [info] Git provider initialized successfully\n12:01:04 PM [info] Initial git state: [object Object]\n",Log,tab +3,1615,"extension-output-pdoom-org.crowd-code-#1-crowd-code",41,0,"",Log,selection_command +4,2200,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"",Log,selection_command +5,3789,"TERMINAL",0,0,"bash",,terminal_focus +6,4679,"TERMINAL",0,0,"bash",,terminal_focus +7,6266,"TERMINAL",0,0,"salloc --time=01:00:00 --partition=dev_accelerated --nodes=1 --gres=gpu:1 --cpus-per-task=8",,terminal_command +8,6324,"TERMINAL",0,0,"]633;Csalloc: Granted job allocation 3512647\r\nsalloc: Waiting for resource configuration\r\n",,terminal_output +9,32134,"TERMINAL",0,0,"s",,terminal_output +10,32215,"TERMINAL",0,0,"o",,terminal_output +11,32340,"TERMINAL",0,0,"u",,terminal_output +12,32392,"TERMINAL",0,0,"r",,terminal_output +13,32598,"TERMINAL",0,0,"c",,terminal_output +14,32705,"TERMINAL",0,0,"e",,terminal_output +15,32864,"TERMINAL",0,0," ",,terminal_output +16,32944,"TERMINAL",0,0,".",,terminal_output +17,33133,"TERMINAL",0,0,"salloc: Nodes hkn0403 are ready for job\r\n",,terminal_output +18,33288,"TERMINAL",0,0,"source .v",,terminal_output +19,33680,"TERMINAL",0,0,"e",,terminal_output +20,33800,"TERMINAL",0,0,"n",,terminal_output +21,34039,"TERMINAL",0,0,"]0;tum_cte0515@hkn0403:~/Projects/jasmine[?2004h[tum_cte0515@hkn0403 jasmine]$ source .venv",,terminal_output +22,34813,"TERMINAL",0,0,"/",,terminal_output +23,35212,"TERMINAL",0,0,"b",,terminal_output +24,35329,"TERMINAL",0,0,"in/",,terminal_output +25,35655,"TERMINAL",0,0,"a",,terminal_output +26,35707,"TERMINAL",0,0,"c",,terminal_output +27,35925,"TERMINAL",0,0,"tivate",,terminal_output +28,36223,"TERMINAL",0,0,"\r\n[?2004l\r]0;tum_cte0515@hkn0403:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0403 jasmine]$ ",,terminal_output +29,36824,"TERMINAL",0,0,"ls",,terminal_output +30,36935,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +31,37086,"TERMINAL",0,0,"debug frame-knoms.png frames gifs killer_partition.sh LICENSE logs overfit_dir __pycache__ requirements-franz.txt sample.py scripts_cremers test.py train_dynamics.py train_tokenizer.py wandb\r\ndiff.diff frame.png genie.py input_pipeline killer.sh log.log models overfit_dir.zip README.md requirements.txt samples slurm tests train_lam.py utils\r\n]0;tum_cte0515@hkn0403:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0403 jasmine]$ ",,terminal_output +32,37532,"TERMINAL",0,0,"qu",,terminal_output +33,37725,"TERMINAL",0,0,"eu",,terminal_output +34,37858,"TERMINAL",0,0,"e",,terminal_output +35,38234,"TERMINAL",0,0,"\r\n[?2004l\r[?1049h(B[?7hEvery 1.0s: squeue --mehkn0403.localdomain: Tue Sep 23 12:01:41 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3511158 accelerat train_dy tum_cte0 PD\t0:00\t 1 (Priority)3511159 accelerat train_dy tum_cte0 PD\t0:00\t 1 (Priority)3512647 dev_accel interact tum_cte0 R\t0:33\t 1 hkn0403",,terminal_output +36,40140,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn0403:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0403 jasmine]$ ",,terminal_output +37,48816,"TERMINAL",0,0,"bash",,terminal_focus +38,49743,"TERMINAL",0,0,"ls",,terminal_command +39,53573,"TERMINAL",0,0,"cd $ws_dir",,terminal_command +40,54015,"TERMINAL",0,0,"ls",,terminal_command +41,54056,"TERMINAL",0,0,"]633;C",,terminal_output +42,54145,"TERMINAL",0,0,"checkpoints count_items.sh data data_atari data_breakout 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""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0213.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0214.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0215.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0216.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0217.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0218.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0219.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0220.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0221.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0222.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0223.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0224.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0225.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0226.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0227.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0228.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0229.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0230.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0231.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0232.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0233.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0234.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0235.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0236.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0237.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0238.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0239.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0240.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0241.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0242.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0243.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0244.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0245.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0246.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0247.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0248.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n },\n {\n ""path"": ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/test/data_0249.array_record"",\n ""num_chunks"": 100,\n ""avg_seq_len"": 16.0\n }\n ]\n}",json,content +54,77467,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/metadata.json",81,0,"",json,selection_mouse +55,77701,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/metadata.json",81,1,"5",json,selection_mouse +56,77747,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/metadata.json",81,2,"50",json,selection_mouse 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for more information.\r\n>>> ",,terminal_output +64,95756,"TERMINAL",0,0,"5",,terminal_output +65,95942,"TERMINAL",0,0,"0",,terminal_output +66,96256,"TERMINAL",0,0,"0",,terminal_output +67,96742,"TERMINAL",0,0,"",,terminal_output +68,96850,"TERMINAL",0,0,"",,terminal_output +69,97635,"TERMINAL",0,0,"0",,terminal_output +70,97686,"TERMINAL",0,0,"0",,terminal_output +71,98051,"TERMINAL",0,0,"_",,terminal_output +72,98552,"TERMINAL",0,0,"0",,terminal_output +73,98781,"TERMINAL",0,0,"0",,terminal_output +74,98845,"TERMINAL",0,0,"0",,terminal_output +75,99581,"TERMINAL",0,0,"*",,terminal_output +76,101063,"TERMINAL",0,0,"1",,terminal_output +77,101503,"TERMINAL",0,0,"6",,terminal_output +78,102026,"TERMINAL",0,0,"\r\n8000000\r\n>>> ",,terminal_output +79,107101,"TERMINAL",0,0,"5",,terminal_output +80,107167,"TERMINAL",0,0,"*",,terminal_output +81,107652,"TERMINAL",0,0,"1",,terminal_output +82,107801,"TERMINAL",0,0,"1",,terminal_output +83,107852,"TERMINAL",0,0,"0",,terminal_output 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+124,145159,"TERMINAL",0,0,"\r\n[?2004l\r]0;tum_cte0515@hkn0403:~/Projects/jasmine_jobs[?2004h(jasmine) [tum_cte0515@hkn0403 jasmine_jobs]$ ",,terminal_output +125,145625,"TERMINAL",0,0,"s",,terminal_output +126,145864,"TERMINAL",0,0,"yn",,terminal_output +127,146441,"TERMINAL",0,0,"c",,terminal_output +128,146567,"TERMINAL",0,0,"-",,terminal_output +129,146809,"TERMINAL",0,0,"r",,terminal_output +130,146885,"TERMINAL",0,0,"u",,terminal_output +131,146950,"TERMINAL",0,0,"n",,terminal_output +132,147117,"TERMINAL",0,0,"n",,terminal_output +133,147187,"TERMINAL",0,0,"e",,terminal_output +134,147244,"TERMINAL",0,0,"r",,terminal_output +135,147363,"TERMINAL",0,0,"\r\n[?2004l\rsending incremental file list\r\n",,terminal_output +136,151058,"TERMINAL",0,0,"genie.py\r\nsample.py\r\ntrain_lam.py\r\n",,terminal_output +137,152054,"TERMINAL",0,0,"slurm/jobs/mihir/horeka/breakout/default_runs/train_lam_default.sh\r\nslurm/jobs/mihir/horeka/preprocessing/breakout_chunked.sh\r\n",,terminal_output +138,152165,"TERMINAL",0,0,"\r\nsent 93,815 bytes received 310 bytes 17,113.64 bytes/sec\r\ntotal size is 128,633,714 speedup is 1,366.63\r\n]0;tum_cte0515@hkn0403:~/Projects/jasmine_jobs[?2004h(jasmine) [tum_cte0515@hkn0403 jasmine_jobs]$ ",,terminal_output +139,156741,"TERMINAL",0,0,"s",,terminal_output +140,156809,"TERMINAL",0,0,"b",,terminal_output +141,156934,"TERMINAL",0,0,"a",,terminal_output +142,157040,"TERMINAL",0,0,"t",,terminal_output +143,157150,"TERMINAL",0,0,"c",,terminal_output +144,157214,"TERMINAL",0,0,"h",,terminal_output +145,157353,"TERMINAL",0,0," ",,terminal_output +146,157655,"TERMINAL",0,0,"slurm/jobs/mihir/horeka/preprocessing/breakout_chunked.sh",,terminal_output +147,158916,"TERMINAL",0,0,"\rslurm/jobs/mihir/horeka/preprocessing/breakout_chunked.sh\r\n[?2004l\rSubmitted batch job 3512651\r\n]0;tum_cte0515@hkn0403:~/Projects/jasmine_jobs[?2004h(jasmine) [tum_cte0515@hkn0403 jasmine_jobs]$ ",,terminal_output +148,162848,"TERMINAL",0,0,"d",,terminal_output +149,162984,"TERMINAL",0,0,"e",,terminal_output +150,163092,"TERMINAL",0,0,"v",,terminal_output +151,163180,"TERMINAL",0,0,"\r\n[?2004l\r]0;tum_cte0515@hkn0403:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0403 jasmine]$ ",,terminal_output +152,313586,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\n#SBATCH --job-name=train_dyn_default_breakout_longer\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_10m_gt_actions_split_small/train\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_10m_gt_actions_split_small/val\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nlam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3502552\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3502552\n\nenv | grep SLURM\n\nexport PYTHONUNBUFFERED=1\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --image_height=10 \\n --image_width=10 \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=120 \\n --init_lr=0 \\n --max_lr=3e-5 \\n 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--output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\n#SBATCH --job-name=train_dyn_default_breakout_longer\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_10m_gt_actions_split/train\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_10m_gt_actions_split/val\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn/$job_name/$slurm_job_id\nmkdir -p 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.venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/train\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/val\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3512502\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=10 \\r\n --image_width=10 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=120 \\r\n --init_lr=0 \\r\n --max_lr=3e-5 \\r\n --log_image_interval=250 \\r\n --log_checkpoint_interval=250 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=breakout-dyn-default-gt-actions-$slurm_job_id \\r\n --tags dyn breakout default \\r\n --entity instant-uv \\r\n --project jafar \\r\n --patch_size 4 \\r\n --lam_patch_size 4 \\r\n --warmup_steps 100 \\r\n --wsd_decay_steps 1000 \\r\n --num_steps 5000 \\r\n --use_gt_actions \\r\n --data_dir $array_records_dir_train \\r\n --val_data_dir $array_records_dir_val \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --val_interval 250 \\r\n --eval_full_frame \\r\n",,terminal_output +258,564043,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=4095309\r\nSLURM_JOB_GPUS=0\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\r\nSLURMD_NODENAME=hkn0403\r\nSLURM_JOB_START_TIME=1758621669\r\nSLURM_STEP_NODELIST=hkn0403\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1758625269\r\nSLURM_PMI2_SRUN_PORT=33711\r\nSLURM_CPUS_ON_NODE=8\r\nSLURM_JOB_CPUS_PER_NODE=8\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=dev_accelerated\r\nSLURM_TRES_PER_TASK=cpu=8\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=1\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3512647\r\nSLURM_PTY_PORT=37671\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.201\r\nSLURM_PTY_WIN_ROW=45\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0403\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.201\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=246\r\nSLURM_NODELIST=hkn0403\r\nSLURM_SRUN_COMM_PORT=35797\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1993.localdomain\r\nSLURM_JOB_ID=3512647\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0403\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_STEP_LAUNCHER_PORT=35797\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0403\r\n",,terminal_output +259,564174,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +260,565754,"TERMINAL",0,0,"^Csrun: interrupt (one more within 1 sec to abort)\r\nsrun: StepId=3512647.0 task 0: running\r\n",,terminal_output +261,565941,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3512647.0\r\nsrun: forcing job termination\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/train_dynamics.py"", line 11, in \r\n from jax.sharding import Mesh, PartitionSpec, NamedSharding\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/jax/__init__.py"", line 82, in \r\n from jax._src.api import effects_barrier as effects_barrier\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/jax/_src/api.py"", line 43, in \r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\n from jax._src import stages\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/jax/_src/stages.py"", line 50, in \r\n from jax._src.interpreters import mlir\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/jax/_src/interpreters/mlir.py"", line 44, in \r\n from jax._src import path\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/jax/_src/path.py"", line 35, in \r\nslurmstepd: error: *** STEP 3512647.0 ON hkn0403 CANCELLED AT 2025-09-23T12:10:29 ***\r\n]0;tum_cte0515@hkn0403:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0403 jasmine]$ ",,terminal_output +262,566092,"TERMINAL",0,0,"^C[?2004l\r[?2004h[?2004l\r\r\n]0;tum_cte0515@hkn0403:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0403 jasmine]$ ",,terminal_output +263,569693,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu_gt_actions.sh",0,0,"",shellscript,tab +264,573143,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu_gt_actions.sh",1999,0,"",shellscript,selection_mouse +265,574081,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu_gt_actions.sh",1998,1,"",shellscript,content +266,574174,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu_gt_actions.sh",1997,1,"",shellscript,content +267,574530,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu_gt_actions.sh",1996,1,"",shellscript,content +268,575579,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu_gt_actions.sh",1996,0,"7",shellscript,content +269,575580,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu_gt_actions.sh",1997,0,"",shellscript,selection_keyboard +270,575657,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu_gt_actions.sh",1997,0,"5",shellscript,content +271,575659,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu_gt_actions.sh",1998,0,"",shellscript,selection_keyboard +272,575757,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu_gt_actions.sh",1998,0,"0",shellscript,content +273,575758,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu_gt_actions.sh",1999,0,"",shellscript,selection_keyboard +274,578500,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu_gt_actions.sh",,terminal_output +275,580326,"TERMINAL",0,0,"\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=1\r\n#SBATCH --time=48:00:00\r\n#SBATCH --partition=accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\r\n#SBATCH --job-name=train_dyn_default_breakout_longer\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/train\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/val\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3512502\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=10 \\r\n --image_width=10 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=120 \\r\n --init_lr=0 \\r\n --max_lr=3e-5 \\r\n --log_image_interval=250 \\r\n --log_checkpoint_interval=250 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=breakout-dyn-default-gt-actions-$slurm_job_id \\r\n --tags dyn breakout default \\r\n --entity instant-uv \\r\n --project jafar \\r\n --patch_size 4 \\r\n --lam_patch_size 4 \\r\n --warmup_steps 100 \\r\n --wsd_decay_steps 1000 \\r\n --num_steps 5000 \\r\n --use_gt_actions \\r\n --data_dir $array_records_dir_train \\r\n --val_data_dir $array_records_dir_val \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --val_interval 750 \\r\n --eval_full_frame \\r\n",,terminal_output +276,580447,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=4095309\r\nSLURM_JOB_GPUS=0\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\r\nSLURMD_NODENAME=hkn0403\r\nSLURM_JOB_START_TIME=1758621669\r\nSLURM_STEP_NODELIST=hkn0403\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1758625269\r\nSLURM_PMI2_SRUN_PORT=33711\r\nSLURM_CPUS_ON_NODE=8\r\nSLURM_JOB_CPUS_PER_NODE=8\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=dev_accelerated\r\nSLURM_TRES_PER_TASK=cpu=8\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=1\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3512647\r\nSLURM_PTY_PORT=37671\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.201\r\nSLURM_PTY_WIN_ROW=45\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0403\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.201\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=246\r\nSLURM_NODELIST=hkn0403\r\nSLURM_SRUN_COMM_PORT=35797\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1993.localdomain\r\nSLURM_JOB_ID=3512647\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0403\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_STEP_LAUNCHER_PORT=35797\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0403\r\n",,terminal_output +277,580580,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +278,583709,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",0,0,"",shellscript,tab +279,586070,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +280,587293,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",2119,0,"",shellscript,selection_mouse +281,588037,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",2120,0,"",shellscript,selection_command +282,588416,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",2117,3,"",shellscript,content +283,589271,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",2117,0,"7",shellscript,content +284,589272,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",2118,0,"",shellscript,selection_keyboard +285,589759,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",2118,0,"5",shellscript,content +286,589760,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",2119,0,"",shellscript,selection_keyboard +287,589823,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",2119,0,"0",shellscript,content +288,589824,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",2120,0,"",shellscript,selection_keyboard +289,590312,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",2119,0,"",shellscript,selection_command +290,591528,"TERMINAL",0,0,"Counting all components: ['action_embed', 'dynamics', 'tokenizer']\r\n",,terminal_output +291,591878,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +292,592662,"TERMINAL",0,0,"wandb: creating run\r\n",,terminal_output +293,592803,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.21.3\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20250923_121055-3rw70eln\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run breakout-dyn-default-gt-actions-3512647\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/3rw70eln\r\n",,terminal_output +294,593160,"TERMINAL",0,0,"Parameter counts:\r\n{'action_embed': 192, 'dynamics': 26555904, 'tokenizer': 33750256, 'total': 60306352}\r\n",,terminal_output +295,600625,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +296,611551,"TERMINAL",0,0,"2025-09-23 12:11:15.537518: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-23 12:11:15.537951: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-23 12:11:15.537971: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-23 12:11:15.539777: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +297,640178,"TERMINAL",0,0,"Total memory size: 1.7 GB, Output size: 0.7 GB, Temp size: 1.0 GB, Argument size: 0.7 GB, Host temp size: 0.0 GB.\r\n",,terminal_output +298,640245,"TERMINAL",0,0,"FLOPs: 3.823e+10, Bytes: 3.007e+10 (28.0 GB), Intensity: 1.3 FLOPs/byte\r\nStarting training from step 0...\r\n",,terminal_output +299,640534,"TERMINAL",0,0,"\r\nMemstats: After params initialized:\r\n\tUsing (GB) 0.85 / 38.7 (2.196382%) on cuda:0\r\n",,terminal_output +300,692634,"TERMINAL",0,0,"Saved checkpoint at step 250\r\n",,terminal_output +301,721748,"TERMINAL",0,0,"Saved checkpoint at step 500\r\n",,terminal_output +302,750670,"TERMINAL",0,0,"Calculating validation metrics...\r\n",,terminal_output +303,757470,"TERMINAL",0,0,"2025-09-23 12:13:41.268865: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-23 12:13:41.268909: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-23 12:13:41.269402: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-23 12:13:41.269423: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +304,810605,"TERMINAL",0,0,"Step 750, validation loss: 1.0221530199050903\r\n",,terminal_output +305,811289,"TERMINAL",0,0,"Saved checkpoint at step 750\r\n",,terminal_output +306,834945,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu_gt_actions.sh",0,0,"",shellscript,tab +307,837933,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu_gt_actions.sh",1442,0,"",shellscript,selection_mouse +308,839001,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu_gt_actions.sh",1441,1,"",shellscript,content +309,839051,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu_gt_actions.sh",1441,0,"1",shellscript,content +310,839054,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu_gt_actions.sh",1442,0,"",shellscript,selection_keyboard +311,863139,"TERMINAL",0,0,"Saved checkpoint at step 1000\r\n",,terminal_output +312,893893,"TERMINAL",0,0,"Saved checkpoint at step 1250\r\n",,terminal_output +313,923975,"TERMINAL",0,0,"Calculating validation metrics...\r\n",,terminal_output +314,946208,"TERMINAL",0,0,"Step 1500, validation loss: 0.8760607838630676\r\n",,terminal_output +315,946612,"TERMINAL",0,0,"Saved checkpoint at step 1500\r\n",,terminal_output +316,977313,"TERMINAL",0,0,"Saved checkpoint at step 1750\r\n",,terminal_output +317,1002922,"genie.py",0,0,"from typing import Dict\n\nimport einops\nimport jax\nimport jax.numpy as jnp\nimport flax.nnx as nnx\nimport orbax.checkpoint as ocp\n\nfrom models.dynamics import DynamicsMaskGIT, DynamicsCausal\nfrom models.lam import LatentActionModel\nfrom models.tokenizer import TokenizerVQVAE\n\n\nclass Genie(nnx.Module):\n """"""Genie model""""""\n\n def __init__(\n self,\n in_dim: int,\n tokenizer_dim: int,\n tokenizer_ffn_dim: int,\n latent_patch_dim: int,\n num_patch_latents: int,\n patch_size: int,\n tokenizer_num_blocks: int,\n tokenizer_num_heads: int,\n lam_dim: int,\n lam_ffn_dim: int,\n latent_action_dim: int,\n num_actions: int,\n lam_patch_size: int,\n lam_num_blocks: int,\n lam_num_heads: int,\n lam_co_train: bool,\n use_gt_actions: bool,\n dyna_type: str,\n dyna_dim: int,\n dyna_ffn_dim: int,\n dyna_num_blocks: int,\n dyna_num_heads: int,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n decode: bool,\n rngs: nnx.Rngs,\n dropout: float = 0.0,\n mask_limit: float = 0.0,\n ):\n # --- Tokenizer ---\n self.in_dim = in_dim\n self.tokenizer_dim = tokenizer_dim\n self.tokenizer_ffn_dim = tokenizer_ffn_dim\n self.latent_patch_dim = latent_patch_dim\n self.num_patch_latents = num_patch_latents\n self.patch_size = patch_size\n self.tokenizer_num_blocks = tokenizer_num_blocks\n self.tokenizer_num_heads = tokenizer_num_heads\n # --- LAM ---\n self.lam_dim = lam_dim\n self.lam_ffn_dim = lam_ffn_dim\n self.latent_action_dim = latent_action_dim\n self.num_actions = num_actions\n self.lam_patch_size = lam_patch_size\n self.lam_num_blocks = lam_num_blocks\n self.lam_num_heads = lam_num_heads\n self.lam_co_train = lam_co_train\n self.use_gt_actions = use_gt_actions\n # --- Dynamics ---\n self.dyna_type = dyna_type\n self.dyna_dim = dyna_dim\n self.dyna_ffn_dim = dyna_ffn_dim\n self.dyna_num_blocks = dyna_num_blocks\n self.dyna_num_heads = dyna_num_heads\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.dropout = dropout\n self.mask_limit = mask_limit\n self.decode = decode\n\n self.tokenizer = TokenizerVQVAE(\n in_dim=self.in_dim,\n model_dim=self.tokenizer_dim,\n ffn_dim=self.tokenizer_ffn_dim,\n latent_dim=self.latent_patch_dim,\n num_latents=self.num_patch_latents,\n patch_size=self.patch_size,\n num_blocks=self.tokenizer_num_blocks,\n num_heads=self.tokenizer_num_heads,\n dropout=0.0,\n codebook_dropout=0.0,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n if self.use_gt_actions:\n self.action_embed = nnx.Embed(\n self.num_actions, self.latent_action_dim, rngs=rngs\n )\n self.lam = None\n else:\n self.lam = LatentActionModel(\n in_dim=self.in_dim,\n model_dim=self.lam_dim,\n ffn_dim=self.lam_ffn_dim,\n latent_dim=self.latent_patch_dim,\n num_latents=self.num_actions,\n patch_size=self.lam_patch_size,\n num_blocks=self.lam_num_blocks,\n num_heads=self.lam_num_heads,\n dropout=0.0,\n codebook_dropout=0.0,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n self.action_embed = None\n if self.dyna_type == ""maskgit"":\n self.dynamics = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n mask_limit=self.mask_limit,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n elif self.dyna_type == ""causal"":\n self.dynamics = DynamicsCausal(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=decode,\n rngs=rngs,\n )\n else:\n raise ValueError(f""Invalid dynamics type: {self.dyna_type}"")\n\n def __call__(\n self,\n batch: Dict[str, jax.Array],\n training: bool = True,\n ) -> Dict[str, jax.Array]:\n videos_BTHWC = batch[""videos""]\n tokenizer_outputs = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_indices_BTN = tokenizer_outputs[""indices""]\n latent_actions_BTm11L = None\n action_embeddings_BTm11L = None\n if self.use_gt_actions:\n assert self.action_embed is not None\n action_indices_E = None\n action_embeddings_BT1L = self.action_embed(batch[""actions""]).reshape(\n *batch[""actions""].shape[:2], 1, self.latent_action_dim\n )\n action_embeddings_BTm11L = action_embeddings_BT1L[:, :-1]\n else:\n assert self.lam is not None\n lam_outputs = self.lam.vq_encode(videos_BTHWC, training=False)\n z_q_BTm11L = lam_outputs[""z_q""]\n action_indices_E = lam_outputs[""indices""]\n latent_actions_BTm11L = jax.lax.cond(\n self.lam_co_train,\n lambda: z_q_BTm11L,\n lambda: jax.lax.stop_gradient(z_q_BTm11L),\n )\n outputs = dict(\n video_tokens=jax.lax.stop_gradient(token_indices_BTN),\n latent_actions=(\n action_embeddings_BTm11L\n if self.use_gt_actions\n else latent_actions_BTm11L\n ),\n )\n outputs[""mask_rng""] = batch[""rng""]\n dyna_logits_BTNV, dyna_mask = self.dynamics(outputs, training)\n outputs[""token_logits""] = dyna_logits_BTNV\n outputs[""mask""] = dyna_mask\n mle_indices_BTN = jnp.argmax(outputs[""token_logits""], axis=-1)\n H, W = batch[""videos""].shape[2:4]\n outputs[""recon""] = self.tokenizer.decode(mle_indices_BTN, (H, W))\n if action_indices_E is not None:\n outputs[""lam_indices""] = action_indices_E\n return outputs\n\n def sample(\n self,\n batch: Dict[str, jax.Array],\n seq_len: int,\n temperature: float = 1,\n sample_argmax: bool = False,\n maskgit_steps: int = 25,\n ) -> tuple[jax.Array, jax.Array]:\n if self.dyna_type == ""maskgit"":\n return self.sample_maskgit(\n batch, seq_len, maskgit_steps, temperature, sample_argmax\n )\n elif self.dyna_type == ""causal"":\n return self.sample_causal(batch, seq_len, temperature, sample_argmax)\n else:\n raise ValueError(f""Dynamics model type unknown: {self.dyna_type}"")\n\n def sample_maskgit(\n self,\n batch: Dict[str, jax.Array],\n seq_len: int,\n steps: int = 25,\n temperature: float = 1,\n sample_argmax: bool = False,\n ) -> tuple[jax.Array, jax.Array]:\n """"""\n Autoregressively samples up to `seq_len` future frames, following Figure 8 of the paper.\n\n - Input frames are tokenized once.\n - Future frames are generated autoregressively in token space.\n - All frames are detokenized in a single pass.\n\n Note:\n - For interactive or step-wise sampling, detokenization should occur after each action.\n - To maintain consistent tensor shapes across timesteps, all current and future frames are decoded at every step.\n - Temporal causal structure is preserved by\n a) reapplying the mask before each decoding step.\n b) a temporal causal mask is applied within each ST-transformer block.\n\n Dimension keys:\n B: batch size\n T: number of input (conditioning) frames\n N: number of patches per frame\n M: model dimension\n S: sequence length\n H: height\n W: width\n E: B * (S - 1)\n P: S * N\n """"""\n assert isinstance(self.dynamics, DynamicsMaskGIT)\n # --- Encode videos and actions ---\n videos_BTHWC = batch[""videos""]\n tokenizer_out = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_idxs_BTN = tokenizer_out[""indices""]\n B, T, N = token_idxs_BTN.shape\n pad_shape = (B, seq_len - T, N)\n pad = jnp.zeros(pad_shape, dtype=token_idxs_BTN.dtype)\n token_idxs_BSN = jnp.concatenate([token_idxs_BTN, pad], axis=1)\n init_logits_BSNV = jnp.zeros(\n shape=(*token_idxs_BSN.shape, self.num_patch_latents)\n )\n if self.use_gt_actions:\n assert self.action_embed is not None\n latent_actions_BT1L = self.action_embed(batch[""actions""]).reshape(\n *batch[""actions""].shape[:2], 1, self.latent_action_dim\n )\n latent_actions_BTm11L = latent_actions_BT1L[:, :-1]\n action_tokens_EL = latent_actions_BTm11L.reshape(-1, self.latent_action_dim)\n else:\n assert self.lam is not None\n latent_actions_E = batch[""latent_actions""]\n action_tokens_EL = self.lam.vq.get_codes(latent_actions_E)\n\n # --- Extract submodule state ---\n dynamics_state = nnx.state(self.dynamics)\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def maskgit_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array],\n step: jax.Array,\n ) -> tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array]:\n rng, token_idxs_BSN, logits_BSNV, mask_BSN, action_tokens_EL = carry\n S, N = token_idxs_BSN.shape[1:]\n L = action_tokens_EL.shape[-1]\n\n # We need to reconstruct the submodule inside scan body to prevent trace context mismatches\n dynamics_maskgit = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n mask_limit=self.mask_limit,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=nnx.Rngs(0),\n )\n nnx.update(dynamics_maskgit, dynamics_state)\n\n # --- Construct + encode video ---\n vid_embed_BSNM = dynamics_maskgit.patch_embed(token_idxs_BSN)\n mask_token_111M = dynamics_maskgit.mask_token.value\n mask_expanded_BSN1 = mask_BSN[..., None]\n vid_embed_BSNM = jnp.where(\n mask_expanded_BSN1, mask_token_111M, vid_embed_BSNM\n )\n\n # --- Predict transition ---\n action_tokens_BSm1L = jnp.reshape(action_tokens_EL, (B, S - 1, L))\n act_embed_BSm1M = dynamics_maskgit.action_up(action_tokens_BSm1L)\n act_embed_BSM = jnp.pad(act_embed_BSm1M, ((0, 0), (1, 0), (0, 0)))\n act_embed_BS1M = jnp.reshape(\n act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1])\n )\n vid_embed_BSNM += act_embed_BS1M\n unmasked_ratio = jnp.cos(jnp.pi * (step + 1) / (steps * 2))\n step_temp = temperature * (1.0 - unmasked_ratio)\n final_logits_BSNV = dynamics_maskgit.transformer(vid_embed_BSNM) / step_temp\n\n # --- Sample new tokens for final frame ---\n if sample_argmax:\n sampled_token_idxs_BSN = jnp.argmax(final_logits_BSNV, axis=-1)\n else:\n rng, _rng = jax.random.split(rng)\n sampled_token_idxs_BSN = jax.random.categorical(_rng, final_logits_BSNV)\n gather_fn = jax.vmap(jax.vmap(jax.vmap(lambda x, y: x[y])))\n final_token_probs_BSN = gather_fn(\n jax.nn.softmax(final_logits_BSNV), sampled_token_idxs_BSN\n )\n final_token_probs_BSN += ~mask_BSN\n # Update masked tokens and logits only\n token_idxs_BSN = jnp.where(mask_BSN, sampled_token_idxs_BSN, token_idxs_BSN)\n logits_BSNV = jnp.where(\n jnp.expand_dims(mask_BSN, -1), final_logits_BSNV, logits_BSNV\n )\n\n # --- Update mask ---\n num_unmasked_tokens = jnp.round(N * (1.0 - unmasked_ratio)).astype(int)\n final_token_probs_flat_BP = einops.rearrange(\n final_token_probs_BSN, ""b s n -> b (s n)""\n )\n idx_mask_P = (\n jnp.arange(final_token_probs_flat_BP.shape[-1])\n <= N - num_unmasked_tokens\n )\n sorted_idxs_BP = jnp.argsort(final_token_probs_flat_BP, axis=-1)\n mask_update_fn = jax.vmap(lambda msk, ids: msk.at[ids].set(idx_mask_P))\n mask_flat_BP = einops.rearrange(mask_BSN, ""b s n -> b (s n)"")\n new_mask_flat_BP = mask_update_fn(mask_flat_BP, sorted_idxs_BP)\n new_mask_BSN = einops.rearrange(new_mask_flat_BP, ""b (s n) -> b s n"", n=N)\n\n new_carry = (\n rng,\n token_idxs_BSN,\n logits_BSNV,\n new_mask_BSN,\n action_tokens_EL,\n )\n return new_carry\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def generation_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array], step_t: jax.Array\n ) -> tuple[jax.Array, jax.Array, jax.Array]:\n rng, current_token_idxs_BSN, current_logits_BSNV = carry\n rng, step_rng = jax.random.split(rng)\n\n # Mask current frame (i.e., t == step_t)\n mask_S = jnp.arange(seq_len) == step_t\n mask_BSN = jnp.broadcast_to(mask_S[None, :, None], (B, seq_len, N)).astype(\n bool\n )\n masked_token_idxs_BSN = current_token_idxs_BSN * ~mask_BSN\n masked_logits_BSNV = current_logits_BSNV * jnp.expand_dims(~mask_BSN, -1)\n\n # --- Initialize and run MaskGIT loop ---\n init_carry_maskgit = (\n step_rng,\n masked_token_idxs_BSN,\n masked_logits_BSNV,\n mask_BSN,\n action_tokens_EL,\n )\n final_carry_maskgit = maskgit_step_fn(init_carry_maskgit, jnp.arange(steps))\n updated_token_idxs_BSN = final_carry_maskgit[1]\n updated_logits_BSNV = final_carry_maskgit[2]\n new_carry = (rng, updated_token_idxs_BSN, updated_logits_BSNV)\n return new_carry\n\n # --- Run the autoregressive generation using jax.lax.scan ---\n initial_carry = (batch[""rng""], token_idxs_BSN, init_logits_BSNV)\n timesteps_to_scan = jnp.arange(T, seq_len)\n final_carry = generation_step_fn(initial_carry, timesteps_to_scan)\n final_token_idxs_BSN = final_carry[1]\n final_logits_BSNV = final_carry[2]\n\n # --- Decode all tokens at once at the end ---\n H, W = batch[""videos""].shape[2:4]\n final_frames_BSHWC = self.tokenizer.decode(\n final_token_idxs_BSN,\n video_hw=(H, W),\n )\n return final_frames_BSHWC, final_logits_BSNV\n\n def sample_causal(\n self,\n batch: Dict[str, jax.Array],\n seq_len: int,\n temperature: float = 1,\n sample_argmax: bool = False,\n ) -> tuple[jax.Array, jax.Array]:\n """"""\n Autoregressively samples up to `seq_len` future frames, following Figure 8 of the paper.\n\n - Input frames are tokenized once.\n - Future frames are generated autoregressively in token space.\n - All frames are detokenized in a single pass.\n\n Note:\n - For interactive or step-wise sampling, detokenization should occur after each action.\n - To maintain consistent tensor shapes across timesteps, all current and future frames are decoded at every step.\n - Temporal causal structure is preserved by\n a) reapplying the mask before each decoding step.\n b) a temporal causal mask is applied within each ST-transformer block.\n\n Dimension keys:\n B: batch size\n T: number of input (conditioning) frames\n N: number of patches per frame\n M: model dimension\n S: sequence length\n H: height\n W: width\n E: B * (S - 1)\n """"""\n assert isinstance(self.dynamics, DynamicsCausal)\n # --- Encode videos and actions ---\n videos_BTHWC = batch[""videos""]\n tokenizer_out = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_idxs_BTN = tokenizer_out[""indices""]\n B, T, N = token_idxs_BTN.shape\n pad_shape = (B, seq_len - T, N)\n pad = jnp.zeros(pad_shape, dtype=token_idxs_BTN.dtype)\n token_idxs_BSN = jnp.concatenate([token_idxs_BTN, pad], axis=1)\n logits_BSNV = jnp.zeros((*token_idxs_BSN.shape, self.num_patch_latents))\n dynamics_state = nnx.state(self.dynamics)\n\n if self.use_gt_actions:\n assert self.action_embed is not None\n latent_actions_BT1L = self.action_embed(batch[""actions""]).reshape(\n *batch[""actions""].shape[:2], 1, self.latent_action_dim\n )\n latent_actions_BTm11L = latent_actions_BT1L[:, 1:]\n action_tokens_EL = latent_actions_BTm11L.reshape(-1, self.latent_action_dim)\n else:\n assert self.lam is not None\n latent_actions_E = batch[""latent_actions""]\n action_tokens_EL = self.lam.vq.get_codes(latent_actions_E)\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def causal_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array],\n step_n: jax.Array,\n ) -> tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array]:\n rng, token_idxs_BSN, logits_BSNV, action_tokens_EL, step_t = carry\n S, N = token_idxs_BSN.shape[1:]\n L = action_tokens_EL.shape[-1]\n\n # We need to reconstruct the submodule inside scan body to prevent trace context mismatches\n dynamics_causal = DynamicsCausal(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=self.decode,\n rngs=nnx.Rngs(0),\n )\n nnx.update(dynamics_causal, dynamics_state)\n\n # --- Construct + encode video ---\n vid_embed_BSNM = dynamics_causal.patch_embed(token_idxs_BSN)\n\n # --- Predict transition ---\n action_tokens_BSm1L = jnp.reshape(action_tokens_EL, (B, S - 1, L))\n act_embed_BSm1M = dynamics_causal.action_up(action_tokens_BSm1L)\n act_embed_BSM = jnp.pad(act_embed_BSm1M, ((0, 0), (1, 0), (0, 0)))\n act_embed_BS1M = jnp.reshape(\n act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1])\n )\n vid_embed_BSNp1M = jnp.concatenate([act_embed_BS1M, vid_embed_BSNM], axis=2)\n final_logits_BTNp1V = (\n dynamics_causal.transformer(vid_embed_BSNp1M, (step_t, step_n))\n / temperature\n )\n final_logits_BV = final_logits_BTNp1V[:, step_t, step_n, :]\n\n # --- Sample new tokens for final frame ---\n if sample_argmax:\n sampled_token_idxs_B = jnp.argmax(final_logits_BV, axis=-1)\n else:\n rng, _rng = jax.random.split(rng)\n sampled_token_idxs_B = jax.random.categorical(_rng, final_logits_BV)\n # Update next tokens only\n token_idxs_BSN = token_idxs_BSN.at[:, step_t, step_n].set(\n sampled_token_idxs_B\n )\n logits_BSNV = logits_BSNV.at[:, step_t, step_n].set(final_logits_BV)\n\n new_carry = (rng, token_idxs_BSN, logits_BSNV, action_tokens_EL, step_t)\n return new_carry\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def generation_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array], step_t: jax.Array\n ) -> tuple[jax.Array, jax.Array, jax.Array]:\n rng, current_token_idxs_BSN, current_logits_BSNV = carry\n rng, step_rng = jax.random.split(rng)\n\n # --- Initialize and run causal loop ---\n init_carry_causal = (\n step_rng,\n current_token_idxs_BSN,\n current_logits_BSNV,\n action_tokens_EL,\n step_t,\n )\n final_carry_causal = causal_step_fn(init_carry_causal, jnp.arange(N))\n updated_token_idxs_BSN = final_carry_causal[1]\n updated_logits_BSNV = final_carry_causal[2]\n new_carry = (rng, updated_token_idxs_BSN, updated_logits_BSNV)\n return new_carry\n\n # --- Run the autoregressive generation using jax.lax.scan ---\n initial_carry = (batch[""rng""], token_idxs_BSN, logits_BSNV)\n timesteps_to_scan = jnp.arange(T, seq_len)\n final_carry = generation_step_fn(initial_carry, timesteps_to_scan)\n final_token_idxs_BSN = final_carry[1]\n final_logits_BSNV = final_carry[2]\n\n # --- Decode all tokens at once at the end ---\n H, W = batch[""videos""].shape[2:4]\n final_frames_BSHWC = self.tokenizer.decode(\n final_token_idxs_BSN,\n video_hw=(H, W),\n )\n return final_frames_BSHWC, final_logits_BSNV\n\n def vq_encode(self, batch: Dict[str, jax.Array], training: bool) -> jax.Array:\n # --- Preprocess videos ---\n assert self.lam is not None\n video_BTHWC = batch[""videos""]\n lam_output: Dict[str, jax.Array] = self.lam.vq_encode(\n video_BTHWC, training=training\n )\n lam_indices_E = lam_output[""indices""]\n return lam_indices_E\n\n\n# FIXME (f.srambical): add conversion script for old checkpoints\ndef restore_genie_components(\n optimizer: nnx.Optimizer,\n sharding: jax.sharding.NamedSharding,\n rng: jax.Array,\n args,\n) -> nnx.Optimizer:\n """"""Restore pre-trained Genie components""""""\n rng_tokenizer, rng_lam = jax.random.split(rng)\n rngs_tokenizer = nnx.Rngs(rng_tokenizer)\n rngs_lam = nnx.Rngs(rng_lam)\n\n tx = optimizer.tx\n model = optimizer.model\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n\n checkpoint_options = ocp.CheckpointManagerOptions(\n step_format_fixed_length=6,\n )\n tokenizer_checkpoint_manager = ocp.CheckpointManager(\n directory=args.tokenizer_checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n dummy_tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.tokenizer_dim,\n ffn_dim=args.tokenizer_ffn_dim,\n latent_dim=args.latent_patch_dim,\n num_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n num_blocks=args.tokenizer_num_blocks,\n num_heads=args.tokenizer_num_heads,\n dropout=args.dropout,\n codebook_dropout=args.dropout,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs_tokenizer,\n )\n dummy_tokenizer_optimizer = nnx.Optimizer(dummy_tokenizer, tx)\n dummy_tokenizer_optimizer_state = nnx.state(dummy_tokenizer_optimizer)\n abstract_sharded_tokenizer_optimizer_state = _create_abstract_sharded_pytree(\n dummy_tokenizer_optimizer_state, sharding\n )\n restored_tokenizer = tokenizer_checkpoint_manager.restore(\n step=tokenizer_checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore( # type: ignore\n abstract_sharded_tokenizer_optimizer_state # type: ignore\n ),\n ),\n )[""model_state""]\n nnx.update(dummy_tokenizer_optimizer.model, restored_tokenizer.model)\n model.tokenizer = dummy_tokenizer_optimizer.model\n tokenizer_checkpoint_manager.close()\n\n if args.lam_checkpoint:\n lam_checkpoint_manager = ocp.CheckpointManager(\n directory=args.lam_checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n dummy_lam = LatentActionModel(\n in_dim=args.image_channels,\n model_dim=args.lam_dim,\n ffn_dim=args.lam_ffn_dim,\n latent_dim=args.latent_patch_dim,\n num_latents=args.num_actions,\n patch_size=args.lam_patch_size,\n num_blocks=args.lam_num_blocks,\n num_heads=args.lam_num_heads,\n dropout=args.dropout,\n codebook_dropout=args.dropout,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs_lam,\n )\n dummy_lam_optimizer = nnx.Optimizer(dummy_lam, tx)\n dummy_lam_optimizer_state = nnx.state(dummy_lam_optimizer)\n abstract_sharded_lam_optimizer_state = _create_abstract_sharded_pytree(\n dummy_lam_optimizer_state, sharding\n )\n restored_lam_optimizer = lam_checkpoint_manager.restore(\n step=lam_checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore( # type: ignore\n abstract_sharded_lam_optimizer_state # type: ignore\n ),\n ),\n )[""model_state""]\n nnx.update(dummy_lam_optimizer.model, restored_lam_optimizer.model)\n model.lam = dummy_lam_optimizer.model\n # Remove the LAM decoder to save memory and avoid unnecessary computation.\n del model.lam.decoder\n lam_checkpoint_manager.close()\n\n # Reinitialize the optimizer states\n optimizer = nnx.Optimizer(model, tx)\n return optimizer\n\n\ndef _create_abstract_sharded_pytree(\n pytree_template: nnx.GraphState, sharding_spec: jax.sharding.NamedSharding\n) -> jax.Array:\n """"""Replaces arrays in a pytree with ShapeDtypeStructs having the given sharding.""""""\n\n def map_fn(leaf_template):\n if hasattr(leaf_template, ""shape"") and hasattr(leaf_template, ""dtype""):\n return jax.ShapeDtypeStruct(\n leaf_template.shape, leaf_template.dtype, sharding=sharding_spec\n )\n return leaf_template\n\n return jax.tree_util.tree_map(map_fn, pytree_template)\n",python,tab +318,1002926,"genie.py",7231,6,"sample",python,selection_command +319,1007673,"TERMINAL",0,0,"Saved checkpoint at step 2000\r\n",,terminal_output +320,1037543,"TERMINAL",0,0,"Calculating validation metrics...\r\n",,terminal_output +321,1053096,"genie.py",18523,0,"",python,selection_mouse +322,1054420,"genie.py",18522,1,"",python,content +323,1054786,"genie.py",18523,0,"",python,selection_command +324,1055399,"genie.py",18523,0,"-",python,content +325,1055401,"genie.py",18524,0,"",python,selection_keyboard +326,1055471,"genie.py",18524,0,"1",python,content +327,1055472,"genie.py",18525,0,"",python,selection_keyboard +328,1057553,"genie.py",18524,0,"",python,selection_command +329,1059848,"TERMINAL",0,0,"Step 2250, validation loss: 0.7719724178314209\r\n",,terminal_output +330,1060290,"TERMINAL",0,0,"Saved checkpoint at step 2250\r\n",,terminal_output +331,1091137,"TERMINAL",0,0,"Saved checkpoint at step 2500\r\n",,terminal_output +332,1121557,"TERMINAL",0,0,"Saved checkpoint at step 2750\r\n",,terminal_output +333,1151473,"TERMINAL",0,0,"Calculating validation metrics...\r\n",,terminal_output +334,1173722,"TERMINAL",0,0,"Step 3000, validation loss: 0.7339697480201721\r\n",,terminal_output +335,1174087,"TERMINAL",0,0,"Saved checkpoint at step 3000\r\n",,terminal_output +336,1181731,"train_tokenizer.py",0,0,"import os\n\nos.environ.setdefault(""XLA_PYTHON_CLIENT_MEM_FRACTION"", ""0.98"")\n\nfrom dataclasses import dataclass, field\nfrom typing import cast, Optional\n\nimport einops\nimport itertools\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\nimport flax.nnx as nnx\n\nfrom models.tokenizer import TokenizerVQVAE\nfrom utils.dataloader import get_dataloader\nfrom utils.train_utils import (\n get_lr_schedule,\n count_parameters_by_component,\n print_mem_stats,\n print_compiled_memory_stats,\n print_compiled_cost_analysis,\n)\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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n vq_beta: float = 0.25\n batch_size: int = 48\n init_lr: float = 0.0\n max_lr: float = 3e-4\n decay_end: float = 0.0\n wsd_decay_steps: int = (\n 20000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n )\n lr_schedule: str = ""wsd"" # supported options: wsd, cos\n warmup_steps: int = 10000\n # Tokenizer\n model_dim: int = 512\n ffn_dim: int = 2048\n latent_dim: int = 32\n num_latents: int = 1024\n patch_size: int = 4\n num_blocks: int = 4\n num_heads: int = 8\n dropout: float = 0.0\n codebook_dropout: float = 0.01\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\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_checkpoint_keep_period: int = 20000\n log_gradients: bool = False\n val_data_dir: str = """"\n val_interval: int = 20_000\n val_steps: int = 50\n wandb_id: str = """"\n use_flash_attention: bool = True\n\n\ndef build_model(args: Args, rng: jax.Array) -> tuple[TokenizerVQVAE, jax.Array]:\n rng, _rng = jax.random.split(rng)\n rngs = nnx.Rngs(_rng)\n return (\n TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.model_dim,\n ffn_dim=args.ffn_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 param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs,\n ),\n rng,\n )\n\n\ndef build_optimizer(\n model: TokenizerVQVAE, args: Args\n) -> tuple[nnx.Optimizer, optax.Schedule]:\n lr_schedule = get_lr_schedule(\n args.lr_schedule,\n args.init_lr,\n args.max_lr,\n args.decay_end,\n args.num_steps,\n args.warmup_steps,\n args.wsd_decay_steps,\n )\n tx = optax.adamw(\n learning_rate=lr_schedule,\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.param_dtype, # moments in full precision\n )\n optimizer = nnx.Optimizer(model, tx)\n return optimizer, lr_schedule\n\n\ndef build_mesh_and_sharding(\n num_devices: int,\n) -> tuple[Mesh, NamedSharding, NamedSharding]:\n device_mesh_arr = create_device_mesh((num_devices,))\n mesh = Mesh(devices=device_mesh_arr, axis_names=(""data"",))\n replicated_sharding = NamedSharding(mesh, PartitionSpec())\n videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n return mesh, replicated_sharding, videos_sharding\n\n\ndef shard_optimizer_states(\n optimizer: nnx.Optimizer, replicated_sharding: NamedSharding\n) -> None:\n model_state = nnx.state(optimizer.model)\n model_sharded_state = jax.lax.with_sharding_constraint(\n model_state, replicated_sharding\n )\n nnx.update(optimizer.model, model_sharded_state)\n optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)\n optimizer_sharded_state = jax.lax.with_sharding_constraint(\n optimizer_state, replicated_sharding\n )\n nnx.update(optimizer, optimizer_sharded_state)\n\n\ndef build_dataloader(args: Args, data_dir: str) -> grain.DataLoaderIterator:\n image_shape = (args.image_height, args.image_width, args.image_channels)\n array_record_files = [\n os.path.join(data_dir, x)\n for x in os.listdir(data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n return grain_iterator\n\n\ndef build_checkpoint_manager(args: Args) -> ocp.CheckpointManager:\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n if args.val_data_dir:\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n return checkpoint_manager\n\n\ndef restore_checkpoint_if_needed(\n args: Args,\n checkpoint_manager: ocp.CheckpointManager,\n optimizer: nnx.Optimizer,\n train_iterator: grain.DataLoaderIterator,\n val_iterator: Optional[grain.DataLoaderIterator],\n restore_step: Optional[int] = None,\n) -> tuple[int, nnx.Optimizer, grain.DataLoaderIterator, grain.DataLoaderIterator]:\n step = 0\n if restore_step is None:\n restore_step = checkpoint_manager.latest_step()\n if args.restore_ckpt:\n abstract_optimizer = nnx.eval_shape(lambda: optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n if val_iterator:\n restore_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointRestore(train_iterator), # type: ignore\n val_dataloader_state=grain.checkpoint.CheckpointRestore(val_iterator), # type: ignore\n )\n else:\n restore_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointRestore(train_iterator), # type: ignore\n )\n restored = checkpoint_manager.restore(restore_step, args=restore_args)\n restored_optimizer_state = restored[""model_state""]\n nnx.update(optimizer, restored_optimizer_state)\n train_iterator = restored[""train_dataloader_state""]\n if val_iterator:\n val_iterator = restored[""val_dataloader_state""]\n step = restore_step or 0\n print(f""Restored dataloader and model state from step {step}"")\n return step, optimizer, train_iterator, val_iterator\n\n\ndef main(args: Args) -> None:\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 rng = jax.random.key(args.seed)\n\n # --- Initialize model ---\n tokenizer, rng = build_model(args, rng)\n\n _, params, _ = nnx.split(tokenizer, nnx.Param, ...)\n param_counts = count_parameters_by_component(params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\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 if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n optimizer, lr_schedule = build_optimizer(tokenizer, args)\n del tokenizer\n\n # FIXME: switch to create_hybrid_device_mesh for runs spanning multiple nodes\n _, replicated_sharding, videos_sharding = build_mesh_and_sharding(num_devices)\n\n shard_optimizer_states(optimizer, replicated_sharding)\n\n # --- Initialize checkpoint manager ---\n checkpoint_manager = build_checkpoint_manager(args)\n\n # --- Create DataLoaderIterator from dataloader ---\n train_iterator = build_dataloader(args, args.data_dir)\n val_iterator = None\n if args.val_data_dir:\n val_iterator = build_dataloader(args, args.val_data_dir)\n\n # --- Restore checkpoint ---\n step, optimizer, train_iterator, val_iterator = restore_checkpoint_if_needed(\n args, checkpoint_manager, optimizer, train_iterator, val_iterator\n )\n\n # --- Define loss and train step (close over args) ---\n def tokenizer_loss_fn(\n model: TokenizerVQVAE, inputs: dict, training: bool = False\n ) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n mse = jnp.square(gt - 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 gt_clipped = gt.clip(0, 1).reshape(-1, *gt.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt_clipped, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt_clipped, 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 @nnx.jit(donate_argnums=0)\n def train_step(\n optimizer: nnx.Optimizer, inputs: dict\n ) -> tuple[jax.Array, jax.Array, dict]:\n def loss_fn(model: TokenizerVQVAE) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n model.train()\n return tokenizer_loss_fn(model, inputs, training=True)\n\n (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(\n optimizer.model\n )\n optimizer.update(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 loss, recon, metrics\n\n @nnx.jit\n def val_step(\n tokenizer: TokenizerVQVAE, inputs: dict\n ) -> tuple[jax.Array, jax.Array, dict]:\n tokenizer.eval()\n (loss, (recon, metrics)) = tokenizer_loss_fn(tokenizer, inputs, training=False)\n return loss, recon, metrics\n\n def calculate_validation_metrics(val_dataloader, tokenizer):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n batch = None\n recon = None\n for batch in val_dataloader:\n loss, recon, metrics = val_step(tokenizer, batch)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(\n f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}""\n )\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([float(m[key]) for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n val_metrics[""val_loss""] = val_loss\n return val_metrics, batch, recon\n\n # --- TRAIN LOOP ---\n dataloader_train = (\n {\n ""videos"": jax.make_array_from_process_local_data(\n videos_sharding, elem[""videos""]\n ),\n }\n for elem in train_iterator\n )\n dataloader_val = None\n if val_iterator:\n dataloader_val = (\n {\n ""videos"": jax.make_array_from_process_local_data(\n videos_sharding, elem[""videos""]\n ),\n }\n for elem in val_iterator\n )\n if jax.process_index() == 0:\n first_batch = next(dataloader_train)\n compiled = train_step.lower(optimizer, first_batch).compile()\n print_compiled_memory_stats(compiled.memory_analysis())\n print_compiled_cost_analysis(compiled.cost_analysis())\n # Do not skip the first batch during training\n dataloader_train = itertools.chain([first_batch], dataloader_train)\n print(f""Starting training from step {step}..."")\n first_step = step\n while step < args.num_steps:\n for batch in dataloader_train:\n # --- Train step ---\n loss, recon, metrics = train_step(optimizer, batch)\n if step == first_step:\n print_mem_stats(""After params initialized"")\n # metrics[""lr""] = lr_schedule(step)\n # print(f""Step {step}, loss: {loss}"")\n step += 1\n\n # --- Validation loss ---\n val_results = {}\n if dataloader_val and step % args.val_interval == 0:\n print(""Calculating validation metrics..."")\n val_metrics, val_gt_batch, val_recon = calculate_validation_metrics(\n dataloader_val, optimizer.model\n )\n print(f""Step {step}, validation loss: {val_metrics['val_loss']}"")\n val_results = {\n ""metrics"": val_metrics,\n ""gt_batch"": val_gt_batch,\n ""recon"": val_recon,\n }\n\n # --- Logging ---\n if args.log:\n if step % args.log_interval == 0 and jax.process_index() == 0:\n log_dict = {""loss"": loss, ""step"": step, **metrics}\n if val_results:\n log_dict.update(val_results[""metrics""])\n wandb.log(log_dict)\n if step % args.log_image_interval == 0:\n gt_seq = batch[""videos""][0].astype(jnp.float32) / 255.0\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 if val_results and step % args.val_interval == 0:\n val_results[""gt_seq_val""] = (\n val_results[""gt_batch""][""videos""][0].astype(jnp.float32)\n / 255.0\n )\n val_results[""recon_seq_val""] = val_results[""recon""][0].clip(\n 0, 1\n )\n val_results[""val_comparison_seq""] = jnp.concatenate(\n (val_results[""gt_seq_val""], val_results[""recon_seq_val""]),\n axis=1,\n )\n val_results[""val_comparison_seq""] = einops.rearrange(\n val_results[""val_comparison_seq""] * 255,\n ""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 if val_results and step % args.val_interval == 0:\n log_images.update(\n dict(\n val_image=wandb.Image(\n np.asarray(val_results[""gt_seq_val""][0])\n ),\n val_recon=wandb.Image(\n np.asarray(val_results[""recon_seq_val""][0])\n ),\n val_true_vs_recon=wandb.Image(\n np.asarray(\n val_results[""val_comparison_seq""].astype(\n np.uint8\n )\n )\n ),\n )\n )\n wandb.log(log_images)\n # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n optimizer_state = nnx.state(optimizer)\n if val_iterator:\n ckpt_manager_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n train_iterator # type: ignore\n ),\n val_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n val_iterator # type: ignore\n ),\n )\n else:\n ckpt_manager_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n train_iterator # type: ignore\n ),\n )\n checkpoint_manager.save(step, args=ckpt_manager_args)\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n checkpoint_manager.close()\n\n\nif __name__ == ""__main__"":\n args = tyro.cli(Args)\n main(args)\n",python,tab +337,1182806,"train_dynamics.py",0,0,"import os\n\n\nos.environ.setdefault(""XLA_PYTHON_CLIENT_MEM_FRACTION"", ""0.98"")\n\nfrom dataclasses import dataclass, field\nimport itertools\nfrom typing import cast, Optional\n\nimport einops\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\nimport flax.nnx as nnx\n\nfrom genie import Genie, restore_genie_components\nfrom utils.dataloader import get_dataloader\nfrom utils.train_utils import (\n get_lr_schedule,\n count_parameters_by_component,\n print_mem_stats,\n print_compiled_memory_stats,\n print_compiled_cost_analysis,\n)\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n batch_size: int = 36\n init_lr: float = 0.0\n max_lr: float = 3e-5\n decay_end: float = 0.0\n wsd_decay_steps: int = (\n 10000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n )\n warmup_steps: int = 5000\n lr_schedule: str = ""wsd"" # supported options: wsd, cos\n # Tokenizer\n tokenizer_dim: int = 512\n tokenizer_ffn_dim: int = 2048\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 4\n tokenizer_num_heads: int = 8\n tokenizer_checkpoint: str = """"\n # LAM\n lam_dim: int = 512\n lam_ffn_dim: int = 2048\n latent_action_dim: int = 32\n num_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 4\n lam_num_heads: int = 8\n lam_checkpoint: str = """"\n # Dynamics\n dyna_type: str = ""maskgit"" # supported options: maskgit, causal\n dyna_dim: int = 512\n dyna_ffn_dim: int = 2048\n dyna_num_blocks: int = 6\n dyna_num_heads: int = 8\n dropout: float = 0.0\n mask_limit: float = 0.5\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\n use_flash_attention: bool = True\n use_gt_actions: bool = False\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n name: str = ""train_dynamics""\n tags: list[str] = field(default_factory=lambda: [""dynamics""])\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 25000\n log_checkpoint_keep_period: int = 20000\n log_gradients: bool = False\n val_data_dir: str = """"\n val_interval: int = 20_000\n val_steps: int = 50\n eval_full_frame: bool = False\n val_maskgit_steps: int = 25\n val_temperature: float = 1\n val_sample_argmax: bool = False\n wandb_id: str = """"\n\n\ndef build_model(args: Args, rng: jax.Array) -> tuple[Genie, jax.Array]:\n rng, _rng = jax.random.split(rng)\n rngs = nnx.Rngs(_rng)\n genie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n tokenizer_ffn_dim=args.tokenizer_ffn_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n lam_ffn_dim=args.lam_ffn_dim,\n latent_action_dim=args.latent_action_dim,\n num_actions=args.num_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n lam_co_train=not args.lam_checkpoint,\n use_gt_actions=args.use_gt_actions,\n # Dynamics\n dyna_type=args.dyna_type,\n dyna_dim=args.dyna_dim,\n dyna_ffn_dim=args.dyna_ffn_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n dropout=args.dropout,\n mask_limit=args.mask_limit,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n decode=False,\n rngs=rngs,\n )\n if args.use_gt_actions:\n assert (\n not args.lam_checkpoint\n ), ""Cannot use LAM when using ground-truth actions.""\n else:\n assert genie.lam is not None\n del genie.lam.decoder\n return genie, rng\n\n\ndef build_optimizer(genie: Genie, args: Args) -> tuple[nnx.Optimizer, optax.Schedule]:\n lr_schedule = get_lr_schedule(\n args.lr_schedule,\n args.init_lr,\n args.max_lr,\n args.decay_end,\n args.num_steps,\n args.warmup_steps,\n args.wsd_decay_steps,\n )\n tx = optax.adamw(\n learning_rate=lr_schedule,\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.param_dtype, # moments in full precision\n )\n optimizer = nnx.Optimizer(genie, tx)\n return optimizer, lr_schedule\n\n\ndef build_mesh_and_sharding(\n num_devices: int,\n) -> tuple[Mesh, NamedSharding, NamedSharding, NamedSharding]:\n device_mesh_arr = create_device_mesh((num_devices,))\n mesh = Mesh(devices=device_mesh_arr, axis_names=(""data"",))\n replicated_sharding = NamedSharding(mesh, PartitionSpec())\n videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n actions_sharding = NamedSharding(mesh, PartitionSpec(""data"", None))\n return mesh, replicated_sharding, videos_sharding, actions_sharding\n\n\ndef shard_optimizer_states(\n optimizer: nnx.Optimizer, replicated_sharding: NamedSharding\n) -> None:\n model_state = nnx.state(optimizer.model)\n model_sharded_state = jax.lax.with_sharding_constraint(\n model_state, replicated_sharding\n )\n nnx.update(optimizer.model, model_sharded_state)\n optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)\n optimizer_sharded_state = jax.lax.with_sharding_constraint(\n optimizer_state, replicated_sharding\n )\n nnx.update(optimizer, optimizer_sharded_state)\n\n\ndef build_dataloader(args: Args, data_dir: str) -> grain.DataLoaderIterator:\n image_shape = (args.image_height, args.image_width, args.image_channels)\n array_record_files = [\n os.path.join(data_dir, x)\n for x in os.listdir(data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n return grain_iterator\n\n\ndef build_checkpoint_manager(args: Args) -> ocp.CheckpointManager:\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n if args.val_data_dir:\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n return checkpoint_manager\n\n\ndef restore_or_initialize_components(\n args: Args,\n checkpoint_manager: ocp.CheckpointManager,\n optimizer: nnx.Optimizer,\n train_iterator: grain.DataLoaderIterator,\n rng: jax.Array,\n replicated_sharding: NamedSharding,\n val_iterator: Optional[grain.DataLoaderIterator],\n restore_step: Optional[int] = None,\n) -> tuple[\n int, nnx.Optimizer, grain.DataLoaderIterator, grain.DataLoaderIterator, jax.Array\n]:\n step = 0\n if restore_step is None:\n restore_step = checkpoint_manager.latest_step()\n if args.restore_ckpt:\n abstract_optimizer = nnx.eval_shape(lambda: optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n if val_iterator:\n restore_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointRestore(train_iterator), # type: ignore\n val_dataloader_state=grain.checkpoint.CheckpointRestore(val_iterator), # type: ignore\n )\n else:\n restore_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointRestore(train_iterator), # type: ignore\n )\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(), args=restore_args\n )\n restored_optimizer_state = restored[""model_state""]\n nnx.update(optimizer, restored_optimizer_state)\n train_iterator = restored[""train_dataloader_state""]\n if val_iterator:\n val_iterator = restored[""val_dataloader_state""]\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored dataloader and model state from step {step}"")\n else:\n # Restore from pre-trained tokenizer (and LAM)\n rng, _rng = jax.random.split(rng)\n optimizer = restore_genie_components(optimizer, replicated_sharding, _rng, args)\n # NOTE: We have to remove the (unused) tokenizer vq dropout due flax.nnx lazily initializing modules.\n # Specifically, the first dynamics model checkpoint will contain the vq dropout module,\n # but the first full restore will fail due to nnx not initializing the module when\n # dropout is set to 0.0.\n del optimizer.model.tokenizer.vq.drop\n return step, optimizer, train_iterator, val_iterator, rng\n\n\ndef _calculate_step_metrics(\n outputs: dict[str, jax.Array],\n gt: jax.Array,\n num_actions: int,\n num_patch_latents: int,\n) -> tuple[jax.Array, dict]:\n mask = outputs[""mask""]\n outputs[""token_logits""] = outputs[""token_logits""].astype(jnp.float32)\n ce_loss = optax.softmax_cross_entropy_with_integer_labels(\n outputs[""token_logits""], outputs[""video_tokens""]\n )\n ce_loss = (mask * ce_loss).sum() / mask.sum()\n acc = outputs[""token_logits""].argmax(-1) == outputs[""video_tokens""]\n acc = (mask * acc).sum() / mask.sum()\n select_probs = jax.nn.softmax(outputs[""token_logits""])\n gt_val = gt.clip(0, 1).reshape(-1, *gt.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt_val, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt_val, recon)).mean()\n _, index_counts_tokenizer = jnp.unique_counts(\n jnp.ravel(outputs[""video_tokens""]),\n size=num_patch_latents,\n fill_value=0,\n )\n codebook_usage_tokenizer = (index_counts_tokenizer != 0).mean()\n metrics = dict(\n cross_entropy_loss=ce_loss,\n masked_token_accuracy=acc,\n select_logit=outputs[""token_logits""].max(-1).mean(),\n select_p=select_probs.max(-1).mean(),\n entropy=jax.scipy.special.entr(select_probs).sum(-1).mean(),\n psnr=psnr,\n ssim=ssim,\n codebook_usage_tokenizer=codebook_usage_tokenizer,\n )\n if ""lam_indices"" in outputs.keys():\n _, index_counts_lam = jnp.unique_counts(\n jnp.ravel(outputs[""lam_indices""]),\n size=num_actions,\n fill_value=0,\n )\n codebook_usage_lam = (index_counts_lam != 0).mean()\n metrics[""codebook_usage_lam""] = codebook_usage_lam\n return ce_loss, metrics\n\n\ndef main(args: Args) -> None:\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 rng = jax.random.key(args.seed)\n\n # --- Initialize model ---\n genie, rng = build_model(args, rng)\n _, params, _ = nnx.split(genie, nnx.Param, ...)\n param_counts = count_parameters_by_component(params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\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 if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n optimizer, lr_schedule = build_optimizer(genie, args)\n del genie\n\n # FIXME: switch to create_hybrid_device_mesh for runs spanning multiple nodes\n _, replicated_sharding, videos_sharding, actions_sharding = build_mesh_and_sharding(\n num_devices\n )\n\n shard_optimizer_states(optimizer, replicated_sharding)\n\n # --- Initialize checkpoint manager ---\n checkpoint_manager = build_checkpoint_manager(args)\n\n # --- Create DataLoaderIterator from dataloader ---\n train_iterator = build_dataloader(args, args.data_dir)\n val_iterator = None\n if args.val_data_dir:\n val_iterator = build_dataloader(args, args.val_data_dir)\n\n # --- Restore checkpoint ---\n step, optimizer, train_iterator, val_iterator, rng = (\n restore_or_initialize_components(\n args,\n checkpoint_manager,\n optimizer,\n train_iterator,\n rng,\n replicated_sharding,\n val_iterator,\n )\n )\n\n # --- Define loss and train step (close over args) ---\n def dynamics_loss_fn(\n model: Genie,\n inputs: dict,\n training: bool = False,\n ) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n ce_loss, metrics = _calculate_step_metrics(\n outputs, gt, args.num_actions, args.num_patch_latents\n )\n return ce_loss, (outputs[""recon""], metrics)\n\n @nnx.jit(donate_argnums=0)\n def train_step(\n optimizer: nnx.Optimizer, inputs: dict\n ) -> tuple[jax.Array, jax.Array, dict]:\n def loss_fn(model: Genie) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n model.train()\n return dynamics_loss_fn(model, inputs, training=True)\n\n (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(\n optimizer.model\n )\n optimizer.update(grads)\n if args.log_gradients:\n metrics[""gradients_std/""] = jax.tree.map(\n lambda x: x.std(), grads[""params""][""dynamics""]\n )\n return loss, recon, metrics\n\n @nnx.jit\n def val_step(genie: Genie, inputs: dict) -> dict:\n """"""Evaluate model and compute metrics""""""\n genie.eval()\n (loss, (recon, metrics)) = dynamics_loss_fn(genie, inputs, training=False)\n val_output = {""loss"": loss, ""recon"": recon, ""metrics"": metrics}\n\n # --- Evaluate full frame prediction (sampling) ---\n if args.eval_full_frame:\n tokenizer_outputs = genie.tokenizer.vq_encode(\n inputs[""videos""], training=False\n )\n tokens_full_frame = tokenizer_outputs[""indices""]\n lam_indices = None\n if not args.use_gt_actions:\n lam_indices = genie.vq_encode(inputs, training=False)\n inputs[""latent_actions""] = lam_indices\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt[:, :-1].astype(\n args.dtype\n ) # remove last frame for generation\n recon_full_frame, logits_full_frame = genie.sample(\n inputs,\n args.seq_len,\n args.val_temperature,\n args.val_sample_argmax,\n args.val_maskgit_steps,\n )\n step_outputs = {\n ""recon"": recon_full_frame,\n ""token_logits"": logits_full_frame,\n ""video_tokens"": tokens_full_frame,\n ""mask"": jnp.zeros_like(tokens_full_frame).at[:, -1].set(True),\n }\n if lam_indices is not None:\n step_outputs[""lam_indices""] = lam_indices\n\n loss_full_frame, metrics_full_frame = _calculate_step_metrics(\n step_outputs, gt, args.num_actions, args.num_patch_latents\n )\n val_output.update(\n {\n ""loss_full_frame"": loss_full_frame,\n ""recon_full_frame"": recon_full_frame,\n ""metrics_full_frame"": metrics_full_frame,\n }\n )\n return val_output\n\n def calculate_validation_metrics(val_dataloader, genie, rng):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n loss_full_frame_per_step = []\n metrics_full_frame_per_step = []\n batch = None\n recon = None\n recon_full_frame = None\n for batch in val_dataloader:\n rng, _rng_mask = jax.random.split(rng, 2)\n batch[""rng""] = _rng_mask\n val_outputs = val_step(genie, batch)\n loss_per_step.append(val_outputs[""loss""])\n metrics_per_step.append(val_outputs[""metrics""])\n recon = val_outputs[""recon""]\n if args.eval_full_frame:\n loss_full_frame_per_step.append(val_outputs[""loss_full_frame""])\n metrics_full_frame_per_step.append(val_outputs[""metrics_full_frame""])\n recon_full_frame = val_outputs[""recon_full_frame""]\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(\n f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}""\n )\n\n val_metrics = {\n f""val_{key}"": np.mean([float(m[key]) for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n val_metrics[""val_loss""] = np.mean(loss_per_step)\n if args.eval_full_frame:\n val_metrics_full_frame = {\n f""val_full_frame_{key}"": np.mean(\n [float(m[key]) for m in metrics_full_frame_per_step]\n )\n for key in metrics_full_frame_per_step[0].keys()\n }\n val_metrics.update(val_metrics_full_frame)\n val_metrics[""val_loss_full_frame""] = np.mean(loss_full_frame_per_step)\n return val_metrics, batch, recon, recon_full_frame\n\n # --- TRAIN LOOP ---\n dataloader_train = (\n {\n ""videos"": jax.make_array_from_process_local_data(\n videos_sharding, local_data=elem[""videos""]\n ),\n ""actions"": (\n jax.make_array_from_process_local_data(\n actions_sharding, elem[""actions""]\n )\n if args.use_gt_actions\n else None\n ),\n }\n for elem in train_iterator\n )\n dataloader_val = None\n if val_iterator:\n dataloader_val = (\n {\n ""videos"": jax.make_array_from_process_local_data(\n videos_sharding, elem[""videos""]\n ),\n ""actions"": (\n jax.make_array_from_process_local_data(\n actions_sharding, elem[""actions""]\n )\n if args.use_gt_actions\n else None\n ),\n }\n for elem in val_iterator\n )\n if jax.process_index() == 0:\n first_batch = next(dataloader_train)\n first_batch[""rng""] = rng # type: ignore\n compiled = train_step.lower(optimizer, first_batch).compile()\n print_compiled_memory_stats(compiled.memory_analysis())\n print_compiled_cost_analysis(compiled.cost_analysis())\n # Do not skip the first batch during training\n dataloader_train = itertools.chain([first_batch], dataloader_train)\n print(f""Starting training from step {step}..."")\n first_step = step\n while step < args.num_steps:\n for batch in dataloader_train:\n # --- Train step ---\n rng, _rng_mask = jax.random.split(rng, 2)\n batch[""rng""] = _rng_mask\n loss, recon, metrics = train_step(optimizer, batch)\n if step == first_step:\n print_mem_stats(""After params initialized"")\n # metrics[""lr""] = lr_schedule(step)\n # print(f""Step {step}, loss: {loss}"")\n step += 1\n\n # --- Validation loss ---\n val_results = {}\n if dataloader_val and step % args.val_interval == 0:\n rng, _rng_mask_val = jax.random.split(rng, 2)\n print(""Calculating validation metrics..."")\n val_metrics, val_gt_batch, val_recon, val_recon_full_frame = (\n calculate_validation_metrics(\n dataloader_val, optimizer.model, _rng_mask_val\n )\n )\n print(f""Step {step}, validation loss: {val_metrics['val_loss']}"")\n val_results = {\n ""metrics"": val_metrics,\n ""gt_batch"": val_gt_batch,\n ""recon"": val_recon,\n ""full_frame"": val_recon_full_frame,\n }\n\n # --- Logging ---\n if args.log:\n if step % args.log_interval == 0 and jax.process_index() == 0:\n log_dict = {""loss"": loss, ""step"": step, **metrics}\n if val_results:\n log_dict.update(val_results[""metrics""])\n wandb.log(log_dict)\n if step % args.log_image_interval == 0:\n gt_seq = batch[""videos""][0].astype(jnp.float32) / 255.0\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 if val_results:\n val_results[""gt_seq_val""] = (\n val_results[""gt_batch""][""videos""][0].astype(jnp.float32)\n / 255.0\n )\n val_results[""recon_seq_val""] = val_results[""recon""][0].clip(\n 0, 1\n )\n val_comparison_seq = jnp.concatenate(\n (val_results[""gt_seq_val""], val_results[""recon_seq_val""]),\n axis=1,\n )\n val_results[""val_comparison_seq""] = einops.rearrange(\n val_comparison_seq * 255, ""t h w c -> h (t w) c""\n )\n if args.eval_full_frame:\n val_results[""full_frame_seq_val""] = val_results[\n ""full_frame""\n ][0].clip(0, 1)\n val_results[""val_full_frame_comparison_seq""] = (\n jnp.concatenate(\n (\n val_results[""gt_seq_val""],\n val_results[""full_frame_seq_val""],\n ),\n axis=1,\n )\n )\n val_results[""val_full_frame_comparison_seq""] = (\n einops.rearrange(\n val_results[""val_full_frame_comparison_seq""] * 255,\n ""t h w c -> h (t w) c"",\n )\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[args.seq_len - 1])),\n recon=wandb.Image(np.asarray(recon_seq[args.seq_len - 1])),\n true_vs_recon=wandb.Image(\n np.asarray(comparison_seq.astype(np.uint8))\n ),\n )\n if val_results:\n log_images.update(\n dict(\n val_image=wandb.Image(\n np.asarray(\n val_results[""gt_seq_val""][args.seq_len - 1]\n )\n ),\n val_recon=wandb.Image(\n np.asarray(\n val_results[""recon_seq_val""][\n args.seq_len - 1\n ]\n )\n ),\n val_true_vs_recon=wandb.Image(\n np.asarray(\n val_results[""val_comparison_seq""].astype(\n np.uint8\n )\n )\n ),\n )\n )\n if args.eval_full_frame:\n log_images.update(\n dict(\n val_full_frame=wandb.Image(\n np.asarray(\n val_results[""full_frame_seq_val""][\n args.seq_len - 1\n ]\n )\n ),\n val_true_vs_full_frame=wandb.Image(\n np.asarray(\n val_results[\n ""val_full_frame_comparison_seq""\n ].astype(np.uint8)\n )\n ),\n )\n )\n wandb.log(log_images)\n # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n optimizer_state = nnx.state(optimizer)\n if val_iterator:\n ckpt_manager_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n train_iterator # type: ignore\n ),\n val_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n val_iterator # type: ignore\n ),\n )\n else:\n ckpt_manager_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n train_iterator # type: ignore\n ),\n )\n checkpoint_manager.save(step, args=ckpt_manager_args)\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n checkpoint_manager.close()\n\n\nif __name__ == ""__main__"":\n args = tyro.cli(Args)\n main(args)\n",python,tab +338,1193094,"train_dynamics.py",22357,0,"",python,selection_mouse +339,1193118,"train_dynamics.py",22356,0,"",python,selection_command +340,1193228,"train_dynamics.py",22356,1,"1",python,selection_mouse +341,1193243,"train_dynamics.py",22357,0,"",python,selection_command +342,1193298,"train_dynamics.py",22325,32,": {loss}"")\n step += 1",python,selection_mouse +343,1193298,"train_dynamics.py",22321,36,"loss: {loss}"")\n step += 1",python,selection_mouse +344,1193312,"train_dynamics.py",22317,40,"p}, loss: {loss}"")\n step += 1",python,selection_mouse +345,1193335,"train_dynamics.py",22313,44,"{step}, loss: {loss}"")\n step += 1",python,selection_mouse +346,1193350,"train_dynamics.py",22308,49,"Step {step}, loss: {loss}"")\n step += 1",python,selection_mouse +347,1193365,"train_dynamics.py",22305,52,"(f""Step {step}, loss: {loss}"")\n step += 1",python,selection_mouse +348,1193418,"train_dynamics.py",22303,54,"nt(f""Step {step}, loss: {loss}"")\n step += 1",python,selection_mouse +349,1193419,"train_dynamics.py",22299,58," print(f""Step {step}, loss: {loss}"")\n step += 1",python,selection_mouse +350,1193467,"train_dynamics.py",22298,59,"# print(f""Step {step}, loss: {loss}"")\n step += 1",python,selection_mouse +351,1193468,"train_dynamics.py",22297,60," # print(f""Step {step}, loss: {loss}"")\n step += 1",python,selection_mouse +352,1193523,"train_dynamics.py",22296,61," # print(f""Step {step}, loss: {loss}"")\n step += 1",python,selection_mouse +353,1193580,"train_dynamics.py",22295,62," # print(f""Step {step}, loss: {loss}"")\n step += 1",python,selection_mouse +354,1193581,"train_dynamics.py",22293,64," # print(f""Step {step}, loss: {loss}"")\n step += 1",python,selection_mouse +355,1193593,"train_dynamics.py",22292,65," # print(f""Step {step}, loss: {loss}"")\n step += 1",python,selection_mouse +356,1193616,"train_dynamics.py",22291,66," # print(f""Step {step}, loss: {loss}"")\n step += 1",python,selection_mouse +357,1193630,"train_dynamics.py",22290,67," # print(f""Step {step}, loss: {loss}"")\n step += 1",python,selection_mouse +358,1193657,"train_dynamics.py",22241,116," # metrics[""lr""] = lr_schedule(step)\n # print(f""Step {step}, loss: {loss}"")\n step += 1",python,selection_mouse +359,1193673,"train_dynamics.py",22240,117," # metrics[""lr""] = lr_schedule(step)\n # print(f""Step {step}, loss: {loss}"")\n step += 1",python,selection_mouse +360,1193699,"train_dynamics.py",22239,118," # metrics[""lr""] = lr_schedule(step)\n # print(f""Step {step}, loss: {loss}"")\n step += 1",python,selection_mouse +361,1194207,"train_dynamics.py",22239,0,"",python,selection_mouse +362,1194208,"train_dynamics.py",22238,12," ",python,selection_mouse +363,1194429,"train_dynamics.py",22238,50," # metrics[""lr""] = lr_schedule(step)\n ",python,selection_mouse +364,1194430,"train_dynamics.py",22238,51," # metrics[""lr""] = lr_schedule(step)\n ",python,selection_mouse +365,1194458,"train_dynamics.py",22238,52," # metrics[""lr""] = lr_schedule(step)\n ",python,selection_mouse +366,1194482,"train_dynamics.py",22238,53," # metrics[""lr""] = lr_schedule(step)\n ",python,selection_mouse +367,1194559,"train_dynamics.py",22238,104," # metrics[""lr""] = lr_schedule(step)\n # print(f""Step {step}, loss: {loss}"")\n ",python,selection_mouse +368,1196017,"train_dynamics.py",22342,0,"",python,selection_mouse +369,1204801,"TERMINAL",0,0,"Saved checkpoint at step 3250\r\n",,terminal_output +370,1235158,"TERMINAL",0,0,"Saved checkpoint at step 3500\r\n",,terminal_output +371,1235804,"train_dynamics.py",0,0,"",python,tab +372,1236053,"train_dynamics.py",16790,0,"",python,selection_command +373,1243457,"train_dynamics.py",0,0,"",python,tab +374,1265022,"TERMINAL",0,0,"Calculating validation metrics...\r\n",,terminal_output +375,1287282,"TERMINAL",0,0,"Step 3750, validation loss: 0.739980936050415\r\n",,terminal_output +376,1287584,"TERMINAL",0,0,"Saved checkpoint at step 3750\r\n",,terminal_output +377,1318043,"TERMINAL",0,0,"Saved checkpoint at step 4000\r\n",,terminal_output +378,1348952,"TERMINAL",0,0,"Saved checkpoint at step 4250\r\n",,terminal_output +379,1378627,"TERMINAL",0,0,"Calculating validation metrics...\r\n",,terminal_output +380,1400950,"TERMINAL",0,0,"Step 4500, validation loss: 0.6556053161621094\r\n",,terminal_output +381,1401277,"TERMINAL",0,0,"Saved checkpoint at step 4500\r\n",,terminal_output +382,1431987,"TERMINAL",0,0,"Saved checkpoint at step 4750\r\n",,terminal_output +383,1462615,"TERMINAL",0,0,"Saved checkpoint at step 5000\r\n",,terminal_output +384,1516181,"TERMINAL",0,0,"wandb: \r\nwandb: 🚀 View run breakout-dyn-default-gt-actions-3512647 at: https://wandb.ai/instant-uv/jafar/runs/3rw70eln\r\nwandb: Find logs at: ../../../../../hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20250923_121055-3rw70eln/logs\r\n",,terminal_output +385,1517976,"TERMINAL",0,0,"]0;tum_cte0515@hkn0403:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0403 jasmine]$ ",,terminal_output +386,1971016,"train_dynamics.py",22552,0,"",python,selection_mouse +387,1971017,"train_dynamics.py",22551,0,"",python,selection_command +388,1971721,"TERMINAL",0,0,"bash",,terminal_focus +389,1977444,"TERMINAL",0,0,"srun",,terminal_focus +390,1977918,"TERMINAL",0,0,"q",,terminal_output +391,1978101,"TERMINAL",0,0,"u",,terminal_output +392,1978171,"TERMINAL",0,0,"e",,terminal_output +393,1978235,"TERMINAL",0,0,"u",,terminal_output +394,1978460,"TERMINAL",0,0,"e",,terminal_output +395,1978597,"TERMINAL",0,0,"\r\n[?2004l\r[?1049h(B[?7hEvery 1.0s: squeue --mehkn0403.localdomain: Tue Sep 23 12:34:02 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3511158 accelerat train_dy tum_cte0 PD\t0:00\t 1 (Priority)3511159 accelerat train_dy tum_cte0 PD\t0:00\t 1 (Priority)3512647 dev_accel interact tum_cte0 R32:53\t 1 hkn04033512651 large preproce tum_cte0 R30:17\t 1 hkn1901",,terminal_output +396,1979889,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn0403:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0403 jasmine]$ ",,terminal_output +397,1984072,"TERMINAL",0,0,"queue",,terminal_output +398,1984752,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu_gt_actions.sh",,terminal_output +399,1986018,"TERMINAL",0,0,"",,terminal_output +400,1986545,"TERMINAL",0,0,"",,terminal_output +401,1986915,"TERMINAL",0,0,"",,terminal_output +402,1987077,"TERMINAL",0,0,"",,terminal_output +403,1987262,"TERMINAL",0,0,"",,terminal_output +404,1987717,"TERMINAL",0,0,"gt_actions.sh ",,terminal_output +405,1988358,"TERMINAL",0,0,"",,terminal_output +406,1989126,"TERMINAL",0,0,"",,terminal_output +407,1989352,"TERMINAL",0,0,"",,terminal_output +408,1989534,"TERMINAL",0,0,"",,terminal_output +409,1989687,"TERMINAL",0,0,"",,terminal_output +410,1989842,"TERMINAL",0,0,"",,terminal_output +411,1990020,"TERMINAL",0,0,"",,terminal_output +412,1990351,"TERMINAL",0,0,".",,terminal_output +413,1990514,"TERMINAL",0,0,"sh ",,terminal_output +414,1996762,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",0,0,"",shellscript,tab +415,1999730,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",1591,0,"",shellscript,selection_mouse +416,1999859,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",1589,3,"500",shellscript,selection_mouse +417,2003391,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",1558,0,"",shellscript,selection_mouse +418,2006150,"TERMINAL",0,0,"\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=1\r\n#SBATCH --time=48:00:00\r\n#SBATCH --partition=accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\r\n#SBATCH --job-name=train_dyn_default_breakout_longer\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/train\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/val\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\nlam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3512576\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3512502\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=10 \\r\n --image_width=10 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=120 \\r\n --init_lr=0 \\r\n --max_lr=3e-5 \\r\n --log_image_interval=500 \\r\n --log_checkpoint_interval=500 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=breakout-dyn-default-$slurm_job_id \\r\n --tags dyn breakout default \\r\n --entity instant-uv \\r\n --project jafar \\r\n --patch_size 4 \\r\n --lam_patch_size 4 \\r\n --warmup_steps 100 \\r\n --wsd_decay_steps 1000 \\r\n --num_steps 5000 \\r\n --data_dir $array_records_dir_train \\r\n --val_data_dir $array_records_dir_val \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --lam_checkpoint $lam_checkpoint \\r\n --val_interval 750 \\r\n --eval_full_frame \\r\n",,terminal_output +419,2006270,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=4095309\r\nSLURM_JOB_GPUS=0\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\r\nSLURMD_NODENAME=hkn0403\r\nSLURM_JOB_START_TIME=1758621669\r\nSLURM_STEP_NODELIST=hkn0403\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1758625269\r\nSLURM_PMI2_SRUN_PORT=33711\r\nSLURM_CPUS_ON_NODE=8\r\nSLURM_JOB_CPUS_PER_NODE=8\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=dev_accelerated\r\nSLURM_TRES_PER_TASK=cpu=8\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=1\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3512647\r\nSLURM_PTY_PORT=37671\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.201\r\nSLURM_PTY_WIN_ROW=45\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0403\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.201\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=246\r\nSLURM_NODELIST=hkn0403\r\nSLURM_SRUN_COMM_PORT=35797\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1993.localdomain\r\nSLURM_JOB_ID=3512647\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0403\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_STEP_LAUNCHER_PORT=35797\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0403\r\n",,terminal_output +420,2006412,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +421,2008647,"TERMINAL",0,0,"bash",,terminal_focus +422,2009113,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +423,2011207,"TERMINAL",0,0,"dev",,terminal_command +424,2012664,"TERMINAL",0,0,"git status",,terminal_command +425,2012715,"TERMINAL",0,0,"]633;C",,terminal_output +426,2012961,"TERMINAL",0,0,"On branch generate-minatar-breakout-dataset\r\n",,terminal_output +427,2013027,"TERMINAL",0,0,"Your branch is ahead of 'origin/generate-minatar-breakout-dataset' by 10 commits.\r\n (use ""git push"" to publish your local commits)\r\n\r\n",,terminal_output +428,2013082,"TERMINAL",0,0,"Last commands done (2 commands done):\r\n pick ba37453 feat: generate coinrun dataset with val split\r\n pick faadd10 feat: implemented validation loss for all three models\r\nNext commands to do (26 remaining commands):\r\n pick 9a17dbb fix: pass val data path to dataloader\r\n pick 6e69cdb fix typo in image logging\r\n (use ""git rebase --edit-todo"" to view and edit)\r\nYou are currently editing a commit while rebasing branch 'gt-actions' on 'c7522f2'.\r\n (use ""git commit --amend"" to amend the current commit)\r\n (use ""git rebase --continue"" once you are satisfied with your changes)\r\n\r\nChanges not staged for commit:\r\n (use ""git add ..."" to update what will be committed)\r\n (use ""git restore ..."" to discard changes in working directory)\r\n\tmodified: genie.py\r\n\tmodified: input_pipeline/generate_breakout_dataset.py\r\n\tmodified: sample.py\r\n\tmodified: train_dynamics.py\r\n\tmodified: train_lam.py\r\n\tmodified: train_tokenizer.py\r\n\r\nUntracked files:\r\n (use ""git add ..."" to include in what will be committed)\r\n\tdiff.diff\r\n\tinput_pipeline/generate_breakout_dataset_agent.py\r\n\tkiller.sh\r\n\tkiller_partition.sh\r\n\tlog.log\r\n\toverfit_dir.zip\r\n\trequirements-franz.txt\r\n\tsamples/\r\n\tscripts_cremers/\r\n\tslurm/\r\n\ttest.py\r\n\tutils/visualizer.py\r\n\r\nno changes added to commit (use ""git add"" and/or ""git commit -a"")\r\n]0;tum_cte0515@hkn1993:~/Projects/jasmine",,terminal_output +429,2015303,"TERMINAL",0,0,"Counting all components: ['dynamics', 'lam', 'tokenizer']\r\n",,terminal_output +430,2015657,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +431,2016491,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.21.3\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20250923_123439-jhpw2lqa\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run breakout-dyn-default-3512647\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/jhpw2lqa\r\n",,terminal_output +432,2016600,"TERMINAL",0,0,"Parameter counts:\r\n{'dynamics': 26555904, 'lam': 16900976, 'tokenizer': 33750256, 'total': 77207136}\r\n",,terminal_output +433,2018776,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +434,2018792,"TERMINAL",0,0,"git diff",,terminal_command +435,2018890,"TERMINAL",0,0,"]633;C[?1h=\rdiff --git a/genie.py b/genie.py\r\nindex bcb23e6..7fa3f40 100644\r\n--- a/genie.py\r\n+++ b/genie.py\r\n@@ -263,7 +263,7 @@ class Genie(nnx.Module):\r\n latent_actions_BT1L = self.action_embed(batch[""actions""]).reshape(\r\n *batch[""actions""].shape[:2], 1, self.latent_action_dim\r\n )\r\n- latent_actions_BTm11L = latent_actions_BT1L[:, 1:]\r\n+ latent_actions_BTm11L = latent_actions_BT1L[:, :-1]\r\n action_tokens_EL = latent_actions_BTm11L.reshape(-1, self.latent_action_dim)\r\n else:\r\n assert self.lam is not None\r\n@@ -452,7 +452,7 @@ class Genie(nnx.Module):\r\n latent_actions_BT1L = self.action_embed(batch[""actions""]).reshape(\r\n *batch[""actions""].shape[:2], 1, self.latent_action_dim\r\n )\r\n:",,terminal_output +436,2019442,"TERMINAL",0,0,"\r- latent_actions_BTm11L = latent_actions_BT1L[:, 1:]\r\n:",,terminal_output +437,2019688,"TERMINAL",0,0,"\r+ latent_actions_BTm11L = latent_actions_BT1L[:, :-1]\r\n:",,terminal_output +438,2019872,"TERMINAL",0,0,"\r action_tokens_EL = latent_actions_BTm11L.reshape(-1, self.latent_action_dim)\r\n:",,terminal_output +439,2020044,"TERMINAL",0,0,"\r else:\r\n:",,terminal_output +440,2020191,"TERMINAL",0,0,"\r assert self.lam is not None\r\n:",,terminal_output +441,2020303,"TERMINAL",0,0,"\rdiff --git a/input_pipeline/generate_breakout_dataset.py b/input_pipeline/generate_breakout_dataset.py\r\n:",,terminal_output +442,2020474,"TERMINAL",0,0,"\rindex 88928b9..4aa5586 100644\r\n:",,terminal_output +443,2020646,"TERMINAL",0,0,"\r--- a/input_pipeline/generate_breakout_dataset.py\r\n:",,terminal_output +444,2020705,"TERMINAL",0,0,"\r+++ b/input_pipeline/generate_breakout_dataset.py\r\n:",,terminal_output +445,2020862,"TERMINAL",0,0,"\r@@ -117,8 +117,8 @@ def generate_episodes(num_episodes: int, split: str):\r\n:",,terminal_output +446,2021701,"TERMINAL",0,0,"\r obs_chunks.extend(obs_chunks_data)\r\n:\r act_chunks.extend(act_chunks_data)\r\n:\r \r\n:\r- ep_metadata, obs_chunks, file_idx, act_chunks = save_chunks(\r\n:\r- obs_chunks, file_idx, args.chunks_per_file, output_dir_split, act_chunks\r\n:\r+ ep_metadata, file_idx, obs_chunks, act_chunks = save_chunks(\r\n:\r+ file_idx, args.chunks_per_file, output_dir_split, obs_chunks, act_chunks\r\n:\r )\r\n:\r episode_metadata.extend(ep_metadata)\r\n:\r \r\n:\rdiff --git a/sample.py b/sample.py\r\n:\rindex b5d5a22..6cf11b4 100644\r\n:",,terminal_output +447,2021868,"TERMINAL",0,0,"\r--- a/sample.py\r\n:",,terminal_output +448,2022308,"TERMINAL",0,0,"\r+++ b/sample.py\r\n:",,terminal_output +449,2022528,"TERMINAL",0,0,"\r@@ -237,7 +237,7 @@ if __name__ == ""__main__"":\r\n:",,terminal_output +450,2022691,"TERMINAL",0,0,"\r if action_batch_E is not None:\r\n:",,terminal_output +451,2022831,"TERMINAL",0,0,"\r action_batch_BSm11 = jnp.reshape(action_batch_E, (B, S - 1, 1))\r\n:",,terminal_output +452,2023001,"TERMINAL",0,0,"\r else:\r\n:",,terminal_output +453,2023175,"TERMINAL",0,0,"\r- action_batch_BSm11 = jnp.expand_dims(batch[""actions""][:, 1:], -1)\r\n:",,terminal_output +454,2023298,"TERMINAL",0,0,"\r+ action_batch_BSm11 = jnp.expand_dims(batch[""actions""][:, :-1], -1)\r\n:",,terminal_output +455,2023917,"TERMINAL",0,0,"\r for t, img in enumerate(imgs[1:]):\r\n:\r d = ImageDraw.Draw(img)\r\n:\r for row in range(B):\r\n:\rdiff --git a/train_dynamics.py b/train_dynamics.py\r\n:\rindex b34ba98..ad89aae 100644\r\n:",,terminal_output +456,2024008,"TERMINAL",0,0,"\r--- a/train_dynamics.py\r\n:\r+++ b/train_dynamics.py\r\n:",,terminal_output +457,2024315,"TERMINAL",0,0,"\r@@ -482,12 +482,14 @@ def main(args: Args) -> None:\r\n:\r \r\n:\r # --- Evaluate full frame prediction (sampling) ---\r\n:\r if args.eval_full_frame:\r\n:\r- lam_indices = genie.vq_encode(inputs, training=False)\r\n:\r tokenizer_outputs = genie.tokenizer.vq_encode(\r\n:\r inputs[""videos""], training=False\r\n:\r )\r\n:\r tokens_full_frame = tokenizer_outputs[""indices""]\r\n:\r- inputs[""latent_actions""] = lam_indices\r\n:\r+ lam_indices = None\r\n:",,terminal_output +458,2024629,"TERMINAL",0,0,"\rM for t, img in enumerate(imgs[1:]):\r\n\r:",,terminal_output +459,2025521,"TERMINAL",0,0,"\rM+ action_batch_BSm11 = jnp.expand_dims(batch[""actions""][:, :-1], -1)\r\n\r:\rM- action_batch_BSm11 = jnp.expand_dims(batch[""actions""][:, 1:], -1)\r\n\r:\rM else:\r\n\r:\rM action_batch_BSm11 = jnp.reshape(action_batch_E, (B, S - 1, 1))\r\n\r:\rM if action_batch_E is not None:\r\n\r:\rM@@ -237,7 +237,7 @@ if __name__ == ""__main__"":\r\n\r:\rM+++ b/sample.py\r\n\r:\rM--- a/sample.py\r\n\r:\rMindex b5d5a22..6cf11b4 100644\r\n\r:\rMdiff --git a/sample.py b/sample.py\r\n\r:\rM \r\n\r:\rM episode_metadata.extend(ep_metadata)\r\n\r:\rM )\r\n\r:\rM+ file_idx, args.chunks_per_file, output_dir_split, obs_chunks, act_chunks\r\n\r:\rM+ ep_metadata, file_idx, obs_chunks, act_chunks = save_chunks(\r\n\r:",,terminal_output +460,2025938,"TERMINAL",0,0,"\r for row in range(B):\r\n:",,terminal_output +461,2026178,"TERMINAL",0,0,"\rdiff --git a/train_dynamics.py b/train_dynamics.py\r\n:",,terminal_output +462,2026304,"TERMINAL",0,0,"\rindex b34ba98..ad89aae 100644\r\n:",,terminal_output +463,2026539,"TERMINAL",0,0,"\r--- a/train_dynamics.py\r\n:",,terminal_output +464,2027439,"TERMINAL",0,0,"\r+++ b/train_dynamics.py\r\n:",,terminal_output +465,2027805,"TERMINAL",0,0,"\r@@ -482,12 +482,14 @@ def main(args: Args) -> None:\r\n:",,terminal_output +466,2029606,"TERMINAL",0,0,"\rMdiff --git a/sample.py b/sample.py\r\n\r:",,terminal_output +467,2030509,"TERMINAL",0,0,"\rM \r\n\r:\rM episode_metadata.extend(ep_metadata)\r\n\r:\rM )\r\n\r:\rM+ file_idx, args.chunks_per_file, output_dir_split, obs_chunks, act_chunks\r\n\r:\rM+ ep_metadata, file_idx, obs_chunks, act_chunks = save_chunks(\r\n\r:\rM- obs_chunks, file_idx, args.chunks_per_file, output_dir_split, act_chunks\r\n\r:\rM- ep_metadata, obs_chunks, file_idx, act_chunks = save_chunks(\r\n\r:\rM \r\n\r:\rM act_chunks.extend(act_chunks_data)\r\n\r:\rM obs_chunks.extend(obs_chunks_data)\r\n\r:\rM@@ -117,8 +117,8 @@ def generate_episodes(num_episodes: int, split: str):\r\n\r:\rM+++ b/input_pipeline/generate_breakout_dataset.py\r\n\r:\rM--- a/input_pipeline/generate_breakout_dataset.py\r\n\r:\rMindex 88928b9..4aa5586 100644\r\n\r:\rMdiff --git a/input_pipeline/generate_breakout_dataset.py b/input_pipeline/generate_breakout_dataset.py\r\n\r:",,terminal_output +468,2030587,"TERMINAL",0,0,"\rM assert self.lam is not None\r\n\r:\rM else:\r\n\r:\rM action_tokens_EL = latent_actions_BTm11L.reshape(-1, self.latent_action_dim)\r\n\r:",,terminal_output +469,2031685,"TERMINAL",0,0,"\r \r\n:",,terminal_output +470,2032206,"TERMINAL",0,0,"\rdiff --git a/sample.py b/sample.py\r\n:",,terminal_output +471,2032380,"TERMINAL",0,0,"\rindex b5d5a22..6cf11b4 100644\r\n:\r--- a/sample.py\r\n:\r+++ b/sample.py\r\n:\r@@ -237,7 +237,7 @@ if __name__ == ""__main__"":\r\n:\r if action_batch_E is not None:\r\n:",,terminal_output +472,2032453,"TERMINAL",0,0,"\r action_batch_BSm11 = jnp.reshape(action_batch_E, (B, S - 1, 1))\r\n:\r else:\r\n:\r- action_batch_BSm11 = jnp.expand_dims(batch[""actions""][:, 1:], -1)\r\n:\r+ action_batch_BSm11 = jnp.expand_dims(batch[""actions""][:, :-1], -1)\r\n:",,terminal_output +473,2032534,"TERMINAL",0,0,"\r for t, img in enumerate(imgs[1:]):\r\n:\r d = ImageDraw.Draw(img)\r\n:",,terminal_output +474,2032821,"TERMINAL",0,0,"\r for row in range(B):\r\n:\rdiff --git a/train_dynamics.py b/train_dynamics.py\r\n:\rindex b34ba98..ad89aae 100644\r\n:\r--- a/train_dynamics.py\r\n:\r+++ b/train_dynamics.py\r\n:\r@@ -482,12 +482,14 @@ def main(args: Args) -> None:\r\n:\r \r\n:\r # --- Evaluate full frame prediction (sampling) ---\r\n:\r if args.eval_full_frame:\r\n:",,terminal_output +475,2033027,"TERMINAL",0,0,"\r- lam_indices = genie.vq_encode(inputs, training=False)\r\n:\r tokenizer_outputs = genie.tokenizer.vq_encode(\r\n:\r inputs[""videos""], training=False\r\n:\r )\r\n:\r tokens_full_frame = tokenizer_outputs[""indices""]\r\n:\r- inputs[""latent_actions""] = lam_indices\r\n:\r+ lam_indices = None\r\n:",,terminal_output +476,2033250,"TERMINAL",0,0,"\r+ if not args.use_gt_actions:\r\n:\r+ lam_indices = genie.vq_encode(inputs, training=False)\r\n:\r+ inputs[""latent_actions""] = lam_indices\r\n:\r gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\r\n:\r inputs[""videos""] = gt[:, :-1].astype(\r\n:",,terminal_output +477,2033305,"TERMINAL",0,0,"\r args.dtype\r\n:",,terminal_output +478,2033372,"TERMINAL",0,0,"\r@@ -504,8 +506,10 @@ def main(args: Args) -> None:\r\n:\r ""token_logits"": logits_full_frame,\r\n:\r ""video_tokens"": tokens_full_frame,\r\n:\r ""mask"": jnp.zeros_like(tokens_full_frame).at[:, -1].set(True),\r\n:\r- ""lam_indices"": lam_indices,\r\n:\r }\r\n:\r+ if lam_indices is not None:\r\n:",,terminal_output +479,2033674,"TERMINAL",0,0,"2025-09-23 12:34:57.659398: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-23 12:34:57.659836: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-23 12:34:57.659863: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-23 12:34:57.660278: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-23 12:34:57.662044: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-23 12:34:57.663597: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +480,2039304,"TERMINAL",0,0,"srun",,terminal_focus +481,2040016,"TERMINAL",0,0,"^Csrun: interrupt (one more within 1 sec to abort)\r\nsrun: StepId=3512647.2 task 0: running\r\n",,terminal_output +482,2040617,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3512647.2\r\nsrun: forcing job termination\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-4:\r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-6:\r\nProcess SpawnProcess-3:\r\n2025-09-23 12:35:04.553034: F external/xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:1136] Non-OK-status: executable.status()\r\nStatus: INTERNAL: ptxas exited with non-zero error code 2, output: - Failure occured when compiling fusion gemm_fusion_dot.84 with config '{block_m:128,block_n:256,block_k:32,split_k:1,num_stages:4,num_warps:8,num_ctas:1}'\r\nFused HLO computation:\r\n%gemm_fusion_dot.84_computation (parameter_0.86: bf16[1800,32], parameter_1.86: bf16[32,512]) -> bf16[1800,512] {\r\n %parameter_0.86 = bf16[1800,32]{1,0} parameter(0)\r\n %parameter_1.86 = bf16[32,512]{1,0} parameter(1)\r\n ROOT %dot.625 = bf16[1800,512]{1,0} dot(%parameter_0.86, %parameter_1.86), lhs_contracting_dims={1}, rhs_contracting_dims={0}, metadata={op_name=""jit(train_step)/jit(main)/dot_general"" source_file=""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/flax/nnx/nn/linear.py"" source_line=387}\r\n}\r\nProcess SpawnProcess-2:\r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\nslurmstepd: error: *** STEP 3512647.2 ON hkn0403 CANCELLED AT 2025-09-23T12:35:04 ***\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\nKeyboardInterrupt\r\nKeyboardInterrupt\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\nTraceback (most recent call last):\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\nTraceback (most recent call last):\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\nKeyboardInterrupt\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\nKeyboardInterrupt\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\nKeyboardInterrupt\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\nKeyboardInterrupt\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\nKeyboardInterrupt\r\nTraceback (most recent call last):\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\nKeyboardInterrupt\r\n",,terminal_output +483,2040846,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3512647.2\r\nsrun: job abort in progress\r\n",,terminal_output +484,2040912,"TERMINAL",0,0,"]0;tum_cte0515@hkn0403:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0403 jasmine]$ ",,terminal_output +485,2041054,"TERMINAL",0,0,"^C[?2004l\r[?2004h[?2004l\r\r\n]0;tum_cte0515@hkn0403:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0403 jasmine]$ ",,terminal_output +486,2042231,"TERMINAL",0,0,"r",,terminal_output +487,2042310,"TERMINAL",0,0,"un",,terminal_output +488,2042438,"TERMINAL",0,0,"n",,terminal_output +489,2042581,"TERMINAL",0,0,"e",,terminal_output +490,2042634,"TERMINAL",0,0,"r",,terminal_output +491,2043937,"TERMINAL",0,0,"\r\n[?2004l\r]0;tum_cte0515@hkn0403:~/Projects/jasmine_jobs[?2004h(jasmine) [tum_cte0515@hkn0403 jasmine_jobs]$ ",,terminal_output +492,2044214,"TERMINAL",0,0,"s",,terminal_output +493,2044382,"TERMINAL",0,0,"y",,terminal_output +494,2044455,"TERMINAL",0,0,"n",,terminal_output +495,2044526,"TERMINAL",0,0,"c",,terminal_output +496,2044717,"TERMINAL",0,0,"-",,terminal_output +497,2044947,"TERMINAL",0,0,"r",,terminal_output +498,2045083,"TERMINAL",0,0,"un",,terminal_output +499,2045293,"TERMINAL",0,0,"ne",,terminal_output +500,2045345,"TERMINAL",0,0,"r",,terminal_output +501,2045434,"TERMINAL",0,0,"\r\n[?2004l\rsending incremental file list\r\ngenie.py\r\nslurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh\r\nslurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu_gt_actions.sh\r\n",,terminal_output +502,2045499,"TERMINAL",0,0,"\r\nsent 65,055 bytes received 272 bytes 130,654.00 bytes/sec\r\ntotal size is 128,633,535 speedup is 1,969.07\r\n]0;tum_cte0515@hkn0403:~/Projects/jasmine_jobs[?2004h(jasmine) [tum_cte0515@hkn0403 jasmine_jobs]$ ",,terminal_output +503,2045852,"TERMINAL",0,0,"sync-runner",,terminal_output +504,2046015,"TERMINAL",0,0,"runner",,terminal_output +505,2046408,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh ",,terminal_output +506,2047157,"TERMINAL",0,0,"\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=1\r\n#SBATCH --time=48:00:00\r\n#SBATCH --partition=accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\r\n#SBATCH --job-name=train_dyn_default_breakout_longer\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/train\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/val\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\nlam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3512576\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3512502\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=10 \\r\n --image_width=10 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=120 \\r\n --init_lr=0 \\r\n --max_lr=3e-5 \\r\n --log_image_interval=500 \\r\n --log_checkpoint_interval=500 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=breakout-dyn-default-$slurm_job_id \\r\n --tags dyn breakout default \\r\n --entity instant-uv \\r\n --project jafar \\r\n --patch_size 4 \\r\n --lam_patch_size 4 \\r\n --warmup_steps 100 \\r\n --wsd_decay_steps 1000 \\r\n --num_steps 5000 \\r\n --data_dir $array_records_dir_train \\r\n --val_data_dir $array_records_dir_val \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --lam_checkpoint $lam_checkpoint \\r\n --val_interval 750 \\r\n --eval_full_frame \\r\n",,terminal_output +507,2047294,"TERMINAL",0,0,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh: line 20: .venv/bin/activate: No such file or directory\r\n]0;tum_cte0515@hkn0403:~/Projects/jasmine_jobs[?2004h(jasmine) [tum_cte0515@hkn0403 jasmine_jobs]$ ",,terminal_output +508,2050365,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh ",,terminal_output +509,2053236,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",0,0,"",shellscript,tab +510,2053237,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",608,0,"",shellscript,selection_command +511,2055314,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",608,0,"#",shellscript,content +512,2055316,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",609,0,"",shellscript,selection_keyboard +513,2055410,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",609,0," ",shellscript,content +514,2055411,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",610,0,"",shellscript,selection_keyboard +515,2055717,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",609,0,"",shellscript,selection_command +516,2057281,"TERMINAL",0,0,"\rync-runner",,terminal_output +517,2057648,"TERMINAL",0,0,"h slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh ",,terminal_output +518,2058112,"TERMINAL",0,0,"\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=1\r\n#SBATCH --time=48:00:00\r\n#SBATCH --partition=accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\r\n#SBATCH --job-name=train_dyn_default_breakout_longer\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/train\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/val\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\nlam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3512576\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3512502\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=10 \\r\n --image_width=10 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=120 \\r\n --init_lr=0 \\r\n --max_lr=3e-5 \\r\n --log_image_interval=500 \\r\n --log_checkpoint_interval=500 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=breakout-dyn-default-$slurm_job_id \\r\n --tags dyn breakout default \\r\n --entity instant-uv \\r\n --project jafar \\r\n --patch_size 4 \\r\n --lam_patch_size 4 \\r\n --warmup_steps 100 \\r\n --wsd_decay_steps 1000 \\r\n --num_steps 5000 \\r\n --data_dir $array_records_dir_train \\r\n --val_data_dir $array_records_dir_val \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --lam_checkpoint $lam_checkpoint \\r\n --val_interval 750 \\r\n --eval_full_frame \\r\n",,terminal_output +519,2058225,"TERMINAL",0,0,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh: line 20: .venv/bin/activate: No such file or directory\r\n]0;tum_cte0515@hkn0403:~/Projects/jasmine_jobs[?2004h(jasmine) [tum_cte0515@hkn0403 jasmine_jobs]$ ",,terminal_output +520,2059624,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh ",,terminal_output +521,2061668,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh",0,0,"",shellscript,tab +522,2063919,"TERMINAL",0,0,"\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=1\r\n#SBATCH --time=48:00:00\r\n#SBATCH --partition=accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\r\n#SBATCH --job-name=train_dyn_default_breakout_longer\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/train\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/val\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\nlam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3512576\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3512502\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=10 \\r\n --image_width=10 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=120 \\r\n --init_lr=0 \\r\n --max_lr=3e-5 \\r\n --log_image_interval=500 \\r\n --log_checkpoint_interval=500 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=breakout-dyn-default-$slurm_job_id \\r\n --tags dyn breakout default \\r\n --entity instant-uv \\r\n --project jafar \\r\n --patch_size 4 \\r\n --lam_patch_size 4 \\r\n --warmup_steps 100 \\r\n --wsd_decay_steps 1000 \\r\n --num_steps 5000 \\r\n --data_dir $array_records_dir_train \\r\n --val_data_dir $array_records_dir_val \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --lam_checkpoint $lam_checkpoint \\r\n --val_interval 750 \\r\n --eval_full_frame \\r\n",,terminal_output +523,2064057,"TERMINAL",0,0,"slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh: line 20: .venv/bin/activate: No such file or directory\r\n]0;tum_cte0515@hkn0403:~/Projects/jasmine_jobs[?2004h(jasmine) [tum_cte0515@hkn0403 jasmine_jobs]$ ",,terminal_output +524,2065430,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh ",,terminal_output +525,2069828,"TERMINAL",0,0,"\r",,terminal_output +526,2070243,"TERMINAL",0,0,"s",,terminal_output +527,2070555,"TERMINAL",0,0,"y",,terminal_output +528,2070620,"TERMINAL",0,0,"n",,terminal_output +529,2070756,"TERMINAL",0,0,"c",,terminal_output +530,2070822,"TERMINAL",0,0,"-",,terminal_output +531,2071012,"TERMINAL",0,0,"r",,terminal_output +532,2071173,"TERMINAL",0,0,"un",,terminal_output +533,2071307,"TERMINAL",0,0,"n",,terminal_output +534,2071418,"TERMINAL",0,0,"e",,terminal_output +535,2071466,"TERMINAL",0,0,"r",,terminal_output +536,2071598,"TERMINAL",0,0,"\r\n[?2004l\rsending incremental file list\r\nslurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh\r\n\r\nsent 34,809 bytes received 226 bytes 70,070.00 bytes/sec\r\ntotal size is 128,633,537 speedup is 3,671.57\r\n]0;tum_cte0515@hkn0403:~/Projects/jasmine_jobs[?2004h(jasmine) [tum_cte0515@hkn0403 jasmine_jobs]$ ",,terminal_output +537,2071994,"TERMINAL",0,0,"sync-runner",,terminal_output +538,2072111,"TERMINAL",0,0,"h slurm/jobs/mihir/horeka/breakout/default_runs/train_dyn_single_gpu.sh ",,terminal_output +539,2072479,"TERMINAL",0,0,"\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=1\r\n#SBATCH --time=48:00:00\r\n#SBATCH --partition=accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/breakout/dyn/%x_%j.log\r\n#SBATCH --job-name=train_dyn_default_breakout_longer\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\n# source .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/train\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_breakout/breakout_episodes_perfect/val\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\nlam_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/lam/interactive/3512576\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/breakout/tokenizer/interactive/3512502\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=10 \\r\n --image_width=10 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=120 \\r\n --init_lr=0 \\r\n --max_lr=3e-5 \\r\n --log_image_interval=500 \\r\n --log_checkpoint_interval=500 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=breakout-dyn-default-$slurm_job_id \\r\n --tags dyn breakout default \\r\n --entity instant-uv \\r\n --project jafar \\r\n --patch_size 4 \\r\n --lam_patch_size 4 \\r\n --warmup_steps 100 \\r\n --wsd_decay_steps 1000 \\r\n --num_steps 5000 \\r\n --data_dir $array_records_dir_train \\r\n --val_data_dir $array_records_dir_val \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --lam_checkpoint $lam_checkpoint \\r\n --val_interval 750 \\r\n --eval_full_frame \\r\n",,terminal_output +540,2072627,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=4095309\r\nSLURM_JOB_GPUS=0\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\r\nSLURMD_NODENAME=hkn0403\r\nSLURM_JOB_START_TIME=1758621669\r\nSLURM_STEP_NODELIST=hkn0403\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1758625269\r\nSLURM_PMI2_SRUN_PORT=33711\r\nSLURM_CPUS_ON_NODE=8\r\nSLURM_JOB_CPUS_PER_NODE=8\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=dev_accelerated\r\nSLURM_TRES_PER_TASK=cpu=8\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=1\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3512647\r\nSLURM_PTY_PORT=37671\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.201\r\nSLURM_PTY_WIN_ROW=45\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0403\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.201\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=246\r\nSLURM_NODELIST=hkn0403\r\nSLURM_SRUN_COMM_PORT=35797\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1993.localdomain\r\nSLURM_JOB_ID=3512647\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0403\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_STEP_LAUNCHER_PORT=35797\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0403\r\n",,terminal_output +541,2072754,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +542,2073305,"TERMINAL",0,0,"git",,terminal_focus +543,2074861,"TERMINAL",0,0,"\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jasmine",,terminal_output +544,2075477,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +545,2076808,"TERMINAL",0,0,"git stash",,terminal_command +546,2076846,"TERMINAL",0,0,"]633;C",,terminal_output +547,2077298,"TERMINAL",0,0,"Saved working directory and index state WIP on generate-minatar-breakout-dataset: 0d69ea3 Merge branch 'gt-actions' into generate-minatar-breakout-dataset\r\n",,terminal_output +548,2077411,"TERMINAL",0,0,"]0;tum_cte0515@hkn1993:~/Projects/jasmine",,terminal_output +549,2081737,"TERMINAL",0,0,"Counting all components: ['dynamics', 'lam', 'tokenizer']\r\n",,terminal_output +550,2082112,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +551,2083025,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.21.3\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine_jobs/wandb/run-20250923_123546-bp9lkamg\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run breakout-dyn-default-3512647\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/bp9lkamg\r\n",,terminal_output +552,2083192,"TERMINAL",0,0,"Parameter counts:\r\n{'dynamics': 26555904, 'lam': 16900976, 'tokenizer': 33750256, 'total': 77207136}\r\n",,terminal_output +553,2085332,"TERMINAL",0,0,"git checkout gt-actions",,terminal_command +554,2085391,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +555,2085422,"",0,0,"Switched from branch 'generate-minatar-breakout-dataset' to 'gt-actions'",,git_branch_checkout +556,2093082,"genie.py",0,0,"from typing import Dict\n\nimport einops\nimport jax\nimport jax.numpy as jnp\nimport flax.nnx as nnx\nimport orbax.checkpoint as ocp\n\nfrom models.dynamics import DynamicsMaskGIT, DynamicsCausal\nfrom models.lam import LatentActionModel\nfrom models.tokenizer import TokenizerVQVAE\n\n\nclass Genie(nnx.Module):\n """"""Genie model""""""\n\n def __init__(\n self,\n in_dim: int,\n tokenizer_dim: int,\n tokenizer_ffn_dim: int,\n latent_patch_dim: int,\n num_patch_latents: int,\n patch_size: int,\n tokenizer_num_blocks: int,\n tokenizer_num_heads: int,\n lam_dim: int,\n lam_ffn_dim: int,\n latent_action_dim: int,\n num_actions: int,\n lam_patch_size: int,\n lam_num_blocks: int,\n lam_num_heads: int,\n lam_co_train: bool,\n use_gt_actions: bool,\n dyna_type: str,\n dyna_dim: int,\n dyna_ffn_dim: int,\n dyna_num_blocks: int,\n dyna_num_heads: int,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n decode: bool,\n rngs: nnx.Rngs,\n dropout: float = 0.0,\n mask_limit: float = 0.0,\n ):\n # --- Tokenizer ---\n self.in_dim = in_dim\n self.tokenizer_dim = tokenizer_dim\n self.tokenizer_ffn_dim = tokenizer_ffn_dim\n self.latent_patch_dim = latent_patch_dim\n self.num_patch_latents = num_patch_latents\n self.patch_size = patch_size\n self.tokenizer_num_blocks = tokenizer_num_blocks\n self.tokenizer_num_heads = tokenizer_num_heads\n # --- LAM ---\n self.lam_dim = lam_dim\n self.lam_ffn_dim = lam_ffn_dim\n self.latent_action_dim = latent_action_dim\n self.num_actions = num_actions\n self.lam_patch_size = lam_patch_size\n self.lam_num_blocks = lam_num_blocks\n self.lam_num_heads = lam_num_heads\n self.lam_co_train = lam_co_train\n self.use_gt_actions = use_gt_actions\n # --- Dynamics ---\n self.dyna_type = dyna_type\n self.dyna_dim = dyna_dim\n self.dyna_ffn_dim = dyna_ffn_dim\n self.dyna_num_blocks = dyna_num_blocks\n self.dyna_num_heads = dyna_num_heads\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.dropout = dropout\n self.mask_limit = mask_limit\n self.decode = decode\n\n self.tokenizer = TokenizerVQVAE(\n in_dim=self.in_dim,\n model_dim=self.tokenizer_dim,\n ffn_dim=self.tokenizer_ffn_dim,\n latent_dim=self.latent_patch_dim,\n num_latents=self.num_patch_latents,\n patch_size=self.patch_size,\n num_blocks=self.tokenizer_num_blocks,\n num_heads=self.tokenizer_num_heads,\n dropout=0.0,\n codebook_dropout=0.0,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n if self.use_gt_actions:\n self.action_embed = nnx.Embed(\n self.num_actions, self.latent_action_dim, rngs=rngs\n )\n self.lam = None\n else:\n self.lam = LatentActionModel(\n in_dim=self.in_dim,\n model_dim=self.lam_dim,\n ffn_dim=self.lam_ffn_dim,\n latent_dim=self.latent_patch_dim,\n num_latents=self.num_actions,\n patch_size=self.lam_patch_size,\n num_blocks=self.lam_num_blocks,\n num_heads=self.lam_num_heads,\n dropout=0.0,\n codebook_dropout=0.0,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n self.action_embed = None\n if self.dyna_type == ""maskgit"":\n self.dynamics = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n mask_limit=self.mask_limit,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n elif self.dyna_type == ""causal"":\n self.dynamics = DynamicsCausal(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=decode,\n rngs=rngs,\n )\n else:\n raise ValueError(f""Invalid dynamics type: {self.dyna_type}"")\n\n def __call__(\n self,\n batch: Dict[str, jax.Array],\n training: bool = True,\n ) -> Dict[str, jax.Array]:\n videos_BTHWC = batch[""videos""]\n tokenizer_outputs = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_indices_BTN = tokenizer_outputs[""indices""]\n latent_actions_BTm11L = None\n action_embeddings_BTm11L = None\n if self.use_gt_actions:\n assert self.action_embed is not None\n action_indices_E = None\n action_embeddings_BT1L = self.action_embed(batch[""actions""]).reshape(\n *batch[""actions""].shape[:2], 1, self.latent_action_dim\n )\n action_embeddings_BTm11L = action_embeddings_BT1L[:, :-1]\n else:\n assert self.lam is not None\n lam_outputs = self.lam.vq_encode(videos_BTHWC, training=False)\n z_q_BTm11L = lam_outputs[""z_q""]\n action_indices_E = lam_outputs[""indices""]\n latent_actions_BTm11L = jax.lax.cond(\n self.lam_co_train,\n lambda: z_q_BTm11L,\n lambda: jax.lax.stop_gradient(z_q_BTm11L),\n )\n outputs = dict(\n video_tokens=jax.lax.stop_gradient(token_indices_BTN),\n latent_actions=(\n action_embeddings_BTm11L\n if self.use_gt_actions\n else latent_actions_BTm11L\n ),\n )\n outputs[""mask_rng""] = batch[""rng""]\n dyna_logits_BTNV, dyna_mask = self.dynamics(outputs, training)\n outputs[""token_logits""] = dyna_logits_BTNV\n outputs[""mask""] = dyna_mask\n mle_indices_BTN = jnp.argmax(outputs[""token_logits""], axis=-1)\n H, W = batch[""videos""].shape[2:4]\n outputs[""recon""] = self.tokenizer.decode(mle_indices_BTN, (H, W))\n if action_indices_E is not None:\n outputs[""lam_indices""] = action_indices_E\n return outputs\n\n def sample(\n self,\n batch: Dict[str, jax.Array],\n seq_len: int,\n temperature: float = 1,\n sample_argmax: bool = False,\n maskgit_steps: int = 25,\n ) -> tuple[jax.Array, jax.Array]:\n if self.dyna_type == ""maskgit"":\n return self.sample_maskgit(\n batch, seq_len, maskgit_steps, temperature, sample_argmax\n )\n elif self.dyna_type == ""causal"":\n return self.sample_causal(batch, seq_len, temperature, sample_argmax)\n else:\n raise ValueError(f""Dynamics model type unknown: {self.dyna_type}"")\n\n def sample_maskgit(\n self,\n batch: Dict[str, jax.Array],\n seq_len: int,\n steps: int = 25,\n temperature: float = 1,\n sample_argmax: bool = False,\n ) -> tuple[jax.Array, jax.Array]:\n """"""\n Autoregressively samples up to `seq_len` future frames, following Figure 8 of the paper.\n\n - Input frames are tokenized once.\n - Future frames are generated autoregressively in token space.\n - All frames are detokenized in a single pass.\n\n Note:\n - For interactive or step-wise sampling, detokenization should occur after each action.\n - To maintain consistent tensor shapes across timesteps, all current and future frames are decoded at every step.\n - Temporal causal structure is preserved by\n a) reapplying the mask before each decoding step.\n b) a temporal causal mask is applied within each ST-transformer block.\n\n Dimension keys:\n B: batch size\n T: number of input (conditioning) frames\n N: number of patches per frame\n M: model dimension\n S: sequence length\n H: height\n W: width\n E: B * (S - 1)\n P: S * N\n """"""\n assert isinstance(self.dynamics, DynamicsMaskGIT)\n # --- Encode videos and actions ---\n videos_BTHWC = batch[""videos""]\n tokenizer_out = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_idxs_BTN = tokenizer_out[""indices""]\n B, T, N = token_idxs_BTN.shape\n pad_shape = (B, seq_len - T, N)\n pad = jnp.zeros(pad_shape, dtype=token_idxs_BTN.dtype)\n token_idxs_BSN = jnp.concatenate([token_idxs_BTN, pad], axis=1)\n init_logits_BSNV = jnp.zeros(\n shape=(*token_idxs_BSN.shape, self.num_patch_latents)\n )\n if self.use_gt_actions:\n assert self.action_embed is not None\n latent_actions_BT1L = self.action_embed(batch[""actions""]).reshape(\n *batch[""actions""].shape[:2], 1, self.latent_action_dim\n )\n latent_actions_BTm11L = latent_actions_BT1L[:, 1:]\n action_tokens_EL = latent_actions_BTm11L.reshape(-1, self.latent_action_dim)\n else:\n assert self.lam is not None\n latent_actions_E = batch[""latent_actions""]\n action_tokens_EL = self.lam.vq.get_codes(latent_actions_E)\n\n # --- Extract submodule state ---\n dynamics_state = nnx.state(self.dynamics)\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def maskgit_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array],\n step: jax.Array,\n ) -> tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array]:\n rng, token_idxs_BSN, logits_BSNV, mask_BSN, action_tokens_EL = carry\n S, N = token_idxs_BSN.shape[1:]\n L = action_tokens_EL.shape[-1]\n\n # We need to reconstruct the submodule inside scan body to prevent trace context mismatches\n dynamics_maskgit = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n mask_limit=self.mask_limit,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=nnx.Rngs(0),\n )\n nnx.update(dynamics_maskgit, dynamics_state)\n\n # --- Construct + encode video ---\n vid_embed_BSNM = dynamics_maskgit.patch_embed(token_idxs_BSN)\n mask_token_111M = dynamics_maskgit.mask_token.value\n mask_expanded_BSN1 = mask_BSN[..., None]\n vid_embed_BSNM = jnp.where(\n mask_expanded_BSN1, mask_token_111M, vid_embed_BSNM\n )\n\n # --- Predict transition ---\n action_tokens_BSm1L = jnp.reshape(action_tokens_EL, (B, S - 1, L))\n act_embed_BSm1M = dynamics_maskgit.action_up(action_tokens_BSm1L)\n act_embed_BSM = jnp.pad(act_embed_BSm1M, ((0, 0), (1, 0), (0, 0)))\n act_embed_BS1M = jnp.reshape(\n act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1])\n )\n vid_embed_BSNM += act_embed_BS1M\n unmasked_ratio = jnp.cos(jnp.pi * (step + 1) / (steps * 2))\n step_temp = temperature * (1.0 - unmasked_ratio)\n final_logits_BSNV = dynamics_maskgit.transformer(vid_embed_BSNM) / step_temp\n\n # --- Sample new tokens for final frame ---\n if sample_argmax:\n sampled_token_idxs_BSN = jnp.argmax(final_logits_BSNV, axis=-1)\n else:\n rng, _rng = jax.random.split(rng)\n sampled_token_idxs_BSN = jax.random.categorical(_rng, final_logits_BSNV)\n gather_fn = jax.vmap(jax.vmap(jax.vmap(lambda x, y: x[y])))\n final_token_probs_BSN = gather_fn(\n jax.nn.softmax(final_logits_BSNV), sampled_token_idxs_BSN\n )\n final_token_probs_BSN += ~mask_BSN\n # Update masked tokens and logits only\n token_idxs_BSN = jnp.where(mask_BSN, sampled_token_idxs_BSN, token_idxs_BSN)\n logits_BSNV = jnp.where(\n jnp.expand_dims(mask_BSN, -1), final_logits_BSNV, logits_BSNV\n )\n\n # --- Update mask ---\n num_unmasked_tokens = jnp.round(N * (1.0 - unmasked_ratio)).astype(int)\n final_token_probs_flat_BP = einops.rearrange(\n final_token_probs_BSN, ""b s n -> b (s n)""\n )\n idx_mask_P = (\n jnp.arange(final_token_probs_flat_BP.shape[-1])\n <= N - num_unmasked_tokens\n )\n sorted_idxs_BP = jnp.argsort(final_token_probs_flat_BP, axis=-1)\n mask_update_fn = jax.vmap(lambda msk, ids: msk.at[ids].set(idx_mask_P))\n mask_flat_BP = einops.rearrange(mask_BSN, ""b s n -> b (s n)"")\n new_mask_flat_BP = mask_update_fn(mask_flat_BP, sorted_idxs_BP)\n new_mask_BSN = einops.rearrange(new_mask_flat_BP, ""b (s n) -> b s n"", n=N)\n\n new_carry = (\n rng,\n token_idxs_BSN,\n logits_BSNV,\n new_mask_BSN,\n action_tokens_EL,\n )\n return new_carry\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def generation_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array], step_t: jax.Array\n ) -> tuple[jax.Array, jax.Array, jax.Array]:\n rng, current_token_idxs_BSN, current_logits_BSNV = carry\n rng, step_rng = jax.random.split(rng)\n\n # Mask current frame (i.e., t == step_t)\n mask_S = jnp.arange(seq_len) == step_t\n mask_BSN = jnp.broadcast_to(mask_S[None, :, None], (B, seq_len, N)).astype(\n bool\n )\n masked_token_idxs_BSN = current_token_idxs_BSN * ~mask_BSN\n masked_logits_BSNV = current_logits_BSNV * jnp.expand_dims(~mask_BSN, -1)\n\n # --- Initialize and run MaskGIT loop ---\n init_carry_maskgit = (\n step_rng,\n masked_token_idxs_BSN,\n masked_logits_BSNV,\n mask_BSN,\n action_tokens_EL,\n )\n final_carry_maskgit = maskgit_step_fn(init_carry_maskgit, jnp.arange(steps))\n updated_token_idxs_BSN = final_carry_maskgit[1]\n updated_logits_BSNV = final_carry_maskgit[2]\n new_carry = (rng, updated_token_idxs_BSN, updated_logits_BSNV)\n return new_carry\n\n # --- Run the autoregressive generation using jax.lax.scan ---\n initial_carry = (batch[""rng""], token_idxs_BSN, init_logits_BSNV)\n timesteps_to_scan = jnp.arange(T, seq_len)\n final_carry = generation_step_fn(initial_carry, timesteps_to_scan)\n final_token_idxs_BSN = final_carry[1]\n final_logits_BSNV = final_carry[2]\n\n # --- Decode all tokens at once at the end ---\n H, W = batch[""videos""].shape[2:4]\n final_frames_BSHWC = self.tokenizer.decode(\n final_token_idxs_BSN,\n video_hw=(H, W),\n )\n return final_frames_BSHWC, final_logits_BSNV\n\n def sample_causal(\n self,\n batch: Dict[str, jax.Array],\n seq_len: int,\n temperature: float = 1,\n sample_argmax: bool = False,\n ) -> tuple[jax.Array, jax.Array]:\n """"""\n Autoregressively samples up to `seq_len` future frames, following Figure 8 of the paper.\n\n - Input frames are tokenized once.\n - Future frames are generated autoregressively in token space.\n - All frames are detokenized in a single pass.\n\n Note:\n - For interactive or step-wise sampling, detokenization should occur after each action.\n - To maintain consistent tensor shapes across timesteps, all current and future frames are decoded at every step.\n - Temporal causal structure is preserved by\n a) reapplying the mask before each decoding step.\n b) a temporal causal mask is applied within each ST-transformer block.\n\n Dimension keys:\n B: batch size\n T: number of input (conditioning) frames\n N: number of patches per frame\n M: model dimension\n S: sequence length\n H: height\n W: width\n E: B * (S - 1)\n """"""\n assert isinstance(self.dynamics, DynamicsCausal)\n # --- Encode videos and actions ---\n videos_BTHWC = batch[""videos""]\n tokenizer_out = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_idxs_BTN = tokenizer_out[""indices""]\n B, T, N = token_idxs_BTN.shape\n pad_shape = (B, seq_len - T, N)\n pad = jnp.zeros(pad_shape, dtype=token_idxs_BTN.dtype)\n token_idxs_BSN = jnp.concatenate([token_idxs_BTN, pad], axis=1)\n logits_BSNV = jnp.zeros((*token_idxs_BSN.shape, self.num_patch_latents))\n dynamics_state = nnx.state(self.dynamics)\n\n if self.use_gt_actions:\n assert self.action_embed is not None\n latent_actions_BT1L = self.action_embed(batch[""actions""]).reshape(\n *batch[""actions""].shape[:2], 1, self.latent_action_dim\n )\n latent_actions_BTm11L = latent_actions_BT1L[:, 1:]\n action_tokens_EL = latent_actions_BTm11L.reshape(-1, self.latent_action_dim)\n else:\n assert self.lam is not None\n latent_actions_E = batch[""latent_actions""]\n action_tokens_EL = self.lam.vq.get_codes(latent_actions_E)\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def causal_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array],\n step_n: jax.Array,\n ) -> tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array]:\n rng, token_idxs_BSN, logits_BSNV, action_tokens_EL, step_t = carry\n S, N = token_idxs_BSN.shape[1:]\n L = action_tokens_EL.shape[-1]\n\n # We need to reconstruct the submodule inside scan body to prevent trace context mismatches\n dynamics_causal = DynamicsCausal(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=self.decode,\n rngs=nnx.Rngs(0),\n )\n nnx.update(dynamics_causal, dynamics_state)\n\n # --- Construct + encode video ---\n vid_embed_BSNM = dynamics_causal.patch_embed(token_idxs_BSN)\n\n # --- Predict transition ---\n action_tokens_BSm1L = jnp.reshape(action_tokens_EL, (B, S - 1, L))\n act_embed_BSm1M = dynamics_causal.action_up(action_tokens_BSm1L)\n act_embed_BSM = jnp.pad(act_embed_BSm1M, ((0, 0), (1, 0), (0, 0)))\n act_embed_BS1M = jnp.reshape(\n act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1])\n )\n vid_embed_BSNp1M = jnp.concatenate([act_embed_BS1M, vid_embed_BSNM], axis=2)\n final_logits_BTNp1V = (\n dynamics_causal.transformer(vid_embed_BSNp1M, (step_t, step_n))\n / temperature\n )\n final_logits_BV = final_logits_BTNp1V[:, step_t, step_n, :]\n\n # --- Sample new tokens for final frame ---\n if sample_argmax:\n sampled_token_idxs_B = jnp.argmax(final_logits_BV, axis=-1)\n else:\n rng, _rng = jax.random.split(rng)\n sampled_token_idxs_B = jax.random.categorical(_rng, final_logits_BV)\n # Update next tokens only\n token_idxs_BSN = token_idxs_BSN.at[:, step_t, step_n].set(\n sampled_token_idxs_B\n )\n logits_BSNV = logits_BSNV.at[:, step_t, step_n].set(final_logits_BV)\n\n new_carry = (rng, token_idxs_BSN, logits_BSNV, action_tokens_EL, step_t)\n return new_carry\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def generation_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array], step_t: jax.Array\n ) -> tuple[jax.Array, jax.Array, jax.Array]:\n rng, current_token_idxs_BSN, current_logits_BSNV = carry\n rng, step_rng = jax.random.split(rng)\n\n # --- Initialize and run causal loop ---\n init_carry_causal = (\n step_rng,\n current_token_idxs_BSN,\n current_logits_BSNV,\n action_tokens_EL,\n step_t,\n )\n final_carry_causal = causal_step_fn(init_carry_causal, jnp.arange(N))\n updated_token_idxs_BSN = final_carry_causal[1]\n updated_logits_BSNV = final_carry_causal[2]\n new_carry = (rng, updated_token_idxs_BSN, updated_logits_BSNV)\n return new_carry\n\n # --- Run the autoregressive generation using jax.lax.scan ---\n initial_carry = (batch[""rng""], token_idxs_BSN, logits_BSNV)\n timesteps_to_scan = jnp.arange(T, seq_len)\n final_carry = generation_step_fn(initial_carry, timesteps_to_scan)\n final_token_idxs_BSN = final_carry[1]\n final_logits_BSNV = final_carry[2]\n\n # --- Decode all tokens at once at the end ---\n H, W = batch[""videos""].shape[2:4]\n final_frames_BSHWC = self.tokenizer.decode(\n final_token_idxs_BSN,\n video_hw=(H, W),\n )\n return final_frames_BSHWC, final_logits_BSNV\n\n def vq_encode(self, batch: Dict[str, jax.Array], training: bool) -> jax.Array:\n # --- Preprocess videos ---\n assert self.lam is not None\n video_BTHWC = batch[""videos""]\n lam_output: Dict[str, jax.Array] = self.lam.vq_encode(\n video_BTHWC, training=training\n )\n lam_indices_E = lam_output[""indices""]\n return lam_indices_E\n\n\n# FIXME (f.srambical): add conversion script for old checkpoints\ndef restore_genie_components(\n optimizer: nnx.Optimizer,\n sharding: jax.sharding.NamedSharding,\n rng: jax.Array,\n args,\n) -> nnx.Optimizer:\n """"""Restore pre-trained Genie components""""""\n rng_tokenizer, rng_lam = jax.random.split(rng)\n rngs_tokenizer = nnx.Rngs(rng_tokenizer)\n rngs_lam = nnx.Rngs(rng_lam)\n\n tx = optimizer.tx\n model = optimizer.model\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n\n checkpoint_options = ocp.CheckpointManagerOptions(\n step_format_fixed_length=6,\n )\n tokenizer_checkpoint_manager = ocp.CheckpointManager(\n directory=args.tokenizer_checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n dummy_tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.tokenizer_dim,\n ffn_dim=args.tokenizer_ffn_dim,\n latent_dim=args.latent_patch_dim,\n num_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n num_blocks=args.tokenizer_num_blocks,\n num_heads=args.tokenizer_num_heads,\n dropout=args.dropout,\n codebook_dropout=args.dropout,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs_tokenizer,\n )\n dummy_tokenizer_optimizer = nnx.Optimizer(dummy_tokenizer, tx)\n dummy_tokenizer_optimizer_state = nnx.state(dummy_tokenizer_optimizer)\n abstract_sharded_tokenizer_optimizer_state = _create_abstract_sharded_pytree(\n dummy_tokenizer_optimizer_state, sharding\n )\n restored_tokenizer = tokenizer_checkpoint_manager.restore(\n step=tokenizer_checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore( # type: ignore\n abstract_sharded_tokenizer_optimizer_state # type: ignore\n ),\n ),\n )[""model_state""]\n nnx.update(dummy_tokenizer_optimizer.model, restored_tokenizer.model)\n model.tokenizer = dummy_tokenizer_optimizer.model\n tokenizer_checkpoint_manager.close()\n\n if args.lam_checkpoint:\n lam_checkpoint_manager = ocp.CheckpointManager(\n directory=args.lam_checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n dummy_lam = LatentActionModel(\n in_dim=args.image_channels,\n model_dim=args.lam_dim,\n ffn_dim=args.lam_ffn_dim,\n latent_dim=args.latent_patch_dim,\n num_latents=args.num_actions,\n patch_size=args.lam_patch_size,\n num_blocks=args.lam_num_blocks,\n num_heads=args.lam_num_heads,\n dropout=args.dropout,\n codebook_dropout=args.dropout,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs_lam,\n )\n dummy_lam_optimizer = nnx.Optimizer(dummy_lam, tx)\n dummy_lam_optimizer_state = nnx.state(dummy_lam_optimizer)\n abstract_sharded_lam_optimizer_state = _create_abstract_sharded_pytree(\n dummy_lam_optimizer_state, sharding\n )\n restored_lam_optimizer = lam_checkpoint_manager.restore(\n step=lam_checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore( # type: ignore\n abstract_sharded_lam_optimizer_state # type: ignore\n ),\n ),\n )[""model_state""]\n nnx.update(dummy_lam_optimizer.model, restored_lam_optimizer.model)\n model.lam = dummy_lam_optimizer.model\n # Remove the LAM decoder to save memory and avoid unnecessary computation.\n del model.lam.decoder\n lam_checkpoint_manager.close()\n\n # Reinitialize the optimizer states\n optimizer = nnx.Optimizer(model, tx)\n return optimizer\n\n\ndef _create_abstract_sharded_pytree(\n pytree_template: nnx.GraphState, sharding_spec: jax.sharding.NamedSharding\n) -> jax.Array:\n """"""Replaces arrays in a pytree with ShapeDtypeStructs having the given sharding.""""""\n\n def map_fn(leaf_template):\n if hasattr(leaf_template, ""shape"") and hasattr(leaf_template, ""dtype""):\n return jax.ShapeDtypeStruct(\n leaf_template.shape, leaf_template.dtype, sharding=sharding_spec\n )\n return leaf_template\n\n return jax.tree_util.tree_map(map_fn, pytree_template)\n",python,tab +557,2093084,"genie.py",9989,2,"1:",python,selection_command +558,2096142,"genie.py",9990,0,"",python,selection_mouse +559,2097088,"genie.py",9991,0,"",python,selection_command +560,2097488,"genie.py",9990,1,"",python,content +561,2097599,"genie.py",9989,1,"",python,content +562,2098248,"genie.py",9989,0,":",python,content +563,2098250,"genie.py",9990,0,"",python,selection_keyboard +564,2098430,"genie.py",9990,0,"-",python,content +565,2098431,"genie.py",9991,0,"",python,selection_keyboard +566,2098489,"genie.py",9991,0,"1",python,content +567,2098490,"genie.py",9992,0,"",python,selection_keyboard +568,2100699,"TERMINAL",0,0,"2025-09-23 12:36:04.685011: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-23 12:36:04.685469: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-23 12:36:04.685499: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-23 12:36:04.685918: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-23 12:36:04.687689: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-23 12:36:04.689259: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +569,2101508,"genie.py",10754,2,"1:",python,selection_command +570,2110187,"genie.py",18522,2,"1:",python,selection_command +571,2112017,"genie.py",19192,2,"1:",python,selection_command +572,2114706,"sample.py",0,0,"from dataclasses import dataclass\nimport time\nimport os\nimport optax\n\nimport dm_pix as pix\nimport einops\nimport jax\nimport jax.numpy as jnp\nimport flax.linen as nn\nimport numpy as np\nimport orbax.checkpoint as ocp\nfrom PIL import Image, ImageDraw\nimport tyro\nfrom flax import nnx\n\nfrom genie import Genie\nfrom utils.dataloader import get_dataloader\n\n\n@dataclass\nclass Args:\n # Experiment\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/coinrun_episodes""\n checkpoint: str = """"\n # Sampling\n batch_size: int = 1\n maskgit_steps: int = 25\n temperature: float = 1.0\n sample_argmax: bool = True\n start_frame: int = 1\n # Tokenizer checkpoint\n tokenizer_dim: int = 512\n tokenizer_ffn_dim: int = 2048\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 4\n tokenizer_num_heads: int = 8\n # LAM checkpoint\n lam_dim: int = 512\n lam_ffn_dim: int = 2048\n latent_action_dim: int = 32\n num_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 4\n lam_num_heads: int = 8\n use_gt_actions: bool = False\n # Dynamics checkpoint\n dyna_type: str = ""maskgit""\n dyna_dim: int = 512\n dyna_ffn_dim: int = 2048\n dyna_num_blocks: int = 6\n dyna_num_heads: int = 8\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\n use_flash_attention: bool = True\n\n\nargs = tyro.cli(Args)\n\nif __name__ == ""__main__"":\n """"""\n Dimension keys:\n B: batch size\n T: number of input (conditioning) frames\n N: number of patches per frame\n S: sequence length\n H: height\n W: width\n E: B * (S - 1)\n """"""\n jax.distributed.initialize()\n\n rng = jax.random.key(args.seed)\n\n # --- Load Genie checkpoint ---\n rngs = nnx.Rngs(rng)\n genie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n tokenizer_ffn_dim=args.tokenizer_ffn_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n lam_ffn_dim=args.lam_ffn_dim,\n latent_action_dim=args.latent_action_dim,\n num_actions=args.num_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n lam_co_train=False,\n use_gt_actions=args.use_gt_actions,\n # Dynamics\n dyna_type=args.dyna_type,\n dyna_dim=args.dyna_dim,\n dyna_ffn_dim=args.dyna_ffn_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n # FIXME (f.srambical): implement spatiotemporal KV caching and set decode=True\n decode=False,\n rngs=rngs,\n )\n\n del genie.tokenizer.vq.drop\n # Need to delete lam decoder for checkpoint loading\n if not args.use_gt_actions:\n assert genie.lam is not None\n del genie.lam.decoder\n\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n checkpoint_options = ocp.CheckpointManagerOptions(\n step_format_fixed_length=6,\n )\n checkpoint_manager = ocp.CheckpointManager(\n args.checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n\n dummy_tx = optax.adamw(\n learning_rate=optax.linear_schedule(0.0001, 0.0001, 10000),\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.dtype,\n )\n dummy_optimizer = nnx.Optimizer(genie, dummy_tx)\n\n abstract_optimizer = nnx.eval_shape(lambda: dummy_optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n ),\n )\n restored_optimizer_state = restored[""model_state""]\n nnx.update(dummy_optimizer, restored_optimizer_state)\n\n # --- Define sampling function ---\n def _sampling_fn(model: Genie, batch: dict) -> jax.Array:\n """"""Runs Genie.sample with pre-defined generation hyper-parameters.""""""\n assert args.dyna_type in [\n ""maskgit"",\n ""causal"",\n ], f""Invalid dynamics type: {args.dyna_type}""\n frames, _ = model.sample(\n batch,\n args.seq_len,\n args.temperature,\n args.sample_argmax,\n args.maskgit_steps,\n )\n return frames\n\n # --- Define autoregressive sampling loop ---\n def _autoreg_sample(genie, rng, batch):\n batch[""videos""] = batch[""videos""][:, : args.start_frame]\n batch[""rng""] = rng\n generated_vid_BSHWC = _sampling_fn(genie, batch)\n return generated_vid_BSHWC\n\n # --- Get video + latent actions ---\n array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n args.batch_size,\n args.image_height,\n args.image_width,\n args.image_channels,\n # We don't use workers in order to avoid grain shutdown issues (https://github.com/google/grain/issues/398)\n num_workers=0,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n dataloader = iter(dataloader)\n batch = next(dataloader)\n gt_video = jnp.asarray(batch[""videos""], dtype=jnp.float32) / 255.0\n batch[""videos""] = gt_video.astype(args.dtype)\n # Get latent actions for all videos in the batch\n action_batch_E = None\n if not args.use_gt_actions:\n action_batch_E = genie.vq_encode(batch, training=False)\n batch[""latent_actions""] = action_batch_E\n\n # --- Sample + evaluate video ---\n recon_video_BSHWC = _autoreg_sample(genie, rng, batch)\n recon_video_BSHWC = recon_video_BSHWC.astype(jnp.float32)\n gt = (\n gt_video[:, : recon_video_BSHWC.shape[1]]\n .clip(0, 1)\n .reshape(-1, *gt_video.shape[2:])\n )\n recon = recon_video_BSHWC.clip(0, 1).reshape(-1, *recon_video_BSHWC.shape[2:])\n ssim = jnp.asarray(\n pix.ssim(gt[:, args.start_frame :], recon[:, args.start_frame :])\n ).mean()\n print(f""SSIM: {ssim}"")\n\n # --- Construct video ---\n true_videos = (gt_video * 255).astype(np.uint8)\n pred_videos = (recon_video_BSHWC * 255).astype(np.uint8)\n video_comparison = np.zeros((2, *recon_video_BSHWC.shape), dtype=np.uint8)\n video_comparison[0] = true_videos[:, : args.seq_len]\n video_comparison[1] = pred_videos\n frames = einops.rearrange(video_comparison, ""n b t h w c -> t (b h) (n w) c"")\n\n # --- Save video ---\n imgs = [Image.fromarray(img) for img in frames]\n # Write actions on each frame, on each row (i.e., for each video in the batch, on the GT row)\n B, S, _, _, _ = batch[""videos""].shape\n if action_batch_E is not None:\n action_batch_BSm11 = jnp.reshape(action_batch_E, (B, S - 1, 1))\n else:\n action_batch_BSm11 = jnp.expand_dims(batch[""actions""][:, 1:], -1)\n for t, img in enumerate(imgs[1:]):\n d = ImageDraw.Draw(img)\n for row in range(B):\n action = action_batch_BSm11[row, t, 0]\n y_offset = row * batch[""videos""].shape[2] + 2\n d.text((2, y_offset), f""{action}"", fill=255)\n imgs[0].save(\n f""generation_{time.time()}.gif"",\n save_all=True,\n append_images=imgs[1:],\n duration=250,\n loop=0,\n )\n",python,tab +573,2114708,"sample.py",7706,2,"1:",python,selection_command +574,2129968,"sample.py",7708,0,"",python,selection_mouse +575,2131220,"sample.py",7707,1,"",python,content +576,2131343,"sample.py",7706,1,"",python,content +577,2131735,"sample.py",7706,0,":",python,content +578,2131736,"sample.py",7707,0,"",python,selection_keyboard +579,2132661,"sample.py",7707,0,"-",python,content +580,2132662,"sample.py",7708,0,"",python,selection_keyboard +581,2132739,"sample.py",7708,0,"1",python,content +582,2132740,"sample.py",7709,0,"",python,selection_keyboard +583,2146498,"TERMINAL",0,0,"Total memory size: 2.5 GB, Output size: 0.9 GB, Temp size: 1.7 GB, Argument size: 0.9 GB, Host temp size: 0.0 GB.\r\n",,terminal_output +584,2146582,"TERMINAL",0,0,"FLOPs: 1.214e+11, Bytes: 4.622e+10 (43.0 GB), Intensity: 2.6 FLOPs/byte\r\nStarting training from step 0...\r\n",,terminal_output +585,2147095,"TERMINAL",0,0,"\r\nMemstats: After params initialized:\r\n\tUsing (GB) 1.1 / 38.7 (2.842377%) on cuda:0\r\n",,terminal_output +586,2148853,"genie.py",0,0,"",python,tab +587,2148854,"genie.py",18522,2,"1:",python,selection_command +588,2150990,"genie.py",18524,0,"",python,selection_mouse +589,2151663,"genie.py",18523,1,"",python,content +590,2151837,"genie.py",18522,1,"",python,content +591,2152165,"genie.py",18522,0,":",python,content +592,2152166,"genie.py",18523,0,"",python,selection_keyboard +593,2152498,"genie.py",18523,0,".",python,content +594,2152500,"genie.py",18524,0,"",python,selection_keyboard +595,2152569,"genie.py",18524,0,"1",python,content +596,2152570,"genie.py",18525,0,"",python,selection_keyboard +597,2153211,"genie.py",18524,1,"",python,content +598,2153329,"genie.py",18523,1,"",python,content +599,2153595,"genie.py",18523,0,"-",python,content +600,2153596,"genie.py",18524,0,"",python,selection_keyboard +601,2153696,"genie.py",18524,0,"1",python,content +602,2153696,"genie.py",18525,0,"",python,selection_keyboard +603,2157359,"sample.py",0,0,"",python,tab +604,2157360,"sample.py",7749,2,"1:",python,selection_command +605,2163629,"sample.py",8091,2,"1:",python,selection_command +606,2165980,"train_lam.py",0,0,"import os\n\nos.environ.setdefault(""XLA_PYTHON_CLIENT_MEM_FRACTION"", ""0.98"")\n\nfrom dataclasses import dataclass, field\nimport itertools\nfrom typing import cast, Optional\n\nimport einops\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\nimport flax.nnx as nnx\n\nfrom models.lam import LatentActionModel\nfrom utils.dataloader import get_dataloader\nfrom utils.train_utils import (\n get_lr_schedule,\n count_parameters_by_component,\n print_mem_stats,\n print_compiled_memory_stats,\n print_compiled_cost_analysis,\n)\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n batch_size: int = 36\n vq_beta: float = 0.25\n init_lr: float = 0.0\n max_lr: float = 3e-5\n decay_end: float = 0.0\n wsd_decay_steps: int = (\n 10000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n )\n warmup_steps: int = 5000\n lr_schedule: str = ""wsd"" # supported options: wsd, cos\n vq_reset_thresh: int = 50\n # LAM\n model_dim: int = 512\n ffn_dim: int = 2048\n latent_dim: int = 32\n num_latents: int = 6\n patch_size: int = 16\n num_blocks: int = 4\n num_heads: int = 8\n dropout: float = 0.0\n codebook_dropout: float = 0.0\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n name: str = ""train_lam""\n tags: list[str] = field(default_factory=lambda: [""lam""])\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 10000\n log_checkpoint_keep_period: int = 20000\n val_data_dir: str = """"\n val_interval: int = 20_000\n val_steps: int = 50\n wandb_id: str = """"\n use_flash_attention: bool = True\n\n\ndef build_model(args: Args, rng: jax.Array) -> tuple[LatentActionModel, jax.Array]:\n rng, _rng = jax.random.split(rng)\n rngs = nnx.Rngs(_rng)\n return (\n LatentActionModel(\n in_dim=args.image_channels,\n model_dim=args.model_dim,\n ffn_dim=args.ffn_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 param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs,\n ),\n rng,\n )\n\n\ndef build_optimizer(\n model: LatentActionModel, args: Args\n) -> tuple[nnx.Optimizer, optax.Schedule]:\n lr_schedule = get_lr_schedule(\n args.lr_schedule,\n args.init_lr,\n args.max_lr,\n args.decay_end,\n args.num_steps,\n args.warmup_steps,\n args.wsd_decay_steps,\n )\n tx = optax.adamw(\n learning_rate=lr_schedule,\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.param_dtype, # moments in full precision\n )\n optimizer = nnx.Optimizer(model, tx)\n return optimizer, lr_schedule\n\n\ndef build_mesh_and_sharding(\n num_devices: int,\n) -> tuple[Mesh, NamedSharding, NamedSharding]:\n device_mesh_arr = create_device_mesh((num_devices,))\n mesh = Mesh(devices=device_mesh_arr, axis_names=(""data"",))\n replicated_sharding = NamedSharding(mesh, PartitionSpec())\n videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n return mesh, replicated_sharding, videos_sharding\n\n\ndef shard_optimizer_states(\n optimizer: nnx.Optimizer, replicated_sharding: NamedSharding\n) -> None:\n model_state = nnx.state(optimizer.model)\n model_sharded_state = jax.lax.with_sharding_constraint(\n model_state, replicated_sharding\n )\n nnx.update(optimizer.model, model_sharded_state)\n optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)\n optimizer_sharded_state = jax.lax.with_sharding_constraint(\n optimizer_state, replicated_sharding\n )\n nnx.update(optimizer, optimizer_sharded_state)\n\n\ndef build_dataloader(args: Args, data_dir: str) -> grain.DataLoaderIterator:\n image_shape = (args.image_height, args.image_width, args.image_channels)\n array_record_files = [\n os.path.join(data_dir, x)\n for x in os.listdir(data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n return grain_iterator\n\n\ndef build_checkpoint_manager(args: Args) -> ocp.CheckpointManager:\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n if args.val_data_dir:\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n return checkpoint_manager\n\n\ndef restore_checkpoint_if_needed(\n args: Args,\n checkpoint_manager: ocp.CheckpointManager,\n optimizer: nnx.Optimizer,\n train_iterator: grain.DataLoaderIterator,\n val_iterator: Optional[grain.DataLoaderIterator],\n restore_step: Optional[int] = None,\n) -> tuple[int, nnx.Optimizer, grain.DataLoaderIterator, grain.DataLoaderIterator]:\n step = 0\n if restore_step is None:\n restore_step = checkpoint_manager.latest_step()\n if args.restore_ckpt:\n abstract_optimizer = nnx.eval_shape(lambda: optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n if val_iterator:\n restore_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointRestore(train_iterator), # type: ignore\n val_dataloader_state=grain.checkpoint.CheckpointRestore(val_iterator), # type: ignore\n )\n else:\n restore_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointRestore(train_iterator), # type: ignore\n )\n restored = checkpoint_manager.restore(restore_step, args=restore_args)\n restored_optimizer_state = restored[""model_state""]\n nnx.update(optimizer, restored_optimizer_state)\n train_iterator = restored[""train_dataloader_state""]\n if val_iterator:\n val_iterator = restored[""val_dataloader_state""]\n step = restore_step or 0\n print(f""Restored dataloader and model state from step {step}"")\n return step, optimizer, train_iterator, val_iterator\n\n\ndef enable_sowing(lam: LatentActionModel) -> None:\n for model in [lam.encoder, lam.decoder]:\n setattr(model, ""sow_logits"", True)\n for blk in getattr(model, ""blocks"", []):\n setattr(blk, ""sow_weights"", True)\n setattr(blk, ""sow_activations"", True)\n\n\ndef main(args: Args) -> None:\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 rng = jax.random.key(args.seed)\n\n # --- Initialize model ---\n lam, rng = build_model(args, rng)\n\n # Count parameters\n _, params, _ = nnx.split(lam, nnx.Param, ...)\n param_counts = count_parameters_by_component(params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\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 if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n optimizer, lr_schedule = build_optimizer(lam, args)\n del lam\n\n # FIXME: switch to create_hybrid_device_mesh for runs spanning multiple nodes\n _, replicated_sharding, videos_sharding = build_mesh_and_sharding(num_devices)\n\n shard_optimizer_states(optimizer, replicated_sharding)\n\n # --- Initialize checkpoint manager ---\n checkpoint_manager = build_checkpoint_manager(args)\n\n # --- Create DataLoaderIterator from dataloader ---\n train_iterator = build_dataloader(args, args.data_dir)\n val_iterator = None\n if args.val_data_dir:\n val_iterator = build_dataloader(args, args.val_data_dir)\n\n # --- Restore checkpoint ---\n step, optimizer, train_iterator, val_iterator = restore_checkpoint_if_needed(\n args, checkpoint_manager, optimizer, train_iterator, val_iterator\n )\n\n # --- Define loss and train step (close over args) ---\n def lam_loss_fn(\n model: LatentActionModel, inputs: dict, training: bool = True\n ) -> tuple[jax.Array, tuple[jax.Array, jax.Array, dict]]:\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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 gt_val = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt_val, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt_val, recon)).mean()\n count_fn = jax.vmap(lambda i: (outputs[""indices""] == i).sum())\n index_counts = count_fn(jnp.arange(args.num_latents))\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=(index_counts != 0).mean(),\n )\n return loss, (outputs[""recon""], index_counts, metrics)\n\n @nnx.jit(donate_argnums=0)\n def train_step(\n optimizer: nnx.Optimizer,\n inputs: dict,\n action_last_active: jax.Array,\n rng: jax.Array,\n ) -> tuple[jax.Array, jax.Array, jax.Array, dict]:\n def loss_fn(\n model: LatentActionModel,\n ) -> tuple[jax.Array, tuple[jax.Array, jax.Array, dict]]:\n model.train()\n return lam_loss_fn(model, inputs, training=True)\n\n # --- Update model ---\n (loss, (recon, idx_counts, metrics)), grads = nnx.value_and_grad(\n loss_fn, has_aux=True\n )(optimizer.model)\n optimizer.update(grads)\n\n # --- Reset inactive latent actions ---\n codebook = optimizer.model.vq.codebook\n num_codes = len(codebook)\n active_codes = idx_counts != 0.0\n action_last_active = jnp.where(active_codes, 0, action_last_active + 1)\n p_code = active_codes / active_codes.sum()\n reset_idxs = jax.random.choice(rng, num_codes, shape=(num_codes,), p=p_code)\n do_reset = action_last_active >= args.vq_reset_thresh\n new_codebook = jnp.where(\n jnp.expand_dims(do_reset, -1), codebook[reset_idxs], codebook.value\n )\n optimizer.model.vq.codebook.value = new_codebook\n action_last_active = jnp.where(do_reset, 0, action_last_active)\n return loss, recon, action_last_active, metrics\n\n @nnx.jit\n def val_step(\n lam: LatentActionModel, inputs: dict\n ) -> tuple[jax.Array, jax.Array, dict]:\n lam.eval()\n (loss, (recon, _, metrics)) = lam_loss_fn(lam, inputs, training=False)\n return loss, recon, metrics\n\n def calculate_validation_metrics(val_dataloader, lam):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n batch = None\n recon = None\n for batch in val_dataloader:\n loss, recon, metrics = val_step(lam, batch)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(\n f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}""\n )\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([float(m[key]) for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n val_metrics[""val_loss""] = val_loss\n return val_metrics, batch, recon\n\n # --- TRAIN LOOP ---\n dataloader_train = (\n {\n ""videos"": jax.make_array_from_process_local_data(\n videos_sharding, elem[""videos""]\n ),\n }\n for elem in train_iterator\n )\n dataloader_val = None\n if val_iterator:\n dataloader_val = (\n {\n ""videos"": jax.make_array_from_process_local_data(\n videos_sharding, elem[""videos""]\n ),\n }\n for elem in val_iterator\n )\n action_last_active = jnp.zeros(args.num_latents, dtype=jnp.int32)\n if jax.process_index() == 0:\n first_batch = next(dataloader_train)\n compiled = train_step.lower(\n optimizer, first_batch, action_last_active, rng\n ).compile()\n print_compiled_memory_stats(compiled.memory_analysis())\n print_compiled_cost_analysis(compiled.cost_analysis())\n # Do not skip the first batch during training\n dataloader_train = itertools.chain([first_batch], dataloader_train)\n print(f""Starting training from step {step}..."")\n first_step = step\n while step < args.num_steps:\n for batch in dataloader_train:\n # --- Train step ---\n rng, _rng = jax.random.split(rng, 2)\n loss, recon, action_last_active, metrics = train_step(\n optimizer, batch, action_last_active, _rng\n )\n if step == first_step:\n print_mem_stats(""After params initialized"")\n metrics[""lr""] = lr_schedule(step)\n print(f""Step {step}, loss: {loss}"")\n step += 1\n\n # --- Validation loss ---\n val_results = {}\n if dataloader_val and step % args.val_interval == 0:\n print(""Calculating validation metrics..."")\n val_metrics, val_gt_batch, val_recon = calculate_validation_metrics(\n dataloader_val, optimizer.model\n )\n print(f""Step {step}, validation loss: {val_metrics['val_loss']}"")\n val_results = {\n ""metrics"": val_metrics,\n ""gt_batch"": val_gt_batch,\n ""recon"": val_recon,\n }\n\n # --- Logging ---\n if args.log:\n if step % args.log_interval == 0 and jax.process_index() == 0:\n log_dict = {""loss"": loss, ""step"": step, **metrics}\n if val_results:\n log_dict.update(val_results[""metrics""])\n wandb.log(log_dict)\n if step % args.log_image_interval == 0:\n gt_seq = batch[""videos""][0, 1:].astype(jnp.float32) / 255.0\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 if val_results:\n val_results[""gt_seq_val""] = (\n val_results[""gt_batch""][""videos""][0, 1:].astype(jnp.float32)\n / 255.0\n )\n val_results[""recon_seq_val""] = val_results[""recon""][0].clip(\n 0, 1\n )\n val_results[""val_comparison_seq""] = jnp.concatenate(\n (val_results[""gt_seq_val""], val_results[""recon_seq_val""]),\n axis=1,\n )\n val_results[""val_comparison_seq""] = einops.rearrange(\n val_results[""val_comparison_seq""] * 255,\n ""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 if val_results:\n log_images.update(\n {\n ""val_image"": wandb.Image(\n np.asarray(val_results[""gt_seq_val""][0])\n ),\n ""val_recon"": wandb.Image(\n np.asarray(val_results[""recon_seq_val""][0])\n ),\n ""val_true_vs_recon"": wandb.Image(\n np.asarray(\n val_results[""val_comparison_seq""].astype(\n np.uint8\n )\n )\n ),\n }\n )\n wandb.log(log_images)\n # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n optimizer_state = nnx.state(optimizer)\n if val_iterator:\n ckpt_manager_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n train_iterator # type: ignore\n ),\n val_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n val_iterator # type: ignore\n ),\n )\n else:\n ckpt_manager_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n train_iterator # type: ignore\n ),\n )\n checkpoint_manager.save(step, args=ckpt_manager_args)\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n checkpoint_manager.close()\n\n\nif __name__ == ""__main__"":\n args = tyro.cli(Args)\n main(args)\n",python,tab +607,2165982,"train_lam.py",11648,2,"1:",python,selection_command +608,2168723,"train_lam.py",17992,2,"1:",python,selection_command +609,2171378,"train_lam.py",18459,2,"1:",python,selection_command +610,2177990,"models/dynamics.py",0,0,"from typing import Dict\n\nimport jax\nimport jax.numpy as jnp\nimport flax.nnx as nnx\n\nfrom utils.nn import STTransformer, Transformer\n\n\nclass DynamicsMaskGIT(nnx.Module):\n """"""\n MaskGIT dynamics model\n\n Dimension keys:\n B: batch size\n T: sequence length\n N: number of patches per frame\n L: latent dimension\n V: vocabulary size (number of latents)\n """"""\n\n def __init__(\n self,\n model_dim: int,\n ffn_dim: int,\n num_latents: int,\n latent_action_dim: int,\n num_blocks: int,\n num_heads: int,\n dropout: float,\n mask_limit: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n rngs: nnx.Rngs,\n ):\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.num_latents = num_latents\n self.latent_action_dim = latent_action_dim\n self.num_blocks = num_blocks\n self.num_heads = num_heads\n self.dropout = dropout\n self.mask_limit = mask_limit\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n\n self.transformer = STTransformer(\n self.model_dim,\n self.model_dim,\n self.ffn_dim,\n self.num_latents,\n self.num_blocks,\n self.num_heads,\n self.dropout,\n self.param_dtype,\n self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)\n self.mask_token = nnx.Param(\n nnx.initializers.lecun_uniform()(rngs.params(), (1, 1, 1, self.model_dim))\n )\n self.action_up = nnx.Linear(\n self.latent_action_dim,\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n def __call__(\n self,\n batch: Dict[str, jax.Array],\n training: bool = True,\n ) -> tuple[jax.Array, jax.Array]:\n # --- Mask videos ---\n video_tokens_BTN = batch[""video_tokens""]\n latent_actions_BTm11L = batch[""latent_actions""]\n vid_embed_BTNM = self.patch_embed(video_tokens_BTN)\n if training:\n batch_size = vid_embed_BTNM.shape[0]\n _rng_prob, *_rngs_mask = jax.random.split(batch[""mask_rng""], batch_size + 1)\n mask_prob = jax.random.uniform(\n _rng_prob, shape=(batch_size,), minval=self.mask_limit\n )\n per_sample_shape = vid_embed_BTNM.shape[1:-1]\n mask = jax.vmap(\n lambda rng, prob: jax.random.bernoulli(rng, prob, per_sample_shape),\n in_axes=(0, 0),\n )(jnp.asarray(_rngs_mask), mask_prob)\n mask = mask.at[:, 0].set(False)\n vid_embed_BTNM = jnp.where(\n jnp.expand_dims(mask, -1), self.mask_token.value, vid_embed_BTNM\n )\n else:\n mask = jnp.ones_like(video_tokens_BTN)\n\n # --- Predict transition ---\n act_embed_BTm11M = self.action_up(latent_actions_BTm11L)\n padded_act_embed_BT1M = jnp.pad(\n act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))\n )\n padded_act_embed_BTNM = jnp.broadcast_to(\n padded_act_embed_BT1M, vid_embed_BTNM.shape\n )\n vid_embed_BTNM += padded_act_embed_BTNM\n logits_BTNV = self.transformer(vid_embed_BTNM)\n return logits_BTNV, mask\n\n\nclass DynamicsCausal(nnx.Module):\n """"""Causal dynamics model""""""\n\n def __init__(\n self,\n model_dim: int,\n ffn_dim: int,\n num_latents: int,\n latent_action_dim: int,\n num_blocks: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n decode: bool,\n rngs: nnx.Rngs,\n ):\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.num_latents = num_latents\n self.latent_action_dim = latent_action_dim\n self.num_blocks = num_blocks\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.decode = decode\n\n self.transformer = Transformer(\n self.model_dim,\n self.model_dim,\n self.ffn_dim,\n self.num_latents,\n self.num_blocks,\n self.num_heads,\n self.dropout,\n self.param_dtype,\n self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=self.decode,\n rngs=rngs,\n )\n self.patch_embed = nnx.Embed(self.num_latents, self.model_dim, rngs=rngs)\n self.action_up = nnx.Linear(\n self.latent_action_dim,\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n def __call__(\n self,\n batch: Dict[str, jax.Array],\n training: bool = True,\n ) -> tuple[jax.Array, jax.Array]:\n video_tokens_BTN = batch[""video_tokens""]\n latent_actions_BTm11L = batch[""latent_actions""]\n vid_embed_BTNM = self.patch_embed(video_tokens_BTN)\n act_embed_BTm11M = self.action_up(latent_actions_BTm11L)\n padded_act_embed_BT1M = jnp.pad(\n act_embed_BTm11M, ((0, 0), (1, 0), (0, 0), (0, 0))\n )\n vid_embed_BTNp1M = jnp.concatenate(\n [padded_act_embed_BT1M, vid_embed_BTNM], axis=2\n )\n logits_BTNp1V = self.transformer(vid_embed_BTNp1M)\n logits_BTNV = logits_BTNp1V[:, :, :-1]\n return logits_BTNV, jnp.ones_like(video_tokens_BTN)\n",python,tab +611,2177991,"models/dynamics.py",2653,2,"1:",python,selection_command +612,2184000,"models/lam.py",0,0,"from typing import Dict\n\nimport jax\nimport jax.numpy as jnp\nimport flax.nnx as nnx\n\nfrom utils.preprocess import patchify, unpatchify\nfrom utils.nn import STTransformer, VectorQuantizer\n\n\nclass LatentActionModel(nnx.Module):\n """"""Latent Action ST-ViVit VQ-VAE\n\n Dimension keys:\n B: batch size\n T: sequence length\n N: number of patches per frame\n M: model dimension\n L: latent dimension\n E: B * (T - 1)\n H: height\n W: width\n C: number of channels (n_dim)\n P: patch token dimension (patch_size^2 * C)\n\n Tm1: T - 1\n Np1: N + 1\n """"""\n\n def __init__(\n self,\n in_dim: int,\n model_dim: int,\n ffn_dim: int,\n latent_dim: int,\n num_latents: int,\n patch_size: int,\n num_blocks: int,\n num_heads: int,\n dropout: float,\n codebook_dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n rngs: nnx.Rngs,\n ):\n self.in_dim = in_dim\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.latent_dim = latent_dim\n self.num_latents = num_latents\n self.patch_size = patch_size\n self.num_blocks = num_blocks\n self.num_heads = num_heads\n self.dropout = dropout\n self.codebook_dropout = codebook_dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n\n self.patch_token_dim = self.in_dim * self.patch_size**2\n self.encoder = STTransformer(\n self.patch_token_dim,\n self.model_dim,\n self.ffn_dim,\n self.latent_dim,\n self.num_blocks,\n self.num_heads,\n self.dropout,\n self.param_dtype,\n self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n self.action_in = nnx.Param(\n nnx.initializers.lecun_uniform()(\n rngs.params(), (1, 1, 1, self.patch_token_dim)\n )\n )\n self.vq = VectorQuantizer(\n self.latent_dim,\n self.num_latents,\n self.codebook_dropout,\n self.dtype,\n rngs=rngs,\n )\n self.patch_up = nnx.Linear(\n self.patch_token_dim,\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.action_up = nnx.Linear(\n self.latent_dim,\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.decoder = STTransformer(\n self.model_dim,\n self.model_dim,\n self.ffn_dim,\n self.patch_token_dim,\n self.num_blocks,\n self.num_heads,\n self.dropout,\n self.param_dtype,\n self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n\n def __call__(\n self, batch: Dict[str, jax.Array], training: bool = True\n ) -> Dict[str, jax.Array]:\n # --- Encode + VQ ---\n H, W = batch[""videos""].shape[2:4]\n videos_BTHWC = batch[""videos""]\n outputs = self.vq_encode(videos_BTHWC, training)\n patch_BTNP = outputs[""patches""]\n z_q_BTm11L = outputs[""z_q""]\n action_BTm11M = self.action_up(z_q_BTm11L)\n patch_BTm1NM = self.patch_up(patch_BTNP[:, :-1])\n action_BTm1NM = jnp.broadcast_to(action_BTm11M, patch_BTm1NM.shape)\n video_action_patches_BTm1NM = action_BTm1NM + patch_BTm1NM\n del outputs[""patches""], patch_BTNP, patch_BTm1NM\n\n # --- Decode ---\n video_recon_BTm1P = self.decoder(video_action_patches_BTm1NM)\n video_recon_BTm1P = video_recon_BTm1P.astype(jnp.float32)\n video_recon_BTm1P = nnx.sigmoid(video_recon_BTm1P)\n video_recon_BTm1P = video_recon_BTm1P.astype(self.dtype)\n video_recon_BTm1HWC = unpatchify(video_recon_BTm1P, self.patch_size, H, W)\n outputs[""recon""] = video_recon_BTm1HWC\n return outputs\n\n def vq_encode(\n self, videos_BTHWC: jax.Array, training: bool = True\n ) -> Dict[str, jax.Array]:\n # --- Preprocess videos ---\n B, T = videos_BTHWC.shape[:2]\n patch_BTNP = patchify(videos_BTHWC, self.patch_size)\n action_pad_BT1P = jnp.broadcast_to(\n self.action_in.value, (B, T, 1, self.patch_token_dim)\n )\n padded_patch_BTNp1P = jnp.concatenate((action_pad_BT1P, patch_BTNP), axis=2)\n\n # --- Encode ---\n z_BTNp1L = self.encoder(padded_patch_BTNp1P)\n # Get latent action for all future frames\n z_BTm1L = z_BTNp1L[:, 1:, 0]\n\n # --- Vector quantize ---\n z_EL = z_BTm1L.reshape(B * (T - 1), self.latent_dim)\n z_q_EL, z_EL, emb_EL, indices_E = self.vq(z_EL, training)\n z_q_BTm11L = z_q_EL.reshape(B, T - 1, 1, self.latent_dim)\n return dict(\n patches=patch_BTNP, z_q=z_q_BTm11L, z=z_EL, emb=emb_EL, indices=indices_E\n )\n",python,tab +613,2184001,"models/lam.py",4812,2,"1:",python,selection_command +614,2192956,"genie.py",0,0,"",python,tab +615,2192957,"genie.py",9930,0,"",python,selection_command +616,2228186,"sample.py",0,0,"",python,tab +617,2228187,"sample.py",7641,0,"",python,selection_command +618,2248251,"TERMINAL",0,0,"git status",,terminal_command +619,2248344,"TERMINAL",0,0,"]633;COn branch gt-actions\r\nYour branch is up to date with 'origin/gt-actions'.\r\n\r\n",,terminal_output +620,2248373,"TERMINAL",0,0,"Last commands done (2 commands done):\r\n pick ba37453 feat: generate coinrun dataset with val split\r\n pick faadd10 feat: implemented validation loss for all three models\r\nNext commands to do (26 remaining commands):\r\n pick 9a17dbb fix: pass val data path to dataloader\r\n pick 6e69cdb fix typo in image logging\r\n (use ""git rebase --edit-todo"" to view and edit)\r\nYou are currently editing a commit while rebasing branch 'gt-actions' on 'c7522f2'.\r\n (use ""git commit --amend"" to amend the current commit)\r\n (use ""git rebase --continue"" once you are satisfied with your changes)\r\n\r\nChanges to be committed:\r\n (use ""git restore --staged ..."" to unstage)\r\n\tmodified: genie.py\r\n\tmodified: sample.py\r\n\r\nUntracked files:\r\n (use ""git add ..."" to include in what will be committed)\r\n\tdiff.diff\r\n\tinput_pipeline/generate_breakout_dataset_agent.py\r\n\tkiller.sh\r\n\tkiller_partition.sh\r\n\tlog.log\r\n\toverfit_dir.zip\r\n\trequirements-franz.txt\r\n\tsamples/\r\n\tscripts_cremers/\r\n\tslurm/\r\n\ttest.py\r\n\tutils/visualizer.py\r\n\r\n]0;tum_cte0515@hkn1993:~/Projects/jasmine",,terminal_output +621,2259714,"TERMINAL",0,0,"git pull",,terminal_command +622,2259765,"TERMINAL",0,0,"]633;C",,terminal_output +623,2261594,"TERMINAL",0,0,"remote: Enumerating objects: 7, done.\r\nremote: Counting objects: 14% (1/7)\rremote: Counting objects: 28% (2/7)\rremote: Counting objects: 42% (3/7)\rremote: Counting objects: 57% (4/7)\rremote: Counting objects: 71% (5/7)\rremote: Counting objects: 85% (6/7)\rremote: Counting objects: 100% (7/7)\rremote: Counting objects: 100% (7/7), done.\r\nremote: Compressing objects: 25% (1/4)\rremote: Compressing objects: 50% (2/4)\rremote: Compressing objects: 75% (3/4)\rremote: Compressing objects: 100% (4/4)\rremote: Compressing objects: 100% (4/4), done.\r\nremote: Total 7 (delta 3), reused 3 (delta 3), pack-reused 0 (from 0)\r\nUnpacking objects: 14% (1/7)\rUnpacking objects: 28% (2/7)\rUnpacking objects: 42% (3/7)\rUnpacking objects: 57% (4/7)\rUnpacking objects: 71% (5/7)\rUnpacking objects: 85% (6/7)\r",,terminal_output +624,2261658,"TERMINAL",0,0,"Unpacking objects: 100% (7/7)\rUnpacking objects: 100% (7/7), 8.34 KiB | 70.00 KiB/s, done.\r\n",,terminal_output +625,2261939,"TERMINAL",0,0,"From github.com:p-doom/jasmine\r\n a5a539c..8fbd8eb gt-actions -> origin/gt-actions\r\n 35e26ae..3b83a0b generate-atari-dataset -> origin/generate-atari-dataset\r\n",,terminal_output +626,2262009,"TERMINAL",0,0,"Saved checkpoint at step 500\r\n",,terminal_output +627,2262020,"TERMINAL",0,0,"Updating a5a539c..8fbd8eb\r\nFast-forward\r\n",,terminal_output +628,2262055,"TERMINAL",0,0," train_dynamics.py | 4 ++--\r\n 1 file changed, 2 insertions(+), 2 deletions(-)\r\n]0;tum_cte0515@hkn1993:~/Projects/jasmine",,terminal_output +629,2273930,"TERMINAL",0,0,"git commit -m ""modify slicing towards new data format""",,terminal_command +630,2273973,"TERMINAL",0,0,"]633;C",,terminal_output +631,2274144,"TERMINAL",0,0,"g",,terminal_output +632,2274210,"TERMINAL",0,0,"i",,terminal_output +633,2274423,"TERMINAL",0,0,"t",,terminal_output +634,2274603,"TERMINAL",0,0," ",,terminal_output +635,2275204,"TERMINAL",0,0,"p",,terminal_output +636,2275314,"TERMINAL",0,0,"u",,terminal_output +637,2275447,"TERMINAL",0,0,"s",,terminal_output +638,2275512,"TERMINAL",0,0,"h",,terminal_output +639,2276617,"TERMINAL",0,0,"black....................................................................",,terminal_output +640,2279658,"TERMINAL",0,0,"Passed\r\n",,terminal_output +641,2279683,"TERMINAL",0,0,"[gt-actions 33b1895] modify slicing towards new data format\r\n 2 files changed, 3 insertions(+), 3 deletions(-)\r\n]0;tum_cte0515@hkn1993:~/Projects/jasmine",,terminal_output +642,2280644,"TERMINAL",0,0,"git push",,terminal_command +643,2280716,"TERMINAL",0,0,"]633;C",,terminal_output +644,2282076,"TERMINAL",0,0,"Enumerating objects: 7, done.\r\nCounting objects: 14% (1/7)\rCounting objects: 28% (2/7)\rCounting objects: 42% (3/7)\rCounting objects: 57% (4/7)\rCounting objects: 71% (5/7)\rCounting objects: 85% (6/7)\rCounting objects: 100% (7/7)\rCounting objects: 100% (7/7), done.\r\nDelta compression using up to 152 threads\r\nCompressing objects: 25% (1/4)\rCompressing objects: 50% (2/4)\rCompressing objects: 75% (3/4)\rCompressing objects: 100% (4/4)\rCompressing objects: 100% (4/4), done.\r\nWriting objects: 25% (1/4)\rWriting objects: 50% (2/4)\rWriting objects: 75% (3/4)\rWriting objects: 100% (4/4)\rWriting objects: 100% (4/4), 407 bytes | 407.00 KiB/s, done.\r\nTotal 4 (delta 3), reused 0 (delta 0), pack-reused 0\r\n",,terminal_output +645,2282162,"TERMINAL",0,0,"remote: Resolving deltas: 0% (0/3)\rremote: Resolving deltas: 33% (1/3)\rremote: Resolving deltas: 66% (2/3)\rremote: Resolving deltas: 100% (3/3)\rremote: Resolving deltas: 100% (3/3), completed with 3 local objects.\r\n",,terminal_output +646,2282403,"TERMINAL",0,0,"To github.com:p-doom/jasmine.git\r\n 8fbd8eb..33b1895 gt-actions -> gt-actions\r\n]0;tum_cte0515@hkn1993:~/Projects/jasmine",,terminal_output +647,2290671,"TERMINAL",0,0,"git branch",,terminal_command +648,2290715,"TERMINAL",0,0,"]633;C[?1h=\r action-mapper\r\n add-wandb-name-and-tags\r\n before-nnx\r\n causal-mem-reduce\r\n causal-spatiotemporal-kv-cache\r\n causal-st-transformer\r\n causal-transformer-dynamics-model\r\n causal-transformer-nnx-no-kv-cache\r\n coinrun-gt-actions\r\n convert-to-jax-array-in-iter\r\n correct-batched-sampling\r\n dev\r\n dont-let-tf-see-gpu\r\n feat/darkness-filter\r\n feat/explicit-image-dims\r\n fix-action-padding-lam-future-information-access\r\n fix-sampling\r\n:",,terminal_output +649,2292586,"TERMINAL",0,0,"\r fix-transformer-forwardpass\r\n:",,terminal_output +650,2293462,"TERMINAL",0,0,"\r fix/spatiotemporal-pe-once-in-STTransformer\r\n:\r generate-minatar-breakout-dataset\r\n:\r grad-norm-log-and-clip\r\n:\r grain-dataloader\r\n:\r* gt-actions\r\n:\r input_pipeline/add-npy2array_record\r\n:\r logging-variants\r\n:\r lr-schedules\r\n:\r main\r\n:\r maskgit-different-maskprob-per-sample\r\n:\r maskgit-sampling-iterative-unmasking-fix\r\n:\r metrics-logging-for-dynamics-model\r\n:\r monkey-patch\r\n:\r new-arch-sampling\r\n:",,terminal_output +651,2293604,"TERMINAL",0,0,"\r preprocess_video\r\n:",,terminal_output +652,2300937,"TERMINAL",0,0,"\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jasmine",,terminal_output +653,2303431,"TERMINAL",0,0,"git checkout generate-minatar-breakout-dataset",,terminal_command +654,2303441,"TERMINAL",0,0,"]633;CSwitched to branch 'generate-minatar-breakout-dataset'\r\nYour branch is ahead of 'origin/generate-minatar-breakout-dataset' by 10 commits.\r\n (use ""git push"" to publish your local commits)\r\n]0;tum_cte0515@hkn1993:~/Projects/jasmine",,terminal_output +655,2303667,"sample.py",7641,75," action_batch_BSm11 = jnp.expand_dims(batch[""actions""][:, 1:], -1)\n",python,content +656,2303711,"TERMINAL",0,0,"Calculating validation metrics...\r\n",,terminal_output +657,2305430,"sample.py",0,0,"Switched from branch 'gt-actions' to 'generate-minatar-breakout-dataset'",python,git_branch_checkout +658,2310942,"TERMINAL",0,0,"2025-09-23 12:39:34.894923: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-23 12:39:34.895522: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-09-23 12:39:34.895542: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +659,2314022,"TERMINAL",0,0,"git stash pop",,terminal_command +660,2314096,"TERMINAL",0,0,"]633;COn branch generate-minatar-breakout-dataset\r\nYour branch is ahead of 'origin/generate-minatar-breakout-dataset' by 10 commits.\r\n (use ""git push"" to publish your local commits)\r\n\r\nLast commands done (2 commands done):\r\n pick ba37453 feat: generate coinrun dataset with val split\r\n pick faadd10 feat: implemented validation loss for all three models\r\nNext commands to do (26 remaining commands):\r\n pick 9a17dbb fix: pass val data path to dataloader\r\n pick 6e69cdb fix typo in image logging\r\n (use ""git rebase --edit-todo"" to view and edit)\r\nYou are currently editing a commit while rebasing branch 'gt-actions' on 'c7522f2'.\r\n (use ""git commit --amend"" to amend the current commit)\r\n (use ""git rebase --continue"" once you are satisfied with your changes)\r\n\r\nChanges not staged for commit:\r\n (use ""git add ..."" to update what will be committed)\r\n (use ""git restore ..."" to discard changes in working directory)\r\n\tmodified: genie.py\r\n\tmodified: input_pipeline/generate_breakout_dataset.py\r\n\tmodified: sample.py\r\n\tmodified: train_dynamics.py\r\n\tmodified: train_lam.py\r\n\tmodified: train_tokenizer.py\r\n\r\nUntracked files:\r\n (use ""git add ..."" to include in what will be committed)\r\n\tdiff.diff\r\n\tinput_pipeline/generate_breakout_dataset_agent.py\r\n\tkiller.sh\r\n\tkiller_partition.sh\r\n\tlog.log\r\n\toverfit_dir.zip\r\n\trequirements-franz.txt\r\n\tsamples/\r\n\tscripts_cremers/\r\n\tslurm/\r\n\ttest.py\r\n\tutils/visualizer.py\r\n\r\nno changes added to commit (use ""git add"" and/or ""git commit -a"")\r\n",,terminal_output +661,2314115,"TERMINAL",0,0,"Dropped refs/stash@{0} (7e8d393bd225e761edba528abd2f41161cf8a0c7)\r\n]0;tum_cte0515@hkn1993:~/Projects/jasmine",,terminal_output +662,2314358,"sample.py",7641,74," action_batch_BSm11 = jnp.expand_dims(batch[""actions""][:, :-1], -1)\n",python,content +663,2318027,"train_dynamics.py",0,0,"import os\n\n\nos.environ.setdefault(""XLA_PYTHON_CLIENT_MEM_FRACTION"", ""0.98"")\n\nfrom dataclasses import dataclass, field\nimport itertools\nfrom typing import cast, Optional\n\nimport einops\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\nimport flax.nnx as nnx\n\nfrom genie import Genie, restore_genie_components\nfrom utils.dataloader import get_dataloader\nfrom utils.train_utils import (\n get_lr_schedule,\n count_parameters_by_component,\n print_mem_stats,\n print_compiled_memory_stats,\n print_compiled_cost_analysis,\n)\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n batch_size: int = 36\n init_lr: float = 0.0\n max_lr: float = 3e-5\n decay_end: float = 0.0\n wsd_decay_steps: int = (\n 10000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n )\n warmup_steps: int = 5000\n lr_schedule: str = ""wsd"" # supported options: wsd, cos\n # Tokenizer\n tokenizer_dim: int = 512\n tokenizer_ffn_dim: int = 2048\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 4\n tokenizer_num_heads: int = 8\n tokenizer_checkpoint: str = """"\n # LAM\n lam_dim: int = 512\n lam_ffn_dim: int = 2048\n latent_action_dim: int = 32\n num_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 4\n lam_num_heads: int = 8\n lam_checkpoint: str = """"\n # Dynamics\n dyna_type: str = ""maskgit"" # supported options: maskgit, causal\n dyna_dim: int = 512\n dyna_ffn_dim: int = 2048\n dyna_num_blocks: int = 6\n dyna_num_heads: int = 8\n dropout: float = 0.0\n mask_limit: float = 0.5\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\n use_flash_attention: bool = True\n use_gt_actions: bool = False\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n name: str = ""train_dynamics""\n tags: list[str] = field(default_factory=lambda: [""dynamics""])\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 25000\n log_checkpoint_keep_period: int = 20000\n log_gradients: bool = False\n val_data_dir: str = """"\n val_interval: int = 20_000\n val_steps: int = 50\n eval_full_frame: bool = False\n val_maskgit_steps: int = 25\n val_temperature: float = 1\n val_sample_argmax: bool = False\n wandb_id: str = """"\n\n\ndef build_model(args: Args, rng: jax.Array) -> tuple[Genie, jax.Array]:\n rng, _rng = jax.random.split(rng)\n rngs = nnx.Rngs(_rng)\n genie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n tokenizer_ffn_dim=args.tokenizer_ffn_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n lam_ffn_dim=args.lam_ffn_dim,\n latent_action_dim=args.latent_action_dim,\n num_actions=args.num_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n lam_co_train=not args.lam_checkpoint,\n use_gt_actions=args.use_gt_actions,\n # Dynamics\n dyna_type=args.dyna_type,\n dyna_dim=args.dyna_dim,\n dyna_ffn_dim=args.dyna_ffn_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n dropout=args.dropout,\n mask_limit=args.mask_limit,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n decode=False,\n rngs=rngs,\n )\n if args.use_gt_actions:\n assert (\n not args.lam_checkpoint\n ), ""Cannot use LAM when using ground-truth actions.""\n else:\n assert genie.lam is not None\n del genie.lam.decoder\n return genie, rng\n\n\ndef build_optimizer(genie: Genie, args: Args) -> tuple[nnx.Optimizer, optax.Schedule]:\n lr_schedule = get_lr_schedule(\n args.lr_schedule,\n args.init_lr,\n args.max_lr,\n args.decay_end,\n args.num_steps,\n args.warmup_steps,\n args.wsd_decay_steps,\n )\n tx = optax.adamw(\n learning_rate=lr_schedule,\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.param_dtype, # moments in full precision\n )\n optimizer = nnx.Optimizer(genie, tx)\n return optimizer, lr_schedule\n\n\ndef build_mesh_and_sharding(\n num_devices: int,\n) -> tuple[Mesh, NamedSharding, NamedSharding, NamedSharding]:\n device_mesh_arr = create_device_mesh((num_devices,))\n mesh = Mesh(devices=device_mesh_arr, axis_names=(""data"",))\n replicated_sharding = NamedSharding(mesh, PartitionSpec())\n videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n actions_sharding = NamedSharding(mesh, PartitionSpec(""data"", None))\n return mesh, replicated_sharding, videos_sharding, actions_sharding\n\n\ndef shard_optimizer_states(\n optimizer: nnx.Optimizer, replicated_sharding: NamedSharding\n) -> None:\n model_state = nnx.state(optimizer.model)\n model_sharded_state = jax.lax.with_sharding_constraint(\n model_state, replicated_sharding\n )\n nnx.update(optimizer.model, model_sharded_state)\n optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)\n optimizer_sharded_state = jax.lax.with_sharding_constraint(\n optimizer_state, replicated_sharding\n )\n nnx.update(optimizer, optimizer_sharded_state)\n\n\ndef build_dataloader(args: Args, data_dir: str) -> grain.DataLoaderIterator:\n image_shape = (args.image_height, args.image_width, args.image_channels)\n array_record_files = [\n os.path.join(data_dir, x)\n for x in os.listdir(data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n return grain_iterator\n\n\ndef build_checkpoint_manager(args: Args) -> ocp.CheckpointManager:\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n if args.val_data_dir:\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n return checkpoint_manager\n\n\ndef restore_or_initialize_components(\n args: Args,\n checkpoint_manager: ocp.CheckpointManager,\n optimizer: nnx.Optimizer,\n train_iterator: grain.DataLoaderIterator,\n rng: jax.Array,\n replicated_sharding: NamedSharding,\n val_iterator: Optional[grain.DataLoaderIterator],\n restore_step: Optional[int] = None,\n) -> tuple[\n int, nnx.Optimizer, grain.DataLoaderIterator, grain.DataLoaderIterator, jax.Array\n]:\n step = 0\n if restore_step is None:\n restore_step = checkpoint_manager.latest_step()\n if args.restore_ckpt:\n abstract_optimizer = nnx.eval_shape(lambda: optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n if val_iterator:\n restore_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointRestore(train_iterator), # type: ignore\n val_dataloader_state=grain.checkpoint.CheckpointRestore(val_iterator), # type: ignore\n )\n else:\n restore_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointRestore(train_iterator), # type: ignore\n )\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(), args=restore_args\n )\n restored_optimizer_state = restored[""model_state""]\n nnx.update(optimizer, restored_optimizer_state)\n train_iterator = restored[""train_dataloader_state""]\n if val_iterator:\n val_iterator = restored[""val_dataloader_state""]\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored dataloader and model state from step {step}"")\n else:\n # Restore from pre-trained tokenizer (and LAM)\n rng, _rng = jax.random.split(rng)\n optimizer = restore_genie_components(optimizer, replicated_sharding, _rng, args)\n # NOTE: We have to remove the (unused) tokenizer vq dropout due flax.nnx lazily initializing modules.\n # Specifically, the first dynamics model checkpoint will contain the vq dropout module,\n # but the first full restore will fail due to nnx not initializing the module when\n # dropout is set to 0.0.\n del optimizer.model.tokenizer.vq.drop\n return step, optimizer, train_iterator, val_iterator, rng\n\n\ndef _calculate_step_metrics(\n outputs: dict[str, jax.Array],\n gt: jax.Array,\n num_actions: int,\n num_patch_latents: int,\n) -> tuple[jax.Array, dict]:\n mask = outputs[""mask""]\n outputs[""token_logits""] = outputs[""token_logits""].astype(jnp.float32)\n ce_loss = optax.softmax_cross_entropy_with_integer_labels(\n outputs[""token_logits""], outputs[""video_tokens""]\n )\n ce_loss = (mask * ce_loss).sum() / mask.sum()\n acc = outputs[""token_logits""].argmax(-1) == outputs[""video_tokens""]\n acc = (mask * acc).sum() / mask.sum()\n select_probs = jax.nn.softmax(outputs[""token_logits""])\n gt_val = gt.clip(0, 1).reshape(-1, *gt.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt_val, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt_val, recon)).mean()\n _, index_counts_tokenizer = jnp.unique_counts(\n jnp.ravel(outputs[""video_tokens""]),\n size=num_patch_latents,\n fill_value=0,\n )\n codebook_usage_tokenizer = (index_counts_tokenizer != 0).mean()\n metrics = dict(\n cross_entropy_loss=ce_loss,\n masked_token_accuracy=acc,\n select_logit=outputs[""token_logits""].max(-1).mean(),\n select_p=select_probs.max(-1).mean(),\n entropy=jax.scipy.special.entr(select_probs).sum(-1).mean(),\n psnr=psnr,\n ssim=ssim,\n codebook_usage_tokenizer=codebook_usage_tokenizer,\n )\n if ""lam_indices"" in outputs.keys():\n _, index_counts_lam = jnp.unique_counts(\n jnp.ravel(outputs[""lam_indices""]),\n size=num_actions,\n fill_value=0,\n )\n codebook_usage_lam = (index_counts_lam != 0).mean()\n metrics[""codebook_usage_lam""] = codebook_usage_lam\n return ce_loss, metrics\n\n\ndef main(args: Args) -> None:\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 rng = jax.random.key(args.seed)\n\n # --- Initialize model ---\n genie, rng = build_model(args, rng)\n _, params, _ = nnx.split(genie, nnx.Param, ...)\n param_counts = count_parameters_by_component(params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\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 if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n optimizer, lr_schedule = build_optimizer(genie, args)\n del genie\n\n # FIXME: switch to create_hybrid_device_mesh for runs spanning multiple nodes\n _, replicated_sharding, videos_sharding, actions_sharding = build_mesh_and_sharding(\n num_devices\n )\n\n shard_optimizer_states(optimizer, replicated_sharding)\n\n # --- Initialize checkpoint manager ---\n checkpoint_manager = build_checkpoint_manager(args)\n\n # --- Create DataLoaderIterator from dataloader ---\n train_iterator = build_dataloader(args, args.data_dir)\n val_iterator = None\n if args.val_data_dir:\n val_iterator = build_dataloader(args, args.val_data_dir)\n\n # --- Restore checkpoint ---\n step, optimizer, train_iterator, val_iterator, rng = (\n restore_or_initialize_components(\n args,\n checkpoint_manager,\n optimizer,\n train_iterator,\n rng,\n replicated_sharding,\n val_iterator,\n )\n )\n\n # --- Define loss and train step (close over args) ---\n def dynamics_loss_fn(\n model: Genie,\n inputs: dict,\n training: bool = False,\n ) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n ce_loss, metrics = _calculate_step_metrics(\n outputs, gt, args.num_actions, args.num_patch_latents\n )\n return ce_loss, (outputs[""recon""], metrics)\n\n @nnx.jit(donate_argnums=0)\n def train_step(\n optimizer: nnx.Optimizer, inputs: dict\n ) -> tuple[jax.Array, jax.Array, dict]:\n def loss_fn(model: Genie) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n model.train()\n return dynamics_loss_fn(model, inputs, training=True)\n\n (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(\n optimizer.model\n )\n optimizer.update(grads)\n if args.log_gradients:\n metrics[""gradients_std/""] = jax.tree.map(\n lambda x: x.std(), grads[""params""][""dynamics""]\n )\n return loss, recon, metrics\n\n @nnx.jit\n def val_step(genie: Genie, inputs: dict) -> dict:\n """"""Evaluate model and compute metrics""""""\n genie.eval()\n (loss, (recon, metrics)) = dynamics_loss_fn(genie, inputs, training=False)\n val_output = {""loss"": loss, ""recon"": recon, ""metrics"": metrics}\n\n # --- Evaluate full frame prediction (sampling) ---\n if args.eval_full_frame:\n tokenizer_outputs = genie.tokenizer.vq_encode(\n inputs[""videos""], training=False\n )\n tokens_full_frame = tokenizer_outputs[""indices""]\n lam_indices = None\n if not args.use_gt_actions:\n lam_indices = genie.vq_encode(inputs, training=False)\n inputs[""latent_actions""] = lam_indices\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt[:, :-1].astype(\n args.dtype\n ) # remove last frame for generation\n recon_full_frame, logits_full_frame = genie.sample(\n inputs,\n args.seq_len,\n args.val_temperature,\n args.val_sample_argmax,\n args.val_maskgit_steps,\n )\n step_outputs = {\n ""recon"": recon_full_frame,\n ""token_logits"": logits_full_frame,\n ""video_tokens"": tokens_full_frame,\n ""mask"": jnp.zeros_like(tokens_full_frame).at[:, -1].set(True),\n }\n if lam_indices is not None:\n step_outputs[""lam_indices""] = lam_indices\n\n loss_full_frame, metrics_full_frame = _calculate_step_metrics(\n step_outputs, gt, args.num_actions, args.num_patch_latents\n )\n val_output.update(\n {\n ""loss_full_frame"": loss_full_frame,\n ""recon_full_frame"": recon_full_frame,\n ""metrics_full_frame"": metrics_full_frame,\n }\n )\n return val_output\n\n def calculate_validation_metrics(val_dataloader, genie, rng):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n loss_full_frame_per_step = []\n metrics_full_frame_per_step = []\n batch = None\n recon = None\n recon_full_frame = None\n for batch in val_dataloader:\n rng, _rng_mask = jax.random.split(rng, 2)\n batch[""rng""] = _rng_mask\n val_outputs = val_step(genie, batch)\n loss_per_step.append(val_outputs[""loss""])\n metrics_per_step.append(val_outputs[""metrics""])\n recon = val_outputs[""recon""]\n if args.eval_full_frame:\n loss_full_frame_per_step.append(val_outputs[""loss_full_frame""])\n metrics_full_frame_per_step.append(val_outputs[""metrics_full_frame""])\n recon_full_frame = val_outputs[""recon_full_frame""]\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(\n f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}""\n )\n\n val_metrics = {\n f""val_{key}"": np.mean([float(m[key]) for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n val_metrics[""val_loss""] = np.mean(loss_per_step)\n if args.eval_full_frame:\n val_metrics_full_frame = {\n f""val_full_frame_{key}"": np.mean(\n [float(m[key]) for m in metrics_full_frame_per_step]\n )\n for key in metrics_full_frame_per_step[0].keys()\n }\n val_metrics.update(val_metrics_full_frame)\n val_metrics[""val_loss_full_frame""] = np.mean(loss_full_frame_per_step)\n return val_metrics, batch, recon, recon_full_frame\n\n # --- TRAIN LOOP ---\n dataloader_train = (\n {\n ""videos"": jax.make_array_from_process_local_data(\n videos_sharding, local_data=elem[""videos""]\n ),\n ""actions"": (\n jax.make_array_from_process_local_data(\n actions_sharding, elem[""actions""]\n )\n if args.use_gt_actions\n else None\n ),\n }\n for elem in train_iterator\n )\n dataloader_val = None\n if val_iterator:\n dataloader_val = (\n {\n ""videos"": jax.make_array_from_process_local_data(\n videos_sharding, elem[""videos""]\n ),\n ""actions"": (\n jax.make_array_from_process_local_data(\n actions_sharding, elem[""actions""]\n )\n if args.use_gt_actions\n else None\n ),\n }\n for elem in val_iterator\n )\n if jax.process_index() == 0:\n first_batch = next(dataloader_train)\n first_batch[""rng""] = rng # type: ignore\n compiled = train_step.lower(optimizer, first_batch).compile()\n print_compiled_memory_stats(compiled.memory_analysis())\n print_compiled_cost_analysis(compiled.cost_analysis())\n # Do not skip the first batch during training\n dataloader_train = itertools.chain([first_batch], dataloader_train)\n print(f""Starting training from step {step}..."")\n first_step = step\n while step < args.num_steps:\n for batch in dataloader_train:\n # --- Train step ---\n rng, _rng_mask = jax.random.split(rng, 2)\n batch[""rng""] = _rng_mask\n loss, recon, metrics = train_step(optimizer, batch)\n if step == first_step:\n print_mem_stats(""After params initialized"")\n # metrics[""lr""] = lr_schedule(step)\n # print(f""Step {step}, loss: {loss}"")\n step += 1\n\n # --- Validation loss ---\n val_results = {}\n if dataloader_val and step % args.val_interval == 0:\n rng, _rng_mask_val = jax.random.split(rng, 2)\n print(""Calculating validation metrics..."")\n val_metrics, val_gt_batch, val_recon, val_recon_full_frame = (\n calculate_validation_metrics(\n dataloader_val, optimizer.model, _rng_mask_val\n )\n )\n print(f""Step {step}, validation loss: {val_metrics['val_loss']}"")\n val_results = {\n ""metrics"": val_metrics,\n ""gt_batch"": val_gt_batch,\n ""recon"": val_recon,\n ""full_frame"": val_recon_full_frame,\n }\n\n # --- Logging ---\n if args.log:\n if step % args.log_interval == 0 and jax.process_index() == 0:\n log_dict = {""loss"": loss, ""step"": step, **metrics}\n if val_results:\n log_dict.update(val_results[""metrics""])\n wandb.log(log_dict)\n if step % args.log_image_interval == 0:\n gt_seq = batch[""videos""][0].astype(jnp.float32) / 255.0\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 if val_results:\n val_results[""gt_seq_val""] = (\n val_results[""gt_batch""][""videos""][0].astype(jnp.float32)\n / 255.0\n )\n val_results[""recon_seq_val""] = val_results[""recon""][0].clip(\n 0, 1\n )\n val_comparison_seq = jnp.concatenate(\n (val_results[""gt_seq_val""], val_results[""recon_seq_val""]),\n axis=1,\n )\n val_results[""val_comparison_seq""] = einops.rearrange(\n val_comparison_seq * 255, ""t h w c -> h (t w) c""\n )\n if args.eval_full_frame:\n val_results[""full_frame_seq_val""] = val_results[\n ""full_frame""\n ][0].clip(0, 1)\n val_results[""val_full_frame_comparison_seq""] = (\n jnp.concatenate(\n (\n val_results[""gt_seq_val""],\n val_results[""full_frame_seq_val""],\n ),\n axis=1,\n )\n )\n val_results[""val_full_frame_comparison_seq""] = (\n einops.rearrange(\n val_results[""val_full_frame_comparison_seq""] * 255,\n ""t h w c -> h (t w) c"",\n )\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[args.seq_len - 1])),\n recon=wandb.Image(np.asarray(recon_seq[args.seq_len - 1])),\n true_vs_recon=wandb.Image(\n np.asarray(comparison_seq.astype(np.uint8))\n ),\n )\n if val_results:\n log_images.update(\n dict(\n val_image=wandb.Image(\n np.asarray(\n val_results[""gt_seq_val""][args.seq_len - 1]\n )\n ),\n val_recon=wandb.Image(\n np.asarray(\n val_results[""recon_seq_val""][\n args.seq_len - 1\n ]\n )\n ),\n val_true_vs_recon=wandb.Image(\n np.asarray(\n val_results[""val_comparison_seq""].astype(\n np.uint8\n )\n )\n ),\n )\n )\n if args.eval_full_frame:\n log_images.update(\n dict(\n val_full_frame=wandb.Image(\n np.asarray(\n val_results[""full_frame_seq_val""][\n args.seq_len - 1\n ]\n )\n ),\n val_true_vs_full_frame=wandb.Image(\n np.asarray(\n val_results[\n ""val_full_frame_comparison_seq""\n ].astype(np.uint8)\n )\n ),\n )\n )\n wandb.log(log_images)\n # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n optimizer_state = nnx.state(optimizer)\n if val_iterator:\n ckpt_manager_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n train_iterator # type: ignore\n ),\n val_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n val_iterator # type: ignore\n ),\n )\n else:\n ckpt_manager_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n train_iterator # type: ignore\n ),\n )\n checkpoint_manager.save(step, args=ckpt_manager_args)\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n checkpoint_manager.close()\n\n\nif __name__ == ""__main__"":\n args = tyro.cli(Args)\n main(args)\n",python,tab +664,2328445,"genie.py",0,0,"from typing import Dict\n\nimport einops\nimport jax\nimport jax.numpy as jnp\nimport flax.nnx as nnx\nimport orbax.checkpoint as ocp\n\nfrom models.dynamics import DynamicsMaskGIT, DynamicsCausal\nfrom models.lam import LatentActionModel\nfrom models.tokenizer import TokenizerVQVAE\n\n\nclass Genie(nnx.Module):\n """"""Genie model""""""\n\n def __init__(\n self,\n in_dim: int,\n tokenizer_dim: int,\n tokenizer_ffn_dim: int,\n latent_patch_dim: int,\n num_patch_latents: int,\n patch_size: int,\n tokenizer_num_blocks: int,\n tokenizer_num_heads: int,\n lam_dim: int,\n lam_ffn_dim: int,\n latent_action_dim: int,\n num_actions: int,\n lam_patch_size: int,\n lam_num_blocks: int,\n lam_num_heads: int,\n lam_co_train: bool,\n use_gt_actions: bool,\n dyna_type: str,\n dyna_dim: int,\n dyna_ffn_dim: int,\n dyna_num_blocks: int,\n dyna_num_heads: int,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n decode: bool,\n rngs: nnx.Rngs,\n dropout: float = 0.0,\n mask_limit: float = 0.0,\n ):\n # --- Tokenizer ---\n self.in_dim = in_dim\n self.tokenizer_dim = tokenizer_dim\n self.tokenizer_ffn_dim = tokenizer_ffn_dim\n self.latent_patch_dim = latent_patch_dim\n self.num_patch_latents = num_patch_latents\n self.patch_size = patch_size\n self.tokenizer_num_blocks = tokenizer_num_blocks\n self.tokenizer_num_heads = tokenizer_num_heads\n # --- LAM ---\n self.lam_dim = lam_dim\n self.lam_ffn_dim = lam_ffn_dim\n self.latent_action_dim = latent_action_dim\n self.num_actions = num_actions\n self.lam_patch_size = lam_patch_size\n self.lam_num_blocks = lam_num_blocks\n self.lam_num_heads = lam_num_heads\n self.lam_co_train = lam_co_train\n self.use_gt_actions = use_gt_actions\n # --- Dynamics ---\n self.dyna_type = dyna_type\n self.dyna_dim = dyna_dim\n self.dyna_ffn_dim = dyna_ffn_dim\n self.dyna_num_blocks = dyna_num_blocks\n self.dyna_num_heads = dyna_num_heads\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.dropout = dropout\n self.mask_limit = mask_limit\n self.decode = decode\n\n self.tokenizer = TokenizerVQVAE(\n in_dim=self.in_dim,\n model_dim=self.tokenizer_dim,\n ffn_dim=self.tokenizer_ffn_dim,\n latent_dim=self.latent_patch_dim,\n num_latents=self.num_patch_latents,\n patch_size=self.patch_size,\n num_blocks=self.tokenizer_num_blocks,\n num_heads=self.tokenizer_num_heads,\n dropout=0.0,\n codebook_dropout=0.0,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n if self.use_gt_actions:\n self.action_embed = nnx.Embed(\n self.num_actions, self.latent_action_dim, rngs=rngs\n )\n self.lam = None\n else:\n self.lam = LatentActionModel(\n in_dim=self.in_dim,\n model_dim=self.lam_dim,\n ffn_dim=self.lam_ffn_dim,\n latent_dim=self.latent_patch_dim,\n num_latents=self.num_actions,\n patch_size=self.lam_patch_size,\n num_blocks=self.lam_num_blocks,\n num_heads=self.lam_num_heads,\n dropout=0.0,\n codebook_dropout=0.0,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n self.action_embed = None\n if self.dyna_type == ""maskgit"":\n self.dynamics = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n mask_limit=self.mask_limit,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n elif self.dyna_type == ""causal"":\n self.dynamics = DynamicsCausal(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=decode,\n rngs=rngs,\n )\n else:\n raise ValueError(f""Invalid dynamics type: {self.dyna_type}"")\n\n def __call__(\n self,\n batch: Dict[str, jax.Array],\n training: bool = True,\n ) -> Dict[str, jax.Array]:\n videos_BTHWC = batch[""videos""]\n tokenizer_outputs = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_indices_BTN = tokenizer_outputs[""indices""]\n latent_actions_BTm11L = None\n action_embeddings_BTm11L = None\n if self.use_gt_actions:\n assert self.action_embed is not None\n action_indices_E = None\n action_embeddings_BT1L = self.action_embed(batch[""actions""]).reshape(\n *batch[""actions""].shape[:2], 1, self.latent_action_dim\n )\n action_embeddings_BTm11L = action_embeddings_BT1L[:, :-1]\n else:\n assert self.lam is not None\n lam_outputs = self.lam.vq_encode(videos_BTHWC, training=False)\n z_q_BTm11L = lam_outputs[""z_q""]\n action_indices_E = lam_outputs[""indices""]\n latent_actions_BTm11L = jax.lax.cond(\n self.lam_co_train,\n lambda: z_q_BTm11L,\n lambda: jax.lax.stop_gradient(z_q_BTm11L),\n )\n outputs = dict(\n video_tokens=jax.lax.stop_gradient(token_indices_BTN),\n latent_actions=(\n action_embeddings_BTm11L\n if self.use_gt_actions\n else latent_actions_BTm11L\n ),\n )\n outputs[""mask_rng""] = batch[""rng""]\n dyna_logits_BTNV, dyna_mask = self.dynamics(outputs, training)\n outputs[""token_logits""] = dyna_logits_BTNV\n outputs[""mask""] = dyna_mask\n mle_indices_BTN = jnp.argmax(outputs[""token_logits""], axis=-1)\n H, W = batch[""videos""].shape[2:4]\n outputs[""recon""] = self.tokenizer.decode(mle_indices_BTN, (H, W))\n if action_indices_E is not None:\n outputs[""lam_indices""] = action_indices_E\n return outputs\n\n def sample(\n self,\n batch: Dict[str, jax.Array],\n seq_len: int,\n temperature: float = 1,\n sample_argmax: bool = False,\n maskgit_steps: int = 25,\n ) -> tuple[jax.Array, jax.Array]:\n if self.dyna_type == ""maskgit"":\n return self.sample_maskgit(\n batch, seq_len, maskgit_steps, temperature, sample_argmax\n )\n elif self.dyna_type == ""causal"":\n return self.sample_causal(batch, seq_len, temperature, sample_argmax)\n else:\n raise ValueError(f""Dynamics model type unknown: {self.dyna_type}"")\n\n def sample_maskgit(\n self,\n batch: Dict[str, jax.Array],\n seq_len: int,\n steps: int = 25,\n temperature: float = 1,\n sample_argmax: bool = False,\n ) -> tuple[jax.Array, jax.Array]:\n """"""\n Autoregressively samples up to `seq_len` future frames, following Figure 8 of the paper.\n\n - Input frames are tokenized once.\n - Future frames are generated autoregressively in token space.\n - All frames are detokenized in a single pass.\n\n Note:\n - For interactive or step-wise sampling, detokenization should occur after each action.\n - To maintain consistent tensor shapes across timesteps, all current and future frames are decoded at every step.\n - Temporal causal structure is preserved by\n a) reapplying the mask before each decoding step.\n b) a temporal causal mask is applied within each ST-transformer block.\n\n Dimension keys:\n B: batch size\n T: number of input (conditioning) frames\n N: number of patches per frame\n M: model dimension\n S: sequence length\n H: height\n W: width\n E: B * (S - 1)\n P: S * N\n """"""\n assert isinstance(self.dynamics, DynamicsMaskGIT)\n # --- Encode videos and actions ---\n videos_BTHWC = batch[""videos""]\n tokenizer_out = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_idxs_BTN = tokenizer_out[""indices""]\n B, T, N = token_idxs_BTN.shape\n pad_shape = (B, seq_len - T, N)\n pad = jnp.zeros(pad_shape, dtype=token_idxs_BTN.dtype)\n token_idxs_BSN = jnp.concatenate([token_idxs_BTN, pad], axis=1)\n init_logits_BSNV = jnp.zeros(\n shape=(*token_idxs_BSN.shape, self.num_patch_latents)\n )\n if self.use_gt_actions:\n assert self.action_embed is not None\n latent_actions_BT1L = self.action_embed(batch[""actions""]).reshape(\n *batch[""actions""].shape[:2], 1, self.latent_action_dim\n )\n latent_actions_BTm11L = latent_actions_BT1L[:, :-1]\n action_tokens_EL = latent_actions_BTm11L.reshape(-1, self.latent_action_dim)\n else:\n assert self.lam is not None\n latent_actions_E = batch[""latent_actions""]\n action_tokens_EL = self.lam.vq.get_codes(latent_actions_E)\n\n # --- Extract submodule state ---\n dynamics_state = nnx.state(self.dynamics)\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def maskgit_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array],\n step: jax.Array,\n ) -> tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array]:\n rng, token_idxs_BSN, logits_BSNV, mask_BSN, action_tokens_EL = carry\n S, N = token_idxs_BSN.shape[1:]\n L = action_tokens_EL.shape[-1]\n\n # We need to reconstruct the submodule inside scan body to prevent trace context mismatches\n dynamics_maskgit = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n mask_limit=self.mask_limit,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=nnx.Rngs(0),\n )\n nnx.update(dynamics_maskgit, dynamics_state)\n\n # --- Construct + encode video ---\n vid_embed_BSNM = dynamics_maskgit.patch_embed(token_idxs_BSN)\n mask_token_111M = dynamics_maskgit.mask_token.value\n mask_expanded_BSN1 = mask_BSN[..., None]\n vid_embed_BSNM = jnp.where(\n mask_expanded_BSN1, mask_token_111M, vid_embed_BSNM\n )\n\n # --- Predict transition ---\n action_tokens_BSm1L = jnp.reshape(action_tokens_EL, (B, S - 1, L))\n act_embed_BSm1M = dynamics_maskgit.action_up(action_tokens_BSm1L)\n act_embed_BSM = jnp.pad(act_embed_BSm1M, ((0, 0), (1, 0), (0, 0)))\n act_embed_BS1M = jnp.reshape(\n act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1])\n )\n vid_embed_BSNM += act_embed_BS1M\n unmasked_ratio = jnp.cos(jnp.pi * (step + 1) / (steps * 2))\n step_temp = temperature * (1.0 - unmasked_ratio)\n final_logits_BSNV = dynamics_maskgit.transformer(vid_embed_BSNM) / step_temp\n\n # --- Sample new tokens for final frame ---\n if sample_argmax:\n sampled_token_idxs_BSN = jnp.argmax(final_logits_BSNV, axis=-1)\n else:\n rng, _rng = jax.random.split(rng)\n sampled_token_idxs_BSN = jax.random.categorical(_rng, final_logits_BSNV)\n gather_fn = jax.vmap(jax.vmap(jax.vmap(lambda x, y: x[y])))\n final_token_probs_BSN = gather_fn(\n jax.nn.softmax(final_logits_BSNV), sampled_token_idxs_BSN\n )\n final_token_probs_BSN += ~mask_BSN\n # Update masked tokens and logits only\n token_idxs_BSN = jnp.where(mask_BSN, sampled_token_idxs_BSN, token_idxs_BSN)\n logits_BSNV = jnp.where(\n jnp.expand_dims(mask_BSN, -1), final_logits_BSNV, logits_BSNV\n )\n\n # --- Update mask ---\n num_unmasked_tokens = jnp.round(N * (1.0 - unmasked_ratio)).astype(int)\n final_token_probs_flat_BP = einops.rearrange(\n final_token_probs_BSN, ""b s n -> b (s n)""\n )\n idx_mask_P = (\n jnp.arange(final_token_probs_flat_BP.shape[-1])\n <= N - num_unmasked_tokens\n )\n sorted_idxs_BP = jnp.argsort(final_token_probs_flat_BP, axis=-1)\n mask_update_fn = jax.vmap(lambda msk, ids: msk.at[ids].set(idx_mask_P))\n mask_flat_BP = einops.rearrange(mask_BSN, ""b s n -> b (s n)"")\n new_mask_flat_BP = mask_update_fn(mask_flat_BP, sorted_idxs_BP)\n new_mask_BSN = einops.rearrange(new_mask_flat_BP, ""b (s n) -> b s n"", n=N)\n\n new_carry = (\n rng,\n token_idxs_BSN,\n logits_BSNV,\n new_mask_BSN,\n action_tokens_EL,\n )\n return new_carry\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def generation_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array], step_t: jax.Array\n ) -> tuple[jax.Array, jax.Array, jax.Array]:\n rng, current_token_idxs_BSN, current_logits_BSNV = carry\n rng, step_rng = jax.random.split(rng)\n\n # Mask current frame (i.e., t == step_t)\n mask_S = jnp.arange(seq_len) == step_t\n mask_BSN = jnp.broadcast_to(mask_S[None, :, None], (B, seq_len, N)).astype(\n bool\n )\n masked_token_idxs_BSN = current_token_idxs_BSN * ~mask_BSN\n masked_logits_BSNV = current_logits_BSNV * jnp.expand_dims(~mask_BSN, -1)\n\n # --- Initialize and run MaskGIT loop ---\n init_carry_maskgit = (\n step_rng,\n masked_token_idxs_BSN,\n masked_logits_BSNV,\n mask_BSN,\n action_tokens_EL,\n )\n final_carry_maskgit = maskgit_step_fn(init_carry_maskgit, jnp.arange(steps))\n updated_token_idxs_BSN = final_carry_maskgit[1]\n updated_logits_BSNV = final_carry_maskgit[2]\n new_carry = (rng, updated_token_idxs_BSN, updated_logits_BSNV)\n return new_carry\n\n # --- Run the autoregressive generation using jax.lax.scan ---\n initial_carry = (batch[""rng""], token_idxs_BSN, init_logits_BSNV)\n timesteps_to_scan = jnp.arange(T, seq_len)\n final_carry = generation_step_fn(initial_carry, timesteps_to_scan)\n final_token_idxs_BSN = final_carry[1]\n final_logits_BSNV = final_carry[2]\n\n # --- Decode all tokens at once at the end ---\n H, W = batch[""videos""].shape[2:4]\n final_frames_BSHWC = self.tokenizer.decode(\n final_token_idxs_BSN,\n video_hw=(H, W),\n )\n return final_frames_BSHWC, final_logits_BSNV\n\n def sample_causal(\n self,\n batch: Dict[str, jax.Array],\n seq_len: int,\n temperature: float = 1,\n sample_argmax: bool = False,\n ) -> tuple[jax.Array, jax.Array]:\n """"""\n Autoregressively samples up to `seq_len` future frames, following Figure 8 of the paper.\n\n - Input frames are tokenized once.\n - Future frames are generated autoregressively in token space.\n - All frames are detokenized in a single pass.\n\n Note:\n - For interactive or step-wise sampling, detokenization should occur after each action.\n - To maintain consistent tensor shapes across timesteps, all current and future frames are decoded at every step.\n - Temporal causal structure is preserved by\n a) reapplying the mask before each decoding step.\n b) a temporal causal mask is applied within each ST-transformer block.\n\n Dimension keys:\n B: batch size\n T: number of input (conditioning) frames\n N: number of patches per frame\n M: model dimension\n S: sequence length\n H: height\n W: width\n E: B * (S - 1)\n """"""\n assert isinstance(self.dynamics, DynamicsCausal)\n # --- Encode videos and actions ---\n videos_BTHWC = batch[""videos""]\n tokenizer_out = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_idxs_BTN = tokenizer_out[""indices""]\n B, T, N = token_idxs_BTN.shape\n pad_shape = (B, seq_len - T, N)\n pad = jnp.zeros(pad_shape, dtype=token_idxs_BTN.dtype)\n token_idxs_BSN = jnp.concatenate([token_idxs_BTN, pad], axis=1)\n logits_BSNV = jnp.zeros((*token_idxs_BSN.shape, self.num_patch_latents))\n dynamics_state = nnx.state(self.dynamics)\n\n if self.use_gt_actions:\n assert self.action_embed is not None\n latent_actions_BT1L = self.action_embed(batch[""actions""]).reshape(\n *batch[""actions""].shape[:2], 1, self.latent_action_dim\n )\n latent_actions_BTm11L = latent_actions_BT1L[:, :-1]\n action_tokens_EL = latent_actions_BTm11L.reshape(-1, self.latent_action_dim)\n else:\n assert self.lam is not None\n latent_actions_E = batch[""latent_actions""]\n action_tokens_EL = self.lam.vq.get_codes(latent_actions_E)\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def causal_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array],\n step_n: jax.Array,\n ) -> tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array]:\n rng, token_idxs_BSN, logits_BSNV, action_tokens_EL, step_t = carry\n S, N = token_idxs_BSN.shape[1:]\n L = action_tokens_EL.shape[-1]\n\n # We need to reconstruct the submodule inside scan body to prevent trace context mismatches\n dynamics_causal = DynamicsCausal(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=self.decode,\n rngs=nnx.Rngs(0),\n )\n nnx.update(dynamics_causal, dynamics_state)\n\n # --- Construct + encode video ---\n vid_embed_BSNM = dynamics_causal.patch_embed(token_idxs_BSN)\n\n # --- Predict transition ---\n action_tokens_BSm1L = jnp.reshape(action_tokens_EL, (B, S - 1, L))\n act_embed_BSm1M = dynamics_causal.action_up(action_tokens_BSm1L)\n act_embed_BSM = jnp.pad(act_embed_BSm1M, ((0, 0), (1, 0), (0, 0)))\n act_embed_BS1M = jnp.reshape(\n act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1])\n )\n vid_embed_BSNp1M = jnp.concatenate([act_embed_BS1M, vid_embed_BSNM], axis=2)\n final_logits_BTNp1V = (\n dynamics_causal.transformer(vid_embed_BSNp1M, (step_t, step_n))\n / temperature\n )\n final_logits_BV = final_logits_BTNp1V[:, step_t, step_n, :]\n\n # --- Sample new tokens for final frame ---\n if sample_argmax:\n sampled_token_idxs_B = jnp.argmax(final_logits_BV, axis=-1)\n else:\n rng, _rng = jax.random.split(rng)\n sampled_token_idxs_B = jax.random.categorical(_rng, final_logits_BV)\n # Update next tokens only\n token_idxs_BSN = token_idxs_BSN.at[:, step_t, step_n].set(\n sampled_token_idxs_B\n )\n logits_BSNV = logits_BSNV.at[:, step_t, step_n].set(final_logits_BV)\n\n new_carry = (rng, token_idxs_BSN, logits_BSNV, action_tokens_EL, step_t)\n return new_carry\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def generation_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array], step_t: jax.Array\n ) -> tuple[jax.Array, jax.Array, jax.Array]:\n rng, current_token_idxs_BSN, current_logits_BSNV = carry\n rng, step_rng = jax.random.split(rng)\n\n # --- Initialize and run causal loop ---\n init_carry_causal = (\n step_rng,\n current_token_idxs_BSN,\n current_logits_BSNV,\n action_tokens_EL,\n step_t,\n )\n final_carry_causal = causal_step_fn(init_carry_causal, jnp.arange(N))\n updated_token_idxs_BSN = final_carry_causal[1]\n updated_logits_BSNV = final_carry_causal[2]\n new_carry = (rng, updated_token_idxs_BSN, updated_logits_BSNV)\n return new_carry\n\n # --- Run the autoregressive generation using jax.lax.scan ---\n initial_carry = (batch[""rng""], token_idxs_BSN, logits_BSNV)\n timesteps_to_scan = jnp.arange(T, seq_len)\n final_carry = generation_step_fn(initial_carry, timesteps_to_scan)\n final_token_idxs_BSN = final_carry[1]\n final_logits_BSNV = final_carry[2]\n\n # --- Decode all tokens at once at the end ---\n H, W = batch[""videos""].shape[2:4]\n final_frames_BSHWC = self.tokenizer.decode(\n final_token_idxs_BSN,\n video_hw=(H, W),\n )\n return final_frames_BSHWC, final_logits_BSNV\n\n def vq_encode(self, batch: Dict[str, jax.Array], training: bool) -> jax.Array:\n # --- Preprocess videos ---\n assert self.lam is not None\n video_BTHWC = batch[""videos""]\n lam_output: Dict[str, jax.Array] = self.lam.vq_encode(\n video_BTHWC, training=training\n )\n lam_indices_E = lam_output[""indices""]\n return lam_indices_E\n\n\n# FIXME (f.srambical): add conversion script for old checkpoints\ndef restore_genie_components(\n optimizer: nnx.Optimizer,\n sharding: jax.sharding.NamedSharding,\n rng: jax.Array,\n args,\n) -> nnx.Optimizer:\n """"""Restore pre-trained Genie components""""""\n rng_tokenizer, rng_lam = jax.random.split(rng)\n rngs_tokenizer = nnx.Rngs(rng_tokenizer)\n rngs_lam = nnx.Rngs(rng_lam)\n\n tx = optimizer.tx\n model = optimizer.model\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n\n checkpoint_options = ocp.CheckpointManagerOptions(\n step_format_fixed_length=6,\n )\n tokenizer_checkpoint_manager = ocp.CheckpointManager(\n directory=args.tokenizer_checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n dummy_tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.tokenizer_dim,\n ffn_dim=args.tokenizer_ffn_dim,\n latent_dim=args.latent_patch_dim,\n num_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n num_blocks=args.tokenizer_num_blocks,\n num_heads=args.tokenizer_num_heads,\n dropout=args.dropout,\n codebook_dropout=args.dropout,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs_tokenizer,\n )\n dummy_tokenizer_optimizer = nnx.Optimizer(dummy_tokenizer, tx)\n dummy_tokenizer_optimizer_state = nnx.state(dummy_tokenizer_optimizer)\n abstract_sharded_tokenizer_optimizer_state = _create_abstract_sharded_pytree(\n dummy_tokenizer_optimizer_state, sharding\n )\n restored_tokenizer = tokenizer_checkpoint_manager.restore(\n step=tokenizer_checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore( # type: ignore\n abstract_sharded_tokenizer_optimizer_state # type: ignore\n ),\n ),\n )[""model_state""]\n nnx.update(dummy_tokenizer_optimizer.model, restored_tokenizer.model)\n model.tokenizer = dummy_tokenizer_optimizer.model\n tokenizer_checkpoint_manager.close()\n\n if args.lam_checkpoint:\n lam_checkpoint_manager = ocp.CheckpointManager(\n directory=args.lam_checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n dummy_lam = LatentActionModel(\n in_dim=args.image_channels,\n model_dim=args.lam_dim,\n ffn_dim=args.lam_ffn_dim,\n latent_dim=args.latent_patch_dim,\n num_latents=args.num_actions,\n patch_size=args.lam_patch_size,\n num_blocks=args.lam_num_blocks,\n num_heads=args.lam_num_heads,\n dropout=args.dropout,\n codebook_dropout=args.dropout,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs_lam,\n )\n dummy_lam_optimizer = nnx.Optimizer(dummy_lam, tx)\n dummy_lam_optimizer_state = nnx.state(dummy_lam_optimizer)\n abstract_sharded_lam_optimizer_state = _create_abstract_sharded_pytree(\n dummy_lam_optimizer_state, sharding\n )\n restored_lam_optimizer = lam_checkpoint_manager.restore(\n step=lam_checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore( # type: ignore\n abstract_sharded_lam_optimizer_state # type: ignore\n ),\n ),\n )[""model_state""]\n nnx.update(dummy_lam_optimizer.model, restored_lam_optimizer.model)\n model.lam = dummy_lam_optimizer.model\n # Remove the LAM decoder to save memory and avoid unnecessary computation.\n del model.lam.decoder\n lam_checkpoint_manager.close()\n\n # Reinitialize the optimizer states\n optimizer = nnx.Optimizer(model, tx)\n return optimizer\n\n\ndef _create_abstract_sharded_pytree(\n pytree_template: nnx.GraphState, sharding_spec: jax.sharding.NamedSharding\n) -> jax.Array:\n """"""Replaces arrays in a pytree with ShapeDtypeStructs having the given sharding.""""""\n\n def map_fn(leaf_template):\n if hasattr(leaf_template, ""shape"") and hasattr(leaf_template, ""dtype""):\n return jax.ShapeDtypeStruct(\n leaf_template.shape, leaf_template.dtype, sharding=sharding_spec\n )\n return leaf_template\n\n return jax.tree_util.tree_map(map_fn, pytree_template)\n",python,tab +665,2328448,"genie.py",9930,0,"",python,selection_command +666,2369109,"TERMINAL",0,0,"Step 750, validation loss: 0.6088705062866211\r\n",,terminal_output +667,2428368,"genie.py",0,0,"from typing import Dict\n\nimport einops\nimport jax\nimport jax.numpy as jnp\nimport flax.nnx as nnx\nimport orbax.checkpoint as ocp\n\nfrom models.dynamics import DynamicsMaskGIT, DynamicsCausal\nfrom models.lam import LatentActionModel\nfrom models.tokenizer import TokenizerVQVAE\n\n\nclass Genie(nnx.Module):\n """"""Genie model""""""\n\n def __init__(\n self,\n in_dim: int,\n tokenizer_dim: int,\n tokenizer_ffn_dim: int,\n latent_patch_dim: int,\n num_patch_latents: int,\n patch_size: int,\n tokenizer_num_blocks: int,\n tokenizer_num_heads: int,\n lam_dim: int,\n lam_ffn_dim: int,\n latent_action_dim: int,\n num_actions: int,\n lam_patch_size: int,\n lam_num_blocks: int,\n lam_num_heads: int,\n lam_co_train: bool,\n use_gt_actions: bool,\n dyna_type: str,\n dyna_dim: int,\n dyna_ffn_dim: int,\n dyna_num_blocks: int,\n dyna_num_heads: int,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n decode: bool,\n rngs: nnx.Rngs,\n dropout: float = 0.0,\n mask_limit: float = 0.0,\n ):\n # --- Tokenizer ---\n self.in_dim = in_dim\n self.tokenizer_dim = tokenizer_dim\n self.tokenizer_ffn_dim = tokenizer_ffn_dim\n self.latent_patch_dim = latent_patch_dim\n self.num_patch_latents = num_patch_latents\n self.patch_size = patch_size\n self.tokenizer_num_blocks = tokenizer_num_blocks\n self.tokenizer_num_heads = tokenizer_num_heads\n # --- LAM ---\n self.lam_dim = lam_dim\n self.lam_ffn_dim = lam_ffn_dim\n self.latent_action_dim = latent_action_dim\n self.num_actions = num_actions\n self.lam_patch_size = lam_patch_size\n self.lam_num_blocks = lam_num_blocks\n self.lam_num_heads = lam_num_heads\n self.lam_co_train = lam_co_train\n self.use_gt_actions = use_gt_actions\n # --- Dynamics ---\n self.dyna_type = dyna_type\n self.dyna_dim = dyna_dim\n self.dyna_ffn_dim = dyna_ffn_dim\n self.dyna_num_blocks = dyna_num_blocks\n self.dyna_num_heads = dyna_num_heads\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.dropout = dropout\n self.mask_limit = mask_limit\n self.decode = decode\n\n self.tokenizer = TokenizerVQVAE(\n in_dim=self.in_dim,\n model_dim=self.tokenizer_dim,\n ffn_dim=self.tokenizer_ffn_dim,\n latent_dim=self.latent_patch_dim,\n num_latents=self.num_patch_latents,\n patch_size=self.patch_size,\n num_blocks=self.tokenizer_num_blocks,\n num_heads=self.tokenizer_num_heads,\n dropout=0.0,\n codebook_dropout=0.0,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n if self.use_gt_actions:\n self.action_embed = nnx.Embed(\n self.num_actions, self.latent_action_dim, rngs=rngs\n )\n self.lam = None\n else:\n self.lam = LatentActionModel(\n in_dim=self.in_dim,\n model_dim=self.lam_dim,\n ffn_dim=self.lam_ffn_dim,\n latent_dim=self.latent_patch_dim,\n num_latents=self.num_actions,\n patch_size=self.lam_patch_size,\n num_blocks=self.lam_num_blocks,\n num_heads=self.lam_num_heads,\n dropout=0.0,\n codebook_dropout=0.0,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n self.action_embed = None\n if self.dyna_type == ""maskgit"":\n self.dynamics = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n mask_limit=self.mask_limit,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n elif self.dyna_type == ""causal"":\n self.dynamics = DynamicsCausal(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=decode,\n rngs=rngs,\n )\n else:\n raise ValueError(f""Invalid dynamics type: {self.dyna_type}"")\n\n def __call__(\n self,\n batch: Dict[str, jax.Array],\n training: bool = True,\n ) -> Dict[str, jax.Array]:\n videos_BTHWC = batch[""videos""]\n tokenizer_outputs = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_indices_BTN = tokenizer_outputs[""indices""]\n latent_actions_BTm11L = None\n action_embeddings_BTm11L = None\n if self.use_gt_actions:\n assert self.action_embed is not None\n action_indices_E = None\n action_embeddings_BT1L = self.action_embed(batch[""actions""]).reshape(\n *batch[""actions""].shape[:2], 1, self.latent_action_dim\n )\n action_embeddings_BTm11L = action_embeddings_BT1L[:, :-1]\n else:\n assert self.lam is not None\n lam_outputs = self.lam.vq_encode(videos_BTHWC, training=False)\n z_q_BTm11L = lam_outputs[""z_q""]\n action_indices_E = lam_outputs[""indices""]\n latent_actions_BTm11L = jax.lax.cond(\n self.lam_co_train,\n lambda: z_q_BTm11L,\n lambda: jax.lax.stop_gradient(z_q_BTm11L),\n )\n outputs = dict(\n video_tokens=jax.lax.stop_gradient(token_indices_BTN),\n latent_actions=(\n action_embeddings_BTm11L\n if self.use_gt_actions\n else latent_actions_BTm11L\n ),\n )\n outputs[""mask_rng""] = batch[""rng""]\n dyna_logits_BTNV, dyna_mask = self.dynamics(outputs, training)\n outputs[""token_logits""] = dyna_logits_BTNV\n outputs[""mask""] = dyna_mask\n mle_indices_BTN = jnp.argmax(outputs[""token_logits""], axis=-1)\n H, W = batch[""videos""].shape[2:4]\n outputs[""recon""] = self.tokenizer.decode(mle_indices_BTN, (H, W))\n if action_indices_E is not None:\n outputs[""lam_indices""] = action_indices_E\n return outputs\n\n def sample(\n self,\n batch: Dict[str, jax.Array],\n seq_len: int,\n temperature: float = 1,\n sample_argmax: bool = False,\n maskgit_steps: int = 25,\n ) -> tuple[jax.Array, jax.Array]:\n if self.dyna_type == ""maskgit"":\n return self.sample_maskgit(\n batch, seq_len, maskgit_steps, temperature, sample_argmax\n )\n elif self.dyna_type == ""causal"":\n return self.sample_causal(batch, seq_len, temperature, sample_argmax)\n else:\n raise ValueError(f""Dynamics model type unknown: {self.dyna_type}"")\n\n def sample_maskgit(\n self,\n batch: Dict[str, jax.Array],\n seq_len: int,\n steps: int = 25,\n temperature: float = 1,\n sample_argmax: bool = False,\n ) -> tuple[jax.Array, jax.Array]:\n """"""\n Autoregressively samples up to `seq_len` future frames, following Figure 8 of the paper.\n\n - Input frames are tokenized once.\n - Future frames are generated autoregressively in token space.\n - All frames are detokenized in a single pass.\n\n Note:\n - For interactive or step-wise sampling, detokenization should occur after each action.\n - To maintain consistent tensor shapes across timesteps, all current and future frames are decoded at every step.\n - Temporal causal structure is preserved by\n a) reapplying the mask before each decoding step.\n b) a temporal causal mask is applied within each ST-transformer block.\n\n Dimension keys:\n B: batch size\n T: number of input (conditioning) frames\n N: number of patches per frame\n M: model dimension\n S: sequence length\n H: height\n W: width\n E: B * (S - 1)\n P: S * N\n """"""\n assert isinstance(self.dynamics, DynamicsMaskGIT)\n # --- Encode videos and actions ---\n videos_BTHWC = batch[""videos""]\n tokenizer_out = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_idxs_BTN = tokenizer_out[""indices""]\n B, T, N = token_idxs_BTN.shape\n pad_shape = (B, seq_len - T, N)\n pad = jnp.zeros(pad_shape, dtype=token_idxs_BTN.dtype)\n token_idxs_BSN = jnp.concatenate([token_idxs_BTN, pad], axis=1)\n init_logits_BSNV = jnp.zeros(\n shape=(*token_idxs_BSN.shape, self.num_patch_latents)\n )\n if self.use_gt_actions:\n assert self.action_embed is not None\n latent_actions_BT1L = self.action_embed(batch[""actions""]).reshape(\n *batch[""actions""].shape[:2], 1, self.latent_action_dim\n )\n latent_actions_BTm11L = latent_actions_BT1L[:, 1:]\n action_tokens_EL = latent_actions_BTm11L.reshape(-1, self.latent_action_dim)\n else:\n assert self.lam is not None\n latent_actions_E = batch[""latent_actions""]\n action_tokens_EL = self.lam.vq.get_codes(latent_actions_E)\n\n # --- Extract submodule state ---\n dynamics_state = nnx.state(self.dynamics)\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def maskgit_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array],\n step: jax.Array,\n ) -> tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array]:\n rng, token_idxs_BSN, logits_BSNV, mask_BSN, action_tokens_EL = carry\n S, N = token_idxs_BSN.shape[1:]\n L = action_tokens_EL.shape[-1]\n\n # We need to reconstruct the submodule inside scan body to prevent trace context mismatches\n dynamics_maskgit = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n mask_limit=self.mask_limit,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=nnx.Rngs(0),\n )\n nnx.update(dynamics_maskgit, dynamics_state)\n\n # --- Construct + encode video ---\n vid_embed_BSNM = dynamics_maskgit.patch_embed(token_idxs_BSN)\n mask_token_111M = dynamics_maskgit.mask_token.value\n mask_expanded_BSN1 = mask_BSN[..., None]\n vid_embed_BSNM = jnp.where(\n mask_expanded_BSN1, mask_token_111M, vid_embed_BSNM\n )\n\n # --- Predict transition ---\n action_tokens_BSm1L = jnp.reshape(action_tokens_EL, (B, S - 1, L))\n act_embed_BSm1M = dynamics_maskgit.action_up(action_tokens_BSm1L)\n act_embed_BSM = jnp.pad(act_embed_BSm1M, ((0, 0), (1, 0), (0, 0)))\n act_embed_BS1M = jnp.reshape(\n act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1])\n )\n vid_embed_BSNM += act_embed_BS1M\n unmasked_ratio = jnp.cos(jnp.pi * (step + 1) / (steps * 2))\n step_temp = temperature * (1.0 - unmasked_ratio)\n final_logits_BSNV = dynamics_maskgit.transformer(vid_embed_BSNM) / step_temp\n\n # --- Sample new tokens for final frame ---\n if sample_argmax:\n sampled_token_idxs_BSN = jnp.argmax(final_logits_BSNV, axis=-1)\n else:\n rng, _rng = jax.random.split(rng)\n sampled_token_idxs_BSN = jax.random.categorical(_rng, final_logits_BSNV)\n gather_fn = jax.vmap(jax.vmap(jax.vmap(lambda x, y: x[y])))\n final_token_probs_BSN = gather_fn(\n jax.nn.softmax(final_logits_BSNV), sampled_token_idxs_BSN\n )\n final_token_probs_BSN += ~mask_BSN\n # Update masked tokens and logits only\n token_idxs_BSN = jnp.where(mask_BSN, sampled_token_idxs_BSN, token_idxs_BSN)\n logits_BSNV = jnp.where(\n jnp.expand_dims(mask_BSN, -1), final_logits_BSNV, logits_BSNV\n )\n\n # --- Update mask ---\n num_unmasked_tokens = jnp.round(N * (1.0 - unmasked_ratio)).astype(int)\n final_token_probs_flat_BP = einops.rearrange(\n final_token_probs_BSN, ""b s n -> b (s n)""\n )\n idx_mask_P = (\n jnp.arange(final_token_probs_flat_BP.shape[-1])\n <= N - num_unmasked_tokens\n )\n sorted_idxs_BP = jnp.argsort(final_token_probs_flat_BP, axis=-1)\n mask_update_fn = jax.vmap(lambda msk, ids: msk.at[ids].set(idx_mask_P))\n mask_flat_BP = einops.rearrange(mask_BSN, ""b s n -> b (s n)"")\n new_mask_flat_BP = mask_update_fn(mask_flat_BP, sorted_idxs_BP)\n new_mask_BSN = einops.rearrange(new_mask_flat_BP, ""b (s n) -> b s n"", n=N)\n\n new_carry = (\n rng,\n token_idxs_BSN,\n logits_BSNV,\n new_mask_BSN,\n action_tokens_EL,\n )\n return new_carry\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def generation_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array], step_t: jax.Array\n ) -> tuple[jax.Array, jax.Array, jax.Array]:\n rng, current_token_idxs_BSN, current_logits_BSNV = carry\n rng, step_rng = jax.random.split(rng)\n\n # Mask current frame (i.e., t == step_t)\n mask_S = jnp.arange(seq_len) == step_t\n mask_BSN = jnp.broadcast_to(mask_S[None, :, None], (B, seq_len, N)).astype(\n bool\n )\n masked_token_idxs_BSN = current_token_idxs_BSN * ~mask_BSN\n masked_logits_BSNV = current_logits_BSNV * jnp.expand_dims(~mask_BSN, -1)\n\n # --- Initialize and run MaskGIT loop ---\n init_carry_maskgit = (\n step_rng,\n masked_token_idxs_BSN,\n masked_logits_BSNV,\n mask_BSN,\n action_tokens_EL,\n )\n final_carry_maskgit = maskgit_step_fn(init_carry_maskgit, jnp.arange(steps))\n updated_token_idxs_BSN = final_carry_maskgit[1]\n updated_logits_BSNV = final_carry_maskgit[2]\n new_carry = (rng, updated_token_idxs_BSN, updated_logits_BSNV)\n return new_carry\n\n # --- Run the autoregressive generation using jax.lax.scan ---\n initial_carry = (batch[""rng""], token_idxs_BSN, init_logits_BSNV)\n timesteps_to_scan = jnp.arange(T, seq_len)\n final_carry = generation_step_fn(initial_carry, timesteps_to_scan)\n final_token_idxs_BSN = final_carry[1]\n final_logits_BSNV = final_carry[2]\n\n # --- Decode all tokens at once at the end ---\n H, W = batch[""videos""].shape[2:4]\n final_frames_BSHWC = self.tokenizer.decode(\n final_token_idxs_BSN,\n video_hw=(H, W),\n )\n return final_frames_BSHWC, final_logits_BSNV\n\n def sample_causal(\n self,\n batch: Dict[str, jax.Array],\n seq_len: int,\n temperature: float = 1,\n sample_argmax: bool = False,\n ) -> tuple[jax.Array, jax.Array]:\n """"""\n Autoregressively samples up to `seq_len` future frames, following Figure 8 of the paper.\n\n - Input frames are tokenized once.\n - Future frames are generated autoregressively in token space.\n - All frames are detokenized in a single pass.\n\n Note:\n - For interactive or step-wise sampling, detokenization should occur after each action.\n - To maintain consistent tensor shapes across timesteps, all current and future frames are decoded at every step.\n - Temporal causal structure is preserved by\n a) reapplying the mask before each decoding step.\n b) a temporal causal mask is applied within each ST-transformer block.\n\n Dimension keys:\n B: batch size\n T: number of input (conditioning) frames\n N: number of patches per frame\n M: model dimension\n S: sequence length\n H: height\n W: width\n E: B * (S - 1)\n """"""\n assert isinstance(self.dynamics, DynamicsCausal)\n # --- Encode videos and actions ---\n videos_BTHWC = batch[""videos""]\n tokenizer_out = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_idxs_BTN = tokenizer_out[""indices""]\n B, T, N = token_idxs_BTN.shape\n pad_shape = (B, seq_len - T, N)\n pad = jnp.zeros(pad_shape, dtype=token_idxs_BTN.dtype)\n token_idxs_BSN = jnp.concatenate([token_idxs_BTN, pad], axis=1)\n logits_BSNV = jnp.zeros((*token_idxs_BSN.shape, self.num_patch_latents))\n dynamics_state = nnx.state(self.dynamics)\n\n if self.use_gt_actions:\n assert self.action_embed is not None\n latent_actions_BT1L = self.action_embed(batch[""actions""]).reshape(\n *batch[""actions""].shape[:2], 1, self.latent_action_dim\n )\n latent_actions_BTm11L = latent_actions_BT1L[:, 1:]\n action_tokens_EL = latent_actions_BTm11L.reshape(-1, self.latent_action_dim)\n else:\n assert self.lam is not None\n latent_actions_E = batch[""latent_actions""]\n action_tokens_EL = self.lam.vq.get_codes(latent_actions_E)\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def causal_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array],\n step_n: jax.Array,\n ) -> tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array]:\n rng, token_idxs_BSN, logits_BSNV, action_tokens_EL, step_t = carry\n S, N = token_idxs_BSN.shape[1:]\n L = action_tokens_EL.shape[-1]\n\n # We need to reconstruct the submodule inside scan body to prevent trace context mismatches\n dynamics_causal = DynamicsCausal(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=self.decode,\n rngs=nnx.Rngs(0),\n )\n nnx.update(dynamics_causal, dynamics_state)\n\n # --- Construct + encode video ---\n vid_embed_BSNM = dynamics_causal.patch_embed(token_idxs_BSN)\n\n # --- Predict transition ---\n action_tokens_BSm1L = jnp.reshape(action_tokens_EL, (B, S - 1, L))\n act_embed_BSm1M = dynamics_causal.action_up(action_tokens_BSm1L)\n act_embed_BSM = jnp.pad(act_embed_BSm1M, ((0, 0), (1, 0), (0, 0)))\n act_embed_BS1M = jnp.reshape(\n act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1])\n )\n vid_embed_BSNp1M = jnp.concatenate([act_embed_BS1M, vid_embed_BSNM], axis=2)\n final_logits_BTNp1V = (\n dynamics_causal.transformer(vid_embed_BSNp1M, (step_t, step_n))\n / temperature\n )\n final_logits_BV = final_logits_BTNp1V[:, step_t, step_n, :]\n\n # --- Sample new tokens for final frame ---\n if sample_argmax:\n sampled_token_idxs_B = jnp.argmax(final_logits_BV, axis=-1)\n else:\n rng, _rng = jax.random.split(rng)\n sampled_token_idxs_B = jax.random.categorical(_rng, final_logits_BV)\n # Update next tokens only\n token_idxs_BSN = token_idxs_BSN.at[:, step_t, step_n].set(\n sampled_token_idxs_B\n )\n logits_BSNV = logits_BSNV.at[:, step_t, step_n].set(final_logits_BV)\n\n new_carry = (rng, token_idxs_BSN, logits_BSNV, action_tokens_EL, step_t)\n return new_carry\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def generation_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array], step_t: jax.Array\n ) -> tuple[jax.Array, jax.Array, jax.Array]:\n rng, current_token_idxs_BSN, current_logits_BSNV = carry\n rng, step_rng = jax.random.split(rng)\n\n # --- Initialize and run causal loop ---\n init_carry_causal = (\n step_rng,\n current_token_idxs_BSN,\n current_logits_BSNV,\n action_tokens_EL,\n step_t,\n )\n final_carry_causal = causal_step_fn(init_carry_causal, jnp.arange(N))\n updated_token_idxs_BSN = final_carry_causal[1]\n updated_logits_BSNV = final_carry_causal[2]\n new_carry = (rng, updated_token_idxs_BSN, updated_logits_BSNV)\n return new_carry\n\n # --- Run the autoregressive generation using jax.lax.scan ---\n initial_carry = (batch[""rng""], token_idxs_BSN, logits_BSNV)\n timesteps_to_scan = jnp.arange(T, seq_len)\n final_carry = generation_step_fn(initial_carry, timesteps_to_scan)\n final_token_idxs_BSN = final_carry[1]\n final_logits_BSNV = final_carry[2]\n\n # --- Decode all tokens at once at the end ---\n H, W = batch[""videos""].shape[2:4]\n final_frames_BSHWC = self.tokenizer.decode(\n final_token_idxs_BSN,\n video_hw=(H, W),\n )\n return final_frames_BSHWC, final_logits_BSNV\n\n def vq_encode(self, batch: Dict[str, jax.Array], training: bool) -> jax.Array:\n # --- Preprocess videos ---\n assert self.lam is not None\n video_BTHWC = batch[""videos""]\n lam_output: Dict[str, jax.Array] = self.lam.vq_encode(\n video_BTHWC, training=training\n )\n lam_indices_E = lam_output[""indices""]\n return lam_indices_E\n\n\n# FIXME (f.srambical): add conversion script for old checkpoints\ndef restore_genie_components(\n optimizer: nnx.Optimizer,\n sharding: jax.sharding.NamedSharding,\n rng: jax.Array,\n args,\n) -> nnx.Optimizer:\n """"""Restore pre-trained Genie components""""""\n rng_tokenizer, rng_lam = jax.random.split(rng)\n rngs_tokenizer = nnx.Rngs(rng_tokenizer)\n rngs_lam = nnx.Rngs(rng_lam)\n\n tx = optimizer.tx\n model = optimizer.model\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n\n checkpoint_options = ocp.CheckpointManagerOptions(\n step_format_fixed_length=6,\n )\n tokenizer_checkpoint_manager = ocp.CheckpointManager(\n directory=args.tokenizer_checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n dummy_tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.tokenizer_dim,\n ffn_dim=args.tokenizer_ffn_dim,\n latent_dim=args.latent_patch_dim,\n num_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n num_blocks=args.tokenizer_num_blocks,\n num_heads=args.tokenizer_num_heads,\n dropout=args.dropout,\n codebook_dropout=args.dropout,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs_tokenizer,\n )\n dummy_tokenizer_optimizer = nnx.Optimizer(dummy_tokenizer, tx)\n dummy_tokenizer_optimizer_state = nnx.state(dummy_tokenizer_optimizer)\n abstract_sharded_tokenizer_optimizer_state = _create_abstract_sharded_pytree(\n dummy_tokenizer_optimizer_state, sharding\n )\n restored_tokenizer = tokenizer_checkpoint_manager.restore(\n step=tokenizer_checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore( # type: ignore\n abstract_sharded_tokenizer_optimizer_state # type: ignore\n ),\n ),\n )[""model_state""]\n nnx.update(dummy_tokenizer_optimizer.model, restored_tokenizer.model)\n model.tokenizer = dummy_tokenizer_optimizer.model\n tokenizer_checkpoint_manager.close()\n\n if args.lam_checkpoint:\n lam_checkpoint_manager = ocp.CheckpointManager(\n directory=args.lam_checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n dummy_lam = LatentActionModel(\n in_dim=args.image_channels,\n model_dim=args.lam_dim,\n ffn_dim=args.lam_ffn_dim,\n latent_dim=args.latent_patch_dim,\n num_latents=args.num_actions,\n patch_size=args.lam_patch_size,\n num_blocks=args.lam_num_blocks,\n num_heads=args.lam_num_heads,\n dropout=args.dropout,\n codebook_dropout=args.dropout,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs_lam,\n )\n dummy_lam_optimizer = nnx.Optimizer(dummy_lam, tx)\n dummy_lam_optimizer_state = nnx.state(dummy_lam_optimizer)\n abstract_sharded_lam_optimizer_state = _create_abstract_sharded_pytree(\n dummy_lam_optimizer_state, sharding\n )\n restored_lam_optimizer = lam_checkpoint_manager.restore(\n step=lam_checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore( # type: ignore\n abstract_sharded_lam_optimizer_state # type: ignore\n ),\n ),\n )[""model_state""]\n nnx.update(dummy_lam_optimizer.model, restored_lam_optimizer.model)\n model.lam = dummy_lam_optimizer.model\n # Remove the LAM decoder to save memory and avoid unnecessary computation.\n del model.lam.decoder\n lam_checkpoint_manager.close()\n\n # Reinitialize the optimizer states\n optimizer = nnx.Optimizer(model, tx)\n return optimizer\n\n\ndef _create_abstract_sharded_pytree(\n pytree_template: nnx.GraphState, sharding_spec: jax.sharding.NamedSharding\n) -> jax.Array:\n """"""Replaces arrays in a pytree with ShapeDtypeStructs having the given sharding.""""""\n\n def map_fn(leaf_template):\n if hasattr(leaf_template, ""shape"") and hasattr(leaf_template, ""dtype""):\n return jax.ShapeDtypeStruct(\n leaf_template.shape, leaf_template.dtype, sharding=sharding_spec\n )\n return leaf_template\n\n return jax.tree_util.tree_map(map_fn, pytree_template)\n",python,tab +668,2428371,"genie.py",9814,0,"",python,selection_mouse +669,2428483,"genie.py",9805,12,"action_embed",python,selection_mouse +670,2429370,"genie.py",9889,0,"",python,selection_mouse +671,2429993,"genie.py",9811,0,"",python,selection_mouse +672,2430121,"genie.py",9805,12,"action_embed",python,selection_mouse +673,2431462,"genie.py",9929,0,"",python,selection_mouse +674,2431463,"genie.py",9928,0,"",python,selection_command +675,2431552,"genie.py",9929,0,"",python,selection_mouse +676,2431553,"genie.py",9928,0,"",python,selection_command +677,2432109,"genie.py",9976,0,"",python,selection_mouse +678,2432244,"genie.py",9966,19,"latent_actions_BT1L",python,selection_mouse +679,2432831,"genie.py",9813,0,"",python,selection_mouse +680,2432990,"genie.py",9805,12,"action_embed",python,selection_mouse +681,2441678,"genie.py",0,0,"",python,tab +682,2441682,"train_dynamics.py",0,0,"",python,tab +683,2441731,"TERMINAL",0,0,"Saved checkpoint at step 1000\r\n",,terminal_output +684,2447133,"sample.py",0,0,"from dataclasses import dataclass\nimport time\nimport os\nimport optax\n\nimport dm_pix as pix\nimport einops\nimport jax\nimport jax.numpy as jnp\nimport flax.linen as nn\nimport numpy as np\nimport orbax.checkpoint as ocp\nfrom PIL import Image, ImageDraw\nimport tyro\nfrom flax import nnx\n\nfrom genie import Genie\nfrom utils.dataloader import get_dataloader\n\n\n@dataclass\nclass Args:\n # Experiment\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/coinrun_episodes""\n checkpoint: str = """"\n # Sampling\n batch_size: int = 1\n maskgit_steps: int = 25\n temperature: float = 1.0\n sample_argmax: bool = True\n start_frame: int = 1\n # Tokenizer checkpoint\n tokenizer_dim: int = 512\n tokenizer_ffn_dim: int = 2048\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 4\n tokenizer_num_heads: int = 8\n # LAM checkpoint\n lam_dim: int = 512\n lam_ffn_dim: int = 2048\n latent_action_dim: int = 32\n num_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 4\n lam_num_heads: int = 8\n use_gt_actions: bool = False\n # Dynamics checkpoint\n dyna_type: str = ""maskgit""\n dyna_dim: int = 512\n dyna_ffn_dim: int = 2048\n dyna_num_blocks: int = 6\n dyna_num_heads: int = 8\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\n use_flash_attention: bool = True\n\n\nargs = tyro.cli(Args)\n\nif __name__ == ""__main__"":\n """"""\n Dimension keys:\n B: batch size\n T: number of input (conditioning) frames\n N: number of patches per frame\n S: sequence length\n H: height\n W: width\n E: B * (S - 1)\n """"""\n jax.distributed.initialize()\n\n rng = jax.random.key(args.seed)\n\n # --- Load Genie checkpoint ---\n rngs = nnx.Rngs(rng)\n genie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n tokenizer_ffn_dim=args.tokenizer_ffn_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n lam_ffn_dim=args.lam_ffn_dim,\n latent_action_dim=args.latent_action_dim,\n num_actions=args.num_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n lam_co_train=False,\n use_gt_actions=args.use_gt_actions,\n # Dynamics\n dyna_type=args.dyna_type,\n dyna_dim=args.dyna_dim,\n dyna_ffn_dim=args.dyna_ffn_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n # FIXME (f.srambical): implement spatiotemporal KV caching and set decode=True\n decode=False,\n rngs=rngs,\n )\n\n del genie.tokenizer.vq.drop\n # Need to delete lam decoder for checkpoint loading\n if not args.use_gt_actions:\n assert genie.lam is not None\n del genie.lam.decoder\n\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n checkpoint_options = ocp.CheckpointManagerOptions(\n step_format_fixed_length=6,\n )\n checkpoint_manager = ocp.CheckpointManager(\n args.checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n\n dummy_tx = optax.adamw(\n learning_rate=optax.linear_schedule(0.0001, 0.0001, 10000),\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.dtype,\n )\n dummy_optimizer = nnx.Optimizer(genie, dummy_tx)\n\n abstract_optimizer = nnx.eval_shape(lambda: dummy_optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n ),\n )\n restored_optimizer_state = restored[""model_state""]\n nnx.update(dummy_optimizer, restored_optimizer_state)\n\n # --- Define sampling function ---\n def _sampling_fn(model: Genie, batch: dict) -> jax.Array:\n """"""Runs Genie.sample with pre-defined generation hyper-parameters.""""""\n assert args.dyna_type in [\n ""maskgit"",\n ""causal"",\n ], f""Invalid dynamics type: {args.dyna_type}""\n frames, _ = model.sample(\n batch,\n args.seq_len,\n args.temperature,\n args.sample_argmax,\n args.maskgit_steps,\n )\n return frames\n\n # --- Define autoregressive sampling loop ---\n def _autoreg_sample(genie, rng, batch):\n batch[""videos""] = batch[""videos""][:, : args.start_frame]\n batch[""rng""] = rng\n generated_vid_BSHWC = _sampling_fn(genie, batch)\n return generated_vid_BSHWC\n\n # --- Get video + latent actions ---\n array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n args.batch_size,\n args.image_height,\n args.image_width,\n args.image_channels,\n # We don't use workers in order to avoid grain shutdown issues (https://github.com/google/grain/issues/398)\n num_workers=0,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n dataloader = iter(dataloader)\n batch = next(dataloader)\n gt_video = jnp.asarray(batch[""videos""], dtype=jnp.float32) / 255.0\n batch[""videos""] = gt_video.astype(args.dtype)\n # Get latent actions for all videos in the batch\n action_batch_E = None\n if not args.use_gt_actions:\n action_batch_E = genie.vq_encode(batch, training=False)\n batch[""latent_actions""] = action_batch_E\n\n # --- Sample + evaluate video ---\n recon_video_BSHWC = _autoreg_sample(genie, rng, batch)\n recon_video_BSHWC = recon_video_BSHWC.astype(jnp.float32)\n gt = (\n gt_video[:, : recon_video_BSHWC.shape[1]]\n .clip(0, 1)\n .reshape(-1, *gt_video.shape[2:])\n )\n recon = recon_video_BSHWC.clip(0, 1).reshape(-1, *recon_video_BSHWC.shape[2:])\n ssim = jnp.asarray(\n pix.ssim(gt[:, args.start_frame :], recon[:, args.start_frame :])\n ).mean()\n print(f""SSIM: {ssim}"")\n\n # --- Construct video ---\n true_videos = (gt_video * 255).astype(np.uint8)\n pred_videos = (recon_video_BSHWC * 255).astype(np.uint8)\n video_comparison = np.zeros((2, *recon_video_BSHWC.shape), dtype=np.uint8)\n video_comparison[0] = true_videos[:, : args.seq_len]\n video_comparison[1] = pred_videos\n frames = einops.rearrange(video_comparison, ""n b t h w c -> t (b h) (n w) c"")\n\n # --- Save video ---\n imgs = [Image.fromarray(img) for img in frames]\n # Write actions on each frame, on each row (i.e., for each video in the batch, on the GT row)\n B, S, _, _, _ = batch[""videos""].shape\n if action_batch_E is not None:\n action_batch_BSm11 = jnp.reshape(action_batch_E, (B, S - 1, 1))\n else:\n action_batch_BSm11 = jnp.expand_dims(batch[""actions""][:, :-1], -1)\n for t, img in enumerate(imgs[1:]):\n d = ImageDraw.Draw(img)\n for row in range(B):\n action = action_batch_BSm11[row, t, 0]\n y_offset = row * batch[""videos""].shape[2] + 2\n d.text((2, y_offset), f""{action}"", fill=255)\n imgs[0].save(\n f""generation_{time.time()}.gif"",\n save_all=True,\n append_images=imgs[1:],\n duration=250,\n loop=0,\n )\n",python,tab +685,2450689,"sample.py",6295,0,"",python,selection_mouse +686,2450779,"sample.py",6291,14,"action_batch_E",python,selection_mouse +687,2451389,"sample.py",6268,0,"",python,selection_mouse +688,2452106,"sample.py",6282,0,"",python,selection_mouse +689,2463247,"sample.py",6367,0,"",python,selection_mouse +690,2463387,"sample.py",6362,14,"latent_actions",python,selection_mouse +691,2473440,"sample.py",6362,0,"",python,selection_mouse +692,2527522,"TERMINAL",0,0,"Calculating validation metrics...\r\n",,terminal_output +693,2541906,"sample.py",7577,0,"",python,selection_mouse +694,2542061,"sample.py",7567,18,"action_batch_BSm11",python,selection_mouse +695,2542697,"sample.py",7662,0,"",python,selection_mouse +696,2542853,"sample.py",7649,18,"action_batch_BSm11",python,selection_mouse +697,2543435,"sample.py",7583,0,"",python,selection_mouse +698,2543647,"sample.py",7567,18,"action_batch_BSm11",python,selection_mouse +699,2546861,"sample.py",6301,0,"",python,selection_mouse +700,2547435,"sample.py",6360,0,"",python,selection_mouse +701,2548036,"sample.py",6361,0,"",python,selection_mouse +702,2548584,"sample.py",6268,0,"",python,selection_mouse +703,2549191,"sample.py",6300,0,"",python,selection_mouse +704,2550300,"TERMINAL",0,0,"Step 1500, validation loss: 0.36969342827796936\r\n",,terminal_output +705,2550387,"TERMINAL",0,0,"Saved checkpoint at step 1500\r\n",,terminal_output +706,2634243,"TERMINAL",0,0,"Saved checkpoint at step 2000\r\n",,terminal_output +707,2677381,"TERMINAL",0,0,"Calculating validation metrics...\r\n",,terminal_output +708,2700307,"TERMINAL",0,0,"Step 2250, validation loss: 0.3902597725391388\r\n",,terminal_output +709,3405594,"sample.py",0,0,"Switched from branch 'generate-minatar-breakout-dataset' to 'gt-actions'",python,git_branch_checkout +710,3835637,"sample.py",0,0,"Switched from branch 'gt-actions' to 'generate-minatar-breakout-dataset'",python,git_branch_checkout +711,4035635,"sample.py",0,0,"Switched from branch 'generate-minatar-breakout-dataset' to 'gt-actions'",python,git_branch_checkout +712,4250664,"sample.py",0,0,"Switched from branch 'gt-actions' to 'generate-minatar-breakout-dataset'",python,git_branch_checkout +713,7570922,"sample.py",0,0,"Switched from branch 'generate-minatar-breakout-dataset' to 'main'",python,git_branch_checkout +714,7655947,"sample.py",0,0,"Switched from branch 'main' to 'hotfix/sampling-shapes-error'",python,git_branch_checkout diff --git a/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-7f667af1-e8dc-4534-8563-a4ef7be9de461754942097068-2025_08_11-21.55.24.740/source.csv b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-7f667af1-e8dc-4534-8563-a4ef7be9de461754942097068-2025_08_11-21.55.24.740/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..3507c0739ae9c80f4c4f83ff4181151a2224f81b --- /dev/null +++ b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-7f667af1-e8dc-4534-8563-a4ef7be9de461754942097068-2025_08_11-21.55.24.740/source.csv @@ -0,0 +1,22 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +2,461,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"9:55:24 PM [info] Activating crowd-code\n9:55:24 PM [info] Recording started\n9:55:24 PM [info] Initializing git provider using file system watchers...\n9:55:24 PM [info] Git repository found\n9:55:24 PM [info] Git provider initialized successfully\n",Log,tab +3,483,"TERMINAL",0,0,"git branch",,terminal_command +4,537,"TERMINAL",0,0,"]633;E;2025-08-11 21:55:24 git branch;686e78ec-8807-43f4-8f60-628f6dc2af90]633;C[?1h=\r",,terminal_output +5,732,"TERMINAL",0,0,"* (HEAD detached at ab43d16)\r\n add-wandb-name-and-tags\r\n before-nnx\r\n causal-mem-reduce\r\n causal-spatiotemporal-kv-cache\r\n causal-st-transformer\r\n causal-transformer-dynamics-model\r\n causal-transformer-nnx-no-kv-cache\r\n convert-to-jax-array-in-iter\r\n correct-batched-sampling\r\n dev\r\n dont-let-tf-see-gpu\r\n feat/explicit-image-dims\r\n fix-action-padding-lam-future-information-access\r\n fix-sampling\r\n fix-transformer-forwardpass\r\n fix/spatiotemporal-pe-once-in-STTransformer\r\n grad-norm-log-and-clip\r\n grain-dataloader\r\n input_pipeline/add-npy2array_record\r\n logging-variants\r\n lr-schedules\r\n main\r\n maskgit-different-maskprob-per-sample\r\n:",,terminal_output +6,2249,"TERMINAL",0,0,"\r maskgit-sampling-iterative-unmasking-fix\r\n:",,terminal_output +7,2427,"TERMINAL",0,0,"\r metrics-logging-for-dynamics-model\r\n:",,terminal_output +8,2573,"TERMINAL",0,0,"\r monkey-patch\r\n:",,terminal_output +9,2811,"TERMINAL",0,0,"\r new-arch-sampling\r\n:",,terminal_output +10,2907,"TERMINAL",0,0,"\r preprocess_video\r\n:",,terminal_output +11,2986,"TERMINAL",0,0,"\r refactor-tmp\r\n:",,terminal_output +12,3470,"TERMINAL",0,0,"\rM causal-st-transformer\r\n\r:",,terminal_output +13,4286,"TERMINAL",0,0,"\rM causal-spatiotemporal-kv-cache\r\n\r:\rM causal-mem-reduce\r\n\r:\rM before-nnx\r\n\r:\rM add-wandb-name-and-tags\r\n\r:\rM* (HEAD detached at ab43d16)\r\n\r:\r\r:\r\r:\r\r:\r\r:\r\r:\r\r:\r\r:",,terminal_output +14,4417,"TERMINAL",0,0,"\r\r:\r\r:\r\r:\r\r:\r\r:",,terminal_output +15,4470,"TERMINAL",0,0,"\r\r:\r\r:",,terminal_output +16,4524,"TERMINAL",0,0,"\r\r:",,terminal_output +17,4848,"TERMINAL",0,0,"\r[?1l>]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +18,7630,"TERMINAL",0,0,"git checkout main",,terminal_command +19,7730,"TERMINAL",0,0,"]633;E;2025-08-11 21:55:32 git checkout main;686e78ec-8807-43f4-8f60-628f6dc2af90]633;Cerror: Your local changes to the following files would be overwritten by checkout:\r\n\ttrain_dynamics.py\r\nPlease commit your changes or stash them before you switch branches.\r\nAborting\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;1",,terminal_output +20,11378,"TERMINAL",0,0,"git stash",,terminal_command +21,11436,"TERMINAL",0,0,"]633;E;2025-08-11 21:55:36 git stash;686e78ec-8807-43f4-8f60-628f6dc2af90]633;C",,terminal_output +22,11610,"TERMINAL",0,0,"Saved working directory and index state WIP on (no branch): ab43d16 fix: missing reshape and shape suffixes in `sample.py` (#134)\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0]633;P;Cwd=/home/hk-project-p0023960/tum_cte0515/Projects/jafar",,terminal_output diff --git a/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-89289833-78d5-4138-b766-a3025aedfd811759399195678-2025_10_02-12.00.33.280/source.csv b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-89289833-78d5-4138-b766-a3025aedfd811759399195678-2025_10_02-12.00.33.280/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..3ebd9c335e968f4ae4aa3aa4b84516b388f35a1e --- /dev/null +++ b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-89289833-78d5-4138-b766-a3025aedfd811759399195678-2025_10_02-12.00.33.280/source.csv @@ -0,0 +1,4696 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +2,365,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"12:00:33 PM [info] Activating crowd-code\n12:00:33 PM [info] Recording started\n12:00:33 PM [info] Initializing git provider using file system watchers...\n",Log,tab +3,479,"extension-output-pdoom-org.crowd-code-#1-crowd-code",153,0,"12:00:33 PM [info] Git repository found\n12:00:33 PM [info] Git provider initialized successfully\n12:00:33 PM [info] Initial git state: [object Object]\n",Log,content +4,1707,"TERMINAL",0,0,"git branc^C",,terminal_command +5,13388,"TERMINAL",0,0,"cat ~/.bashrc",,terminal_command +6,13398,"TERMINAL",0,0,"]633;C# .bashrc\r\n\r\n# Source global definitions\r\nif [ -f /etc/bashrc ]; then\r\n\t. /etc/bashrc\r\nfi\r\n\r\n# User specific environment\r\nif ! [[ ""$PATH"" =~ ""$HOME/.local/bin:$HOME/bin:"" ]]\r\nthen\r\n PATH=""$HOME/.local/bin:$HOME/bin:$PATH""\r\nfi\r\nexport PATH\r\n\r\nalias idle='sinfo_t_idle'\r\nexport ws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\r\nalias salloc_node='salloc --time=01:00:00 --partition=dev_accelerated --nodes=1 --gres=gpu:1 --cpus-per-task=8'\r\nalias salloc_cpu='salloc --time=01:00:00 --partition=dev_cpuonly --nodes=1 --cpus-per-task=128'\r\nalias sync-runner=""sh /home/hk-project-p0023960/tum_cte0515/sync_runner.sh /home/hk-project-p0023960/tum_cte0515/Projects/jasmine /home/hk-project-p0023960/tum_cte0515/Projects/jasmine_jobs/""\r\nalias sync-jafar=""sh /home/hk-project-p0023960/tum_cte0515/sync_runner.sh /home/hk-project-p0023960/tum_cte0515/Projects/jafar /home/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/""\r\nalias sync-runner-2=""sh /home/hk-project-p0023960/tum_cte0515/sync_runner.sh /home/hk-project-p0023960/tum_cte0515/Projects/jasmine /home/hk-project-p0023960/tum_cte0515/Projects/jasmine_jobs_2/""\r\nalias sync-runner-3=""sh /home/hk-project-p0023960/tum_cte0515/sync_runner.sh /home/hk-project-p0023960/tum_cte0515/Projects/jasmine /home/hk-project-p0023960/tum_cte0515/Projects/jasmine_jobs_3/""\r\nalias runner=""cd /home/hk-project-p0023960/tum_cte0515/Projects/jasmine_jobs/""\r\nalias runner-2=""cd /home/hk-project-p0023960/tum_cte0515/Projects/jasmine_jobs_2/""\r\nalias runner-3=""cd /home/hk-project-p0023960/tum_cte0515/Projects/jasmine_jobs_3/""\r\nalias jafar=""cd /home/hk-project-p0023960/tum_cte0515/Projects/jafar/""\r\nalias jafar-runner=""cd /home/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/""\r\nalias dev=""cd /home/hk-project-p0023960/tum_cte0515/Projects/jasmine/""\r\nalias smi=""watch -n1 nvidia-smi""\r\nalias idling=""watch -n1 sinfo_t_idle""\r\nalias queue='watch -n1 squeue --me'\r\nalias fqueue='watch -n 1 ""squeue -o \""%.10i %.16P %.30j %.8u %.8T %.10M %.9l %.6D %R\""""'\r\nalias fsacct_week='sacct --format=""JobID%15,JobName%30,Partition%16,AllocCPUS%3,State%12,Elapsed%10,Timelimit%10"" --starttime $(date -d ""last week"" +%Y-%m-%d) | grep -vE ""*.batch|*.extern|*.inter|bash|python|CANCELLED|echo""'\r\nalias logs=""cd /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir""\r\n\r\nalias sbatch_dir=""sh /home/hk-project-p0023960/tum_cte0515/sbatch_dir.sh $@""\r\nalias branch=""git rev-parse --abbrev-ref HEAD""\r\n\r\n\r\noverlapper() {\r\n if [ -z ""$1"" ]; then\r\n echo ""Usage: overlap ""\r\n else\r\n srun --jobid=""$1"" --pty /usr/bin/bash\r\n fi\r\n}\r\n# Uncomment the following line if you don't like systemctl's auto-paging feature:\r\n# export SYSTEMD_PAGER=\r\n\r\n# User specific aliases and functions\r\nexport PATH=""$HOME/ffmpeg:$PATH""\r\n\r\n]0;tum_cte0515@hkn1991:~/Projects/jasmine",,terminal_output +7,249939,"TERMINAL",0,0,"bash",,terminal_focus +8,383493,"TERMINAL",0,0,"bash",,terminal_focus +9,468500,"TERMINAL",0,0,"branch",,terminal_command +10,468533,"TERMINAL",0,0,"]633;Cdynamics_coinrun_500m_dataset_29519\r\n]0;tum_cte0515@hkn1991:~/Projects/jasmine",,terminal_output +11,480525,"TERMINAL",0,0,"git branch | pytorch",,terminal_command +12,480596,"TERMINAL",0,0,"]633;Cbash: pytorch: command not found...\r\n",,terminal_output +13,481877,"TERMINAL",0,0,"]0;tum_cte0515@hkn1991:~/Projects/jasmine",,terminal_output +14,485678,"TERMINAL",0,0,"git branch | grep pytorch",,terminal_command +15,490887,"TERMINAL",0,0,"git checkout ablation/use-pytorch-dataloader",,terminal_command +16,490924,"TERMINAL",0,0,"]633;C",,terminal_output +17,491441,"TERMINAL",0,0,"Switched to branch 'ablation/use-pytorch-dataloader'\r\n]0;tum_cte0515@hkn1991:~/Projects/jasmine",,terminal_output +18,493756,"TERMINAL",0,0,"git pull",,terminal_command +19,493810,"TERMINAL",0,0,"]633;C",,terminal_output +20,495309,"",0,0,"Switched from branch 'dynamics_coinrun_500m_dataset_29519' to 'ablation/use-pytorch-dataloader'",,git_branch_checkout +21,495685,"TERMINAL",0,0,"There is no tracking information for the current branch.\r\nPlease specify which branch you want to merge with.\r\nSee git-pull(1) for details.\r\n\r\n git pull \r\n\r\nIf you wish to set tracking information for this branch you can do so with:\r\n\r\n git branch --set-upstream-to=origin/ ablation/use-pytorch-dataloader\r\n\r\n]0;tum_cte0515@hkn1991:~/Projects/jasmine",,terminal_output +22,505387,"jasmine/train_dynamics.py",0,0,"import os\n\n\nos.environ.setdefault(""XLA_PYTHON_CLIENT_MEM_FRACTION"", ""0.98"")\n\nfrom dataclasses import dataclass, field\nimport itertools\nfrom typing import cast, Optional\n\nimport einops\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport flax.nnx as nnx\nfrom torch.utils.data import DataLoader\n\nfrom genie import Genie, restore_genie_components\nfrom utils.dataloader_torch import get_dataloader\nfrom utils.train_utils import (\n get_lr_schedule,\n count_parameters_by_component,\n print_mem_stats,\n print_compiled_memory_stats,\n print_compiled_cost_analysis,\n)\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n batch_size: int = 36\n init_lr: float = 0.0\n max_lr: float = 3e-5\n decay_end: float = 0.0\n wsd_decay_steps: int = (\n 10000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n )\n warmup_steps: int = 5000\n lr_schedule: str = ""wsd"" # supported options: wsd, cos\n # Tokenizer\n tokenizer_dim: int = 512\n tokenizer_ffn_dim: int = 2048\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 4\n tokenizer_num_heads: int = 8\n tokenizer_checkpoint: str = """"\n # LAM\n lam_dim: int = 512\n lam_ffn_dim: int = 2048\n latent_action_dim: int = 32\n num_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 4\n lam_num_heads: int = 8\n lam_checkpoint: str = """"\n # Dynamics\n dyna_type: str = ""maskgit"" # supported options: maskgit, causal\n dyna_dim: int = 512\n dyna_ffn_dim: int = 2048\n dyna_num_blocks: int = 6\n dyna_num_heads: int = 8\n dropout: float = 0.0\n mask_limit: float = 0.5\n z_loss_weight: float = 0.0\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\n use_flash_attention: bool = True\n use_gt_actions: bool = False\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n name: str = ""train_dynamics""\n tags: list[str] = field(default_factory=lambda: [""dynamics""])\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 25000\n log_checkpoint_keep_period: int = 20000\n log_gradients: bool = False\n val_data_dir: str = """"\n val_interval: int = 20_000\n val_steps: int = 50\n eval_full_frame: bool = False\n val_maskgit_steps: int = 25\n val_temperature: float = 1\n val_sample_argmax: bool = False\n wandb_id: str = """"\n\n\ndef build_model(args: Args, rng: jax.Array) -> tuple[Genie, jax.Array]:\n rng, _rng = jax.random.split(rng)\n rngs = nnx.Rngs(_rng)\n genie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n tokenizer_ffn_dim=args.tokenizer_ffn_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n lam_ffn_dim=args.lam_ffn_dim,\n latent_action_dim=args.latent_action_dim,\n num_actions=args.num_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n lam_co_train=not args.lam_checkpoint,\n use_gt_actions=args.use_gt_actions,\n # Dynamics\n dyna_type=args.dyna_type,\n dyna_dim=args.dyna_dim,\n dyna_ffn_dim=args.dyna_ffn_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n dropout=args.dropout,\n mask_limit=args.mask_limit,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n decode=False,\n rngs=rngs,\n )\n if args.use_gt_actions:\n assert (\n not args.lam_checkpoint\n ), ""Cannot use LAM when using ground-truth actions.""\n else:\n assert genie.lam is not None\n del genie.lam.decoder\n return genie, rng\n\n\ndef build_optimizer(genie: Genie, args: Args) -> nnx.ModelAndOptimizer:\n lr_schedule = get_lr_schedule(\n args.lr_schedule,\n args.init_lr,\n args.max_lr,\n args.decay_end,\n args.num_steps,\n args.warmup_steps,\n args.wsd_decay_steps,\n )\n tx = optax.adamw(\n learning_rate=lr_schedule,\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.param_dtype, # moments in full precision\n )\n optimizer = nnx.ModelAndOptimizer(genie, tx)\n return optimizer\n\n\ndef build_mesh_and_sharding(\n num_devices: int,\n) -> tuple[Mesh, NamedSharding, NamedSharding, NamedSharding]:\n device_mesh_arr = create_device_mesh((num_devices,))\n mesh = Mesh(devices=device_mesh_arr, axis_names=(""data"",))\n replicated_sharding = NamedSharding(mesh, PartitionSpec())\n videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n actions_sharding = NamedSharding(mesh, PartitionSpec(""data"", None))\n return mesh, replicated_sharding, videos_sharding, actions_sharding\n\n\ndef shard_optimizer_states(\n optimizer: nnx.ModelAndOptimizer, replicated_sharding: NamedSharding\n) -> None:\n model_state = nnx.state(optimizer.model)\n model_sharded_state = jax.lax.with_sharding_constraint(\n model_state, replicated_sharding\n )\n nnx.update(optimizer.model, model_sharded_state)\n optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)\n optimizer_sharded_state = jax.lax.with_sharding_constraint(\n optimizer_state, replicated_sharding\n )\n nnx.update(optimizer, optimizer_sharded_state)\n\n\ndef build_dataloader(args: Args, data_dir: str) -> DataLoader:\n return get_dataloader(data_dir, args.seq_len, args.batch_size)\n\n\ndef build_checkpoint_manager(args: Args) -> Optional[ocp.CheckpointManager]:\n if args.restore_ckpt or args.save_ckpt:\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n return checkpoint_manager\n else:\n return None\n\n\ndef restore_or_initialize_components(\n args: Args,\n checkpoint_manager: Optional[ocp.CheckpointManager],\n optimizer: nnx.ModelAndOptimizer,\n rng: jax.Array,\n replicated_sharding: NamedSharding,\n restore_step: Optional[int] = None,\n) -> tuple[\n int,\n nnx.ModelAndOptimizer,\n jax.Array,\n]:\n step = 0\n if checkpoint_manager and restore_step is None:\n restore_step = checkpoint_manager.latest_step()\n if args.restore_ckpt:\n assert checkpoint_manager is not None\n abstract_optimizer = nnx.eval_shape(lambda: optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n restore_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n )\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(), args=restore_args\n )\n restored_optimizer_state = restored[""model_state""]\n nnx.update(optimizer, restored_optimizer_state)\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored model state from step {step}"")\n else:\n # Restore from pre-trained tokenizer (and LAM)\n rng, _rng = jax.random.split(rng)\n optimizer = restore_genie_components(optimizer, replicated_sharding, _rng, args)\n return step, optimizer, rng\n\n\ndef _calculate_top_k_accuracy(\n token_logits_BTNV: jax.Array,\n video_tokens_BTN: jax.Array,\n mask_BTN: jax.Array,\n k: int,\n) -> jax.Array:\n _, topk_indices_BTNK = jax.lax.top_k(token_logits_BTNV, k)\n topk_correct = jnp.any(\n topk_indices_BTNK == video_tokens_BTN[..., jnp.newaxis], axis=-1\n )\n topk_acc = (mask_BTN * topk_correct).sum() / mask_BTN.sum()\n return topk_acc\n\n\ndef _calculate_step_metrics(\n outputs: dict[str, jax.Array],\n gt: jax.Array,\n num_actions: int,\n num_patch_latents: int,\n) -> tuple[jax.Array, dict]:\n mask_BTN = outputs[""mask""]\n outputs[""token_logits""] = outputs[""token_logits""].astype(jnp.float32)\n ce_loss = optax.softmax_cross_entropy_with_integer_labels(\n outputs[""token_logits""], outputs[""video_tokens""]\n )\n ce_loss = (mask_BTN * ce_loss).sum() / mask_BTN.sum()\n z_val = jax.nn.logsumexp(outputs[""token_logits""], axis=-1)\n z_loss_metric = (mask_BTN * (z_val**2)).sum() / mask_BTN.sum()\n\n masked_token_top_1_acc = _calculate_top_k_accuracy(\n outputs[""token_logits""], outputs[""video_tokens""], mask_BTN, 1\n )\n masked_token_top_2_acc = _calculate_top_k_accuracy(\n outputs[""token_logits""], outputs[""video_tokens""], mask_BTN, 2\n )\n masked_token_top_5_acc = _calculate_top_k_accuracy(\n outputs[""token_logits""], outputs[""video_tokens""], mask_BTN, 5\n )\n masked_token_top_16_acc = _calculate_top_k_accuracy(\n outputs[""token_logits""], outputs[""video_tokens""], mask_BTN, 16\n )\n\n select_probs = jax.nn.softmax(outputs[""token_logits""])\n gt_val = gt.clip(0, 1).reshape(-1, *gt.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt_val, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt_val, recon)).mean()\n _, index_counts_tokenizer = jnp.unique_counts(\n jnp.ravel(outputs[""video_tokens""]),\n size=num_patch_latents,\n fill_value=0,\n )\n codebook_usage_tokenizer = (index_counts_tokenizer != 0).mean()\n metrics = dict(\n cross_entropy_loss=ce_loss,\n masked_token_top1_accuracy=masked_token_top_1_acc,\n masked_token_top2_accuracy=masked_token_top_2_acc,\n masked_token_top5_accuracy=masked_token_top_5_acc,\n masked_token_top16_accuracy=masked_token_top_16_acc,\n select_logit=outputs[""token_logits""].max(-1).mean(),\n select_p=select_probs.max(-1).mean(),\n entropy=jax.scipy.special.entr(select_probs).sum(-1).mean(),\n z_loss=z_loss_metric,\n psnr=psnr,\n ssim=ssim,\n codebook_usage_tokenizer=codebook_usage_tokenizer,\n )\n if ""lam_indices"" in outputs.keys():\n _, index_counts_lam = jnp.unique_counts(\n jnp.ravel(outputs[""lam_indices""]),\n size=num_actions,\n fill_value=0,\n )\n codebook_usage_lam = (index_counts_lam != 0).mean()\n metrics[""codebook_usage_lam""] = codebook_usage_lam\n return ce_loss, metrics\n\n\ndef main(args: Args) -> None:\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 rng = jax.random.key(args.seed)\n\n # --- Initialize model ---\n genie, rng = build_model(args, rng)\n _, params, _ = nnx.split(genie, nnx.Param, ...)\n param_counts = count_parameters_by_component(params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\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 if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n optimizer = build_optimizer(genie, args)\n del genie\n\n # FIXME: switch to create_hybrid_device_mesh for runs spanning multiple nodes\n _, replicated_sharding, videos_sharding, actions_sharding = build_mesh_and_sharding(\n num_devices\n )\n\n shard_optimizer_states(optimizer, replicated_sharding)\n\n # --- Initialize checkpoint manager ---\n checkpoint_manager = build_checkpoint_manager(args)\n\n # --- Create DataLoaderIterator from dataloader ---\n print(""============\n\n"", args.data_dir, ""\n\n============"")\n train_iterator = build_dataloader(args, args.data_dir)\n val_iterator = None\n if args.val_data_dir:\n val_iterator = build_dataloader(args, args.val_data_dir)\n\n # --- Restore checkpoint ---\n step, optimizer, rng = restore_or_initialize_components(\n args,\n checkpoint_manager,\n optimizer,\n rng,\n replicated_sharding,\n )\n\n # --- Define loss and train step (close over args) ---\n def dynamics_loss_fn(\n model: Genie,\n inputs: dict,\n ) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs)\n ce_loss, metrics = _calculate_step_metrics(\n outputs, gt, args.num_actions, args.num_patch_latents\n )\n z_loss = metrics[""z_loss""]\n total_loss = ce_loss + args.z_loss_weight * z_loss\n metrics[""total_loss""] = total_loss\n return total_loss, (outputs[""recon""], metrics)\n\n @nnx.jit(donate_argnums=0)\n def train_step(\n optimizer: nnx.ModelAndOptimizer, inputs: dict\n ) -> tuple[jax.Array, jax.Array, dict]:\n def loss_fn(model: Genie) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n model.train()\n return dynamics_loss_fn(model, inputs)\n\n (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(\n optimizer.model\n )\n optimizer.update(grads)\n if args.log_gradients:\n metrics[""gradients_std/""] = jax.tree.map(\n lambda x: x.std(), grads[""params""][""dynamics""]\n )\n return loss, recon, metrics\n\n @nnx.jit\n def val_step(genie: Genie, inputs: dict) -> dict:\n """"""Evaluate model and compute metrics""""""\n genie.eval()\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n (loss, (recon, metrics)) = dynamics_loss_fn(genie, inputs)\n val_output = {""loss"": loss, ""recon"": recon, ""metrics"": metrics}\n\n # --- Evaluate full frame prediction (sampling) ---\n if args.eval_full_frame:\n inputs[""videos""] = gt.astype(args.dtype)\n tokenizer_outputs = genie.tokenizer.vq_encode(\n inputs[""videos""], training=False\n )\n tokens_full_frame = tokenizer_outputs[""indices""]\n lam_indices_E = None\n if not args.use_gt_actions:\n lam_indices_E = genie.vq_encode(inputs, training=False)\n inputs[""latent_actions""] = lam_indices_E\n inputs[""videos""] = inputs[""videos""][\n :, :-1\n ] # remove last frame for generation\n recon_full_frame, logits_full_frame = genie.sample(\n inputs,\n args.seq_len,\n args.val_temperature,\n args.val_sample_argmax,\n args.val_maskgit_steps,\n )\n # Calculate metrics for the last frame only\n step_outputs = {\n ""recon"": recon_full_frame[:, -1],\n ""token_logits"": logits_full_frame[:, -1],\n ""video_tokens"": tokens_full_frame[:, -1],\n ""mask"": jnp.ones_like(tokens_full_frame[:, -1]),\n }\n if lam_indices_E is not None:\n lam_indices_B = lam_indices_E.reshape((-1, args.seq_len - 1))[:, -1]\n step_outputs[""lam_indices""] = lam_indices_B\n\n loss_full_frame, metrics_full_frame = _calculate_step_metrics(\n step_outputs, gt[:, -1], args.num_actions, args.num_patch_latents\n )\n val_output.update(\n {\n ""loss_full_frame"": loss_full_frame,\n ""recon_full_frame"": recon_full_frame,\n ""metrics_full_frame"": metrics_full_frame,\n }\n )\n return val_output\n\n def calculate_validation_metrics(val_dataloader, genie, rng):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n loss_full_frame_per_step = []\n metrics_full_frame_per_step = []\n batch = None\n recon = None\n recon_full_frame = None\n for batch in val_dataloader:\n rng, _rng_mask = jax.random.split(rng, 2)\n batch[""rng""] = _rng_mask\n val_outputs = val_step(genie, batch)\n loss_per_step.append(val_outputs[""loss""])\n metrics_per_step.append(val_outputs[""metrics""])\n recon = val_outputs[""recon""]\n if args.eval_full_frame:\n loss_full_frame_per_step.append(val_outputs[""loss_full_frame""])\n metrics_full_frame_per_step.append(val_outputs[""metrics_full_frame""])\n recon_full_frame = val_outputs[""recon_full_frame""]\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(\n f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}""\n )\n\n val_metrics = {\n f""val_{key}"": np.mean([float(m[key]) for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n val_metrics[""val_loss""] = np.mean(loss_per_step)\n if args.eval_full_frame:\n val_metrics_full_frame = {\n f""val_full_frame_{key}"": np.mean(\n [float(m[key]) for m in metrics_full_frame_per_step]\n )\n for key in metrics_full_frame_per_step[0].keys()\n }\n val_metrics.update(val_metrics_full_frame)\n val_metrics[""val_full_frame_loss""] = np.mean(loss_full_frame_per_step)\n return val_metrics, batch, recon, recon_full_frame\n\n # --- TRAIN LOOP ---\n dataloader_train = (\n {\n ""videos"": jax.make_array_from_process_local_data(\n videos_sharding, local_data=elem[""videos""]\n ),\n ""actions"": (\n jax.make_array_from_process_local_data(\n actions_sharding, elem[""actions""]\n )\n if args.use_gt_actions\n else None\n ),\n }\n for elem in train_iterator\n )\n dataloader_val = None\n if val_iterator:\n dataloader_val = (\n {\n ""videos"": jax.make_array_from_process_local_data(\n videos_sharding, elem[""videos""]\n ),\n ""actions"": (\n jax.make_array_from_process_local_data(\n actions_sharding, elem[""actions""]\n )\n if args.use_gt_actions\n else None\n ),\n }\n for elem in val_iterator\n )\n if jax.process_index() == 0:\n first_batch = next(dataloader_train)\n first_batch[""rng""] = rng # type: ignore\n compiled = train_step.lower(optimizer, first_batch).compile()\n print_compiled_memory_stats(compiled.memory_analysis())\n print_compiled_cost_analysis(compiled.cost_analysis())\n # Do not skip the first batch during training\n dataloader_train = itertools.chain([first_batch], dataloader_train)\n print(f""Starting training from step {step}..."")\n first_step = step\n while step < args.num_steps:\n for batch in dataloader_train:\n # --- Train step ---\n rng, _rng_mask = jax.random.split(rng, 2)\n batch[""rng""] = _rng_mask\n loss, recon, metrics = train_step(optimizer, batch)\n if step == first_step:\n print_mem_stats(""After params initialized"")\n step += 1\n\n # --- Validation loss ---\n val_results = {}\n if dataloader_val and step % args.val_interval == 0:\n rng, _rng_mask_val = jax.random.split(rng, 2)\n print(""Calculating validation metrics..."")\n val_metrics, val_gt_batch, val_recon, val_recon_full_frame = (\n calculate_validation_metrics(\n dataloader_val, optimizer.model, _rng_mask_val\n )\n )\n print(f""Step {step}, validation loss: {val_metrics['val_loss']}"")\n val_results = {\n ""metrics"": val_metrics,\n ""gt_batch"": val_gt_batch,\n ""recon"": val_recon,\n ""full_frame"": val_recon_full_frame,\n }\n\n # --- Logging ---\n if args.log:\n if step % args.log_interval == 0 and jax.process_index() == 0:\n log_dict = {""loss"": loss, ""step"": step, **metrics}\n if val_results:\n log_dict.update(val_results[""metrics""])\n wandb.log(log_dict)\n if step % args.log_image_interval == 0:\n gt_seq = batch[""videos""][0].astype(jnp.float32) / 255.0\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 if val_results:\n val_results[""gt_seq_val""] = (\n val_results[""gt_batch""][""videos""][0].astype(jnp.float32)\n / 255.0\n )\n val_results[""recon_seq_val""] = val_results[""recon""][0].clip(\n 0, 1\n )\n val_comparison_seq = jnp.concatenate(\n (val_results[""gt_seq_val""], val_results[""recon_seq_val""]),\n axis=1,\n )\n val_results[""val_comparison_seq""] = einops.rearrange(\n val_comparison_seq * 255, ""t h w c -> h (t w) c""\n )\n if args.eval_full_frame:\n val_results[""full_frame_seq_val""] = val_results[\n ""full_frame""\n ][0].clip(0, 1)\n val_results[""val_full_frame_comparison_seq""] = (\n jnp.concatenate(\n (\n val_results[""gt_seq_val""],\n val_results[""full_frame_seq_val""],\n ),\n axis=1,\n )\n )\n val_results[""val_full_frame_comparison_seq""] = (\n einops.rearrange(\n val_results[""val_full_frame_comparison_seq""] * 255,\n ""t h w c -> h (t w) c"",\n )\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[args.seq_len - 1])),\n recon=wandb.Image(np.asarray(recon_seq[args.seq_len - 1])),\n true_vs_recon=wandb.Image(\n np.asarray(comparison_seq.astype(np.uint8))\n ),\n )\n if val_results:\n log_images.update(\n dict(\n val_image=wandb.Image(\n np.asarray(\n val_results[""gt_seq_val""][args.seq_len - 1]\n )\n ),\n val_recon=wandb.Image(\n np.asarray(\n val_results[""recon_seq_val""][\n args.seq_len - 1\n ]\n )\n ),\n val_true_vs_recon=wandb.Image(\n np.asarray(\n val_results[""val_comparison_seq""].astype(\n np.uint8\n )\n )\n ),\n )\n )\n if args.eval_full_frame:\n log_images.update(\n dict(\n val_full_frame=wandb.Image(\n np.asarray(\n val_results[""full_frame_seq_val""][\n args.seq_len - 1\n ]\n )\n ),\n val_true_vs_full_frame=wandb.Image(\n np.asarray(\n val_results[\n ""val_full_frame_comparison_seq""\n ].astype(np.uint8)\n )\n ),\n )\n )\n wandb.log(log_images)\n # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n assert checkpoint_manager is not None\n optimizer_state = nnx.state(optimizer)\n ckpt_manager_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n )\n checkpoint_manager.save(step, args=ckpt_manager_args)\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n if checkpoint_manager:\n checkpoint_manager.close()\n\n\nif __name__ == ""__main__"":\n args = tyro.cli(Args)\n main(args)\n",python,tab +23,539750,"jasmine/train_dynamics.py",6179,0,"",python,selection_command +24,540933,"jasmine/train_dynamics.py",6220,0,"",python,selection_command +25,541349,"jasmine/train_dynamics.py",6247,0,"",python,selection_command +26,541559,"jasmine/train_dynamics.py",13394,0,"",python,selection_command +27,541730,"jasmine/train_dynamics.py",13418,0,"",python,selection_command +28,543722,"jasmine/train_dynamics.py",13474,0,"",python,selection_command +29,544632,"jasmine/train_dynamics.py",13418,0,"",python,selection_command +30,544838,"jasmine/train_dynamics.py",13474,0,"",python,selection_command +31,587544,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=2-00:00:00\n#SBATCH --cpus-per-task=8\n#SBATCH --gres=gpu:1\n#SBATCH --partition=accelerated\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/jafar_og_reproduction/dynamics/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/jafar_og_reproduction/dynamics/%x_%j.log\n#SBATCH --job-name=train_dynamics_coinrun_og_reproduction\n\n# Log the sbatch script\ncat $0\n\nsource .venv/bin/activate\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\ntags=""coinrun_og dynanmics 10m_dataset repro_mihir""\n\nnpy_records_dir=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m""\n\n# TODO mihir: update the tokenizer and lam checkpoints\ntokenizer_ckpt_dir=""""\nlam_ckpt_dir=""""\n\nCHECKPOINT_DIR=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/dynamics/${job_name}/${slurm_job_id}""\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python jasmine/train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --tokenizer_checkpoint=""${tokenizer_ckpt_dir}"" \\n --lam_checkpoint=""${lam_ckpt_dir}"" \\n --log_image_interval=1000 \\n --log \\n --name=""${job_name}_${slurm_job_id}"" \\n --tags ${tags} \\n --entity instant-uv \\n --project jafar \\n --data_dir $npy_records_dir \\n --wandb_id $slurm_job_id\n",shellscript,tab +32,601514,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",760,0,"",shellscript,selection_mouse +33,601684,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",741,20,"coinrun_episodes_10m",shellscript,selection_mouse +34,601856,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",741,22,"coinrun_episodes_10m""\n",shellscript,selection_mouse +35,602431,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",683,78,"workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m",shellscript,selection_mouse +36,602513,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",682,79,"/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m",shellscript,selection_mouse +37,602540,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",678,83,"work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m",shellscript,selection_mouse +38,602703,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",677,84,"/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m",shellscript,selection_mouse +39,602731,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",673,88,"hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m",shellscript,selection_mouse +40,603481,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",672,89,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m",shellscript,selection_mouse +41,604408,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",668,0,"",shellscript,selection_mouse +42,604559,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",655,15,"npy_records_dir",shellscript,selection_mouse +43,604787,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",655,108,"npy_records_dir=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m""\n",shellscript,selection_mouse +44,609780,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=00:20:00\n#SBATCH --partition=dev_accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\n#SBATCH --job-name=train_dyn_single_gpu\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\n# array_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_test\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_split/train\n# array_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_split/val\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_default/3528955\n\nenv | grep SLURM\n\nexport PYTHONUNBUFFERED=1\n\nsrun python jasmine/train_dynamics.py \\n --save_ckpt \\n --image_height=64 \\n --image_width=64 \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=110 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=50 \\n --log_checkpoint_interval=2 \\n --dyna_type=maskgit \\n --log \\n --name=coinrun-dyn-dev-$slurm_job_id \\n --tags dyn coinrun dev \\n --entity instant-uv \\n --project jafar \\n --warmup_steps 0 \\n --wsd_decay_steps 0 \\n --num_steps 10 \\n --data_dir $array_records_dir_train \\n --tokenizer_checkpoint $tokenizer_checkpoint \\n --val_data_dir $array_records_dir_val \\n --val_interval 2 \\n --eval_full_frame \\n --val_steps 5\n",shellscript,tab +45,617289,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1114,0,"",shellscript,selection_mouse +46,617416,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1092,26,"coinrun_episodes_10m_split",shellscript,selection_mouse +47,619609,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1023,0,"",shellscript,selection_mouse +48,619728,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1023,2,"/h",shellscript,selection_mouse +49,619796,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1023,3,"/hk",shellscript,selection_mouse +50,619827,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1023,4,"/hkf",shellscript,selection_mouse +51,619864,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1023,5,"/hkfs",shellscript,selection_mouse +52,619894,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1023,6,"/hkfs/",shellscript,selection_mouse +53,619925,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1023,7,"/hkfs/w",shellscript,selection_mouse +54,619926,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1023,8,"/hkfs/wo",shellscript,selection_mouse +55,619959,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1023,9,"/hkfs/wor",shellscript,selection_mouse +56,619995,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1023,11,"/hkfs/work/",shellscript,selection_mouse +57,619996,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1023,12,"/hkfs/work/w",shellscript,selection_mouse +58,620024,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1023,13,"/hkfs/work/wo",shellscript,selection_mouse +59,620069,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",912,111,"k/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +60,620069,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",913,110,"/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +61,620104,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",914,109,"workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +62,620104,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",916,107,"rkspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +63,620144,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",918,105,"space/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +64,620145,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",920,103,"ace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +65,620145,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",923,100,"/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +66,620178,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",925,98,"cratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +67,620179,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",802,221,"h/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_split/train\n# array_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +68,620209,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",806,217,"m_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_split/train\n# array_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +69,620210,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",809,214,"nd3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_split/train\n# array_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +70,620242,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",812,211,"695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_split/train\n# array_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +71,620243,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",816,207,"jafa_ws_shared/data_coinrun/coinrun_episodes_10m_split/train\n# array_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +72,620274,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",819,204,"a_ws_shared/data_coinrun/coinrun_episodes_10m_split/train\n# array_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +73,620275,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",821,202,"ws_shared/data_coinrun/coinrun_episodes_10m_split/train\n# array_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +74,620302,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",823,200,"_shared/data_coinrun/coinrun_episodes_10m_split/train\n# array_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +75,620336,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",826,197,"ared/data_coinrun/coinrun_episodes_10m_split/train\n# array_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +76,620337,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",829,194,"d/data_coinrun/coinrun_episodes_10m_split/train\n# array_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +77,620368,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",833,190,"ta_coinrun/coinrun_episodes_10m_split/train\n# array_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +78,620369,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",837,186,"oinrun/coinrun_episodes_10m_split/train\n# array_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +79,620397,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",966,57,"inrun/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +80,620398,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",968,55,"run/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +81,620429,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",970,53,"n/coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +82,620430,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",972,51,"coinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +83,620463,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",973,50,"oinrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +84,620571,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",974,49,"inrun_episodes_10m_chunked\narray_records_dir_val=",shellscript,selection_mouse +85,620572,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1023,75,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinru",shellscript,selection_mouse +86,620572,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1023,77,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_",shellscript,selection_mouse +87,620588,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1023,82,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episo",shellscript,selection_mouse +88,620602,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1023,84,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episode",shellscript,selection_mouse +89,620621,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1023,100,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_split/val\n",shellscript,selection_mouse +90,620980,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1023,93,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_spl",shellscript,selection_mouse +91,621014,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1023,94,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_spli",shellscript,selection_mouse +92,621193,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1023,95,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_split",shellscript,selection_mouse +93,623330,"TERMINAL",0,0,"bash",,terminal_focus +94,625081,"TERMINAL",0,0,"cd /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_split",,terminal_command +95,625656,"TERMINAL",0,0,"ls",,terminal_command +96,625688,"TERMINAL",0,0,"]633;Cmetadata.json test train val\r\n]0;tum_cte0515@hkn1991:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_split",,terminal_output +97,631060,"TERMINAL",0,0,"cd val/",,terminal_command +98,636527,"TERMINAL",0,0,"ls -l | head",,terminal_command +99,636601,"TERMINAL",0,0,"]633;C",,terminal_output +100,637135,"TERMINAL",0,0,"total 371520\r\n-rw-rw----+ 1 tum_cte0515 hk-project-p0023960 655360 Sep 4 19:48 episode_0.array_record\r\n-rw-rw----+ 1 tum_cte0515 hk-project-p0023960 851968 Sep 4 19:48 episode_100.array_record\r\n-rw-rw----+ 1 tum_cte0515 hk-project-p0023960 983040 Sep 4 19:48 episode_101.array_record\r\n-rw-rw----+ 1 tum_cte0515 hk-project-p0023960 786432 Sep 4 19:48 episode_102.array_record\r\n-rw-rw----+ 1 tum_cte0515 hk-project-p0023960 524288 Sep 4 19:48 episode_103.array_record\r\n-rw-rw----+ 1 tum_cte0515 hk-project-p0023960 983040 Sep 4 19:48 episode_104.array_record\r\n-rw-rw----+ 1 tum_cte0515 hk-project-p0023960 524288 Sep 4 19:48 episode_105.array_record\r\n-rw-rw----+ 1 tum_cte0515 hk-project-p0023960 720896 Sep 4 19:48 episode_106.array_record\r\n-rw-rw----+ 1 tum_cte0515 hk-project-p0023960 720896 Sep 4 19:48 episode_107.array_record\r\n]0;tum_cte0515@hkn1991:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_split/val",,terminal_output +101,639536,"TERMINAL",0,0,"cd ..",,terminal_command +102,639740,"TERMINAL",0,0,"ls",,terminal_command +103,640417,"TERMINAL",0,0,"cd ..",,terminal_command +104,640720,"TERMINAL",0,0,"ls",,terminal_command +105,640791,"TERMINAL",0,0,"]633;C",,terminal_output +106,640889,"TERMINAL",0,0,"array_records coinrun_episodes_10m_chunked coinrun_episodes_10m_gt_actions_split coinrun_episodes_500m_gt_actions_split\r\ncoinrun_episodes coinrun_episodes_10m_gt_actions coinrun_episodes_10m_gt_actions_split_test coinrun_episodes_test\r\ncoinrun_episodes_10m coinrun_episodes_10m_gt_actions_distinct_seed coinrun_episodes_10m_split\r\n]0;tum_cte0515@hkn1991:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun",,terminal_output +107,643515,"TERMINAL",0,0,"cd coinrun_episodes",,terminal_command +108,646310,"TERMINAL",0,0,"ls",,terminal_command +109,646382,"TERMINAL",0,0,"]633;C",,terminal_output +110,647578,"TERMINAL",0,0,"^C\r\n]0;tum_cte0515@hkn1991:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes",,terminal_output +111,651003,"TERMINAL",0,0,"ls -l | head",,terminal_command +112,651067,"TERMINAL",0,0,"]633;C",,terminal_output +113,651449,"TERMINAL",0,0,"total 73932112\r\n-rw-r--r-- 1 tum_cte0515 hk-project-p0023960 3244160 Jun 10 16:09 episode_0.npy\r\n-rw-r--r-- 1 tum_cte0515 hk-project-p0023960 4067456 Jun 10 16:12 episode_1000.npy\r\n-rw-r--r-- 1 tum_cte0515 hk-project-p0023960 8810624 Jun 10 16:12 episode_1001.npy\r\n-rw-r--r-- 1 tum_cte0515 hk-project-p0023960 3244160 Jun 10 16:12 episode_1002.npy\r\n-rw-r--r-- 1 tum_cte0515 hk-project-p0023960 6094976 Jun 10 16:12 episode_1003.npy\r\n-rw-r--r-- 1 tum_cte0515 hk-project-p0023960 8736896 Jun 10 16:12 episode_1004.npy\r\n-rw-r--r-- 1 tum_cte0515 hk-project-p0023960 1953920 Jun 10 16:12 episode_1005.npy\r\n-rw-r--r-- 1 tum_cte0515 hk-project-p0023960 9990272 Jun 10 16:12 episode_1006.npy\r\n-rw-r--r-- 1 tum_cte0515 hk-project-p0023960 5456000 Jun 10 16:12 episode_1007.npy\r\n]0;tum_cte0515@hkn1991:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes",,terminal_output +114,656744,"TERMINAL",0,0,"pwd",,terminal_command +115,659880,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",876,0,"",shellscript,selection_mouse +116,659884,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",875,0,"",shellscript,selection_command +117,659920,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",875,1,"n",shellscript,selection_mouse +118,659956,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",876,0,"",shellscript,selection_command +119,660989,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",871,5,"",shellscript,content 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source .venv/bin/activate\r\n[?2004l\r]0;tum_cte0515@hkn0401:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +259,719703,"TERMINAL",0,0,"s",,terminal_output +260,719758,"TERMINAL",0,0,"h",,terminal_output +261,719988,"TERMINAL",0,0," ",,terminal_output +262,720329,"TERMINAL",0,0,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",,terminal_output +263,720838,"TERMINAL",0,0,"\rslurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=4\r\n#SBATCH --time=00:20:00\r\n#SBATCH --partition=dev_accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --job-name=train_dyn_single_gpu\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_default/3528955\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python jasmine/train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=64 \\r\n --image_width=64 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=110 \\r\n --init_lr=0 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=50 \\r\n --log_checkpoint_interval=2 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=coinrun-dyn-dev-$slurm_job_id \\r\n --tags dyn coinrun dev \\r\n --entity instant-uv \\r\n --project jafar \\r\n --warmup_steps 0 \\r\n --wsd_decay_steps 0 \\r\n --num_steps 10 \\r\n --data_dir $array_records_dir_train \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --val_data_dir $array_records_dir_val \\r\n --val_interval 2 \\r\n --eval_full_frame \\r\n --val_steps 5\r\n",,terminal_output +264,721026,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=1469415\r\nSLURM_JOB_GPUS=0\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\r\nSLURMD_NODENAME=hkn0401\r\nSLURM_JOB_START_TIME=1759399921\r\nSLURM_STEP_NODELIST=hkn0401\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1759403521\r\nSLURM_PMI2_SRUN_PORT=44089\r\nSLURM_CPUS_ON_NODE=8\r\nSLURM_JOB_CPUS_PER_NODE=8\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=dev_accelerated\r\nSLURM_TRES_PER_TASK=cpu=8\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=1\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3536670\r\nSLURM_PTY_PORT=41791\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.199\r\nSLURM_PTY_WIN_ROW=40\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0401\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.199\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=182\r\nSLURM_NODELIST=hkn0401\r\nSLURM_SRUN_COMM_PORT=44895\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1991.localdomain\r\nSLURM_JOB_ID=3536670\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0401\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_STEP_LAUNCHER_PORT=44895\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0401\r\n",,terminal_output +265,721127,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +266,734794,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +267,741412,"TERMINAL",0,0,"Counting all components: ['dynamics', 'lam', 'tokenizer']\r\n",,terminal_output +268,741827,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +269,742768,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.22.0\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_121254-u5gwj4e9\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run coinrun-dyn-dev-3536670\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/u5gwj4e9\r\n",,terminal_output +270,743098,"TERMINAL",0,0,"Parameter counts:\r\n{'dynamics': 26555904, 'lam': 17640416, 'tokenizer': 33750256, 'total': 77946576}\r\n",,terminal_output +271,744540,"TERMINAL",0,0,"============\r\n\r\n /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes \r\n\r\n============\r\n",,terminal_output +272,745384,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +273,749809,"TERMINAL",0,0,"Traceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 745, in \r\n main(args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 587, in main\r\n first_batch = next(dataloader_train)\r\n ^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 554, in \r\n dataloader_train = (\r\n ^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/torch/utils/data/dataloader.py"", line 734, in __next__\r\n data = self._next_data()\r\n ^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/torch/utils/data/dataloader.py"", line 790, in _next_data\r\n data = self._dataset_fetcher.fetch(index) # may raise StopIteration\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/torch/utils/data/_utils/fetch.py"", line 52, in fetch\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n ~~~~~~~~~~~~^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/dataloader_torch.py"", line 19, in __getitem__\r\n episode = np.load(self.metadata[idx][""path""])\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/numpy/lib/_npyio_impl.py"", line 454, in load\r\n fid = stack.enter_context(open(os.fspath(file), ""rb""))\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nFileNotFoundError: [Errno 2] No such file or directory: 'data/coinrun_episodes/episode_15.npy'\r\n",,terminal_output +274,750831,"TERMINAL",0,0,"wandb: \r\nwandb: 🚀 View run coinrun-dyn-dev-3536670 at: https://wandb.ai/instant-uv/jafar/runs/u5gwj4e9\r\nwandb: Find logs at: ../../../../../hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_121254-u5gwj4e9/logs\r\n",,terminal_output +275,756158,"TERMINAL",0,0,"W1002 12:13:09.144347 1469662 pjrt_client.cc:1469] WatchJobStateAsync failed for task goo.gle/debugonly job_name: ""jax_worker"": UNAVAILABLE: Cancelling all calls\r\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/WatchJobState:\r\n:UNKNOWN:Error received from peer {grpc_status:14, grpc_message:""Cancelling all calls""}\r\n",,terminal_output +276,756690,"TERMINAL",0,0,"srun: error: hkn0401: task 0: Exited with exit code 1\r\n]0;tum_cte0515@hkn0401:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +277,758111,"jasmine/train_dynamics.py",0,0,"",python,tab +278,759246,"jasmine/train_dynamics.py",13414,0,"",python,selection_mouse +279,759661,"jasmine/train_dynamics.py",13469,0,"",python,selection_mouse +280,759829,"jasmine/train_dynamics.py",13468,8,"data_dir",python,selection_mouse +281,760144,"jasmine/train_dynamics.py",13466,0,"",python,selection_mouse +282,760325,"jasmine/train_dynamics.py",13463,4,"args",python,selection_mouse +283,760627,"jasmine/train_dynamics.py",13472,0,"",python,selection_mouse +284,760825,"jasmine/train_dynamics.py",13468,8,"data_dir",python,selection_mouse +285,761166,"jasmine/train_dynamics.py",13551,0,"",python,selection_mouse +286,761295,"jasmine/train_dynamics.py",13547,8,"data_dir",python,selection_mouse +287,761683,"jasmine/train_dynamics.py",13544,0,"",python,selection_mouse 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+314,770710,"jasmine/train_dynamics.py",6258,8,"data_dir",python,selection_mouse +315,772568,"jasmine/train_dynamics.py",6269,0,"",python,selection_mouse +316,772713,"jasmine/train_dynamics.py",6268,4,"args",python,selection_mouse +317,773115,"jasmine/train_dynamics.py",6274,0,"",python,selection_mouse +318,773274,"jasmine/train_dynamics.py",6273,7,"seq_len",python,selection_mouse +319,774237,"jasmine/train_dynamics.py",6252,0,"",python,selection_mouse +320,774606,"jasmine/utils/dataloader_torch.py",0,0,"# file copied from https://raw.githubusercontent.com/FLAIROx/jafar/refs/heads/main/utils/dataloader.py\nfrom pathlib import Path\n\nimport jax.numpy as jnp\nimport numpy as np\nfrom torch.utils.data import Dataset, DataLoader\n\n\nclass VideoDataset(Dataset):\n def __init__(self, data_dir, seq_len):\n self.data_dir = Path(data_dir)\n self.seq_len = seq_len\n self.metadata = np.load(self.data_dir / ""metadata.npy"", allow_pickle=True)\n\n def __len__(self):\n return len(self.metadata)\n\n def __getitem__(self, idx):\n episode = np.load(self.metadata[idx][""path""])\n start_idx = np.random.randint(0, len(episode) - self.seq_len + 1)\n seq = episode[start_idx : start_idx + self.seq_len]\n return seq.astype(np.float32) / 255.0\n\n\ndef collate_fn(batch):\n """"""Convert batch of numpy arrays to JAX array""""""\n return jnp.array(np.stack(batch))\n\n\ndef get_dataloader(data_dir, seq_len, batch_size):\n dataset = VideoDataset(data_dir, seq_len)\n return DataLoader(\n dataset,\n batch_size=batch_size,\n shuffle=True,\n collate_fn=collate_fn,\n )\n",python,tab +321,774608,"jasmine/utils/dataloader_torch.py",895,0,"",python,selection_command +322,775674,"jasmine/utils/dataloader_torch.py",913,0,"",python,selection_mouse +323,775815,"jasmine/utils/dataloader_torch.py",910,8,"data_dir",python,selection_mouse +324,786703,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/dataloader_torch.py",0,0,"# file copied from https://raw.githubusercontent.com/FLAIROx/jafar/refs/heads/main/utils/dataloader.py\nfrom pathlib import Path\n\nimport jax.numpy as jnp\nimport numpy as np\nfrom torch.utils.data import Dataset, DataLoader\n\n\nclass VideoDataset(Dataset):\n def __init__(self, data_dir, seq_len):\n self.data_dir = Path(data_dir)\n self.seq_len = seq_len\n self.metadata = np.load(self.data_dir / ""metadata.npy"", allow_pickle=True)\n\n def __len__(self):\n return len(self.metadata)\n\n def __getitem__(self, idx):\n episode = np.load(self.metadata[idx][""path""])\n start_idx = np.random.randint(0, len(episode) - self.seq_len + 1)\n seq = episode[start_idx : start_idx + self.seq_len]\n return seq.astype(np.float32) / 255.0\n\n\ndef collate_fn(batch):\n """"""Convert batch of numpy arrays to JAX array""""""\n return jnp.array(np.stack(batch))\n\n\ndef get_dataloader(data_dir, seq_len, batch_size):\n dataset = VideoDataset(data_dir, seq_len)\n return DataLoader(\n dataset,\n batch_size=batch_size,\n shuffle=True,\n collate_fn=collate_fn,\n )\n",python,tab +325,786704,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/dataloader_torch.py",539,0,"",python,selection_command +326,789098,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/dataloader_torch.py",577,0,"",python,selection_mouse +327,789228,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/dataloader_torch.py",570,8,"metadata",python,selection_mouse +328,793039,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/dataloader_torch.py",404,0,"",python,selection_mouse +329,793186,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/dataloader_torch.py",402,8,"data_dir",python,selection_mouse +330,793833,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/dataloader_torch.py",397,0,"",python,selection_mouse 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+353,830184,"/home/hk-project-p0023960/tum_cte0515/Projects/jafar/generate_dataset.py",0,0,"""""""\nGenerates a dataset of random-action CoinRun episodes.\nEpisodes are saved individually as memory-mapped files for efficient loading.\n""""""\n\nfrom dataclasses import dataclass\nfrom pathlib import Path\n\nfrom gym3 import types_np\nimport numpy as np\nfrom procgen import ProcgenGym3Env\nimport tyro\n\n\n@dataclass\nclass Args:\n num_episodes: int = 10000\n output_dir: str = ""data/coinrun_episodes""\n min_episode_length: int = 50\n\n\nargs = tyro.cli(Args)\noutput_dir = Path(args.output_dir)\noutput_dir.mkdir(parents=True, exist_ok=True)\n\n# --- Generate episodes ---\ni = 0\nmetadata = []\nwhile i < args.num_episodes:\n seed = np.random.randint(0, 10000)\n env = ProcgenGym3Env(num=1, env_name=""coinrun"", start_level=seed)\n dataseq = []\n\n # --- Run episode ---\n for j in range(1000):\n env.act(types_np.sample(env.ac_space, bshape=(env.num,)))\n rew, obs, first = env.observe()\n dataseq.append(obs[""rgb""])\n if first:\n break\n\n # --- Save episode ---\n if len(dataseq) >= args.min_episode_length:\n episode_data = np.concatenate(dataseq, axis=0)\n episode_path = output_dir / f""episode_{i}.npy""\n np.save(episode_path, episode_data.astype(np.uint8))\n metadata.append({""path"": str(episode_path), ""length"": len(dataseq)})\n print(f""Episode {i} completed, length: {len(dataseq)}"")\n i += 1\n else:\n print(f""Episode too short ({len(dataseq)}), resampling..."")\n\n# --- Save metadata ---\nnp.save(output_dir / ""metadata.npy"", metadata)\nprint(f""Dataset generated with {len(metadata)} valid episodes"")\n",python,tab +354,830930,"/home/hk-project-p0023960/tum_cte0515/Projects/jafar/generate_dataset.py",227,0,"",python,selection_mouse +355,830963,"/home/hk-project-p0023960/tum_cte0515/Projects/jafar/generate_dataset.py",226,0,"",python,selection_command 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+607,1066715,"TERMINAL",0,0,"Episode 36 completed, length: 242\r\nEpisode 37 completed, length: 719\r\n",,terminal_output +608,1066829,"TERMINAL",0,0,"Episode 38 completed, length: 681\r\n",,terminal_output +609,1067055,"TERMINAL",0,0,"Episode 39 completed, length: 1000\r\n",,terminal_output +610,1067327,"TERMINAL",0,0,"Episode 40 completed, length: 1000\r\n",,terminal_output +611,1067773,"TERMINAL",0,0,"Episode 41 completed, length: 1000\r\nEpisode 42 completed, length: 157\r\nEpisode 43 completed, length: 433\r\nEpisode 44 completed, length: 166\r\nEpisode 45 completed, length: 250\r\n",,terminal_output +612,1067991,"TERMINAL",0,0,"Episode 46 completed, length: 1000\r\nEpisode 47 completed, length: 1000\r\nEpisode 48 completed, length: 54\r\nEpisode 49 completed, length: 361\r\nEpisode 50 completed, length: 248\r\n",,terminal_output +613,1068057,"TERMINAL",0,0,"Episode 51 completed, length: 223\r\nEpisode 52 completed, length: 57\r\n",,terminal_output +614,1068256,"TERMINAL",0,0,"Episode 53 completed, length: 988\r\n",,terminal_output +615,1068627,"TERMINAL",0,0,"Episode 54 completed, length: 1000\r\nEpisode 55 completed, length: 197\r\n",,terminal_output +616,1068722,"TERMINAL",0,0,"Episode 56 completed, length: 342\r\n",,terminal_output +617,1068953,"TERMINAL",0,0,"Episode 57 completed, length: 1000\r\n",,terminal_output +618,1069105,"TERMINAL",0,0,"Episode 58 completed, length: 1000\r\n",,terminal_output +619,1069184,"TERMINAL",0,0,"Episode 59 completed, length: 1000\r\n",,terminal_output +620,1069502,"TERMINAL",0,0,"Episode 60 completed, length: 1000\r\n",,terminal_output +621,1069732,"TERMINAL",0,0,"Episode 61 completed, length: 1000\r\nEpisode 62 completed, length: 298\r\n",,terminal_output +622,1070053,"TERMINAL",0,0,"Episode 63 completed, length: 1000\r\nEpisode 64 completed, length: 221\r\nEpisode 65 completed, length: 62\r\n",,terminal_output +623,1070182,"TERMINAL",0,0,"Episode 66 completed, length: 849\r\nEpisode 67 completed, length: 186\r\nEpisode 68 completed, length: 730\r\n",,terminal_output +624,1070233,"TERMINAL",0,0,"Episode 69 completed, length: 328\r\n",,terminal_output +625,1070467,"TERMINAL",0,0,"Episode 70 completed, length: 1000\r\n",,terminal_output +626,1070966,"TERMINAL",0,0,"Episode 71 completed, length: 1000\r\nEpisode 72 completed, length: 365\r\nEpisode 73 completed, length: 251\r\nEpisode 74 completed, length: 79\r\n",,terminal_output +627,1071388,"TERMINAL",0,0,"Episode 75 completed, length: 575\r\n",,terminal_output +628,1071468,"TERMINAL",0,0,"Episode 76 completed, length: 1000\r\n",,terminal_output +629,1071728,"TERMINAL",0,0,"Episode 77 completed, length: 927\r\nEpisode 78 completed, length: 211\r\nEpisode 79 completed, length: 287\r\n",,terminal_output +630,1071868,"TERMINAL",0,0,"Episode 80 completed, length: 423\r\nEpisode 81 completed, length: 502\r\n",,terminal_output +631,1071966,"TERMINAL",0,0,"Episode 82 completed, length: 841\r\n",,terminal_output +632,1072069,"TERMINAL",0,0,"Episode 83 completed, length: 645\r\n",,terminal_output +633,1072607,"TERMINAL",0,0,"Episode 84 completed, length: 669\r\nEpisode 85 completed, length: 293\r\nEpisode 86 completed, length: 262\r\nEpisode 87 completed, length: 381\r\nEpisode 88 completed, length: 570\r\nEpisode 89 completed, length: 238\r\nEpisode 90 completed, length: 1000\r\nEpisode 91 completed, length: 306\r\n",,terminal_output +634,1072748,"TERMINAL",0,0,"Episode 92 completed, length: 1000\r\n",,terminal_output +635,1072984,"TERMINAL",0,0,"Episode 93 completed, length: 1000\r\n",,terminal_output +636,1073127,"TERMINAL",0,0,"Episode 94 completed, length: 460\r\nEpisode 95 completed, length: 189\r\n",,terminal_output +637,1073500,"TERMINAL",0,0,"Episode 96 completed, length: 1000\r\n",,terminal_output +638,1073567,"TERMINAL",0,0,"Episode 97 completed, length: 599\r\n",,terminal_output +639,1073817,"TERMINAL",0,0,"Episode 98 completed, length: 1000\r\nEpisode 99 completed, length: 69\r\nDataset generated with 100 valid episodes\r\n",,terminal_output +640,1073993,"TERMINAL",0,0,"]0;tum_cte0515@hkn0401:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0401 jafar]$ ",,terminal_output +641,1075879,"TERMINAL",0,0,"d",,terminal_output +642,1076042,"TERMINAL",0,0,"e",,terminal_output +643,1076228,"TERMINAL",0,0,"a",,terminal_output +644,1076297,"TERMINAL",0,0,"c",,terminal_output +645,1076411,"TERMINAL",0,0,"t",,terminal_output +646,1076568,"TERMINAL",0,0,"i",,terminal_output +647,1076745,"TERMINAL",0,0,"v",,terminal_output +648,1076828,"TERMINAL",0,0,"at",,terminal_output +649,1076889,"TERMINAL",0,0,"e",,terminal_output +650,1077510,"TERMINAL",0,0,"\r\n[?2004l\r]0;tum_cte0515@hkn0401:~/Projects/jafar[?2004h[tum_cte0515@hkn0401 jafar]$ ",,terminal_output +651,1077909,"TERMINAL",0,0,"c",,terminal_output +652,1078007,"TERMINAL",0,0,"d",,terminal_output +653,1078161,"TERMINAL",0,0," ",,terminal_output +654,1078232,"TERMINAL",0,0,".",,terminal_output +655,1078426,"TERMINAL",0,0,".",,terminal_output +656,1078526,"TERMINAL",0,0,"\r\n[?2004l\r]0;tum_cte0515@hkn0401:~/Projects[?2004h[tum_cte0515@hkn0401 Projects]$ ",,terminal_output +657,1078708,"TERMINAL",0,0,"l",,terminal_output +658,1078801,"TERMINAL",0,0,"s",,terminal_output +659,1078873,"TERMINAL",0,0,"\r\n[?2004l\rcheckpoints jafar jasmine jasmine_jobs jasmine_jobs_2 jasmine_jobs_3 tmp\r\n]0;tum_cte0515@hkn0401:~/Projects[?2004h[tum_cte0515@hkn0401 Projects]$ ",,terminal_output +660,1079411,"TERMINAL",0,0,"cd",,terminal_output +661,1079543,"TERMINAL",0,0," ",,terminal_output +662,1079892,"TERMINAL",0,0,"j",,terminal_output +663,1079987,"TERMINAL",0,0,"a",,terminal_output +664,1080227,"TERMINAL",0,0,"m",,terminal_output +665,1080620,"TERMINAL",0,0,"s",,terminal_output +666,1080692,"TERMINAL",0,0,"m",,terminal_output +667,1080784,"TERMINAL",0,0,"ine",,terminal_output +668,1081052,"TERMINAL",0,0,"\r\n[?2004l\r]0;tum_cte0515@hkn0401:~/Projects/jasmine[?2004h[tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +669,1081115,"TERMINAL",0,0,"l",,terminal_output +670,1081216,"TERMINAL",0,0,"s",,terminal_output +671,1081301,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +672,1081444,"TERMINAL",0,0,"ali-old-branch.diff diff2.diff frame.png input_pipeline killer.sh logs overfit_dir.zip README.md scripts_cremers tests wandb\r\ndata diff.diff frames jasmine LICENSE message.md __pycache__ requirements-franz.txt slurm utils\r\ndebug frame-knoms.png gifs killer_partition.sh log.log models pyproject.toml samples test.py uv.lock\r\n]0;tum_cte0515@hkn0401:~/Projects/jasmine[?2004h[tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +673,1083662,"TERMINAL",0,0,"ls",,terminal_output +674,1083754,"TERMINAL",0,0,"cd jasmine",,terminal_output +675,1083987,"TERMINAL",0,0,"ls",,terminal_output +676,1084101,"TERMINAL",0,0,"cd ..",,terminal_output +677,1084206,"TERMINAL",0,0,"deactivate",,terminal_output +678,1084404,"TERMINAL",0,0,"python generate_dataset.py --output_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test --num_episodes=100",,terminal_output +679,1084560,"TERMINAL",0,0,"\rsource .venv/bin/activate",,terminal_output +680,1084646,"TERMINAL",0,0,"deactivate",,terminal_output +681,1085311,"TERMINAL",0,0,"python generate_dataset.py --output_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test --num_episodes=100",,terminal_output +682,1085435,"TERMINAL",0,0,"\rls",,terminal_output +683,1085964,"TERMINAL",0,0,"cd jafar/",,terminal_output +684,1086312,"TERMINAL",0,0,"ls",,terminal_output +685,1086595,"TERMINAL",0,0,"cd jafar/",,terminal_output +686,1086746,"TERMINAL",0,0,"..",,terminal_output +687,1086991,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",,terminal_output +688,1087242,"TERMINAL",0,0,"\rource .venv/bin/activate",,terminal_output +689,1087830,"TERMINAL",0,0,"h slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",,terminal_output +690,1089193,"TERMINAL",0,0,"bash",,terminal_focus +691,1090273,"TERMINAL",0,0,"ls",,terminal_command +692,1090304,"TERMINAL",0,0,"]633;C",,terminal_output +693,1090394,"TERMINAL",0,0,"array_records coinrun_episodes_10m_chunked coinrun_episodes_10m_gt_actions_split coinrun_episodes_500m_gt_actions_split\r\ncoinrun_episodes coinrun_episodes_10m_gt_actions coinrun_episodes_10m_gt_actions_split_test coinrun_episodes_test\r\ncoinrun_episodes_10m coinrun_episodes_10m_gt_actions_distinct_seed coinrun_episodes_10m_split npy_test\r\n]0;tum_cte0515@hkn1991:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun",,terminal_output +694,1096669,"TERMINAL",0,0,"cd npy_test/",,terminal_command +695,1097421,"TERMINAL",0,0,"pwd",,terminal_command +696,1100492,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",0,0,"",shellscript,tab +697,1103081,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",658,0,"",shellscript,selection_mouse +698,1104197,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",658,85,"",shellscript,content +699,1104210,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",657,0,"",shellscript,selection_command +700,1104547,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",658,0,"",shellscript,selection_command +701,1104828,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",658,0,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test",shellscript,content +702,1106455,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",759,0,"",shellscript,selection_mouse +703,1107239,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",758,0,"",shellscript,selection_command +704,1108066,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",757,0,"",shellscript,selection_command +705,1108203,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",758,0,"",shellscript,selection_command +706,1109276,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",758,85,"",shellscript,content +707,1109279,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",757,0,"",shellscript,selection_command +708,1109628,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",758,0,"",shellscript,selection_command +709,1110319,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",758,0,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test",shellscript,content +710,1110962,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",834,0,"",shellscript,selection_command +711,1113680,"TERMINAL",0,0,"srun",,terminal_focus +712,1114963,"TERMINAL",0,0,"\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=4\r\n#SBATCH --time=00:20:00\r\n#SBATCH --partition=dev_accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --job-name=train_dyn_single_gpu\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_default/3528955\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python jasmine/train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=64 \\r\n --image_width=64 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=110 \\r\n --init_lr=0 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=50 \\r\n --log_checkpoint_interval=2 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=coinrun-dyn-dev-$slurm_job_id \\r\n --tags dyn coinrun dev \\r\n --entity instant-uv \\r\n --project jafar \\r\n --warmup_steps 0 \\r\n --wsd_decay_steps 0 \\r\n --num_steps 10 \\r\n --data_dir $array_records_dir_train \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --val_data_dir $array_records_dir_val \\r\n --val_interval 2 \\r\n --eval_full_frame \\r\n --val_steps 5\r\n",,terminal_output +713,1115091,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=1469415\r\nSLURM_JOB_GPUS=0\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\r\nSLURMD_NODENAME=hkn0401\r\nSLURM_JOB_START_TIME=1759399921\r\nSLURM_STEP_NODELIST=hkn0401\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1759403521\r\nSLURM_PMI2_SRUN_PORT=44089\r\nSLURM_CPUS_ON_NODE=8\r\nSLURM_JOB_CPUS_PER_NODE=8\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=dev_accelerated\r\nSLURM_TRES_PER_TASK=cpu=8\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=1\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3536670\r\nSLURM_PTY_PORT=41791\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.199\r\nSLURM_PTY_WIN_ROW=40\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0401\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.199\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=182\r\nSLURM_NODELIST=hkn0401\r\nSLURM_SRUN_COMM_PORT=44895\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1991.localdomain\r\nSLURM_JOB_ID=3536670\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0401\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_STEP_LAUNCHER_PORT=44895\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0401\r\n",,terminal_output +714,1115208,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +715,1119169,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +716,1125595,"TERMINAL",0,0,"Counting all components: ['dynamics', 'lam', 'tokenizer']\r\n",,terminal_output +717,1126028,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +718,1126955,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.22.0\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_121919-cdtzddag\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run coinrun-dyn-dev-3536670\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/cdtzddag\r\nParameter counts:\r\n{'dynamics': 26555904, 'lam': 17640416, 'tokenizer': 33750256, 'total': 77946576}\r\n",,terminal_output +719,1128418,"TERMINAL",0,0,"============\r\n\r\n /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test \r\n\r\n============\r\n",,terminal_output +720,1129106,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +721,1133843,"TERMINAL",0,0,"Traceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 745, in \r\n main(args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 587, in main\r\n first_batch = next(dataloader_train)\r\n ^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 557, in \r\n videos_sharding, local_data=elem[""videos""]\r\n ~~~~^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/array.py"", line 386, in __getitem__\r\n return indexing.rewriting_take(self, idx)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/numpy/indexing.py"", line 655, in rewriting_take\r\n treedef, static_idx, dynamic_idx = split_index_for_jit(idx, arr.shape)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/numpy/indexing.py"", line 751, in split_index_for_jit\r\n raise TypeError(f""JAX does not support string indexing; got {idx=}"")\r\nTypeError: JAX does not support string indexing; got idx=('videos',)\r\n",,terminal_output +722,1134472,"TERMINAL",0,0,"wandb: \r\nwandb: 🚀 View run coinrun-dyn-dev-3536670 at: https://wandb.ai/instant-uv/jafar/runs/cdtzddag\r\nwandb: Find logs at: ../../../../../hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_121919-cdtzddag/logs\r\n",,terminal_output +723,1134637,"TERMINAL",0,0,"W1002 12:19:27.778152 1470987 pjrt_client.cc:1469] WatchJobStateAsync failed for task goo.gle/debugproto job_name: ""jax_worker"": CANCELLED: CANCELLED\r\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/WatchJobState:\r\n:UNKNOWN:Error received from peer {grpc_status:1, grpc_message:""CANCELLED""} [type.googleapis.com/tensorflow.DerivedStatus='']\r\n",,terminal_output +724,1135310,"TERMINAL",0,0,"srun: error: hkn0401: task 0: Exited with exit code 1\r\n]0;tum_cte0515@hkn0401:~/Projects/jasmine[?2004h[tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +725,1152022,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"import os\n\n\nos.environ.setdefault(""XLA_PYTHON_CLIENT_MEM_FRACTION"", ""0.98"")\n\nfrom dataclasses import dataclass, field\nimport itertools\nfrom typing import cast, Optional\n\nimport einops\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport flax.nnx as nnx\nfrom torch.utils.data import DataLoader\n\nfrom genie import Genie, restore_genie_components\nfrom utils.dataloader_torch import get_dataloader\nfrom utils.train_utils import (\n get_lr_schedule,\n count_parameters_by_component,\n print_mem_stats,\n print_compiled_memory_stats,\n print_compiled_cost_analysis,\n)\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n batch_size: int = 36\n init_lr: float = 0.0\n max_lr: float = 3e-5\n decay_end: float = 0.0\n wsd_decay_steps: int = (\n 10000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n )\n warmup_steps: int = 5000\n lr_schedule: str = ""wsd"" # supported options: wsd, cos\n # Tokenizer\n tokenizer_dim: int = 512\n tokenizer_ffn_dim: int = 2048\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 4\n tokenizer_num_heads: int = 8\n tokenizer_checkpoint: str = """"\n # LAM\n lam_dim: int = 512\n lam_ffn_dim: int = 2048\n latent_action_dim: int = 32\n num_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 4\n lam_num_heads: int = 8\n lam_checkpoint: str = """"\n # Dynamics\n dyna_type: str = ""maskgit"" # supported options: maskgit, causal\n dyna_dim: int = 512\n dyna_ffn_dim: int = 2048\n dyna_num_blocks: int = 6\n dyna_num_heads: int = 8\n dropout: float = 0.0\n mask_limit: float = 0.5\n z_loss_weight: float = 0.0\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\n use_flash_attention: bool = True\n use_gt_actions: bool = False\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n name: str = ""train_dynamics""\n tags: list[str] = field(default_factory=lambda: [""dynamics""])\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 25000\n log_checkpoint_keep_period: int = 20000\n log_gradients: bool = False\n val_data_dir: str = """"\n val_interval: int = 20_000\n val_steps: int = 50\n eval_full_frame: bool = False\n val_maskgit_steps: int = 25\n val_temperature: float = 1\n val_sample_argmax: bool = False\n wandb_id: str = """"\n\n\ndef build_model(args: Args, rng: jax.Array) -> tuple[Genie, jax.Array]:\n rng, _rng = jax.random.split(rng)\n rngs = nnx.Rngs(_rng)\n genie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n tokenizer_ffn_dim=args.tokenizer_ffn_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n lam_ffn_dim=args.lam_ffn_dim,\n latent_action_dim=args.latent_action_dim,\n num_actions=args.num_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n lam_co_train=not args.lam_checkpoint,\n use_gt_actions=args.use_gt_actions,\n # Dynamics\n dyna_type=args.dyna_type,\n dyna_dim=args.dyna_dim,\n dyna_ffn_dim=args.dyna_ffn_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n dropout=args.dropout,\n mask_limit=args.mask_limit,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n decode=False,\n rngs=rngs,\n )\n if args.use_gt_actions:\n assert (\n not args.lam_checkpoint\n ), ""Cannot use LAM when using ground-truth actions.""\n else:\n assert genie.lam is not None\n del genie.lam.decoder\n return genie, rng\n\n\ndef build_optimizer(genie: Genie, args: Args) -> nnx.ModelAndOptimizer:\n lr_schedule = get_lr_schedule(\n args.lr_schedule,\n args.init_lr,\n args.max_lr,\n args.decay_end,\n args.num_steps,\n args.warmup_steps,\n args.wsd_decay_steps,\n )\n tx = optax.adamw(\n learning_rate=lr_schedule,\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.param_dtype, # moments in full precision\n )\n optimizer = nnx.ModelAndOptimizer(genie, tx)\n return optimizer\n\n\ndef build_mesh_and_sharding(\n num_devices: int,\n) -> tuple[Mesh, NamedSharding, NamedSharding, NamedSharding]:\n device_mesh_arr = create_device_mesh((num_devices,))\n mesh = Mesh(devices=device_mesh_arr, axis_names=(""data"",))\n replicated_sharding = NamedSharding(mesh, PartitionSpec())\n videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n actions_sharding = NamedSharding(mesh, PartitionSpec(""data"", None))\n return mesh, replicated_sharding, videos_sharding, actions_sharding\n\n\ndef shard_optimizer_states(\n optimizer: nnx.ModelAndOptimizer, replicated_sharding: NamedSharding\n) -> None:\n model_state = nnx.state(optimizer.model)\n model_sharded_state = jax.lax.with_sharding_constraint(\n model_state, replicated_sharding\n )\n nnx.update(optimizer.model, model_sharded_state)\n optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)\n optimizer_sharded_state = jax.lax.with_sharding_constraint(\n optimizer_state, replicated_sharding\n )\n nnx.update(optimizer, optimizer_sharded_state)\n\n\ndef build_dataloader(args: Args, data_dir: str) -> DataLoader:\n return get_dataloader(data_dir, args.seq_len, args.batch_size)\n\n\ndef build_checkpoint_manager(args: Args) -> Optional[ocp.CheckpointManager]:\n if args.restore_ckpt or args.save_ckpt:\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n return checkpoint_manager\n else:\n return None\n\n\ndef restore_or_initialize_components(\n args: Args,\n checkpoint_manager: Optional[ocp.CheckpointManager],\n optimizer: nnx.ModelAndOptimizer,\n rng: jax.Array,\n replicated_sharding: NamedSharding,\n restore_step: Optional[int] = None,\n) -> tuple[\n int,\n nnx.ModelAndOptimizer,\n jax.Array,\n]:\n step = 0\n if checkpoint_manager and restore_step is None:\n restore_step = checkpoint_manager.latest_step()\n if args.restore_ckpt:\n assert checkpoint_manager is not None\n abstract_optimizer = nnx.eval_shape(lambda: optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n restore_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n )\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(), args=restore_args\n )\n restored_optimizer_state = restored[""model_state""]\n nnx.update(optimizer, restored_optimizer_state)\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored model state from step {step}"")\n else:\n # Restore from pre-trained tokenizer (and LAM)\n rng, _rng = jax.random.split(rng)\n optimizer = restore_genie_components(optimizer, replicated_sharding, _rng, args)\n return step, optimizer, rng\n\n\ndef _calculate_top_k_accuracy(\n token_logits_BTNV: jax.Array,\n video_tokens_BTN: jax.Array,\n mask_BTN: jax.Array,\n k: int,\n) -> jax.Array:\n _, topk_indices_BTNK = jax.lax.top_k(token_logits_BTNV, k)\n topk_correct = jnp.any(\n topk_indices_BTNK == video_tokens_BTN[..., jnp.newaxis], axis=-1\n )\n topk_acc = (mask_BTN * topk_correct).sum() / mask_BTN.sum()\n return topk_acc\n\n\ndef _calculate_step_metrics(\n outputs: dict[str, jax.Array],\n gt: jax.Array,\n num_actions: int,\n num_patch_latents: int,\n) -> tuple[jax.Array, dict]:\n mask_BTN = outputs[""mask""]\n outputs[""token_logits""] = outputs[""token_logits""].astype(jnp.float32)\n ce_loss = optax.softmax_cross_entropy_with_integer_labels(\n outputs[""token_logits""], outputs[""video_tokens""]\n )\n ce_loss = (mask_BTN * ce_loss).sum() / mask_BTN.sum()\n z_val = jax.nn.logsumexp(outputs[""token_logits""], axis=-1)\n z_loss_metric = (mask_BTN * (z_val**2)).sum() / mask_BTN.sum()\n\n masked_token_top_1_acc = _calculate_top_k_accuracy(\n outputs[""token_logits""], outputs[""video_tokens""], mask_BTN, 1\n )\n masked_token_top_2_acc = _calculate_top_k_accuracy(\n outputs[""token_logits""], outputs[""video_tokens""], mask_BTN, 2\n )\n masked_token_top_5_acc = _calculate_top_k_accuracy(\n outputs[""token_logits""], outputs[""video_tokens""], mask_BTN, 5\n )\n masked_token_top_16_acc = _calculate_top_k_accuracy(\n outputs[""token_logits""], outputs[""video_tokens""], mask_BTN, 16\n )\n\n select_probs = jax.nn.softmax(outputs[""token_logits""])\n gt_val = gt.clip(0, 1).reshape(-1, *gt.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt_val, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt_val, recon)).mean()\n _, index_counts_tokenizer = jnp.unique_counts(\n jnp.ravel(outputs[""video_tokens""]),\n size=num_patch_latents,\n fill_value=0,\n )\n codebook_usage_tokenizer = (index_counts_tokenizer != 0).mean()\n metrics = dict(\n cross_entropy_loss=ce_loss,\n masked_token_top1_accuracy=masked_token_top_1_acc,\n masked_token_top2_accuracy=masked_token_top_2_acc,\n masked_token_top5_accuracy=masked_token_top_5_acc,\n masked_token_top16_accuracy=masked_token_top_16_acc,\n select_logit=outputs[""token_logits""].max(-1).mean(),\n select_p=select_probs.max(-1).mean(),\n entropy=jax.scipy.special.entr(select_probs).sum(-1).mean(),\n z_loss=z_loss_metric,\n psnr=psnr,\n ssim=ssim,\n codebook_usage_tokenizer=codebook_usage_tokenizer,\n )\n if ""lam_indices"" in outputs.keys():\n _, index_counts_lam = jnp.unique_counts(\n jnp.ravel(outputs[""lam_indices""]),\n size=num_actions,\n fill_value=0,\n )\n codebook_usage_lam = (index_counts_lam != 0).mean()\n metrics[""codebook_usage_lam""] = codebook_usage_lam\n return ce_loss, metrics\n\n\ndef main(args: Args) -> None:\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 rng = jax.random.key(args.seed)\n\n # --- Initialize model ---\n genie, rng = build_model(args, rng)\n _, params, _ = nnx.split(genie, nnx.Param, ...)\n param_counts = count_parameters_by_component(params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\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 if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n optimizer = build_optimizer(genie, args)\n del genie\n\n # FIXME: switch to create_hybrid_device_mesh for runs spanning multiple nodes\n _, replicated_sharding, videos_sharding, actions_sharding = build_mesh_and_sharding(\n num_devices\n )\n\n shard_optimizer_states(optimizer, replicated_sharding)\n\n # --- Initialize checkpoint manager ---\n checkpoint_manager = build_checkpoint_manager(args)\n\n # --- Create DataLoaderIterator from dataloader ---\n print(""============\n\n"", args.data_dir, ""\n\n============"")\n train_iterator = build_dataloader(args, args.data_dir)\n val_iterator = None\n if args.val_data_dir:\n val_iterator = build_dataloader(args, args.val_data_dir)\n\n # --- Restore checkpoint ---\n step, optimizer, rng = restore_or_initialize_components(\n args,\n checkpoint_manager,\n optimizer,\n rng,\n replicated_sharding,\n )\n\n # --- Define loss and train step (close over args) ---\n def dynamics_loss_fn(\n model: Genie,\n inputs: dict,\n ) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs)\n ce_loss, metrics = _calculate_step_metrics(\n outputs, gt, args.num_actions, args.num_patch_latents\n )\n z_loss = metrics[""z_loss""]\n total_loss = ce_loss + args.z_loss_weight * z_loss\n metrics[""total_loss""] = total_loss\n return total_loss, (outputs[""recon""], metrics)\n\n @nnx.jit(donate_argnums=0)\n def train_step(\n optimizer: nnx.ModelAndOptimizer, inputs: dict\n ) -> tuple[jax.Array, jax.Array, dict]:\n def loss_fn(model: Genie) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n model.train()\n return dynamics_loss_fn(model, inputs)\n\n (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(\n optimizer.model\n )\n optimizer.update(grads)\n if args.log_gradients:\n metrics[""gradients_std/""] = jax.tree.map(\n lambda x: x.std(), grads[""params""][""dynamics""]\n )\n return loss, recon, metrics\n\n @nnx.jit\n def val_step(genie: Genie, inputs: dict) -> dict:\n """"""Evaluate model and compute metrics""""""\n genie.eval()\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n (loss, (recon, metrics)) = dynamics_loss_fn(genie, inputs)\n val_output = {""loss"": loss, ""recon"": recon, ""metrics"": metrics}\n\n # --- Evaluate full frame prediction (sampling) ---\n if args.eval_full_frame:\n inputs[""videos""] = gt.astype(args.dtype)\n tokenizer_outputs = genie.tokenizer.vq_encode(\n inputs[""videos""], training=False\n )\n tokens_full_frame = tokenizer_outputs[""indices""]\n lam_indices_E = None\n if not args.use_gt_actions:\n lam_indices_E = genie.vq_encode(inputs, training=False)\n inputs[""latent_actions""] = lam_indices_E\n inputs[""videos""] = inputs[""videos""][\n :, :-1\n ] # remove last frame for generation\n recon_full_frame, logits_full_frame = genie.sample(\n inputs,\n args.seq_len,\n args.val_temperature,\n args.val_sample_argmax,\n args.val_maskgit_steps,\n )\n # Calculate metrics for the last frame only\n step_outputs = {\n ""recon"": recon_full_frame[:, -1],\n ""token_logits"": logits_full_frame[:, -1],\n ""video_tokens"": tokens_full_frame[:, -1],\n ""mask"": jnp.ones_like(tokens_full_frame[:, -1]),\n }\n if lam_indices_E is not None:\n lam_indices_B = lam_indices_E.reshape((-1, args.seq_len - 1))[:, -1]\n step_outputs[""lam_indices""] = lam_indices_B\n\n loss_full_frame, metrics_full_frame = _calculate_step_metrics(\n step_outputs, gt[:, -1], args.num_actions, args.num_patch_latents\n )\n val_output.update(\n {\n ""loss_full_frame"": loss_full_frame,\n ""recon_full_frame"": recon_full_frame,\n ""metrics_full_frame"": metrics_full_frame,\n }\n )\n return val_output\n\n def calculate_validation_metrics(val_dataloader, genie, rng):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n loss_full_frame_per_step = []\n metrics_full_frame_per_step = []\n batch = None\n recon = None\n recon_full_frame = None\n for batch in val_dataloader:\n rng, _rng_mask = jax.random.split(rng, 2)\n batch[""rng""] = _rng_mask\n val_outputs = val_step(genie, batch)\n loss_per_step.append(val_outputs[""loss""])\n metrics_per_step.append(val_outputs[""metrics""])\n recon = val_outputs[""recon""]\n if args.eval_full_frame:\n loss_full_frame_per_step.append(val_outputs[""loss_full_frame""])\n metrics_full_frame_per_step.append(val_outputs[""metrics_full_frame""])\n recon_full_frame = val_outputs[""recon_full_frame""]\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(\n f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}""\n )\n\n val_metrics = {\n f""val_{key}"": np.mean([float(m[key]) for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n val_metrics[""val_loss""] = np.mean(loss_per_step)\n if args.eval_full_frame:\n val_metrics_full_frame = {\n f""val_full_frame_{key}"": np.mean(\n [float(m[key]) for m in metrics_full_frame_per_step]\n )\n for key in metrics_full_frame_per_step[0].keys()\n }\n val_metrics.update(val_metrics_full_frame)\n val_metrics[""val_full_frame_loss""] = np.mean(loss_full_frame_per_step)\n return val_metrics, batch, recon, recon_full_frame\n\n # --- TRAIN LOOP ---\n dataloader_train = (\n {\n ""videos"": jax.make_array_from_process_local_data(\n videos_sharding, local_data=elem[""videos""]\n ),\n ""actions"": (\n jax.make_array_from_process_local_data(\n actions_sharding, elem[""actions""]\n )\n if args.use_gt_actions\n else None\n ),\n }\n for elem in train_iterator\n )\n dataloader_val = None\n if val_iterator:\n dataloader_val = (\n {\n ""videos"": jax.make_array_from_process_local_data(\n videos_sharding, elem[""videos""]\n ),\n ""actions"": (\n jax.make_array_from_process_local_data(\n actions_sharding, elem[""actions""]\n )\n if args.use_gt_actions\n else None\n ),\n }\n for elem in val_iterator\n )\n if jax.process_index() == 0:\n first_batch = next(dataloader_train)\n first_batch[""rng""] = rng # type: ignore\n compiled = train_step.lower(optimizer, first_batch).compile()\n print_compiled_memory_stats(compiled.memory_analysis())\n print_compiled_cost_analysis(compiled.cost_analysis())\n # Do not skip the first batch during training\n dataloader_train = itertools.chain([first_batch], dataloader_train)\n print(f""Starting training from step {step}..."")\n first_step = step\n while step < args.num_steps:\n for batch in dataloader_train:\n # --- Train step ---\n rng, _rng_mask = jax.random.split(rng, 2)\n batch[""rng""] = _rng_mask\n loss, recon, metrics = train_step(optimizer, batch)\n if step == first_step:\n print_mem_stats(""After params initialized"")\n step += 1\n\n # --- Validation loss ---\n val_results = {}\n if dataloader_val and step % args.val_interval == 0:\n rng, _rng_mask_val = jax.random.split(rng, 2)\n print(""Calculating validation metrics..."")\n val_metrics, val_gt_batch, val_recon, val_recon_full_frame = (\n calculate_validation_metrics(\n dataloader_val, optimizer.model, _rng_mask_val\n )\n )\n print(f""Step {step}, validation loss: {val_metrics['val_loss']}"")\n val_results = {\n ""metrics"": val_metrics,\n ""gt_batch"": val_gt_batch,\n ""recon"": val_recon,\n ""full_frame"": val_recon_full_frame,\n }\n\n # --- Logging ---\n if args.log:\n if step % args.log_interval == 0 and jax.process_index() == 0:\n log_dict = {""loss"": loss, ""step"": step, **metrics}\n if val_results:\n log_dict.update(val_results[""metrics""])\n wandb.log(log_dict)\n if step % args.log_image_interval == 0:\n gt_seq = batch[""videos""][0].astype(jnp.float32) / 255.0\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 if val_results:\n val_results[""gt_seq_val""] = (\n val_results[""gt_batch""][""videos""][0].astype(jnp.float32)\n / 255.0\n )\n val_results[""recon_seq_val""] = val_results[""recon""][0].clip(\n 0, 1\n )\n val_comparison_seq = jnp.concatenate(\n (val_results[""gt_seq_val""], val_results[""recon_seq_val""]),\n axis=1,\n )\n val_results[""val_comparison_seq""] = einops.rearrange(\n val_comparison_seq * 255, ""t h w c -> h (t w) c""\n )\n if args.eval_full_frame:\n val_results[""full_frame_seq_val""] = val_results[\n ""full_frame""\n ][0].clip(0, 1)\n val_results[""val_full_frame_comparison_seq""] = (\n jnp.concatenate(\n (\n val_results[""gt_seq_val""],\n val_results[""full_frame_seq_val""],\n ),\n axis=1,\n )\n )\n val_results[""val_full_frame_comparison_seq""] = (\n einops.rearrange(\n val_results[""val_full_frame_comparison_seq""] * 255,\n ""t h w c -> h (t w) c"",\n )\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[args.seq_len - 1])),\n recon=wandb.Image(np.asarray(recon_seq[args.seq_len - 1])),\n true_vs_recon=wandb.Image(\n np.asarray(comparison_seq.astype(np.uint8))\n ),\n )\n if val_results:\n log_images.update(\n dict(\n val_image=wandb.Image(\n np.asarray(\n val_results[""gt_seq_val""][args.seq_len - 1]\n )\n ),\n val_recon=wandb.Image(\n np.asarray(\n val_results[""recon_seq_val""][\n args.seq_len - 1\n ]\n )\n ),\n val_true_vs_recon=wandb.Image(\n np.asarray(\n val_results[""val_comparison_seq""].astype(\n np.uint8\n )\n )\n ),\n )\n )\n if args.eval_full_frame:\n log_images.update(\n dict(\n val_full_frame=wandb.Image(\n 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+800,1211653,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19491,0,"",python,selection_command +801,1211936,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19492,0,"",python,selection_command +802,1212034,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19491,1,"",python,content +803,1212200,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19490,0,"",python,selection_command +804,1215037,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",,terminal_output +805,1215134,"TERMINAL",0,0,"\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=4\r\n#SBATCH --time=00:20:00\r\n#SBATCH --partition=dev_accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --job-name=train_dyn_single_gpu\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_default/3528955\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python jasmine/train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=64 \\r\n --image_width=64 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=110 \\r\n --init_lr=0 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=50 \\r\n --log_checkpoint_interval=2 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=coinrun-dyn-dev-$slurm_job_id \\r\n --tags dyn coinrun dev \\r\n --entity instant-uv \\r\n --project jafar \\r\n --warmup_steps 0 \\r\n --wsd_decay_steps 0 \\r\n --num_steps 10 \\r\n --data_dir $array_records_dir_train \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --val_data_dir $array_records_dir_val \\r\n --val_interval 2 \\r\n --eval_full_frame \\r\n --val_steps 5\r\n",,terminal_output +806,1215263,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=1469415\r\nSLURM_JOB_GPUS=0\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\r\nSLURMD_NODENAME=hkn0401\r\nSLURM_JOB_START_TIME=1759399921\r\nSLURM_STEP_NODELIST=hkn0401\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1759403521\r\nSLURM_PMI2_SRUN_PORT=44089\r\nSLURM_CPUS_ON_NODE=8\r\nSLURM_JOB_CPUS_PER_NODE=8\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=dev_accelerated\r\nSLURM_TRES_PER_TASK=cpu=8\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=1\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3536670\r\nSLURM_PTY_PORT=41791\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.199\r\nSLURM_PTY_WIN_ROW=40\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0401\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.199\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=182\r\nSLURM_NODELIST=hkn0401\r\nSLURM_SRUN_COMM_PORT=44895\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1991.localdomain\r\nSLURM_JOB_ID=3536670\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0401\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_STEP_LAUNCHER_PORT=44895\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0401\r\n",,terminal_output +807,1215396,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +808,1219118,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +809,1225558,"TERMINAL",0,0,"Counting all components: ['dynamics', 'lam', 'tokenizer']\r\n",,terminal_output +810,1226071,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +811,1226908,"TERMINAL",0,0,"wandb: creating run\r\nwandb: Tracking run with wandb version 0.22.0\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_122059-y4usbd13\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run coinrun-dyn-dev-3536670\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/y4usbd13\r\n",,terminal_output +812,1227037,"TERMINAL",0,0,"Parameter counts:\r\n{'dynamics': 26555904, 'lam': 17640416, 'tokenizer': 33750256, 'total': 77946576}\r\n",,terminal_output +813,1228392,"TERMINAL",0,0,"============\r\n\r\n /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test \r\n\r\n============\r\n",,terminal_output +814,1229146,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +815,1249084,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"",python,tab +816,1249534,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19523,0,"",python,selection_mouse +817,1250961,"TERMINAL",0,0,"Traceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 731, in \r\n main(args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 575, in main\r\n compiled = train_step.lower(optimizer, first_batch).compile()\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/transforms/compilation.py"", line 476, in lower\r\n lowered = self.jitted_fn.lower(*pure_args, **pure_kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/transforms/compilation.py"", line 126, in __call__\r\n out = self.f(*args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 446, in train_step\r\n (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/graph.py"", line 2045, in update_context_manager_wrapper\r\n return f(*args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/transforms/autodiff.py"", line 163, in grad_wrapper\r\n fn_out = gradded_fn(*pure_args)\r\n ^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/transforms/autodiff.py"", line 88, in __call__\r\n out = self.f(*args)\r\n ^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 444, in loss_fn\r\n return dynamics_loss_fn(model, inputs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 429, in dynamics_loss_fn\r\n outputs = model(inputs)\r\n ^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/genie.py"", line 158, in __call__\r\n tokenizer_outputs = self.tokenizer.vq_encode(videos_BTHWC, training=False)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/models/tokenizer.py"", line 115, in vq_encode\r\n x_BTNL = self.encoder(patch_BTNP)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py"", line 250, in __call__\r\n x_BTNI = self.input_norm1(x_BTNI)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/nn/normalization.py"", line 505, in __call__\r\n return _normalize(\r\n ^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/nn/normalization.py"", line 172, in _normalize\r\n scale = scale.reshape(feature_shape)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/numpy/array_methods.py"", line 1147, in meth\r\n return getattr(self.aval, name).fun(self, *args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/numpy/array_methods.py"", line 316, in _reshape\r\n newshape = _compute_newshape(self, args[0] if len(args) == 1 else args)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/numpy/array_methods.py"", line 477, in _compute_newshape\r\n raise TypeError(f""cannot reshape array of shape {arr.shape} (size {arr.size}) ""\r\nTypeError: cannot reshape array of shape (768,) (size 768) into shape [1, 1, 1, 48] (size 48)\r\n--------------------\r\nFor simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n",,terminal_output +818,1251635,"TERMINAL",0,0,"wandb: \r\nwandb: 🚀 View run coinrun-dyn-dev-3536670 at: https://wandb.ai/instant-uv/jafar/runs/y4usbd13\r\nwandb: Find logs at: ../../../../../hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_122059-y4usbd13/logs\r\n",,terminal_output +819,1251782,"TERMINAL",0,0,"W1002 12:21:24.963197 1471562 pjrt_client.cc:1469] WatchJobStateAsync failed for task goo.gle/debugonly job_name: ""jax_worker"": CANCELLED: CANCELLED\r\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/WatchJobState:\r\n:UNKNOWN:Error received from peer {grpc_status:1, grpc_message:""CANCELLED""} [type.googleapis.com/tensorflow.DerivedStatus='']\r\n",,terminal_output +820,1252239,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20017,0,"",python,selection_command +821,1252670,"TERMINAL",0,0,"srun: error: hkn0401: task 0: Exited with exit code 1\r\n]0;tum_cte0515@hkn0401:~/Projects/jasmine[?2004h[tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +822,1252783,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",18437,0,"",python,selection_command +823,1252936,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",13437,0,"",python,selection_command +824,1254222,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",13433,65,"",python,content +825,1254235,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",13437,0,"",python,selection_command +826,1307820,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",0,0,"",shellscript,tab +827,1369119,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/genie.py",0,0,"from typing import Dict\n\nimport einops\nimport jax\nimport jax.numpy as jnp\nimport flax.nnx as nnx\nimport orbax.checkpoint as ocp\n\nfrom models.dynamics import DynamicsMaskGIT, DynamicsCausal\nfrom models.lam import LatentActionModel\nfrom models.tokenizer import TokenizerVQVAE\n\n\nclass Genie(nnx.Module):\n """"""Genie model""""""\n\n def __init__(\n self,\n in_dim: int,\n tokenizer_dim: int,\n tokenizer_ffn_dim: int,\n latent_patch_dim: int,\n num_patch_latents: int,\n patch_size: int,\n tokenizer_num_blocks: int,\n tokenizer_num_heads: int,\n lam_dim: int,\n lam_ffn_dim: int,\n latent_action_dim: int,\n num_actions: int,\n lam_patch_size: int,\n lam_num_blocks: int,\n lam_num_heads: int,\n lam_co_train: bool,\n use_gt_actions: bool,\n dyna_type: str,\n dyna_dim: int,\n dyna_ffn_dim: int,\n dyna_num_blocks: int,\n dyna_num_heads: int,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n decode: bool,\n rngs: nnx.Rngs,\n dropout: float = 0.0,\n mask_limit: float = 0.0,\n ):\n # --- Tokenizer ---\n self.in_dim = in_dim\n self.tokenizer_dim = tokenizer_dim\n self.tokenizer_ffn_dim = tokenizer_ffn_dim\n self.latent_patch_dim = latent_patch_dim\n self.num_patch_latents = num_patch_latents\n self.patch_size = patch_size\n self.tokenizer_num_blocks = tokenizer_num_blocks\n self.tokenizer_num_heads = tokenizer_num_heads\n # --- LAM ---\n self.lam_dim = lam_dim\n self.lam_ffn_dim = lam_ffn_dim\n self.latent_action_dim = latent_action_dim\n self.num_actions = num_actions\n self.lam_patch_size = lam_patch_size\n self.lam_num_blocks = lam_num_blocks\n self.lam_num_heads = lam_num_heads\n self.lam_co_train = lam_co_train\n self.use_gt_actions = use_gt_actions\n # --- Dynamics ---\n self.dyna_type = dyna_type\n self.dyna_dim = dyna_dim\n self.dyna_ffn_dim = dyna_ffn_dim\n self.dyna_num_blocks = dyna_num_blocks\n self.dyna_num_heads = dyna_num_heads\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.dropout = dropout\n self.mask_limit = mask_limit\n self.decode = decode\n\n self.tokenizer = TokenizerVQVAE(\n in_dim=self.in_dim,\n model_dim=self.tokenizer_dim,\n ffn_dim=self.tokenizer_ffn_dim,\n latent_dim=self.latent_patch_dim,\n num_latents=self.num_patch_latents,\n patch_size=self.patch_size,\n num_blocks=self.tokenizer_num_blocks,\n num_heads=self.tokenizer_num_heads,\n dropout=0.0,\n codebook_dropout=0.0,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n if self.use_gt_actions:\n self.action_embed = nnx.Embed(\n self.num_actions, self.latent_action_dim, rngs=rngs\n )\n self.lam = None\n else:\n self.lam = LatentActionModel(\n in_dim=self.in_dim,\n model_dim=self.lam_dim,\n ffn_dim=self.lam_ffn_dim,\n latent_dim=self.latent_patch_dim,\n num_latents=self.num_actions,\n patch_size=self.lam_patch_size,\n num_blocks=self.lam_num_blocks,\n num_heads=self.lam_num_heads,\n dropout=0.0,\n codebook_dropout=0.0,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n self.action_embed = None\n if self.dyna_type == ""maskgit"":\n self.dynamics = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n mask_limit=self.mask_limit,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n elif self.dyna_type == ""causal"":\n self.dynamics = DynamicsCausal(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=decode,\n rngs=rngs,\n )\n else:\n raise ValueError(f""Invalid dynamics type: {self.dyna_type}"")\n\n def __call__(\n self,\n batch: Dict[str, jax.Array],\n ) -> Dict[str, jax.Array]:\n videos_BTHWC = batch[""videos""]\n tokenizer_outputs = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_indices_BTN = tokenizer_outputs[""indices""]\n latent_actions_BTm11L = None\n action_embeddings_BTm11L = None\n if self.use_gt_actions:\n assert self.action_embed is not None\n action_indices_E = None\n action_embeddings_BT1L = self.action_embed(batch[""actions""]).reshape(\n *batch[""actions""].shape[:2], 1, self.latent_action_dim\n )\n action_embeddings_BTm11L = action_embeddings_BT1L[:, :-1]\n else:\n assert self.lam is not None\n lam_outputs = self.lam.vq_encode(videos_BTHWC, training=False)\n z_q_BTm11L = lam_outputs[""z_q""]\n action_indices_E = lam_outputs[""indices""]\n latent_actions_BTm11L = jax.lax.cond(\n self.lam_co_train,\n lambda: z_q_BTm11L,\n lambda: jax.lax.stop_gradient(z_q_BTm11L),\n )\n outputs = dict(\n video_tokens=jax.lax.stop_gradient(token_indices_BTN),\n latent_actions=(\n action_embeddings_BTm11L\n if self.use_gt_actions\n else latent_actions_BTm11L\n ),\n )\n outputs[""mask_rng""] = batch[""rng""]\n dyna_logits_BTNV, dyna_mask = self.dynamics(outputs)\n outputs[""token_logits""] = dyna_logits_BTNV\n outputs[""mask""] = dyna_mask\n mle_indices_BTN = jnp.argmax(outputs[""token_logits""], axis=-1)\n H, W = batch[""videos""].shape[2:4]\n outputs[""recon""] = self.tokenizer.decode(mle_indices_BTN, (H, W))\n if action_indices_E is not None:\n outputs[""lam_indices""] = action_indices_E\n return outputs\n\n def sample(\n self,\n batch: Dict[str, jax.Array],\n seq_len: int,\n temperature: float = 1,\n sample_argmax: bool = False,\n maskgit_steps: int = 25,\n ) -> tuple[jax.Array, jax.Array]:\n if self.dyna_type == ""maskgit"":\n return self.sample_maskgit(\n batch, seq_len, maskgit_steps, temperature, sample_argmax\n )\n elif self.dyna_type == ""causal"":\n return self.sample_causal(batch, seq_len, temperature, sample_argmax)\n else:\n raise ValueError(f""Dynamics model type unknown: {self.dyna_type}"")\n\n def sample_maskgit(\n self,\n batch: Dict[str, jax.Array],\n seq_len: int,\n steps: int = 25,\n temperature: float = 1,\n sample_argmax: bool = False,\n ) -> tuple[jax.Array, jax.Array]:\n """"""\n Autoregressively samples up to `seq_len` future frames, following Figure 8 of the paper.\n\n - Input frames are tokenized once.\n - Future frames are generated autoregressively in token space.\n - All frames are detokenized in a single pass.\n\n Note:\n - For interactive or step-wise sampling, detokenization should occur after each action.\n - To maintain consistent tensor shapes across timesteps, all current and future frames are decoded at every step.\n - Temporal causal structure is preserved by\n a) reapplying the mask before each decoding step.\n b) a temporal causal mask is applied within each ST-transformer block.\n\n Dimension keys:\n B: batch size\n T: number of input (conditioning) frames\n N: number of patches per frame\n M: model dimension\n S: sequence length\n H: height\n W: width\n E: B * (S - 1)\n P: S * N\n """"""\n assert isinstance(self.dynamics, DynamicsMaskGIT)\n # --- Encode videos and actions ---\n videos_BTHWC = batch[""videos""]\n tokenizer_out = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_idxs_BTN = tokenizer_out[""indices""]\n B, T, N = token_idxs_BTN.shape\n pad_shape = (B, seq_len - T, N)\n pad = jnp.zeros(pad_shape, dtype=token_idxs_BTN.dtype)\n token_idxs_BSN = jnp.concatenate([token_idxs_BTN, pad], axis=1)\n init_logits_BSNV = jnp.zeros(\n shape=(*token_idxs_BSN.shape, self.num_patch_latents)\n )\n if self.use_gt_actions:\n assert self.action_embed is not None\n latent_actions_BT1L = self.action_embed(batch[""actions""]).reshape(\n *batch[""actions""].shape[:2], 1, self.latent_action_dim\n )\n latent_actions_BTm11L = latent_actions_BT1L[:, :-1]\n action_tokens_EL = latent_actions_BTm11L.reshape(-1, self.latent_action_dim)\n else:\n assert self.lam is not None\n latent_actions_E = batch[""latent_actions""]\n action_tokens_EL = self.lam.vq.get_codes(latent_actions_E)\n\n # --- Extract submodule state ---\n dynamics_state = nnx.state(self.dynamics)\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def maskgit_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array],\n step: jax.Array,\n ) -> tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array]:\n rng, token_idxs_BSN, logits_BSNV, mask_BSN, action_tokens_EL = carry\n S, N = token_idxs_BSN.shape[1:]\n L = action_tokens_EL.shape[-1]\n\n # We need to reconstruct the submodule inside scan body to prevent trace context mismatches\n dynamics_maskgit = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n mask_limit=self.mask_limit,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=nnx.Rngs(0),\n )\n nnx.update(dynamics_maskgit, dynamics_state)\n\n # --- Construct + encode video ---\n vid_embed_BSNM = dynamics_maskgit.patch_embed(token_idxs_BSN)\n mask_token_111M = dynamics_maskgit.mask_token.value\n mask_expanded_BSN1 = mask_BSN[..., None]\n vid_embed_BSNM = jnp.where(\n mask_expanded_BSN1, mask_token_111M, vid_embed_BSNM\n )\n\n # --- Predict transition ---\n action_tokens_BSm1L = jnp.reshape(action_tokens_EL, (B, S - 1, L))\n act_embed_BSm1M = dynamics_maskgit.action_up(action_tokens_BSm1L)\n act_embed_BSM = jnp.pad(act_embed_BSm1M, ((0, 0), (1, 0), (0, 0)))\n act_embed_BS1M = jnp.reshape(\n act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1])\n )\n vid_embed_BSNM += act_embed_BS1M\n unmasked_ratio = jnp.cos(jnp.pi * (step + 1) / (steps * 2))\n step_temp = temperature * (1.0 - unmasked_ratio)\n final_logits_BSNV = dynamics_maskgit.transformer(vid_embed_BSNM) / step_temp\n\n # --- Sample new tokens for final frame ---\n if sample_argmax:\n sampled_token_idxs_BSN = jnp.argmax(final_logits_BSNV, axis=-1)\n else:\n rng, _rng = jax.random.split(rng)\n sampled_token_idxs_BSN = jax.random.categorical(_rng, final_logits_BSNV)\n gather_fn = jax.vmap(jax.vmap(jax.vmap(lambda x, y: x[y])))\n final_token_probs_BSN = gather_fn(\n jax.nn.softmax(final_logits_BSNV), sampled_token_idxs_BSN\n )\n final_token_probs_BSN += ~mask_BSN\n # Update masked tokens and logits only\n token_idxs_BSN = jnp.where(mask_BSN, sampled_token_idxs_BSN, token_idxs_BSN)\n logits_BSNV = jnp.where(\n jnp.expand_dims(mask_BSN, -1), final_logits_BSNV, logits_BSNV\n )\n\n # --- Update mask ---\n num_unmasked_tokens = jnp.round(N * (1.0 - unmasked_ratio)).astype(int)\n final_token_probs_flat_BP = einops.rearrange(\n final_token_probs_BSN, ""b s n -> b (s n)""\n )\n idx_mask_P = (\n jnp.arange(final_token_probs_flat_BP.shape[-1])\n <= N - num_unmasked_tokens\n )\n sorted_idxs_BP = jnp.argsort(final_token_probs_flat_BP, axis=-1)\n mask_update_fn = jax.vmap(lambda msk, ids: msk.at[ids].set(idx_mask_P))\n mask_flat_BP = einops.rearrange(mask_BSN, ""b s n -> b (s n)"")\n new_mask_flat_BP = mask_update_fn(mask_flat_BP, sorted_idxs_BP)\n new_mask_BSN = einops.rearrange(new_mask_flat_BP, ""b (s n) -> b s n"", n=N)\n\n new_carry = (\n rng,\n token_idxs_BSN,\n logits_BSNV,\n new_mask_BSN,\n action_tokens_EL,\n )\n return new_carry\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def generation_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array], step_t: jax.Array\n ) -> tuple[jax.Array, jax.Array, jax.Array]:\n rng, current_token_idxs_BSN, current_logits_BSNV = carry\n rng, step_rng = jax.random.split(rng)\n\n # Mask current frame (i.e., t == step_t)\n mask_S = jnp.arange(seq_len) == step_t\n mask_BSN = jnp.broadcast_to(mask_S[None, :, None], (B, seq_len, N)).astype(\n bool\n )\n masked_token_idxs_BSN = current_token_idxs_BSN * ~mask_BSN\n masked_logits_BSNV = current_logits_BSNV * jnp.expand_dims(~mask_BSN, -1)\n\n # --- Initialize and run MaskGIT loop ---\n init_carry_maskgit = (\n step_rng,\n masked_token_idxs_BSN,\n masked_logits_BSNV,\n mask_BSN,\n action_tokens_EL,\n )\n final_carry_maskgit = maskgit_step_fn(init_carry_maskgit, jnp.arange(steps))\n updated_token_idxs_BSN = final_carry_maskgit[1]\n updated_logits_BSNV = final_carry_maskgit[2]\n new_carry = (rng, updated_token_idxs_BSN, updated_logits_BSNV)\n return new_carry\n\n # --- Run the autoregressive generation using jax.lax.scan ---\n initial_carry = (batch[""rng""], token_idxs_BSN, init_logits_BSNV)\n timesteps_to_scan = jnp.arange(T, seq_len)\n final_carry = generation_step_fn(initial_carry, timesteps_to_scan)\n final_token_idxs_BSN = final_carry[1]\n final_logits_BSNV = final_carry[2]\n\n # --- Decode all tokens at once at the end ---\n H, W = batch[""videos""].shape[2:4]\n final_frames_BSHWC = self.tokenizer.decode(\n final_token_idxs_BSN,\n video_hw=(H, W),\n )\n return final_frames_BSHWC, final_logits_BSNV\n\n def sample_causal(\n self,\n batch: Dict[str, jax.Array],\n seq_len: int,\n temperature: float = 1,\n sample_argmax: bool = False,\n ) -> tuple[jax.Array, jax.Array]:\n """"""\n Autoregressively samples up to `seq_len` future frames, following Figure 8 of the paper.\n\n - Input frames are tokenized once.\n - Future frames are generated autoregressively in token space.\n - All frames are detokenized in a single pass.\n\n Note:\n - For interactive or step-wise sampling, detokenization should occur after each action.\n - To maintain consistent tensor shapes across timesteps, all current and future frames are decoded at every step.\n - Temporal causal structure is preserved by\n a) reapplying the mask before each decoding step.\n b) a temporal causal mask is applied within each ST-transformer block.\n\n Dimension keys:\n B: batch size\n T: number of input (conditioning) frames\n N: number of patches per frame\n M: model dimension\n S: sequence length\n H: height\n W: width\n E: B * (S - 1)\n """"""\n assert isinstance(self.dynamics, DynamicsCausal)\n # --- Encode videos and actions ---\n videos_BTHWC = batch[""videos""]\n tokenizer_out = self.tokenizer.vq_encode(videos_BTHWC, training=False)\n token_idxs_BTN = tokenizer_out[""indices""]\n B, T, N = token_idxs_BTN.shape\n pad_shape = (B, seq_len - T, N)\n pad = jnp.zeros(pad_shape, dtype=token_idxs_BTN.dtype)\n token_idxs_BSN = jnp.concatenate([token_idxs_BTN, pad], axis=1)\n logits_BSNV = jnp.zeros((*token_idxs_BSN.shape, self.num_patch_latents))\n dynamics_state = nnx.state(self.dynamics)\n\n if self.use_gt_actions:\n assert self.action_embed is not None\n latent_actions_BT1L = self.action_embed(batch[""actions""]).reshape(\n *batch[""actions""].shape[:2], 1, self.latent_action_dim\n )\n latent_actions_BTm11L = latent_actions_BT1L[:, :-1]\n action_tokens_EL = latent_actions_BTm11L.reshape(-1, self.latent_action_dim)\n else:\n assert self.lam is not None\n latent_actions_E = batch[""latent_actions""]\n action_tokens_EL = self.lam.vq.get_codes(latent_actions_E)\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def causal_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array],\n step_n: jax.Array,\n ) -> tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array]:\n rng, token_idxs_BSN, logits_BSNV, action_tokens_EL, step_t = carry\n S, N = token_idxs_BSN.shape[1:]\n L = action_tokens_EL.shape[-1]\n\n # We need to reconstruct the submodule inside scan body to prevent trace context mismatches\n dynamics_causal = DynamicsCausal(\n model_dim=self.dyna_dim,\n ffn_dim=self.dyna_ffn_dim,\n num_latents=self.num_patch_latents,\n latent_action_dim=self.latent_action_dim,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=self.decode,\n rngs=nnx.Rngs(0),\n )\n nnx.update(dynamics_causal, dynamics_state)\n\n # --- Construct + encode video ---\n vid_embed_BSNM = dynamics_causal.patch_embed(token_idxs_BSN)\n\n # --- Predict transition ---\n action_tokens_BSm1L = jnp.reshape(action_tokens_EL, (B, S - 1, L))\n act_embed_BSm1M = dynamics_causal.action_up(action_tokens_BSm1L)\n act_embed_BSM = jnp.pad(act_embed_BSm1M, ((0, 0), (1, 0), (0, 0)))\n act_embed_BS1M = jnp.reshape(\n act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1])\n )\n vid_embed_BSNp1M = jnp.concatenate([act_embed_BS1M, vid_embed_BSNM], axis=2)\n final_logits_BTNp1V = (\n dynamics_causal.transformer(vid_embed_BSNp1M, (step_t, step_n))\n / temperature\n )\n final_logits_BV = final_logits_BTNp1V[:, step_t, step_n, :]\n\n # --- Sample new tokens for final frame ---\n if sample_argmax:\n sampled_token_idxs_B = jnp.argmax(final_logits_BV, axis=-1)\n else:\n rng, _rng = jax.random.split(rng)\n sampled_token_idxs_B = jax.random.categorical(_rng, final_logits_BV)\n # Update next tokens only\n token_idxs_BSN = token_idxs_BSN.at[:, step_t, step_n].set(\n sampled_token_idxs_B\n )\n logits_BSNV = logits_BSNV.at[:, step_t, step_n].set(final_logits_BV)\n\n new_carry = (rng, token_idxs_BSN, logits_BSNV, action_tokens_EL, step_t)\n return new_carry\n\n @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)\n def generation_step_fn(\n carry: tuple[jax.Array, jax.Array, jax.Array], step_t: jax.Array\n ) -> tuple[jax.Array, jax.Array, jax.Array]:\n rng, current_token_idxs_BSN, current_logits_BSNV = carry\n rng, step_rng = jax.random.split(rng)\n\n # --- Initialize and run causal loop ---\n init_carry_causal = (\n step_rng,\n current_token_idxs_BSN,\n current_logits_BSNV,\n action_tokens_EL,\n step_t,\n )\n final_carry_causal = causal_step_fn(init_carry_causal, jnp.arange(N))\n updated_token_idxs_BSN = final_carry_causal[1]\n updated_logits_BSNV = final_carry_causal[2]\n new_carry = (rng, updated_token_idxs_BSN, updated_logits_BSNV)\n return new_carry\n\n # --- Run the autoregressive generation using jax.lax.scan ---\n initial_carry = (batch[""rng""], token_idxs_BSN, logits_BSNV)\n timesteps_to_scan = jnp.arange(T, seq_len)\n final_carry = generation_step_fn(initial_carry, timesteps_to_scan)\n final_token_idxs_BSN = final_carry[1]\n final_logits_BSNV = final_carry[2]\n\n # --- Decode all tokens at once at the end ---\n H, W = batch[""videos""].shape[2:4]\n final_frames_BSHWC = self.tokenizer.decode(\n final_token_idxs_BSN,\n video_hw=(H, W),\n )\n return final_frames_BSHWC, final_logits_BSNV\n\n def vq_encode(self, batch: Dict[str, jax.Array], training: bool) -> jax.Array:\n # --- Preprocess videos ---\n assert self.lam is not None\n video_BTHWC = batch[""videos""]\n lam_output: Dict[str, jax.Array] = self.lam.vq_encode(\n video_BTHWC, training=training\n )\n lam_indices_E = lam_output[""indices""]\n return lam_indices_E\n\n\n# FIXME (f.srambical): add conversion script for old checkpoints\ndef restore_genie_components(\n optimizer: nnx.ModelAndOptimizer,\n sharding: jax.sharding.NamedSharding,\n rng: jax.Array,\n args,\n) -> nnx.ModelAndOptimizer:\n """"""Restore pre-trained Genie components""""""\n rng_tokenizer, rng_lam = jax.random.split(rng)\n rngs_tokenizer = nnx.Rngs(rng_tokenizer)\n rngs_lam = nnx.Rngs(rng_lam)\n\n tx = optimizer.tx\n model = optimizer.model\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n\n checkpoint_options = ocp.CheckpointManagerOptions(\n step_format_fixed_length=6,\n )\n tokenizer_checkpoint_manager = ocp.CheckpointManager(\n directory=args.tokenizer_checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n dummy_tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.tokenizer_dim,\n ffn_dim=args.tokenizer_ffn_dim,\n latent_dim=args.latent_patch_dim,\n num_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n num_blocks=args.tokenizer_num_blocks,\n num_heads=args.tokenizer_num_heads,\n dropout=args.dropout,\n codebook_dropout=args.dropout,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs_tokenizer,\n )\n dummy_tokenizer_optimizer = nnx.ModelAndOptimizer(dummy_tokenizer, tx)\n dummy_tokenizer_optimizer_state = nnx.state(dummy_tokenizer_optimizer)\n abstract_sharded_tokenizer_optimizer_state = _create_abstract_sharded_pytree(\n dummy_tokenizer_optimizer_state, sharding\n )\n restored_tokenizer = tokenizer_checkpoint_manager.restore(\n step=tokenizer_checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore( # type: ignore\n abstract_sharded_tokenizer_optimizer_state # type: ignore\n ),\n ),\n )[""model_state""]\n nnx.update(dummy_tokenizer_optimizer.model, restored_tokenizer.model)\n model.tokenizer = dummy_tokenizer_optimizer.model\n tokenizer_checkpoint_manager.close()\n\n if args.lam_checkpoint:\n lam_checkpoint_manager = ocp.CheckpointManager(\n directory=args.lam_checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n dummy_lam = LatentActionModel(\n in_dim=args.image_channels,\n model_dim=args.lam_dim,\n ffn_dim=args.lam_ffn_dim,\n latent_dim=args.latent_patch_dim,\n num_latents=args.num_actions,\n patch_size=args.lam_patch_size,\n num_blocks=args.lam_num_blocks,\n num_heads=args.lam_num_heads,\n dropout=args.dropout,\n codebook_dropout=args.dropout,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs_lam,\n )\n dummy_lam_optimizer = nnx.ModelAndOptimizer(dummy_lam, tx)\n dummy_lam_optimizer_state = nnx.state(dummy_lam_optimizer)\n abstract_sharded_lam_optimizer_state = _create_abstract_sharded_pytree(\n dummy_lam_optimizer_state, sharding\n )\n restored_lam_optimizer = lam_checkpoint_manager.restore(\n step=lam_checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore( # type: ignore\n abstract_sharded_lam_optimizer_state # type: ignore\n ),\n ),\n )[""model_state""]\n nnx.update(dummy_lam_optimizer.model, restored_lam_optimizer.model)\n model.lam = dummy_lam_optimizer.model\n # Remove the LAM decoder to save memory and avoid unnecessary computation.\n del model.lam.decoder\n lam_checkpoint_manager.close()\n\n # Reinitialize the optimizer states\n optimizer = nnx.ModelAndOptimizer(model, tx)\n return optimizer\n\n\ndef _create_abstract_sharded_pytree(\n pytree_template: nnx.GraphState, sharding_spec: jax.sharding.NamedSharding\n) -> jax.Array:\n """"""Replaces arrays in a pytree with ShapeDtypeStructs having the given sharding.""""""\n\n def map_fn(leaf_template):\n if hasattr(leaf_template, ""shape"") and hasattr(leaf_template, ""dtype""):\n return jax.ShapeDtypeStruct(\n leaf_template.shape, leaf_template.dtype, sharding=sharding_spec\n )\n return leaf_template\n\n return jax.tree_util.tree_map(map_fn, pytree_template)\n",python,tab +828,1369120,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/genie.py",5425,0,"",python,selection_command +829,1372036,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/genie.py",5470,0,"",python,selection_mouse +830,1373187,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/genie.py",5472,0,"",python,selection_mouse +831,1374265,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/genie.py",5464,0,"",python,selection_mouse +832,1374445,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/genie.py",2447,0,"",python,selection_command +833,1375329,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/genie.py",2464,0,"",python,selection_mouse +834,1376186,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/genie.py",2459,14,"TokenizerVQVAE",python,selection_mouse +835,1376757,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/genie.py",2464,0,"",python,selection_mouse +836,1379645,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/genie.py",247,0,"",python,selection_mouse +837,1381166,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/genie.py",404,0,"",python,selection_mouse +838,1381254,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/genie.py",403,0,"",python,selection_command +839,1381254,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/genie.py",403,1,",",python,selection_mouse +840,1381255,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/genie.py",404,0,"",python,selection_command +841,1394601,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/genie.py",2462,0,"",python,selection_mouse +842,1402348,"jasmine/models/tokenizer.py",0,0,"from typing import Dict, Tuple\n\nimport flax.nnx as nnx\nimport jax.numpy as jnp\nimport jax\n\nfrom utils.preprocess import patchify, unpatchify\nfrom utils.nn import STTransformer, VectorQuantizer\n\n\nclass TokenizerVQVAE(nnx.Module):\n """"""\n ST-ViVit VQ-VAE\n\n Dimension keys:\n B: batch size\n T: sequence length\n N: number of patches per frame\n L: latent dimension\n D: B * T * N\n H: height\n W: width\n C: number of channels\n P: patch token dimension (patch_size^2 * C)\n """"""\n\n def __init__(\n self,\n in_dim: int,\n model_dim: int,\n ffn_dim: int,\n latent_dim: int,\n num_latents: int,\n patch_size: int,\n num_blocks: int,\n num_heads: int,\n dropout: float,\n codebook_dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n rngs: nnx.Rngs,\n ):\n self.in_dim = in_dim\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.latent_dim = latent_dim\n self.num_latents = num_latents\n self.patch_size = patch_size\n self.num_blocks = num_blocks\n self.num_heads = num_heads\n self.dropout = dropout\n self.codebook_dropout = codebook_dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n\n self.encoder = STTransformer(\n self.in_dim * self.patch_size**2,\n self.model_dim,\n self.ffn_dim,\n self.latent_dim,\n self.num_blocks,\n self.num_heads,\n self.dropout,\n self.param_dtype,\n self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n self.vq = VectorQuantizer(\n self.latent_dim,\n self.num_latents,\n self.codebook_dropout,\n self.dtype,\n rngs=rngs,\n )\n self.out_dim = self.in_dim * self.patch_size**2\n self.decoder = STTransformer(\n self.latent_dim,\n self.model_dim,\n self.ffn_dim,\n self.out_dim,\n self.num_blocks,\n self.num_heads,\n self.dropout,\n self.param_dtype,\n self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n\n def __call__(\n self, batch: Dict[str, jax.Array], training: bool = True\n ) -> Dict[str, jax.Array]:\n H, W = batch[""videos""].shape[2:4]\n videos_BTHWC = batch[""videos""]\n outputs = self.vq_encode(videos_BTHWC, training)\n z_q_BTNL = outputs[""z_q""]\n recon_BTHWC = self.decoder(z_q_BTNL)\n recon_BTHWC = recon_BTHWC.astype(jnp.float32)\n recon_BTHWC = nnx.sigmoid(recon_BTHWC)\n recon_BTHWC = recon_BTHWC.astype(self.dtype)\n recon_BTHWC = unpatchify(recon_BTHWC, self.patch_size, H, W)\n outputs[""recon""] = recon_BTHWC\n return outputs\n\n def vq_encode(\n self, videos: jax.Array, training: bool = True\n ) -> Dict[str, jax.Array]:\n # --- Preprocess + encode ---\n B, T = videos.shape[:2]\n patch_BTNP = patchify(videos, self.patch_size)\n N = patch_BTNP.shape[2]\n x_BTNL = self.encoder(patch_BTNP)\n\n # --- Vector quantize ---\n x_DL = x_BTNL.reshape(B * T * N, self.latent_dim)\n z_q_DL, z_DL, emb_DL, indices_D = self.vq(x_DL, training)\n z_q_BTNL = z_q_DL.reshape(B, T, N, self.latent_dim)\n indices_BTN = indices_D.reshape(B, T, N)\n return dict(z_q=z_q_BTNL, z=z_DL, emb=emb_DL, indices=indices_BTN)\n\n def decode(self, indices_BTN: jax.Array, video_hw: Tuple[int, int]) -> jax.Array:\n z_BTNL = self.vq.codebook[indices_BTN]\n recon_BTNP = self.decoder(z_BTNL)\n recon_BTNP = recon_BTNP.astype(jnp.float32)\n recon_BTNP = nnx.sigmoid(recon_BTNP)\n recon_BTNP = recon_BTNP.astype(self.dtype)\n return unpatchify(recon_BTNP, self.patch_size, *video_hw)\n",python,tab +843,1408128,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/models/tokenizer.py",0,0,"from typing import Dict, Tuple\n\nimport flax.nnx as nnx\nimport jax.numpy as jnp\nimport jax\n\nfrom utils.preprocess import patchify, unpatchify\nfrom utils.nn import STTransformer, VectorQuantizer\n\n\nclass TokenizerVQVAE(nnx.Module):\n """"""\n ST-ViVit VQ-VAE\n\n Dimension keys:\n B: batch size\n T: sequence length\n N: number of patches per frame\n L: latent dimension\n D: B * T * N\n H: height\n W: width\n C: number of channels\n P: patch token dimension (patch_size^2 * C)\n """"""\n\n def __init__(\n self,\n in_dim: int,\n model_dim: int,\n ffn_dim: int,\n latent_dim: int,\n num_latents: int,\n patch_size: int,\n num_blocks: int,\n num_heads: int,\n dropout: float,\n codebook_dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n rngs: nnx.Rngs,\n ):\n self.in_dim = in_dim\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.latent_dim = latent_dim\n self.num_latents = num_latents\n self.patch_size = patch_size\n self.num_blocks = num_blocks\n self.num_heads = num_heads\n self.dropout = dropout\n self.codebook_dropout = codebook_dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n\n self.encoder = STTransformer(\n self.in_dim * self.patch_size**2,\n self.model_dim,\n self.ffn_dim,\n self.latent_dim,\n self.num_blocks,\n self.num_heads,\n self.dropout,\n self.param_dtype,\n self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n self.vq = VectorQuantizer(\n self.latent_dim,\n self.num_latents,\n self.codebook_dropout,\n self.dtype,\n rngs=rngs,\n )\n self.out_dim = self.in_dim * self.patch_size**2\n self.decoder = STTransformer(\n self.latent_dim,\n self.model_dim,\n self.ffn_dim,\n self.out_dim,\n self.num_blocks,\n self.num_heads,\n self.dropout,\n self.param_dtype,\n self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n )\n\n def __call__(\n self, batch: Dict[str, jax.Array], training: bool = True\n ) -> Dict[str, jax.Array]:\n H, W = batch[""videos""].shape[2:4]\n videos_BTHWC = batch[""videos""]\n outputs = self.vq_encode(videos_BTHWC, training)\n z_q_BTNL = outputs[""z_q""]\n recon_BTHWC = self.decoder(z_q_BTNL)\n recon_BTHWC = recon_BTHWC.astype(jnp.float32)\n recon_BTHWC = nnx.sigmoid(recon_BTHWC)\n recon_BTHWC = recon_BTHWC.astype(self.dtype)\n recon_BTHWC = unpatchify(recon_BTHWC, self.patch_size, H, W)\n outputs[""recon""] = recon_BTHWC\n return outputs\n\n def vq_encode(\n self, videos: jax.Array, training: bool = True\n ) -> Dict[str, jax.Array]:\n # --- Preprocess + encode ---\n B, T = videos.shape[:2]\n patch_BTNP = patchify(videos, self.patch_size)\n N = patch_BTNP.shape[2]\n x_BTNL = self.encoder(patch_BTNP)\n\n # --- Vector quantize ---\n x_DL = x_BTNL.reshape(B * T * N, self.latent_dim)\n z_q_DL, z_DL, emb_DL, indices_D = self.vq(x_DL, training)\n z_q_BTNL = z_q_DL.reshape(B, T, N, self.latent_dim)\n indices_BTN = indices_D.reshape(B, T, N)\n return dict(z_q=z_q_BTNL, z=z_DL, emb=emb_DL, indices=indices_BTN)\n\n def decode(self, indices_BTN: jax.Array, video_hw: Tuple[int, int]) -> jax.Array:\n z_BTNL = self.vq.codebook[indices_BTN]\n recon_BTNP = self.decoder(z_BTNL)\n recon_BTNP = recon_BTNP.astype(jnp.float32)\n recon_BTNP = nnx.sigmoid(recon_BTNP)\n recon_BTNP = recon_BTNP.astype(self.dtype)\n return unpatchify(recon_BTNP, self.patch_size, *video_hw)\n",python,tab +844,1408129,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/models/tokenizer.py",3320,0,"",python,selection_command +845,1411065,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/models/tokenizer.py",3345,0,"",python,selection_mouse +846,1411302,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/models/tokenizer.py",1441,0,"",python,selection_command +847,1415882,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py",0,0,"import math\nfrom typing import Tuple, Callable, List\n\nfrom flax import nnx\nimport jax\nimport jax.numpy as jnp\nimport einops\n\n\ndef _get_spatiotemporal_positional_encoding(d_model: int, max_len: int = 5000):\n """"""\n Creates a function that applies separate sinusoidal positional encodings to the temporal and spatial dimensions.\n """"""\n pe = jnp.zeros((max_len, d_model))\n position = jnp.arange(0, max_len, dtype=jnp.float32)[:, None]\n div_term = jnp.exp(jnp.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))\n pe = pe.at[:, 0::2].set(jnp.sin(position * div_term))\n pe = pe.at[:, 1::2].set(jnp.cos(position * div_term))\n\n def _encode(x: jax.Array) -> jax.Array:\n """"""\n Args:\n x: The input tensor of shape (Batch, Time, Space, Dimension).\n\n Returns:\n The input tensor with positional encodings added.\n """"""\n assert x.ndim == 4, f""Input must be 4-dimensional, but got shape {x.shape}""\n\n num_timesteps = x.shape[1]\n num_spatial_patches = x.shape[2]\n\n # Temporal positional encoding: (1, T, 1, D)\n temporal_pe = pe[None, :num_timesteps, None, :]\n x = x + temporal_pe\n\n # Spatial positional encoding: (1, 1, S, D)\n spatial_pe = pe[None, None, :num_spatial_patches, :]\n x = x + spatial_pe\n\n return x\n\n return _encode\n\n\nclass STBlock(nnx.Module):\n def __init__(\n self,\n dim: int,\n ffn_dim: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n rngs: nnx.Rngs,\n sow_weights: bool,\n sow_activations: bool,\n ):\n self.dim = dim\n self.ffn_dim = ffn_dim\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.sow_weights = sow_weights\n self.sow_activations = sow_activations\n\n self.spatial_norm = nnx.LayerNorm(\n num_features=self.dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.spatial_attention = nnx.MultiHeadAttention(\n num_heads=self.num_heads,\n in_features=self.dim,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n attention_fn=_create_flash_attention_fn(\n self.use_flash_attention, is_causal=False\n ),\n rngs=rngs,\n decode=False,\n )\n\n self.temporal_norm = nnx.LayerNorm(\n num_features=self.dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.temporal_attention = nnx.MultiHeadAttention(\n num_heads=self.num_heads,\n in_features=self.dim,\n qkv_features=self.dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n attention_fn=_create_flash_attention_fn(\n self.use_flash_attention, is_causal=True\n ),\n rngs=rngs,\n decode=False,\n )\n\n self.ffn_norm = nnx.LayerNorm(\n num_features=self.dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.ffn_dense1 = nnx.Linear(\n in_features=self.dim,\n out_features=self.ffn_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.ffn_dense2 = nnx.Linear(\n in_features=self.ffn_dim,\n out_features=self.dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n @nnx.remat\n def __call__(self, x_BTNM: jax.Array) -> jax.Array:\n # --- Spatial attention ---\n z_BTNM = self.spatial_norm(x_BTNM)\n z_BTNM = self.spatial_attention(z_BTNM, sow_weights=self.sow_weights)\n x_BTNM = x_BTNM + z_BTNM\n\n # --- Temporal attention ---\n x_BNTM = x_BTNM.swapaxes(1, 2)\n z_BNTM = self.temporal_norm(x_BNTM)\n z_BNTM = self.temporal_attention(z_BNTM, sow_weights=self.sow_weights)\n x_BNTM = x_BNTM + z_BNTM\n x_BTNM = x_BNTM.swapaxes(1, 2)\n\n # --- Feedforward ---\n z_BTNM = self.ffn_norm(x_BTNM)\n z_BTND = self.ffn_dense1(z_BTNM)\n z_BTND = jax.nn.gelu(z_BTND)\n z_BTNM = self.ffn_dense2(z_BTND)\n x_BTNM = x_BTNM + z_BTNM\n if self.sow_activations:\n self.sow(nnx.Intermediate, ""activations"", x_BTNM)\n return x_BTNM\n\n\nclass STTransformer(nnx.Module):\n """"""\n Dimension keys:\n B: batch size\n T: number of frames\n N: number of patches per frame\n I: number of input features\n M: model dimension\n D: FFN dimension\n V: vocabulary size\n """"""\n\n def __init__(\n self,\n input_dim: int,\n model_dim: int,\n ffn_dim: int,\n out_dim: int,\n num_blocks: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n rngs: nnx.Rngs,\n sow_weights: bool = False,\n sow_activations: bool = False,\n sow_logits: bool = False,\n max_len: int = 5000,\n ):\n self.input_dim = input_dim\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.out_dim = out_dim\n self.num_blocks = num_blocks\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.sow_logits = sow_logits\n self.sow_weights = sow_weights\n self.sow_activations = sow_activations\n\n self.input_norm1 = nnx.LayerNorm(\n num_features=self.input_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.input_dense = nnx.Linear(\n in_features=self.input_dim,\n out_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.input_norm2 = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n\n self.pos_enc = _get_spatiotemporal_positional_encoding(\n self.model_dim, max_len=max_len\n )\n\n self.blocks = []\n for _ in range(self.num_blocks):\n self.blocks.append(\n STBlock(\n dim=self.model_dim,\n ffn_dim=self.ffn_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n rngs=rngs,\n sow_weights=self.sow_weights,\n sow_activations=self.sow_activations,\n )\n )\n\n self.output_dense = nnx.Linear(\n in_features=self.model_dim,\n out_features=self.out_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n def __call__(self, x_BTNI: jax.Array) -> jax.Array:\n x_BTNI = self.input_norm1(x_BTNI)\n x_BTNM = self.input_dense(x_BTNI)\n x_BTNM = self.input_norm2(x_BTNM)\n x_BTNM = self.pos_enc(x_BTNM)\n for block in self.blocks:\n x_BTNM = block(x_BTNM)\n\n x_BTNV = self.output_dense(x_BTNM)\n if self.sow_logits:\n self.sow(nnx.Intermediate, ""logits"", x_BTNV)\n return x_BTNV\n\n\nclass TransformerBlock(nnx.Module):\n def __init__(\n self,\n model_dim: int,\n ffn_dim: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n decode: bool,\n rngs: nnx.Rngs,\n sow_weights: bool,\n sow_activations: bool,\n ):\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.decode = decode\n self.sow_weights = sow_weights\n self.sow_activations = sow_activations\n\n self.temporal_norm = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.spatial_norm = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.ffn_norm = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.temporal_attention = nnx.MultiHeadAttention(\n num_heads=self.num_heads,\n in_features=self.model_dim,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n attention_fn=_create_flash_attention_fn(\n self.use_flash_attention, is_causal=True\n ),\n rngs=rngs,\n decode=self.decode,\n )\n self.spatial_attention = nnx.MultiHeadAttention(\n num_heads=self.num_heads,\n in_features=self.model_dim,\n qkv_features=self.model_dim,\n dropout_rate=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n attention_fn=_create_flash_attention_fn(\n self.use_flash_attention, is_causal=True\n ),\n rngs=rngs,\n decode=self.decode,\n )\n self.ffn_dense1 = nnx.Linear(\n in_features=self.model_dim,\n out_features=self.ffn_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.ffn_dense2 = nnx.Linear(\n in_features=self.ffn_dim,\n out_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n @nnx.remat\n def __call__(\n self, x_BTNM: jax.Array, pos_index: Tuple[jax.Array, jax.Array] | None = None\n ) -> jax.Array:\n # --- Spatial attention ---\n B, T, N, M = x_BTNM.shape\n z_FNM = einops.rearrange(x_BTNM, ""b t n m -> (b t) n m"")\n z_FNM = self.spatial_norm(z_FNM)\n z_FNM = self.spatial_attention(z_FNM, sow_weights=self.sow_weights)\n z_BTNM = einops.rearrange(z_FNM, ""(b t) n m -> b t n m"", t=T)\n x_BTNM = x_BTNM + z_BTNM\n # --- Temporal attention ---\n z_PTM = einops.rearrange(x_BTNM, ""b t n m -> (b n) t m"")\n z_PTM = self.temporal_norm(z_PTM)\n z_PTM = self.temporal_attention(z_PTM, sow_weights=self.sow_weights)\n z_BTNM = einops.rearrange(z_PTM, ""(b n) t m -> b t n m"", n=N)\n x_BTNM = x_BTNM + z_BTNM\n # --- Feedforward ---\n z_BTNM = self.ffn_norm(x_BTNM)\n z_BTND = self.ffn_dense1(z_BTNM)\n z_BTND = jax.nn.gelu(z_BTND)\n z_BTNM = self.ffn_dense2(z_BTND)\n x_BTNM = x_BTNM + z_BTNM\n if self.sow_activations:\n self.sow(nnx.Intermediate, ""activations"", x_BTNM)\n\n return x_BTNM\n\n\nclass Transformer(nnx.Module):\n """"""\n Dimension keys:\n B: batch size\n T: number of frames\n N: number of patches per frame\n I: number of input features\n M: model dimension\n D: FFN dimension\n V: vocabulary size\n F: number of frames in batch\n P: number of patch positions in batch\n """"""\n\n def __init__(\n self,\n input_dim: int,\n model_dim: int,\n ffn_dim: int,\n out_dim: int,\n num_blocks: int,\n num_heads: int,\n dropout: float,\n param_dtype: jnp.dtype,\n dtype: jnp.dtype,\n use_flash_attention: bool,\n decode: bool,\n rngs: nnx.Rngs,\n sow_logits: bool = False,\n sow_weights: bool = False,\n sow_activations: bool = False,\n max_len: int = 5000,\n ):\n self.input_dim = input_dim\n self.model_dim = model_dim\n self.ffn_dim = ffn_dim\n self.out_dim = out_dim\n self.num_blocks = num_blocks\n self.num_heads = num_heads\n self.dropout = dropout\n self.param_dtype = param_dtype\n self.dtype = dtype\n self.use_flash_attention = use_flash_attention\n self.sow_logits = sow_logits\n self.sow_weights = sow_weights\n self.sow_activations = sow_activations\n\n self.input_norm1 = nnx.LayerNorm(\n num_features=self.input_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n self.input_dense = nnx.Linear(\n in_features=self.input_dim,\n out_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n self.input_norm2 = nnx.LayerNorm(\n num_features=self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.param_dtype, # layer norm in full precision\n rngs=rngs,\n )\n\n self.pos_enc = _get_spatiotemporal_positional_encoding(\n self.model_dim, max_len=max_len\n )\n\n self.blocks: List[TransformerBlock] = []\n for _ in range(self.num_blocks):\n self.blocks.append(\n TransformerBlock(\n model_dim=self.model_dim,\n ffn_dim=self.ffn_dim,\n num_heads=self.num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n decode=decode,\n sow_weights=self.sow_weights,\n sow_activations=self.sow_activations,\n rngs=rngs,\n )\n )\n self.output_dense = nnx.Linear(\n in_features=self.model_dim,\n out_features=self.out_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n rngs=rngs,\n )\n\n def __call__(\n self, x_BTNI: jax.Array, pos_index: Tuple[jax.Array, jax.Array] | None = None\n ) -> jax.Array:\n x_BTNI = self.input_norm1(x_BTNI)\n x_BTNM = self.input_dense(x_BTNI)\n x_BTNM = self.input_norm2(x_BTNM)\n x_BTNM = self.pos_enc(x_BTNM)\n for block in self.blocks:\n x_BTNM = block(x_BTNM, pos_index)\n\n x_BTNV = self.output_dense(x_BTNM)\n if self.sow_logits:\n self.sow(nnx.Intermediate, ""logits"", x_BTNV)\n return x_BTNV\n\n\ndef normalize(x: jax.Array) -> jax.Array:\n return x / (jnp.linalg.norm(x, ord=2, axis=-1, keepdims=True) + 1e-8)\n\n\nclass VectorQuantizer(nnx.Module):\n """"""\n Dimension keys:\n D: B * T * N\n K: number of latents\n L: latent dimension\n """"""\n\n def __init__(\n self,\n latent_dim: int,\n num_latents: int,\n dropout: float,\n dtype: jnp.dtype,\n rngs: nnx.Rngs,\n ):\n self.latent_dim = latent_dim\n self.num_latents = num_latents\n self.dropout = dropout\n self.dtype = dtype\n\n self.codebook = nnx.Param(\n normalize(\n nnx.initializers.normal(stddev=1)(\n rngs.params(), (self.num_latents, self.latent_dim)\n )\n )\n )\n self.drop = nnx.Dropout(self.dropout, rngs=rngs)\n\n def __call__(\n self, x_DL: jax.Array, training: bool\n ) -> Tuple[jax.Array, jax.Array, jax.Array, jax.Array]:\n # --- Compute distances ---\n x_DL = x_DL.astype(self.dtype)\n codebook = self.codebook.value.astype(self.dtype)\n\n x_DL = normalize(x_DL)\n normalized_codebook_KL = normalize(codebook)\n distance_DK = -jnp.matmul(x_DL, normalized_codebook_KL.T)\n if training:\n distance_DK = self.drop(distance_DK)\n\n # --- Get indices and embeddings ---\n indices_D = jnp.argmin(distance_DK, axis=-1)\n z_DL = codebook[indices_D]\n\n # --- Straight through estimator ---\n z_q_DL = x_DL + jax.lax.stop_gradient(z_DL - x_DL)\n return z_q_DL, z_DL, x_DL, indices_D\n\n def get_codes(self, indices_E: jax.Array) -> jax.Array:\n return self.codebook[indices_E]\n\n\ndef _create_flash_attention_fn(use_flash_attention: bool, is_causal: bool) -> Callable:\n """"""\n Create an attention function that uses flash attention if enabled.\n\n flax.nnx.MultiHeadAttention provides tensors with shape (batch..., length, num_heads, head_dim),\n but jax.nn.dot_product_attention expects (batch, length, num_heads, head_dim). We reshape to\n ensure compatibility. cuDNN's flash attention additionally requires a sequence length that\n is a multiple of 4. We pad the sequence length to the nearest multiple of 4 and mask\n accordingly. Note that cuDNN requires the mask to be broadcast before calling the attention\n function due to strict shape checking.\n """"""\n\n def attention_fn(\n query_BTHD, key_BSHD, value_BSHD, bias=None, mask_B111=None, **kwargs\n ):\n implementation = ""cudnn"" if use_flash_attention else None\n\n def _merge_batch_dims(x):\n return einops.rearrange(x, ""... l h k -> (...) l h k"")\n\n def _pad(x, pad_size):\n return jnp.pad(x, ((0, 0), (0, pad_size), (0, 0), (0, 0)))\n\n original_shape = query_BTHD.shape\n T = query_BTHD.shape[-3]\n S = key_BSHD.shape[-3]\n\n # Pad to nearest multiple of 4\n Q = ((T + 3) // 4) * 4\n pad_size_Q = Q - T\n K = ((S + 3) // 4) * 4\n pad_size_K = K - S\n\n query_BQHD = _pad(_merge_batch_dims(query_BTHD), pad_size_Q)\n key_BKHD = _pad(_merge_batch_dims(key_BSHD), pad_size_K)\n value_BKHD = _pad(_merge_batch_dims(value_BSHD), pad_size_K)\n\n attention_mask = jnp.ones((Q, K), dtype=jnp.bool_)\n attention_mask = attention_mask.at[T:, :].set(False)\n attention_mask = attention_mask.at[:, S:].set(False)\n\n mask_11TS = attention_mask[jnp.newaxis, jnp.newaxis, :, :]\n\n bias_4d = (\n jnp.pad(\n _merge_batch_dims(bias),\n ((0, 0), (0, 0), (0, pad_size_Q), (0, pad_size_K)),\n )\n if bias is not None\n else None\n )\n\n # NOTE: jax.nn.dot_product_attention does not support dropout\n output_4d = jax.nn.dot_product_attention(\n query=query_BQHD,\n key=key_BKHD,\n value=value_BKHD,\n bias=bias_4d,\n mask=mask_11TS,\n implementation=implementation,\n is_causal=is_causal,\n )\n return output_4d[..., :T, :, :].reshape(original_shape)\n\n return attention_fn\n",python,tab +848,1415883,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py",7815,0,"",python,selection_command +849,1416992,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py",7885,0,"",python,selection_mouse 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mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_default/3528955\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python jasmine/train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=64 \\r\n --image_width=64 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=110 \\r\n --patch_size=4 \\r\n --init_lr=0 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=50 \\r\n --log_checkpoint_interval=2 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=coinrun-dyn-dev-$slurm_job_id \\r\n --tags dyn coinrun dev \\r\n --entity instant-uv \\r\n --project jafar \\r\n --warmup_steps 0 \\r\n --wsd_decay_steps 0 \\r\n --num_steps 10 \\r\n --data_dir $array_records_dir_train \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --val_data_dir $array_records_dir_val \\r\n --val_interval 2 \\r\n --eval_full_frame \\r\n --val_steps 5\r\n",,terminal_output 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Use `wandb login --relogin` to force relogin\r\n",,terminal_output +1013,1508869,"jasmine/train_dynamics.py",831,0,"",python,selection_command +1014,1508898,"jasmine/train_dynamics.py",860,0,"",python,selection_command +1015,1508899,"jasmine/train_dynamics.py",878,0,"",python,selection_command +1016,1508930,"jasmine/train_dynamics.py",900,0,"",python,selection_command +1017,1508958,"jasmine/train_dynamics.py",928,0,"",python,selection_command +1018,1508986,"jasmine/train_dynamics.py",955,0,"",python,selection_command +1019,1509047,"jasmine/train_dynamics.py",982,0,"",python,selection_command +1020,1509079,"jasmine/train_dynamics.py",1005,0,"",python,selection_command +1021,1509110,"jasmine/train_dynamics.py",1033,0,"",python,selection_command +1022,1509137,"jasmine/train_dynamics.py",1064,0,"",python,selection_command +1023,1509168,"jasmine/train_dynamics.py",1083,0,"",python,selection_command +1024,1509197,"jasmine/train_dynamics.py",1108,0,"",python,selection_command +1025,1509227,"jasmine/train_dynamics.py",1133,0,"",python,selection_command +1026,1509228,"jasmine/train_dynamics.py",1158,0,"",python,selection_command +1027,1509294,"jasmine/train_dynamics.py",1185,0,"",python,selection_command +1028,1509295,"jasmine/train_dynamics.py",1214,0,"",python,selection_command +1029,1509326,"jasmine/train_dynamics.py",1297,0,"",python,selection_command +1030,1509355,"jasmine/train_dynamics.py",1303,0,"",python,selection_command +1031,1509385,"jasmine/train_dynamics.py",1332,0,"",python,selection_command +1032,1509414,"jasmine/train_dynamics.py",1392,0,"",python,selection_command +1033,1509445,"jasmine/train_dynamics.py",1408,0,"",python,selection_command +1034,1509475,"jasmine/train_dynamics.py",1437,0,"",python,selection_command +1035,1509628,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.22.0\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_122541-td4jceyg\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run coinrun-dyn-dev-3536670\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/td4jceyg\r\n",,terminal_output +1036,1509757,"TERMINAL",0,0,"Parameter counts:\r\n{'dynamics': 26555904, 'lam': 17640416, 'tokenizer': 33750256, 'total': 77946576}\r\n",,terminal_output +1037,1509951,"jasmine/train_dynamics.py",1471,0,"",python,selection_command +1038,1510459,"jasmine/train_dynamics.py",1502,0,"",python,selection_command +1039,1510494,"jasmine/train_dynamics.py",1536,0,"",python,selection_command +1040,1510554,"jasmine/train_dynamics.py",1560,0,"",python,selection_command +1041,1510591,"jasmine/train_dynamics.py",1594,0,"",python,selection_command +1042,1510624,"jasmine/train_dynamics.py",1627,0,"",python,selection_command +1043,1511361,"jasmine/train_dynamics.py",1662,0,"",python,selection_command +1044,1511860,"jasmine/train_dynamics.py",1672,0,"",python,selection_command +1045,1511893,"jasmine/train_dynamics.py",1695,0,"",python,selection_command +1046,1511917,"jasmine/train_dynamics.py",1723,0,"",python,selection_command +1047,1511926,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. 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Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +1048,1511949,"jasmine/train_dynamics.py",1755,0,"",python,selection_command +1049,1511981,"jasmine/train_dynamics.py",1780,0,"",python,selection_command +1050,1512014,"jasmine/train_dynamics.py",1809,0,"",python,selection_command +1051,1512050,"jasmine/train_dynamics.py",1837,0,"",python,selection_command +1052,1512080,"jasmine/train_dynamics.py",1864,0,"",python,selection_command +1053,1512110,"jasmine/train_dynamics.py",1893,0,"",python,selection_command +1054,1512141,"jasmine/train_dynamics.py",1908,0,"",python,selection_command +1055,1512173,"jasmine/train_dynamics.py",1977,0,"",python,selection_command +1056,1512204,"jasmine/train_dynamics.py",2001,0,"",python,selection_command +1057,1512238,"jasmine/train_dynamics.py",2030,0,"",python,selection_command +1058,1512271,"jasmine/train_dynamics.py",2059,0,"",python,selection_command 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+1072,1514573,"jasmine/train_dynamics.py",2474,0,"",python,selection_command +1073,1517644,"TERMINAL",0,0,"Traceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 730, in \r\n main(args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 574, in main\r\n compiled = train_step.lower(optimizer, first_batch).compile()\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/transforms/compilation.py"", line 476, in lower\r\n lowered = self.jitted_fn.lower(*pure_args, **pure_kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/transforms/compilation.py"", line 126, in __call__\r\n out = self.f(*args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 445, in train_step\r\n (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/graph.py"", line 2045, in update_context_manager_wrapper\r\n return f(*args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/transforms/autodiff.py"", line 163, in grad_wrapper\r\n fn_out = gradded_fn(*pure_args)\r\n ^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/transforms/autodiff.py"", line 88, in __call__\r\n out = self.f(*args)\r\n ^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 443, in loss_fn\r\n return dynamics_loss_fn(model, inputs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 428, in dynamics_loss_fn\r\n outputs = model(inputs)\r\n ^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/genie.py"", line 158, in __call__\r\n tokenizer_outputs = self.tokenizer.vq_encode(videos_BTHWC, training=False)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/models/tokenizer.py"", line 115, in vq_encode\r\n x_BTNL = self.encoder(patch_BTNP)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py"", line 250, in __call__\r\n x_BTNI = self.input_norm1(x_BTNI)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/nn/normalization.py"", line 505, in __call__\r\n return _normalize(\r\n ^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/nn/normalization.py"", line 172, in _normalize\r\n scale = scale.reshape(feature_shape)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/numpy/array_methods.py"", line 1147, in meth\r\n return getattr(self.aval, name).fun(self, *args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/numpy/array_methods.py"", line 316, in _reshape\r\n newshape = _compute_newshape(self, args[0] if len(args) == 1 else args)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/numpy/array_methods.py"", line 477, in _compute_newshape\r\n raise TypeError(f""cannot reshape array of shape {arr.shape} (size {arr.size}) ""\r\nTypeError: cannot reshape array of shape (768,) (size 768) into shape [1, 1, 1, 48] (size 48)\r\n--------------------\r\nFor simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n",,terminal_output +1074,1518258,"TERMINAL",0,0,"wandb: \r\nwandb: 🚀 View run coinrun-dyn-dev-3536670 at: https://wandb.ai/instant-uv/jafar/runs/td4jceyg\r\nwandb: Find logs at: ../../../../../hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_122541-td4jceyg/logs\r\n",,terminal_output +1075,1523424,"TERMINAL",0,0,"W1002 12:25:56.565449 1472303 pjrt_client.cc:1469] WatchJobStateAsync failed for task goo.gle/debugproto job_name: ""jax_worker"": UNAVAILABLE: Cancelling all calls\r\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/WatchJobState:\r\n:UNKNOWN:Error received from peer {grpc_message:""Cancelling all calls"", grpc_status:14}\r\n",,terminal_output +1076,1524251,"TERMINAL",0,0,"srun: error: hkn0401: task 0: Exited with exit code 1\r\n]0;tum_cte0515@hkn0401:~/Projects/jasmine[?2004h[tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +1077,1531308,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"",python,tab +1078,1531309,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",14719,0,"",python,selection_command +1079,1532728,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",14762,0,"",python,selection_mouse +1080,1533017,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",14762,6,"inputs",python,selection_mouse +1081,1534971,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",14747,0,"",python,selection_mouse +1082,1535116,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",14738,16,"dynamics_loss_fn",python,selection_mouse 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+1110,1559601,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19762,0,"",python,selection_command +1111,1560237,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19735,0,"",python,selection_mouse +1112,1564610,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=4\r\n#SBATCH --time=00:20:00\r\n#SBATCH --partition=dev_accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --job-name=train_dyn_single_gpu\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_default/3528955\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python jasmine/train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=64 \\r\n --image_width=64 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=110 \\r\n --patch_size=4 \\r\n --init_lr=0 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=50 \\r\n --log_checkpoint_interval=2 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=coinrun-dyn-dev-$slurm_job_id \\r\n --tags dyn coinrun dev \\r\n --entity instant-uv \\r\n --project jafar \\r\n --warmup_steps 0 \\r\n --wsd_decay_steps 0 \\r\n --num_steps 10 \\r\n --data_dir $array_records_dir_train \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --val_data_dir $array_records_dir_val \\r\n --val_interval 2 \\r\n --eval_full_frame \\r\n --val_steps 5\r\n",,terminal_output +1113,1564727,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=1469415\r\nSLURM_JOB_GPUS=0\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\r\nSLURMD_NODENAME=hkn0401\r\nSLURM_JOB_START_TIME=1759399921\r\nSLURM_STEP_NODELIST=hkn0401\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1759403521\r\nSLURM_PMI2_SRUN_PORT=44089\r\nSLURM_CPUS_ON_NODE=8\r\nSLURM_JOB_CPUS_PER_NODE=8\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=dev_accelerated\r\nSLURM_TRES_PER_TASK=cpu=8\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=1\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3536670\r\nSLURM_PTY_PORT=41791\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.199\r\nSLURM_PTY_WIN_ROW=40\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0401\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.199\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=182\r\nSLURM_NODELIST=hkn0401\r\nSLURM_SRUN_COMM_PORT=44895\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1991.localdomain\r\nSLURM_JOB_ID=3536670\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0401\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_STEP_LAUNCHER_PORT=44895\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0401\r\n",,terminal_output +1114,1564850,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +1115,1568565,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +1116,1575259,"TERMINAL",0,0,"Counting all components: ['dynamics', 'lam', 'tokenizer']\r\n",,terminal_output +1117,1575387,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +1118,1576219,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.22.0\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_122648-c720yljw\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run coinrun-dyn-dev-3536670\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/c720yljw\r\n",,terminal_output +1119,1576380,"TERMINAL",0,0,"Parameter counts:\r\n{'dynamics': 26555904, 'lam': 17640416, 'tokenizer': 33750256, 'total': 77946576}\r\n",,terminal_output +1120,1578510,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +1121,1584071,"TERMINAL",0,0,"Traceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 730, in \r\n main(args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 574, in main\r\n compiled = train_step.lower(optimizer, first_batch).compile()\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/transforms/compilation.py"", line 476, in lower\r\n lowered = self.jitted_fn.lower(*pure_args, **pure_kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/transforms/compilation.py"", line 126, in __call__\r\n out = self.f(*args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 445, in train_step\r\n (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/graph.py"", line 2045, in update_context_manager_wrapper\r\n return f(*args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/transforms/autodiff.py"", line 163, in grad_wrapper\r\n fn_out = gradded_fn(*pure_args)\r\n ^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/transforms/autodiff.py"", line 88, in __call__\r\n out = self.f(*args)\r\n ^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 443, in loss_fn\r\n return dynamics_loss_fn(model, inputs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 428, in dynamics_loss_fn\r\n outputs = model(inputs)\r\n ^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/genie.py"", line 158, in __call__\r\n tokenizer_outputs = self.tokenizer.vq_encode(videos_BTHWC, training=False)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/models/tokenizer.py"", line 115, in vq_encode\r\n x_BTNL = self.encoder(patch_BTNP)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py"", line 250, in __call__\r\n x_BTNI = self.input_norm1(x_BTNI)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/nn/normalization.py"", line 505, in __call__\r\n return _normalize(\r\n ^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/flax/nnx/nn/normalization.py"", line 172, in _normalize\r\n scale = scale.reshape(feature_shape)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/numpy/array_methods.py"", line 1147, in meth\r\n return getattr(self.aval, name).fun(self, *args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/numpy/array_methods.py"", line 316, in _reshape\r\n newshape = _compute_newshape(self, args[0] if len(args) == 1 else args)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/numpy/array_methods.py"", line 477, in _compute_newshape\r\n raise TypeError(f""cannot reshape array of shape {arr.shape} (size {arr.size}) ""\r\nTypeError: cannot reshape array of shape (768,) (size 768) into shape [1, 1, 1, 48] (size 48)\r\n--------------------\r\nFor simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n",,terminal_output +1122,1584649,"TERMINAL",0,0,"wandb: \r\nwandb: 🚀 View run coinrun-dyn-dev-3536670 at: https://wandb.ai/instant-uv/jafar/runs/c720yljw\r\nwandb: Find logs at: ../../../../../hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_122648-c720yljw/logs\r\n",,terminal_output +1123,1584788,"TERMINAL",0,0,"W1002 12:26:57.975221 1472833 pjrt_client.cc:1469] WatchJobStateAsync failed for task goo.gle/debugproto job_name: ""jax_worker"": UNAVAILABLE: failed to connect to all addresses; last error: UNKNOWN: ipv4:10.0.1.33:63262: Failed to connect to remote host: Connection refused\r\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/WatchJobState:\r\n:UNKNOWN:Error received from peer {grpc_status:14, grpc_message:""failed to connect to all addresses; last error: UNKNOWN: ipv4:10.0.1.33:63262: Failed to connect to remote host: Connection refused""}\r\n",,terminal_output +1124,1585674,"TERMINAL",0,0,"srun: error: hkn0401: task 0: Exited with exit code 1\r\n]0;tum_cte0515@hkn0401:~/Projects/jasmine[?2004h[tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +1125,1588228,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"",python,tab +1126,1598122,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",0,0,"",shellscript,tab +1127,1602888,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1378,0,"",shellscript,selection_mouse +1128,1603023,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1377,3,"110",shellscript,selection_mouse +1129,1604138,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1378,0,"",shellscript,selection_mouse +1130,1604184,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1377,3,"110",shellscript,selection_mouse +1131,1614627,"TERMINAL",0,0,"p",,terminal_output +1132,1614760,"TERMINAL",0,0,"y",,terminal_output +1133,1614934,"TERMINAL",0,0,"t",,terminal_output +1134,1615002,"TERMINAL",0,0,"h",,terminal_output +1135,1615153,"TERMINAL",0,0,"o",,terminal_output +1136,1615210,"TERMINAL",0,0,"n",,terminal_output +1137,1615489,"TERMINAL",0,0,"\r\n[?2004l\rPython 3.9.18 (main, Sep 4 2025, 00:00:00) \r\n[GCC 11.4.1 20231218 (Red Hat 11.4.1-3)] on linux\r\nType ""help"", ""copyright"", ""credits"" or ""license"" for more information.\r\n>>> ",,terminal_output +1138,1616415,"TERMINAL",0,0,"4",,terminal_output +1139,1616714,"TERMINAL",0,0,"*",,terminal_output +1140,1616836,"TERMINAL",0,0,"*",,terminal_output +1141,1617479,"TERMINAL",0,0,"2",,terminal_output +1142,1617980,"TERMINAL",0,0,"\r\n16\r\n>>> ",,terminal_output +1143,1619657,"TERMINAL",0,0,"1",,terminal_output +1144,1620227,"TERMINAL",0,0,"6",,terminal_output +1145,1620482,"TERMINAL",0,0,"*",,terminal_output +1146,1620733,"TERMINAL",0,0,"*",,terminal_output +1147,1620833,"TERMINAL",0,0,"2",,terminal_output +1148,1620914,"TERMINAL",0,0,"\r\n256\r\n>>> ",,terminal_output +1149,1639343,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py",0,0,"",python,tab +1150,1639344,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py",7815,0,"",python,selection_command +1151,1643338,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py",13508,0,"",python,selection_mouse +1152,1643499,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py",13501,11,"input_norm1",python,selection_mouse +1153,1644071,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py",13525,0,"",python,selection_mouse +1154,1644238,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py",13519,9,"LayerNorm",python,selection_mouse +1155,1644918,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py",13509,0,"",python,selection_mouse +1156,1645034,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py",13501,11,"input_norm1",python,selection_mouse +1157,1645491,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py",13519,0,"",python,selection_mouse +1158,1645635,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py",13519,9,"LayerNorm",python,selection_mouse +1159,1646263,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py",13503,0,"",python,selection_mouse +1160,1647271,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py",5082,0,"",python,selection_command +1161,1649953,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py",6417,0,"",python,selection_mouse +1162,1650103,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py",6412,9,"input_dim",python,selection_mouse +1163,1652324,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py",5699,0,"",python,selection_mouse +1164,1652478,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py",5698,9,"model_dim",python,selection_mouse +1165,1652688,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py",5665,0,"",python,selection_mouse +1166,1652927,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py",5663,9,"input_dim",python,selection_mouse +1167,1659485,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/models/tokenizer.py",0,0,"",python,tab +1168,1661654,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/models/tokenizer.py",1479,0,"",python,selection_mouse +1169,1661771,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/models/tokenizer.py",1478,4,"self",python,selection_mouse +1170,1662317,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/models/tokenizer.py",1484,0,"",python,selection_mouse +1171,1662408,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/models/tokenizer.py",1483,6,"in_dim",python,selection_mouse +1172,1667591,"TERMINAL",0,0,"bash",,terminal_focus +1173,1669704,"TERMINAL",0,0,"dev",,terminal_command +1174,1672047,"TERMINAL",0,0,"python",,terminal_command +1175,1672124,"TERMINAL",0,0,"]633;CPython 3.9.18 (main, Sep 4 2025, 00:00:00) \r\n[GCC 11.4.1 20231218 (Red Hat 11.4.1-3)] on linux\r\nType ""help"", ""copyright"", ""credits"" or ""license"" for more information.\r\n>>> ",,terminal_output +1176,1673379,"TERMINAL",0,0,"4",,terminal_output +1177,1673630,"TERMINAL",0,0,"*",,terminal_output +1178,1673866,"TERMINAL",0,0,"*",,terminal_output +1179,1674040,"TERMINAL",0,0,"2",,terminal_output +1180,1674169,"TERMINAL",0,0,"\r\n16\r\n>>> ",,terminal_output +1181,1679109,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"",python,tab +1182,1690966,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/genie.py",0,0,"",python,tab +1183,1696467,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"",python,tab +1184,1701514,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",3119,0,"",python,selection_mouse +1185,1701660,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",3109,14,"image_channels",python,selection_mouse +1186,1702344,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",3141,0,"",python,selection_mouse +1187,1702756,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",3106,0,"",python,selection_mouse +1188,1705865,"TERMINAL",0,0,"8",,terminal_output +1189,1706703,"TERMINAL",0,0,"",,terminal_output +1190,1707104,"TERMINAL",0,0,"1",,terminal_output +1191,1707429,"TERMINAL",0,0,"6",,terminal_output +1192,1707733,"TERMINAL",0,0," ",,terminal_output +1193,1707983,"TERMINAL",0,0,"*",,terminal_output +1194,1708108,"TERMINAL",0,0,"*",,terminal_output +1195,1709029,"TERMINAL",0,0,"2",,terminal_output +1196,1709097,"TERMINAL",0,0," ",,terminal_output +1197,1709830,"TERMINAL",0,0,"*",,terminal_output +1198,1710468,"TERMINAL",0,0,"",,terminal_output +1199,1711003,"TERMINAL",0,0,"\r\n256\r\n>>> ",,terminal_output +1200,1712764,"TERMINAL",0,0,"16 **2 ",,terminal_output +1201,1713201,"TERMINAL",0,0,"",,terminal_output +1202,1713284,"TERMINAL",0,0,"",,terminal_output +1203,1714254,"TERMINAL",0,0,"[1@3",,terminal_output +1204,1714518,"TERMINAL",0,0,"[1@ ",,terminal_output +1205,1714680,"TERMINAL",0,0,"[1@*",,terminal_output +1206,1714740,"TERMINAL",0,0,"[1@ ",,terminal_output +1207,1715067,"TERMINAL",0,0,"\r\n768\r\n>>> ",,terminal_output +1208,1721144,"TERMINAL",0,0,"1",,terminal_output +1209,1721547,"TERMINAL",0,0,"6",,terminal_output +1210,1721827,"TERMINAL",0,0," ",,terminal_output +1211,1722105,"TERMINAL",0,0,"*",,terminal_output +1212,1722563,"TERMINAL",0,0,"3",,terminal_output +1213,1722644,"TERMINAL",0,0,"\r\n48\r\n>>> ",,terminal_output +1214,1726558,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",3442,0,"",python,selection_mouse +1215,1728479,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",3386,0,"",python,selection_mouse +1216,1732721,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",3643,0,"",python,selection_command +1217,1733751,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",3339,0,"",python,selection_command +1218,1734102,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",3323,0,"",python,selection_command +1219,1734461,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1788,0,"",python,selection_command +1220,1736668,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1540,0,"",python,selection_command +1221,1738108,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",3663,0,"",python,selection_command +1222,1739246,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1540,0,"",python,selection_command +1223,1741077,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1510,0,"",python,selection_mouse +1224,1741220,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1506,17,"num_patch_latents",python,selection_mouse +1225,1741505,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1546,0,"",python,selection_mouse +1226,1741628,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1540,10,"patch_size",python,selection_mouse +1227,1742424,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1559,0,"",python,selection_mouse +1228,1742427,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1558,0,"",python,selection_command +1229,1742460,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1558,1,"4",python,selection_mouse +1230,1742461,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1559,0,"",python,selection_command +1231,1742579,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1558,1,"4",python,selection_mouse +1232,1742580,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1554,5,"t = 4",python,selection_mouse +1233,1742580,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1551,8," int = 4",python,selection_mouse +1234,1742614,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1548,11,"ze: int = 4",python,selection_mouse +1235,1742615,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1546,13,"size: int = 4",python,selection_mouse +1236,1742648,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1544,15,"h_size: int = 4",python,selection_mouse +1237,1742649,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1542,17,"tch_size: int = 4",python,selection_mouse +1238,1742681,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1539,20," patch_size: int = 4",python,selection_mouse +1239,1742681,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1538,21," patch_size: int = 4",python,selection_mouse +1240,1742711,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1537,22," patch_size: int = 4",python,selection_mouse +1241,1742747,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1559,1,"\n",python,selection_mouse +1242,1743126,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1540,0,"",python,selection_mouse +1243,1743486,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1542,0,"",python,selection_mouse +1244,1743905,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1545,0,"",python,selection_mouse +1245,1744060,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1540,10,"patch_size",python,selection_mouse +1246,1744739,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",1559,0,"",python,selection_mouse 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--error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/tokenizer/%x_%j.log\n#SBATCH --job-name=train_tokenizer_1e-4\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\n# array_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_test\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_test/train\n# array_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_test/val\n\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nexport PYTHONUNBUFFERED=1\n\nsrun python jasmine/train_tokenizer.py \\n --save_ckpt \\n --image_height=64 \\n --image_width=64 \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=120 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=50 \\n --log_checkpoint_interval=2 \\n --log \\n --name=coinrun-tokenizer-dataset-test-$slurm_job_id \\n --tags tokenizer coinrun dev \\n --entity instant-uv \\n --project jafar \\n --warmup_steps 0 \\n --wsd_decay_steps 0 \\n --num_steps 10 \\n --data_dir $array_records_dir_train \\n --val_data_dir $array_records_dir_val \\n --val_interval 3 \\n --val_steps 5\n",shellscript,tab +1262,1769712,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1564,0,"",shellscript,selection_mouse +1263,1770216,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1546,0,"",shellscript,selection_mouse +1264,1770221,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1545,0,"",shellscript,selection_command +1265,1771311,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1546,0,"\n ",shellscript,content +1266,1771850,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1551,0,"-",shellscript,content +1267,1771851,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1552,0,"",shellscript,selection_keyboard +1268,1771994,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1552,0,"-",shellscript,content 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+1280,1787577,"TERMINAL",0,0,"]0;tum_cte0515@hkn0401:~/Projects/jasmine[?2004h[tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +1281,1788679,"TERMINAL",0,0,"s",,terminal_output +1282,1788767,"TERMINAL",0,0,"o",,terminal_output +1283,1788853,"TERMINAL",0,0,"u",,terminal_output +1284,1788907,"TERMINAL",0,0,"r",,terminal_output +1285,1789095,"TERMINAL",0,0,"c",,terminal_output +1286,1789146,"TERMINAL",0,0,"e",,terminal_output +1287,1789242,"TERMINAL",0,0," ",,terminal_output +1288,1789422,"TERMINAL",0,0,".",,terminal_output +1289,1789506,"TERMINAL",0,0,"",,terminal_output +1290,1789658,"TERMINAL",0,0,"b",,terminal_output +1291,1789790,"TERMINAL",0,0,"",,terminal_output +1292,1790279,"TERMINAL",0,0,"",,terminal_output +1293,1790868,"TERMINAL",0,0,"v",,terminal_output +1294,1790951,"TERMINAL",0,0,"e",,terminal_output +1295,1791131,"TERMINAL",0,0,"nv/",,terminal_output +1296,1791499,"TERMINAL",0,0,"n",,terminal_output +1297,1791579,"TERMINAL",0,0,"",,terminal_output +1298,1792070,"TERMINAL",0,0,"",,terminal_output +1299,1792373,"TERMINAL",0,0,"b",,terminal_output +1300,1792455,"TERMINAL",0,0,"in/",,terminal_output +1301,1792842,"TERMINAL",0,0,"a",,terminal_output +1302,1792968,"TERMINAL",0,0,"c",,terminal_output +1303,1793124,"TERMINAL",0,0,"tivate",,terminal_output +1304,1793567,"TERMINAL",0,0,"\r\n[?2004l\r]0;tum_cte0515@hkn0401:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +1305,1794547,"TERMINAL",0,0,"s",,terminal_output +1306,1794606,"TERMINAL",0,0,"h",,terminal_output +1307,1794698,"TERMINAL",0,0," ",,terminal_output +1308,1795006,"TERMINAL",0,0,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",,terminal_output +1309,1795236,"TERMINAL",0,0,"\rslurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=4\r\n#SBATCH --time=00:20:00\r\n#SBATCH --partition=dev_accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/tokenizer/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/tokenizer/%x_%j.log\r\n#SBATCH --job-name=train_tokenizer_1e-4\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\n# array_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_test\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_test/train\r\n# array_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_chunked\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_test/val\r\n\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python jasmine/train_tokenizer.py \\r\n --save_ckpt \\r\n --image_height=64 \\r\n --image_width=64 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=120 \\r\n --init_lr=0 \\r\n --patch_size=4 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=50 \\r\n --log_checkpoint_interval=2 \\r\n --log \\r\n --name=coinrun-tokenizer-dataset-test-$slurm_job_id \\r\n --tags tokenizer coinrun dev \\r\n --entity instant-uv \\r\n --project jafar \\r\n --warmup_steps 0 \\r\n --wsd_decay_steps 0 \\r\n --num_steps 10 \\r\n --data_dir $array_records_dir_train \\r\n --val_data_dir $array_records_dir_val \\r\n --val_interval 3 \\r\n --val_steps 5\r\n",,terminal_output +1310,1795451,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=1469415\r\nSLURM_JOB_GPUS=0\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\r\nSLURMD_NODENAME=hkn0401\r\nSLURM_JOB_START_TIME=1759399921\r\nSLURM_STEP_NODELIST=hkn0401\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1759403521\r\nSLURM_PMI2_SRUN_PORT=44089\r\nSLURM_CPUS_ON_NODE=8\r\nSLURM_JOB_CPUS_PER_NODE=8\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=dev_accelerated\r\nSLURM_TRES_PER_TASK=cpu=8\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=1\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3536670\r\nSLURM_PTY_PORT=41791\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.199\r\nSLURM_PTY_WIN_ROW=40\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0401\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.199\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=182\r\nSLURM_NODELIST=hkn0401\r\nSLURM_SRUN_COMM_PORT=44895\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1991.localdomain\r\nSLURM_JOB_ID=3536670\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0401\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_STEP_LAUNCHER_PORT=44895\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0401\r\n",,terminal_output +1311,1795577,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +1312,1798488,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +1313,1802514,"TERMINAL",0,0,"Counting all components: ['decoder', 'encoder', 'vq']\r\n",,terminal_output +1314,1802865,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +1315,1803720,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.22.0\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_123036-5adyg0sl\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run coinrun-tokenizer-dataset-test-3536670\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/5adyg0sl\r\n",,terminal_output +1316,1803873,"TERMINAL",0,0,"Parameter counts:\r\n{'decoder': 16858736, 'encoder': 16858752, 'vq': 32768, 'total': 33750256}\r\n",,terminal_output +1317,1847132,"TERMINAL",0,0,"Total memory size: 29.3 GB, Output size: 0.5 GB, Temp size: 28.8 GB, Argument size: 0.4 GB, Host temp size: 0.0 GB.\r\nFLOPs: 1.335e+13, Bytes: 7.598e+11 (707.6 GB), Intensity: 17.6 FLOPs/byte\r\nStarting training from step 0...\r\n",,terminal_output +1318,1847341,"TERMINAL",0,0,"\r\nMemstats: After params initialized:\r\n\tUsing (GB) 0.52 / 38.7 (1.343669%) on cuda:0\r\n",,terminal_output +1319,1864260,"TERMINAL",0,0,"WARNING:absl:[process=0][thread=MainThread][operation_id=1] _SignalingThread.join() waiting for signals ([, ]) blocking the main thread will slow down blocking save times. This is likely due to main thread calling result() on a CommitFuture.\r\n",,terminal_output +1320,1864668,"TERMINAL",0,0,"WARNING:absl:[process=0][thread=MainThread][operation_id=1] _SignalingThread.join() waiting for signals ([, ]) blocking the main thread will slow down blocking save times. This is likely due to main thread calling result() on a CommitFuture.\r\n",,terminal_output +1321,1866372,"TERMINAL",0,0,"Saved checkpoint at step 2\r\n",,terminal_output +1322,1866556,"TERMINAL",0,0,"Calculating validation metrics...\r\n",,terminal_output +1323,1880142,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",0,0,"",shellscript,tab +1324,1882644,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1185,0,"",shellscript,selection_mouse +1325,1882743,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1185,1,"/",shellscript,selection_mouse +1326,1882744,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1185,2,"/h",shellscript,selection_mouse +1327,1882760,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1185,4,"/hkf",shellscript,selection_mouse +1328,1882780,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1185,8,"/hkfs/wo",shellscript,selection_mouse +1329,1882828,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1169,16,"\nCHECKPOINT_DIR=",shellscript,selection_mouse +1330,1882872,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1168,17,"\n\nCHECKPOINT_DIR=",shellscript,selection_mouse +1331,1882910,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1169,16,"\nCHECKPOINT_DIR=",shellscript,selection_mouse +1332,1883288,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1185,97,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/$job_name/",shellscript,selection_mouse +1333,1883297,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1185,96,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/$job_name",shellscript,selection_mouse +1334,1883331,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1169,16,"\nCHECKPOINT_DIR=",shellscript,selection_mouse +1335,1883407,"TERMINAL",0,0,"Step 3, validation loss: 0.19625379145145416\r\n",,terminal_output +1336,1883764,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1185,88,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/$",shellscript,selection_mouse +1337,1883839,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1185,87,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/",shellscript,selection_mouse +1338,1883919,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1185,86,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer",shellscript,selection_mouse +1339,1883958,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1185,85,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenize",shellscript,selection_mouse +1340,1884348,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",1185,86,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer",shellscript,selection_mouse +1341,1887488,"TERMINAL",0,0,"python",,terminal_focus +1342,1888443,"TERMINAL",0,0,"\r\n",,terminal_output +1343,1888466,"TERMINAL",0,0,"]0;tum_cte0515@hkn1991:~/Projects/jasmine",,terminal_output +1344,1900096,"TERMINAL",0,0,"WARNING:absl:[process=0][thread=MainThread][operation_id=2] _SignalingThread.join() waiting for signals ([, ]) blocking the main thread will slow down blocking save times. This is likely due to main thread calling result() on a CommitFuture.\r\nWARNING:absl:[process=0][thread=MainThread][operation_id=2] _SignalingThread.join() waiting for signals ([, ]) blocking the main thread will slow down blocking save times. This is likely due to main thread calling result() on a CommitFuture.\r\n",,terminal_output +1345,1902314,"TERMINAL",0,0,"Saved checkpoint at step 4\r\n",,terminal_output +1346,1904271,"TERMINAL",0,0,"Calculating validation metrics...\r\n",,terminal_output +1347,1908958,"TERMINAL",0,0,"Step 6, validation loss: 0.17459146678447723\r\nWARNING:absl:[process=0][thread=MainThread][operation_id=3] _SignalingThread.join() waiting for signals ([, ]) blocking the main thread will slow down blocking save times. This is likely due to main thread calling result() on a CommitFuture.\r\nWARNING:absl:[process=0][thread=MainThread][operation_id=3] _SignalingThread.join() waiting for signals ([, ]) blocking the main thread will slow down blocking save times. This is likely due to main thread calling result() on a CommitFuture.\r\n",,terminal_output +1348,1909365,"TERMINAL",0,0,"Saved checkpoint at step 6\r\n",,terminal_output +1349,1910115,"TERMINAL",0,0,"WARNING:absl:[process=0][thread=MainThread][operation_id=4] _SignalingThread.join() waiting for signals ([, ]) blocking the main thread will slow down blocking save times. This is likely due to main thread calling result() on a CommitFuture.\r\nWARNING:absl:[process=0][thread=MainThread][operation_id=4] _SignalingThread.join() waiting for signals ([, ]) blocking the main thread will slow down blocking save times. This is likely due to main thread calling result() on a CommitFuture.\r\n",,terminal_output +1350,1910141,"TERMINAL",0,0,"cd /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670",,terminal_command +1351,1910145,"TERMINAL",0,0,"]633;C]0;tum_cte0515@hkn1991:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670",,terminal_output +1352,1910860,"TERMINAL",0,0,"ls",,terminal_command +1353,1910893,"TERMINAL",0,0,"]633;C000002 000004 000006 000008.orbax-checkpoint-tmp\r\n]0;tum_cte0515@hkn1991:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670",,terminal_output +1354,1913477,"TERMINAL",0,0,"Saved checkpoint at step 8\r\n",,terminal_output +1355,1913621,"TERMINAL",0,0,"Calculating validation metrics...\r\n",,terminal_output +1356,1915027,"TERMINAL",0,0,"WARNING:absl:Path /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670/000008 could not be identified as a temporary checkpoint path using . Got error: Expected AtomicRenameTemporaryPath (/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670/000008) to end with "".orbax-checkpoint-tmp"".\r\nWARNING:absl:Path /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670/000002 could not be identified as a temporary checkpoint path using . Got error: Expected AtomicRenameTemporaryPath (/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670/000002) to end with "".orbax-checkpoint-tmp"".\r\nWARNING:absl:Path /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670/000006 could not be identified as a temporary checkpoint path using . Got error: Expected AtomicRenameTemporaryPath (/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670/000006) to end with "".orbax-checkpoint-tmp"".\r\nWARNING:absl:Path /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670/000004 could not be identified as a temporary checkpoint path using . Got error: Expected AtomicRenameTemporaryPath (/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670/000004) to end with "".orbax-checkpoint-tmp"".\r\n",,terminal_output +1357,1915921,"TERMINAL",0,0,"srun",,terminal_focus +1358,1917133,"TERMINAL",0,0,"^Csrun: interrupt (one more within 1 sec to abort)\r\nsrun: StepId=3536670.5 task 0: running\r\n",,terminal_output +1359,1917600,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3536670.5\r\nsrun: forcing job termination\r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-6:\r\nProcess SpawnProcess-12:\r\nProcess SpawnProcess-11:\r\nProcess SpawnProcess-9:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-16:\r\nProcess SpawnProcess-10:\r\nProcess SpawnProcess-13:\r\nProcess SpawnProcess-15:\r\nProcess SpawnProcess-14:\r\nProcess SpawnProcess-4:\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\nTraceback (most recent call last):\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\nKeyboardInterrupt\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\nTraceback (most recent call last):\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\nTraceback (most recent call last):\r\nKeyboardInterrupt\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\nTraceback (most recent call last):\r\nTraceback (most recent call last):\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\nTraceback (most recent call last):\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\nTraceback (most recent call last):\r\nslurmstepd: error: *** STEP 3536670.5 ON hkn0401 CANCELLED AT 2025-10-02T12:32:30 ***\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 314, in _bootstrap\r\n self.run()\r\n File ""/usr/lib64/python3.12/multiprocessing/process.py"", line 108, in run\r\n self._target(*self._args, **self._kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/grain_pool.py"", line 261, in _worker_loop\r\n if not multiprocessing_common.add_element_to_queue( # pytype: disable=wrong-arg-types\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/grain/_src/python/multiprocessing_common.py"", line 54, in add_element_to_queue\r\n elements_queue.put(element, timeout=_QUEUE_WAIT_TIMEOUT_SECONDS)\r\n File ""/usr/lib64/python3.12/multiprocessing/queues.py"", line 89, in put\r\n if not self._sem.acquire(block, timeout):\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n",,terminal_output +1360,1917769,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3536670.5\r\nsrun: job abort in progress\r\n",,terminal_output +1361,1918010,"TERMINAL",0,0,"bash",,terminal_focus +1362,1918048,"TERMINAL",0,0,"]0;tum_cte0515@hkn0401:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +1363,1919031,"TERMINAL",0,0,"ls",,terminal_command +1364,1924925,"TERMINAL",0,0,"pwd",,terminal_command +1365,1929389,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",0,0,"",shellscript,tab +1366,1931701,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",0,0,"",shellscript,tab +1367,1934091,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1175,0,"",shellscript,selection_mouse +1368,1934128,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1174,0,"",shellscript,selection_command +1369,1934208,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1174,1,"5",shellscript,selection_mouse +1370,1934208,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1175,0,"",shellscript,selection_command 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+1385,1935080,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1049,126,"ckpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_default/3528955",shellscript,selection_mouse +1386,1935107,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1050,125,"kpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_default/3528955",shellscript,selection_mouse +1387,1935141,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1051,124,"point=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_default/3528955",shellscript,selection_mouse +1388,1935180,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1052,123,"oint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_default/3528955",shellscript,selection_mouse +1389,1935237,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1053,122,"int=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_default/3528955",shellscript,selection_mouse +1390,1935251,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1054,121,"nt=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_default/3528955",shellscript,selection_mouse +1391,1935370,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1055,120,"t=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_default/3528955",shellscript,selection_mouse +1392,1935395,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1056,119,"=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_default/3528955",shellscript,selection_mouse +1393,1935601,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1057,118,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/train_tokenizer_default/3528955",shellscript,selection_mouse +1394,1936551,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1057,118,"",shellscript,content +1395,1936570,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1056,0,"",shellscript,selection_command +1396,1936923,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1057,0,"",shellscript,selection_command +1397,1937404,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1057,0,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670",shellscript,content +1398,1937965,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1162,0,"",shellscript,selection_command +1399,1942334,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",0,0,"",shellscript,tab +1400,1947092,"TERMINAL",0,0,"srun",,terminal_focus +1401,1948252,"TERMINAL",0,0,"s",,terminal_output +1402,1948692,"TERMINAL",0,0,"",,terminal_output +1403,1948841,"TERMINAL",0,0,"",,terminal_output +1404,1949090,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",,terminal_output +1405,1949227,"TERMINAL",0,0,"\rource .venv/bin/activate",,terminal_output +1406,1949912,"TERMINAL",0,0,"python",,terminal_output +1407,1950239,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",,terminal_output +1408,1951683,"TERMINAL",0,0,"\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=4\r\n#SBATCH --time=00:20:00\r\n#SBATCH --partition=dev_accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --job-name=train_dyn_single_gpu\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python jasmine/train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=64 \\r\n --image_width=64 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=110 \\r\n --patch_size=4 \\r\n --init_lr=0 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=50 \\r\n --log_checkpoint_interval=2 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=coinrun-dyn-dev-$slurm_job_id \\r\n --tags dyn coinrun dev \\r\n --entity instant-uv \\r\n --project jafar \\r\n --warmup_steps 0 \\r\n --wsd_decay_steps 0 \\r\n --num_steps 10 \\r\n --data_dir $array_records_dir_train \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --val_data_dir $array_records_dir_val \\r\n --val_interval 2 \\r\n --eval_full_frame \\r\n --val_steps 5\r\n",,terminal_output +1409,1951832,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=1469415\r\nSLURM_JOB_GPUS=0\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\r\nSLURMD_NODENAME=hkn0401\r\nSLURM_JOB_START_TIME=1759399921\r\nSLURM_STEP_NODELIST=hkn0401\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1759403521\r\nSLURM_PMI2_SRUN_PORT=44089\r\nSLURM_CPUS_ON_NODE=8\r\nSLURM_JOB_CPUS_PER_NODE=8\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=dev_accelerated\r\nSLURM_TRES_PER_TASK=cpu=8\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=1\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3536670\r\nSLURM_PTY_PORT=41791\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.199\r\nSLURM_PTY_WIN_ROW=40\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0401\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.199\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=182\r\nSLURM_NODELIST=hkn0401\r\nSLURM_SRUN_COMM_PORT=44895\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1991.localdomain\r\nSLURM_JOB_ID=3536670\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0401\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_STEP_LAUNCHER_PORT=44895\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0401\r\n",,terminal_output +1410,1951962,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +1411,1955788,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +1412,1962150,"TERMINAL",0,0,"Counting all components: ['dynamics', 'lam', 'tokenizer']\r\n",,terminal_output +1413,1962503,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +1414,1963331,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.22.0\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_123315-4ycgzjbl\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run coinrun-dyn-dev-3536670\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/4ycgzjbl\r\n",,terminal_output +1415,1963492,"TERMINAL",0,0,"Parameter counts:\r\n{'dynamics': 26555904, 'lam': 17640416, 'tokenizer': 33750256, 'total': 77946576}\r\n",,terminal_output +1416,1965604,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +1417,2043573,"TERMINAL",0,0,"Total memory size: 23.3 GB, Output size: 0.9 GB, Temp size: 22.3 GB, Argument size: 0.9 GB, Host temp size: 0.0 GB.\r\nFLOPs: 1.367e+13, Bytes: 6.890e+11 (641.7 GB), Intensity: 19.8 FLOPs/byte\r\nStarting training from step 0...\r\n",,terminal_output +1418,2044260,"TERMINAL",0,0,"\r\nMemstats: After params initialized:\r\n\tUsing (GB) 1.15 / 38.7 (2.971576%) on cuda:0\r\n",,terminal_output +1419,2223011,"TERMINAL",0,0,"bash",,terminal_focus +1420,2242025,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"",python,tab +1421,2258437,"TERMINAL",0,0,"srun",,terminal_focus +1422,2260641,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"",python,tab 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+1430,2275888,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20603,15,"print_mem_stats",python,selection_mouse +1431,2289174,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"",python,tab +1432,2295164,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",0,0,"",shellscript,tab +1433,2328417,"TERMINAL",0,0,"^Csrun: interrupt (one more within 1 sec to abort)\r\nsrun: StepId=3536670.6 task 0: running\r\n",,terminal_output +1434,2328733,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3536670.6\r\nsrun: forcing job termination\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 730, in \r\n main(args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 581, in main\r\n while step < args.num_steps:\r\n ^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\nWARNING:asyncio:socket.send() raised exception.\r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\nslurmstepd: error: *** STEP 3536670.6 ON hkn0401 CANCELLED AT 2025-10-02T12:39:21 ***\r\n",,terminal_output +1435,2329194,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3536670.6\r\nsrun: job abort in progress\r\n^Csrun: sending Ctrl-C to StepId=3536670.6\r\nsrun: job abort in progress\r\n]0;tum_cte0515@hkn0401:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +1436,2334481,"jasmine/train_dynamics.py",0,0,"",python,tab +1437,2338789,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",0,0,"",shellscript,tab +1438,2340065,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1881,0,"",shellscript,selection_mouse +1439,2340103,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1880,0,"",shellscript,selection_command 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+1495,2371554,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1877,0,"",shellscript,selection_command +1496,2375951,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",,terminal_output +1497,2376127,"TERMINAL",0,0,"\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=4\r\n#SBATCH --time=00:20:00\r\n#SBATCH --partition=dev_accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --job-name=train_dyn_single_gpu\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python jasmine/train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=64 \\r\n --image_width=64 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=110 \\r\n --patch_size=4 \\r\n --init_lr=0 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=50 \\r\n --log_checkpoint_interval=2 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=coinrun-dyn-dev-$slurm_job_id \\r\n --tags dyn coinrun dev \\r\n --entity instant-uv \\r\n --project jafar \\r\n --warmup_steps 0 \\r\n --wsd_decay_steps 0 \\r\n --num_steps 10 \\r\n --data_dir $array_records_dir_train \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --val_data_dir $array_records_dir_val \\r\n --log_interval 1 \\r\n --val_interval 2 \\r\n --eval_full_frame \\r\n --val_steps 5\r\n",,terminal_output 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+1499,2376362,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +1500,2380230,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +1501,2386611,"TERMINAL",0,0,"Counting all components: ['dynamics', 'lam', 'tokenizer']\r\n",,terminal_output +1502,2387241,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +1503,2387929,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.22.0\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_124020-66kv8l9p\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run coinrun-dyn-dev-3536670\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/66kv8l9p\r\n",,terminal_output +1504,2388084,"TERMINAL",0,0,"Parameter counts:\r\n{'dynamics': 26555904, 'lam': 17640416, 'tokenizer': 33750256, 'total': 77946576}\r\n",,terminal_output +1505,2390202,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +1506,2402114,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"",python,tab +1507,2403830,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20668,0,"",python,selection_mouse +1508,2403831,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20667,0,"",python,selection_command +1509,2404724,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20668,0,"\n ",python,content +1510,2404922,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20681,0,"p",python,content +1511,2404923,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20682,0,"",python,selection_keyboard +1512,2405036,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20682,0,"r",python,content +1513,2405037,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20683,0,"",python,selection_keyboard +1514,2405173,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20683,0,"i",python,content +1515,2405174,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20684,0,"",python,selection_keyboard +1516,2405264,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20684,0,"n",python,content +1517,2405266,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20685,0,"",python,selection_keyboard +1518,2405332,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20685,0,"t",python,content +1519,2405333,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20686,0,"",python,selection_keyboard +1520,2405887,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20686,0,"()",python,content +1521,2405889,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20687,0,"",python,selection_keyboard +1522,2406127,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20687,0,"""""",python,content +1523,2406128,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20688,0,"",python,selection_keyboard +1524,2407132,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20688,0,"S",python,content +1525,2407134,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20689,0,"",python,selection_keyboard +1526,2407580,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20690,0,", step",python,content +1527,2407580,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20689,0,"tep",python,content +1528,2407584,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20699,0,"",python,selection_command +1529,2408305,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20698,0,"",python,selection_command +1530,2410470,"TERMINAL",0,0,"^Csrun: interrupt (one more within 1 sec to abort)\r\nsrun: StepId=3536670.7 task 0: running\r\n",,terminal_output +1531,2410787,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3536670.7\r\nsrun: forcing job termination\r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\nslurmstepd: error: *** STEP 3536670.7 ON hkn0401 CANCELLED AT 2025-10-02T12:40:43 ***\r\n",,terminal_output +1532,2411128,"TERMINAL",0,0,"]0;tum_cte0515@hkn0401:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +1533,2412108,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",,terminal_output +1534,2412371,"TERMINAL",0,0,"\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=4\r\n#SBATCH --time=00:20:00\r\n#SBATCH --partition=dev_accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --job-name=train_dyn_single_gpu\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python jasmine/train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=64 \\r\n --image_width=64 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=110 \\r\n --patch_size=4 \\r\n --init_lr=0 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=50 \\r\n --log_checkpoint_interval=2 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=coinrun-dyn-dev-$slurm_job_id \\r\n --tags dyn coinrun dev \\r\n --entity instant-uv \\r\n --project jafar \\r\n --warmup_steps 0 \\r\n --wsd_decay_steps 0 \\r\n --num_steps 10 \\r\n --data_dir $array_records_dir_train \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --val_data_dir $array_records_dir_val \\r\n --log_interval 1 \\r\n --val_interval 2 \\r\n --eval_full_frame \\r\n --val_steps 5\r\n",,terminal_output +1535,2412514,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=1469415\r\nSLURM_JOB_GPUS=0\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\r\nSLURMD_NODENAME=hkn0401\r\nSLURM_JOB_START_TIME=1759399921\r\nSLURM_STEP_NODELIST=hkn0401\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1759403521\r\nSLURM_PMI2_SRUN_PORT=44089\r\nSLURM_CPUS_ON_NODE=8\r\nSLURM_JOB_CPUS_PER_NODE=8\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=dev_accelerated\r\nSLURM_TRES_PER_TASK=cpu=8\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=1\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3536670\r\nSLURM_PTY_PORT=41791\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.199\r\nSLURM_PTY_WIN_ROW=40\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0401\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.199\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=182\r\nSLURM_NODELIST=hkn0401\r\nSLURM_SRUN_COMM_PORT=44895\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1991.localdomain\r\nSLURM_JOB_ID=3536670\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0401\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_STEP_LAUNCHER_PORT=44895\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0401\r\n",,terminal_output +1536,2412596,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +1537,2416525,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +1538,2422826,"TERMINAL",0,0,"Counting all components: ['dynamics', 'lam', 'tokenizer']\r\n",,terminal_output +1539,2423168,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +1540,2423878,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.22.0\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_124056-an7mepk5\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run coinrun-dyn-dev-3536670\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/an7mepk5\r\n",,terminal_output +1541,2424076,"TERMINAL",0,0,"Parameter counts:\r\n{'dynamics': 26555904, 'lam': 17640416, 'tokenizer': 33750256, 'total': 77946576}\r\n",,terminal_output +1542,2426157,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +1543,2495085,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"",python,tab +1544,2499517,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",15021,0,"",python,selection_mouse +1545,2503442,"TERMINAL",0,0,"Total memory size: 23.3 GB, Output size: 0.9 GB, Temp size: 22.3 GB, Argument size: 0.9 GB, Host temp size: 0.0 GB.\r\nFLOPs: 7.842e+11, Bytes: 6.638e+11 (618.2 GB), Intensity: 1.2 FLOPs/byte\r\nStarting training from step 0...\r\n",,terminal_output +1546,2503931,"TERMINAL",0,0,"\r\nMemstats: After params initialized:\r\n\tUsing (GB) 1.21 / 38.7 (3.126615%) on cuda:0\r\nStep 1\r\n",,terminal_output 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+1642,2563012,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",18997,0,"",python,selection_mouse +1643,2580924,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"",python,tab +1644,2583617,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19370,0,"",python,selection_mouse +1645,2583778,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19358,16,"dataloader_train",python,selection_mouse +1646,2714580,"TERMINAL",0,0,"^Csrun: interrupt (one more within 1 sec to abort)\r\nsrun: StepId=3536670.8 task 0: running\r\n",,terminal_output +1647,2714831,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3536670.8\r\nsrun: forcing job termination\r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\nslurmstepd: error: *** STEP 3536670.8 ON hkn0401 CANCELLED AT 2025-10-02T12:45:47 ***\r\n",,terminal_output +1648,2714957,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3536670.8\r\nsrun: job abort in progress\r\n",,terminal_output +1649,2715107,"TERMINAL",0,0,"]0;tum_cte0515@hkn0401:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +1650,2715488,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",,terminal_output +1651,2715740,"TERMINAL",0,0,"\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=4\r\n#SBATCH --time=00:20:00\r\n#SBATCH --partition=dev_accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --job-name=train_dyn_single_gpu\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python jasmine/train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=64 \\r\n --image_width=64 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=110 \\r\n --patch_size=4 \\r\n --init_lr=0 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=50 \\r\n --log_checkpoint_interval=2 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=coinrun-dyn-dev-$slurm_job_id \\r\n --tags dyn coinrun dev \\r\n --entity instant-uv \\r\n --project jafar \\r\n --warmup_steps 0 \\r\n --wsd_decay_steps 0 \\r\n --num_steps 10 \\r\n --data_dir $array_records_dir_train \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --val_data_dir $array_records_dir_val \\r\n --log_interval 1 \\r\n --val_interval 2 \\r\n --eval_full_frame \\r\n --val_steps 5\r\n",,terminal_output +1652,2715869,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=1469415\r\nSLURM_JOB_GPUS=0\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\r\nSLURMD_NODENAME=hkn0401\r\nSLURM_JOB_START_TIME=1759399921\r\nSLURM_STEP_NODELIST=hkn0401\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1759403521\r\nSLURM_PMI2_SRUN_PORT=44089\r\nSLURM_CPUS_ON_NODE=8\r\nSLURM_JOB_CPUS_PER_NODE=8\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=dev_accelerated\r\nSLURM_TRES_PER_TASK=cpu=8\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=1\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3536670\r\nSLURM_PTY_PORT=41791\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.199\r\nSLURM_PTY_WIN_ROW=40\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0401\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.199\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=182\r\nSLURM_NODELIST=hkn0401\r\nSLURM_SRUN_COMM_PORT=44895\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1991.localdomain\r\nSLURM_JOB_ID=3536670\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0401\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_STEP_LAUNCHER_PORT=44895\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0401\r\n",,terminal_output +1653,2715950,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +1654,2719530,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +1655,2726102,"TERMINAL",0,0,"Counting all components: ['dynamics', 'lam', 'tokenizer']\r\n",,terminal_output +1656,2726440,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +1657,2727187,"TERMINAL",0,0,"wandb: creating run\r\n",,terminal_output +1658,2727286,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.22.0\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_124559-6ee9v61v\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run coinrun-dyn-dev-3536670\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/6ee9v61v\r\n",,terminal_output +1659,2727424,"TERMINAL",0,0,"Parameter counts:\r\n{'dynamics': 26555904, 'lam': 17640416, 'tokenizer': 33750256, 'total': 77946576}\r\n",,terminal_output +1660,2729500,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +1661,2733383,"TERMINAL",0,0,"Traceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 718, in \r\n main(args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 559, in main\r\n first_batch = next(dataloader_train)\r\n ^^^^^^^^^^^^^^^^^^^^^^\r\nTypeError: 'DataLoader' object is not an iterator\r\n",,terminal_output +1662,2734117,"TERMINAL",0,0,"wandb: \r\nwandb: 🚀 View run coinrun-dyn-dev-3536670 at: https://wandb.ai/instant-uv/jafar/runs/6ee9v61v\r\nwandb: Find logs at: ../../../../../hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_124559-6ee9v61v/logs\r\n",,terminal_output +1663,2739242,"TERMINAL",0,0,"W1002 12:46:12.423430 1479842 pjrt_client.cc:1469] WatchJobStateAsync failed for task goo.gle/debugonly job_name: ""jax_worker"": UNAVAILABLE: Cancelling all calls\r\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/WatchJobState:\r\n:UNKNOWN:Error received from peer {grpc_message:""Cancelling all calls"", grpc_status:14}\r\n",,terminal_output +1664,2740189,"TERMINAL",0,0,"srun: error: hkn0401: task 0: Exited with exit code 1\r\n]0;tum_cte0515@hkn0401:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +1665,2742788,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"",python,tab +1666,2742789,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19510,0,"",python,selection_command +1667,2744192,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19547,0,"",python,selection_mouse 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+1745,2802710,"jasmine/utils/dataloader_torch.py",774,0,"",python,selection_mouse +1746,2802828,"jasmine/utils/dataloader_torch.py",773,1,"\n",python,selection_mouse +1747,2807592,"jasmine/utils/dataloader_torch.py",1005,0,"",python,selection_mouse +1748,2809735,".venv/lib64/python3.12/site-packages/torch/utils/data/dataloader.py",0,0,"# mypy: allow-untyped-defs\nr""""""Definition of the DataLoader and associated iterators that subclass _BaseDataLoaderIter.\n\nTo support these two classes, in `./_utils` we define many utility methods and\nfunctions to be run in multiprocessing. E.g., the data loading worker loop is\nin `./_utils/worker.py`.\n""""""\nfrom __future__ import annotations\n\nimport functools\nimport itertools\nimport logging\nimport multiprocessing as python_multiprocessing\nimport os\nimport queue\nimport threading\nimport warnings\nfrom typing import Any, Callable, Generic, Optional, TYPE_CHECKING, TypeVar, Union\nfrom typing_extensions import Self\n\nimport torch\nimport torch.distributed as dist\nimport torch.utils.data.graph_settings\nfrom torch._utils import ExceptionWrapper\nfrom torch.utils.data import _utils\nfrom torch.utils.data.datapipes.datapipe import (\n _IterDataPipeSerializationWrapper,\n _MapDataPipeSerializationWrapper,\n IterDataPipe,\n MapDataPipe,\n)\nfrom torch.utils.data.dataset import Dataset, IterableDataset\nfrom torch.utils.data.sampler import (\n BatchSampler,\n RandomSampler,\n Sampler,\n SequentialSampler,\n)\n\n\nif TYPE_CHECKING:\n from collections.abc import Iterable\n\n__all__ = [\n ""DataLoader"",\n ""get_worker_info"",\n ""default_collate"",\n ""default_convert"",\n]\n\n\n_T = TypeVar(""_T"")\n_T_co = TypeVar(""_T_co"", covariant=True)\n_worker_init_fn_t = Callable[[int], None]\n\n# Ideally we would parameterize `DataLoader` by the return type of `collate_fn`, but there is currently no way to have that\n# type parameter set to a default value if the user doesn't pass in a custom 'collate_fn'.\n# See https://github.com/python/mypy/issues/3737.\n_collate_fn_t = Callable[[list[_T]], Any]\n\n\n# These functions used to be defined in this file. However, it was moved to\n# _utils/collate.py. Although it is rather hard to access this from user land\n# (one has to explicitly directly `import torch.utils.data.dataloader`), there\n# probably is user code out there using it. This aliasing maintains BC in this\n# aspect.\ndefault_collate: _collate_fn_t = _utils.collate.default_collate\ndefault_convert = _utils.collate.default_convert\n\nget_worker_info = _utils.worker.get_worker_info\n\nlogger = logging.getLogger(__name__)\n\n\nclass _DatasetKind:\n Map = 0\n Iterable = 1\n\n @staticmethod\n def create_fetcher(kind, dataset, auto_collation, collate_fn, drop_last):\n if kind == _DatasetKind.Map:\n return _utils.fetch._MapDatasetFetcher(\n dataset, auto_collation, collate_fn, drop_last\n )\n else:\n return _utils.fetch._IterableDatasetFetcher(\n dataset, auto_collation, collate_fn, drop_last\n )\n\n\nclass _InfiniteConstantSampler(Sampler):\n r""""""Analogous to ``itertools.repeat(None, None)``.\n\n Used as sampler for :class:`~torch.utils.data.IterableDataset`.\n """"""\n\n def __iter__(self):\n while True:\n yield None\n\n\ndef _get_distributed_settings():\n if dist.is_available() and dist.is_initialized():\n return dist.get_world_size(), dist.get_rank()\n else:\n return 1, 0\n\n\ndef _sharding_worker_init_fn(worker_init_fn, world_size, rank_id, worker_id):\n global_worker_id = worker_id\n info = torch.utils.data.get_worker_info()\n assert info is not None\n total_workers = info.num_workers\n datapipe = info.dataset\n assert isinstance(datapipe, (IterDataPipe, MapDataPipe))\n # To distribute elements across distributed process evenly, we should shard data on distributed\n # processes first then shard on worker processes\n total_workers *= world_size\n global_worker_id = global_worker_id * world_size + rank_id\n # For BC, use default SHARDING_PRIORITIES\n torch.utils.data.graph_settings.apply_sharding(\n datapipe, total_workers, global_worker_id\n )\n if worker_init_fn is not None:\n worker_init_fn(worker_id)\n\n\ndef _share_dist_seed(generator, pg):\n _shared_seed = torch.empty((), dtype=torch.int64).random_(generator=generator)\n if isinstance(pg, dist.ProcessGroup):\n dist.broadcast(_shared_seed, src=0, group=pg)\n return _shared_seed.item()\n\n\nclass DataLoader(Generic[_T_co]):\n r""""""\n Data loader combines a dataset and a sampler, and provides an iterable over the given dataset.\n\n The :class:`~torch.utils.data.DataLoader` supports both map-style and\n iterable-style datasets with single- or multi-process loading, customizing\n loading order and optional automatic batching (collation) and memory pinning.\n\n See :py:mod:`torch.utils.data` documentation page for more details.\n\n Args:\n dataset (Dataset): dataset from which to load the data.\n batch_size (int, optional): how many samples per batch to load\n (default: ``1``).\n shuffle (bool, optional): set to ``True`` to have the data reshuffled\n at every epoch (default: ``False``).\n sampler (Sampler or Iterable, optional): defines the strategy to draw\n samples from the dataset. Can be any ``Iterable`` with ``__len__``\n implemented. If specified, :attr:`shuffle` must not be specified.\n batch_sampler (Sampler or Iterable, optional): like :attr:`sampler`, but\n returns a batch of indices at a time. Mutually exclusive with\n :attr:`batch_size`, :attr:`shuffle`, :attr:`sampler`,\n and :attr:`drop_last`.\n num_workers (int, optional): how many subprocesses to use for data\n loading. ``0`` means that the data will be loaded in the main process.\n (default: ``0``)\n collate_fn (Callable, optional): merges a list of samples to form a\n mini-batch of Tensor(s). Used when using batched loading from a\n map-style dataset.\n pin_memory (bool, optional): If ``True``, the data loader will copy Tensors\n into device/CUDA pinned memory before returning them. If your data elements\n are a custom type, or your :attr:`collate_fn` returns a batch that is a custom type,\n see the example below.\n drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,\n if the dataset size is not divisible by the batch size. If ``False`` and\n the size of dataset is not divisible by the batch size, then the last batch\n will be smaller. (default: ``False``)\n timeout (numeric, optional): if positive, the timeout value for collecting a batch\n from workers. Should always be non-negative. (default: ``0``)\n worker_init_fn (Callable, optional): If not ``None``, this will be called on each\n worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as\n input, after seeding and before data loading. (default: ``None``)\n multiprocessing_context (str or multiprocessing.context.BaseContext, optional): If\n ``None``, the default\n `multiprocessing context `_ # noqa: D401\n of your operating system will\n be used. (default: ``None``)\n generator (torch.Generator, optional): If not ``None``, this RNG will be used\n by RandomSampler to generate random indexes and multiprocessing to generate\n ``base_seed`` for workers. (default: ``None``)\n prefetch_factor (int, optional, keyword-only arg): Number of batches loaded\n in advance by each worker. ``2`` means there will be a total of\n 2 * num_workers batches prefetched across all workers. (default value depends\n on the set value for num_workers. If value of num_workers=0 default is ``None``.\n Otherwise, if value of ``num_workers > 0`` default is ``2``).\n persistent_workers (bool, optional): If ``True``, the data loader will not shut down\n the worker processes after a dataset has been consumed once. This allows to\n maintain the workers `Dataset` instances alive. (default: ``False``)\n pin_memory_device (str, optional): the device to :attr:`pin_memory` on if ``pin_memory`` is\n ``True``. If not given, the current :ref:`accelerator` will be the\n default. This argument is discouraged and subject to deprecated.\n in_order (bool, optional): If ``False``, the data loader will not enforce that batches\n are returned in a first-in, first-out order. Only applies when ``num_workers > 0``. (default: ``True``)\n\n\n .. warning:: If the ``spawn`` start method is used, :attr:`worker_init_fn`\n cannot be an unpicklable object, e.g., a lambda function. See\n :ref:`multiprocessing-best-practices` on more details related\n to multiprocessing in PyTorch.\n\n .. warning:: ``len(dataloader)`` heuristic is based on the length of the sampler used.\n When :attr:`dataset` is an :class:`~torch.utils.data.IterableDataset`,\n it instead returns an estimate based on ``len(dataset) / batch_size``, with proper\n rounding depending on :attr:`drop_last`, regardless of multi-process loading\n configurations. This represents the best guess PyTorch can make because PyTorch\n trusts user :attr:`dataset` code in correctly handling multi-process\n loading to avoid duplicate data.\n\n However, if sharding results in multiple workers having incomplete last batches,\n this estimate can still be inaccurate, because (1) an otherwise complete batch can\n be broken into multiple ones and (2) more than one batch worth of samples can be\n dropped when :attr:`drop_last` is set. Unfortunately, PyTorch can not detect such\n cases in general.\n\n See `Dataset Types`_ for more details on these two types of datasets and how\n :class:`~torch.utils.data.IterableDataset` interacts with\n `Multi-process data loading`_.\n\n .. warning:: See :ref:`reproducibility`, and :ref:`dataloader-workers-random-seed`, and\n :ref:`data-loading-randomness` notes for random seed related questions.\n\n .. warning:: Setting `in_order` to `False` can harm reproducibility and may lead to a skewed data\n distribution being fed to the trainer in cases with imbalanced data.\n """"""\n\n dataset: Dataset[_T_co]\n batch_size: Optional[int]\n num_workers: int\n pin_memory: bool\n drop_last: bool\n timeout: float\n sampler: Union[Sampler, Iterable]\n pin_memory_device: str\n prefetch_factor: Optional[int]\n _iterator: Optional[_BaseDataLoaderIter]\n __initialized = False\n\n def __init__(\n self,\n dataset: Dataset[_T_co],\n batch_size: Optional[int] = 1,\n shuffle: Optional[bool] = None,\n sampler: Union[Sampler, Iterable, None] = None,\n batch_sampler: Union[Sampler[list], Iterable[list], None] = None,\n num_workers: int = 0,\n collate_fn: Optional[_collate_fn_t] = None,\n pin_memory: bool = False,\n drop_last: bool = False,\n timeout: float = 0,\n worker_init_fn: Optional[_worker_init_fn_t] = None,\n multiprocessing_context=None,\n generator=None,\n *,\n prefetch_factor: Optional[int] = None,\n persistent_workers: bool = False,\n pin_memory_device: str = """",\n in_order: bool = True,\n ) -> None:\n torch._C._log_api_usage_once(""python.data_loader"")\n\n if num_workers < 0:\n raise ValueError(\n ""num_workers option should be non-negative; ""\n ""use num_workers=0 to disable multiprocessing.""\n )\n\n if timeout < 0:\n raise ValueError(""timeout option should be non-negative"")\n\n if num_workers == 0 and prefetch_factor is not None:\n raise ValueError(\n ""prefetch_factor option could only be specified in multiprocessing.""\n ""let num_workers > 0 to enable multiprocessing, otherwise set prefetch_factor to None.""\n )\n elif num_workers > 0 and prefetch_factor is None:\n prefetch_factor = 2\n elif prefetch_factor is not None and prefetch_factor < 0:\n raise ValueError(""prefetch_factor option should be non-negative"")\n\n if persistent_workers and num_workers == 0:\n raise ValueError(""persistent_workers option needs num_workers > 0"")\n\n self.dataset = dataset\n self.num_workers = num_workers\n self.prefetch_factor = prefetch_factor\n self.pin_memory = pin_memory\n self.pin_memory_device = pin_memory_device\n self.timeout = timeout\n self.worker_init_fn = worker_init_fn\n self.multiprocessing_context = multiprocessing_context\n self.in_order = in_order\n\n # Adds forward compatibilities so classic DataLoader can work with DataPipes:\n # _DataPipeSerializationWrapper container makes it easier to serialize without redefining pickler\n if isinstance(self.dataset, IterDataPipe):\n self.dataset = _IterDataPipeSerializationWrapper(self.dataset)\n elif isinstance(self.dataset, MapDataPipe):\n self.dataset = _MapDataPipeSerializationWrapper(self.dataset)\n\n # Arg-check dataset related before checking samplers because we want to\n # tell users that iterable-style datasets are incompatible with custom\n # samplers first, so that they don't learn that this combo doesn't work\n # after spending time fixing the custom sampler errors.\n if isinstance(dataset, IterableDataset):\n self._dataset_kind = _DatasetKind.Iterable\n # NOTE [ Custom Samplers and IterableDataset ]\n #\n # `IterableDataset` does not support custom `batch_sampler` or\n # `sampler` since the key is irrelevant (unless we support\n # generator-style dataset one day...).\n #\n # For `sampler`, we always create a dummy sampler. This is an\n # infinite sampler even when the dataset may have an implemented\n # finite `__len__` because in multi-process data loading, naive\n # settings will return duplicated data (which may be desired), and\n # thus using a sampler with length matching that of dataset will\n # cause data lost (you may have duplicates of the first couple\n # batches, but never see anything afterwards). Therefore,\n # `Iterabledataset` always uses an infinite sampler, an instance of\n # `_InfiniteConstantSampler` defined above.\n #\n # A custom `batch_sampler` essentially only controls the batch size.\n # However, it is unclear how useful it would be since an iterable-style\n # dataset can handle that within itself. Moreover, it is pointless\n # in multi-process data loading as the assignment order of batches\n # to workers is an implementation detail so users can not control\n # how to batchify each worker's iterable. Thus, we disable this\n # option. If this turns out to be useful in future, we can re-enable\n # this, and support custom samplers that specify the assignments to\n # specific workers.\n if isinstance(dataset, IterDataPipe):\n if shuffle is not None:\n dataset = torch.utils.data.graph_settings.apply_shuffle_settings(\n dataset, shuffle=shuffle\n )\n # We cannot check `shuffle is not None` here, since previously `shuffle=False` was the default.\n elif shuffle not in {False, None}:\n raise ValueError(\n f""DataLoader with IterableDataset: expected unspecified shuffle option, but got shuffle={shuffle}""\n )\n\n if sampler is not None:\n # See NOTE [ Custom Samplers and IterableDataset ]\n raise ValueError(\n f""DataLoader with IterableDataset: expected unspecified sampler option, but got sampler={sampler}""\n )\n elif batch_sampler is not None:\n # See NOTE [ Custom Samplers and IterableDataset ]\n raise ValueError(\n ""DataLoader with IterableDataset: expected unspecified ""\n f""batch_sampler option, but got batch_sampler={batch_sampler}""\n )\n else:\n shuffle = bool(shuffle)\n self._dataset_kind = _DatasetKind.Map\n\n if sampler is not None and shuffle:\n raise ValueError(""sampler option is mutually exclusive with shuffle"")\n\n if batch_sampler is not None:\n # auto_collation with custom batch_sampler\n if batch_size != 1 or shuffle or sampler is not None or drop_last:\n raise ValueError(\n ""batch_sampler option is mutually exclusive ""\n ""with batch_size, shuffle, sampler, and ""\n ""drop_last""\n )\n batch_size = None\n drop_last = False\n elif batch_size is None:\n # no auto_collation\n if drop_last:\n raise ValueError(\n ""batch_size=None option disables auto-batching ""\n ""and is mutually exclusive with drop_last""\n )\n\n if sampler is None: # give default samplers\n if self._dataset_kind == _DatasetKind.Iterable:\n # See NOTE [ Custom Samplers and IterableDataset ]\n sampler = _InfiniteConstantSampler()\n else: # map-style\n if shuffle:\n sampler = RandomSampler(dataset, generator=generator) # type: ignore[arg-type]\n else:\n sampler = SequentialSampler(dataset) # type: ignore[arg-type]\n\n if batch_size is not None and batch_sampler is None:\n # auto_collation without custom batch_sampler\n batch_sampler = BatchSampler(sampler, batch_size, drop_last)\n\n self.batch_size = batch_size\n self.drop_last = drop_last\n self.sampler = sampler\n self.batch_sampler = batch_sampler\n self.generator = generator\n\n if collate_fn is None:\n if self._auto_collation:\n collate_fn = _utils.collate.default_collate\n else:\n collate_fn = _utils.collate.default_convert\n\n self.collate_fn = collate_fn\n self.persistent_workers = persistent_workers\n\n self.__initialized = True\n self._IterableDataset_len_called = (\n None # See NOTE [ IterableDataset and __len__ ]\n )\n\n self._iterator = None\n\n self.check_worker_number_rationality()\n\n torch.set_vital(""Dataloader"", ""enabled"", ""True"") # type: ignore[attr-defined]\n\n def _get_iterator(self) -> _BaseDataLoaderIter:\n if self.num_workers == 0:\n return _SingleProcessDataLoaderIter(self)\n else:\n self.check_worker_number_rationality()\n return _MultiProcessingDataLoaderIter(self)\n\n @property\n def multiprocessing_context(self):\n return self.__multiprocessing_context\n\n @multiprocessing_context.setter\n def multiprocessing_context(self, multiprocessing_context):\n if multiprocessing_context is not None:\n if self.num_workers > 0:\n if isinstance(multiprocessing_context, str):\n valid_start_methods = torch.multiprocessing.get_all_start_methods()\n if multiprocessing_context not in valid_start_methods:\n raise ValueError(\n ""multiprocessing_context option ""\n f""should specify a valid start method in {valid_start_methods!r}, but got ""\n f""multiprocessing_context={multiprocessing_context!r}""\n )\n multiprocessing_context = torch.multiprocessing.get_context(\n multiprocessing_context\n )\n\n if not isinstance(\n multiprocessing_context, python_multiprocessing.context.BaseContext\n ):\n raise TypeError(\n ""multiprocessing_context option should be a valid context ""\n ""object or a string specifying the start method, but got ""\n f""multiprocessing_context={multiprocessing_context}""\n )\n else:\n raise ValueError(\n ""multiprocessing_context can only be used with ""\n ""multi-process loading (num_workers > 0), but got ""\n f""num_workers={self.num_workers}""\n )\n\n self.__multiprocessing_context = multiprocessing_context\n\n def __setattr__(self, attr, val):\n if self.__initialized and attr in (\n ""batch_size"",\n ""batch_sampler"",\n ""sampler"",\n ""drop_last"",\n ""dataset"",\n ""persistent_workers"",\n ):\n raise ValueError(\n f""{attr} attribute should not be set after {self.__class__.__name__} is initialized""\n )\n\n super().__setattr__(attr, val)\n\n def __iter__(self) -> _BaseDataLoaderIter:\n # When using a single worker the returned iterator should be\n # created everytime to avoid resetting its state\n # However, in the case of a multiple workers iterator\n # the iterator is only created once in the lifetime of the\n # DataLoader object so that workers can be reused\n if self.persistent_workers and self.num_workers > 0:\n if self._iterator is None:\n self._iterator = self._get_iterator()\n else:\n self._iterator._reset(self)\n return self._iterator\n else:\n return self._get_iterator()\n\n @property\n def _auto_collation(self):\n return self.batch_sampler is not None\n\n @property\n def _index_sampler(self):\n # The actual sampler used for generating indices for `_DatasetFetcher`\n # (see _utils/fetch.py) to read data at each time. This would be\n # `.batch_sampler` if in auto-collation mode, and `.sampler` otherwise.\n # We can't change `.sampler` and `.batch_sampler` attributes for BC\n # reasons.\n if self._auto_collation:\n return self.batch_sampler\n else:\n return self.sampler\n\n def __len__(self) -> int:\n if self._dataset_kind == _DatasetKind.Iterable:\n # NOTE [ IterableDataset and __len__ ]\n #\n # For `IterableDataset`, `__len__` could be inaccurate when one naively\n # does multi-processing data loading, since the samples will be duplicated.\n # However, no real use case should be actually using that behavior, so\n # it should count as a user error. We should generally trust user\n # code to do the proper thing (e.g., configure each replica differently\n # in `__iter__`), and give us the correct `__len__` if they choose to\n # implement it (this will still throw if the dataset does not implement\n # a `__len__`).\n #\n # To provide a further warning, we track if `__len__` was called on the\n # `DataLoader`, save the returned value in `self._len_called`, and warn\n # if the iterator ends up yielding more than this number of samples.\n\n # Cannot statically verify that dataset is Sized\n length = self._IterableDataset_len_called = len(self.dataset) # type: ignore[assignment, arg-type]\n if (\n self.batch_size is not None\n ): # IterableDataset doesn't allow custom sampler or batch_sampler\n from math import ceil\n\n if self.drop_last:\n length = length // self.batch_size\n else:\n length = ceil(length / self.batch_size)\n return length\n else:\n return len(self._index_sampler)\n\n def check_worker_number_rationality(self):\n # This function check whether the dataloader's worker number is rational based on\n # current system's resource. Current rule is that if the number of workers this\n # Dataloader will create is bigger than the number of logical cpus that is allowed to\n # use, than we will pop up a warning to let user pay attention.\n #\n # eg. If current system has 2 physical CPUs with 16 cores each. And each core support 2\n # threads, then the total logical cpus here is 2 * 16 * 2 = 64. Let's say current\n # DataLoader process can use half of them which is 32, then the rational max number of\n # worker that initiated from this process is 32.\n # Now, let's say the created DataLoader has num_works = 40, which is bigger than 32.\n # So the warning message is triggered to notify the user to lower the worker number if\n # necessary.\n #\n #\n # [Note] Please note that this function repects `cpuset` only when os.sched_getaffinity is\n # available (available in most of Linux system, but not OSX and Windows).\n # When os.sched_getaffinity is not available, os.cpu_count() is called instead, but\n # it doesn't repect cpuset.\n # We don't take threading into account since each worker process is single threaded\n # at this time.\n #\n # We don't set any threading flags (eg. OMP_NUM_THREADS, MKL_NUM_THREADS, etc)\n # other than `torch.set_num_threads` to 1 in the worker process, if the passing\n # in functions use 3rd party modules that rely on those threading flags to determine\n # how many thread to create (eg. numpy, etc), then it is caller's responsibility to\n # set those flags correctly.\n def _create_warning_msg(num_worker_suggest, num_worker_created, cpuset_checked):\n suggested_max_worker_msg = (\n (\n (\n ""Our suggested max number of worker in current system is {}{}, which is smaller ""\n ""than what this DataLoader is going to create.""\n ).format(\n num_worker_suggest,\n (\n """"\n if cpuset_checked\n else "" (`cpuset` is not taken into account)""\n ),\n )\n )\n if num_worker_suggest is not None\n else (\n ""DataLoader is not able to compute a suggested max number of worker in current system.""\n )\n )\n\n warn_msg = (\n f""This DataLoader will create {num_worker_created} worker processes in total. {suggested_max_worker_msg} ""\n ""Please be aware that excessive worker creation might get DataLoader running slow or even freeze, ""\n ""lower the worker number to avoid potential slowness/freeze if necessary.""\n )\n return warn_msg\n\n if not self.num_workers or self.num_workers == 0:\n return\n\n # try to compute a suggested max number of worker based on system's resource\n max_num_worker_suggest = None\n cpuset_checked = False\n if hasattr(os, ""sched_getaffinity""):\n try:\n max_num_worker_suggest = len(os.sched_getaffinity(0))\n cpuset_checked = True\n except Exception:\n pass\n if max_num_worker_suggest is None:\n # os.cpu_count() could return Optional[int]\n # get cpu count first and check None in order to satisfy mypy check\n cpu_count = os.cpu_count()\n if cpu_count is not None:\n max_num_worker_suggest = cpu_count\n\n if max_num_worker_suggest is None:\n warnings.warn(\n _create_warning_msg(\n max_num_worker_suggest, self.num_workers, cpuset_checked\n )\n )\n return\n\n if self.num_workers > max_num_worker_suggest:\n warnings.warn(\n _create_warning_msg(\n max_num_worker_suggest, self.num_workers, cpuset_checked\n )\n )\n\n\nclass _BaseDataLoaderIter:\n def __init__(self, loader: DataLoader) -> None:\n self._dataset = loader.dataset\n self._shared_seed = None\n self._pg = None\n if isinstance(self._dataset, IterDataPipe):\n if dist.is_available() and dist.is_initialized():\n self._pg = dist.new_group(backend=""gloo"")\n self._shared_seed = _share_dist_seed(loader.generator, self._pg)\n shared_rng = torch.Generator()\n shared_rng.manual_seed(self._shared_seed)\n self._dataset = torch.utils.data.graph_settings.apply_random_seed(\n self._dataset, shared_rng\n )\n self._dataset_kind = loader._dataset_kind\n self._IterableDataset_len_called = loader._IterableDataset_len_called\n self._auto_collation = loader._auto_collation\n self._drop_last = loader.drop_last\n self._index_sampler = loader._index_sampler\n self._num_workers = loader.num_workers\n ws, rank = _get_distributed_settings()\n self._world_size = ws\n self._rank = rank\n # If pin_memory_device not set, default behaviour is current accelerator.\n # If pin_memory_device is set but pin_memory is not set, the default\n # behaviour false.\n if len(loader.pin_memory_device) == 0:\n if loader.pin_memory and not torch.accelerator.is_available():\n warn_msg = (\n ""'pin_memory' argument is set as true but no accelerator is found, ""\n ""then device pinned memory won't be used.""\n )\n warnings.warn(warn_msg)\n\n self._pin_memory = loader.pin_memory and torch.accelerator.is_available()\n self._pin_memory_device = None\n # Currently, pin_memory would raise error on the MPS backend (see\n # https://github.com/pytorch/pytorch/issues/86060), so forcibly\n # disable pin_memory on MPS. Remove this restriction once pinned\n # memory allocation for MPS is fixed.\n if (\n self._pin_memory\n and (acc := torch.accelerator.current_accelerator()) is not None\n and acc.type == ""mps""\n ):\n self._pin_memory = False\n warn_msg = (\n ""'pin_memory' argument is set as true but not supported on MPS now, ""\n ""then device pinned memory won't be used.""\n )\n warnings.warn(warn_msg)\n else:\n if not loader.pin_memory:\n warn_msg = (\n ""'pin_memory_device' is set but 'pin_memory' argument is not set, ""\n ""then device pinned memory won't be used.""\n ""please set 'pin_memory' to true, if you need to use the device pin memory""\n )\n warnings.warn(warn_msg)\n\n self._pin_memory = loader.pin_memory\n self._pin_memory_device = loader.pin_memory_device\n self._timeout = loader.timeout\n self._collate_fn = loader.collate_fn\n self._sampler_iter = iter(self._index_sampler)\n self._base_seed = (\n torch.empty((), dtype=torch.int64)\n .random_(generator=loader.generator)\n .item()\n )\n self._persistent_workers = loader.persistent_workers\n self._num_yielded = 0\n self._profile_name = f""enumerate(DataLoader)#{self.__class__.__name__}.__next__""\n\n def __iter__(self) -> Self:\n return self\n\n def _reset(self, loader, first_iter=False):\n self._sampler_iter = iter(self._index_sampler)\n self._num_yielded = 0\n self._IterableDataset_len_called = loader._IterableDataset_len_called\n if isinstance(self._dataset, IterDataPipe):\n self._shared_seed = _share_dist_seed(loader.generator, self._pg)\n shared_rng = torch.Generator()\n shared_rng.manual_seed(self._shared_seed)\n self._dataset = torch.utils.data.graph_settings.apply_random_seed(\n self._dataset, shared_rng\n )\n\n def _next_index(self):\n return next(self._sampler_iter) # may raise StopIteration\n\n def _next_data(self):\n raise NotImplementedError\n\n def __next__(self) -> Any:\n with torch.autograd.profiler.record_function(self._profile_name):\n if self._sampler_iter is None:\n # TODO(https://github.com/pytorch/pytorch/issues/76750)\n self._reset() # type: ignore[call-arg]\n data = self._next_data()\n self._num_yielded += 1\n if (\n self._dataset_kind == _DatasetKind.Iterable\n and self._IterableDataset_len_called is not None\n and self._num_yielded > self._IterableDataset_len_called\n ):\n warn_msg = (\n f""Length of IterableDataset {self._dataset} was reported to be {self._IterableDataset_len_called}""\n f""(when accessing len(dataloader)), but {self._num_yielded} samples have been fetched. ""\n )\n if self._num_workers > 0:\n warn_msg += (\n ""For multiprocessing data-loading, this could be caused by not properly configuring the ""\n ""IterableDataset replica at each worker. Please see ""\n ""https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset for examples.""\n )\n warnings.warn(warn_msg)\n return data\n\n def __len__(self) -> int:\n return len(self._index_sampler)\n\n def __getstate__(self):\n # TODO: add limited pickling support for sharing an iterator\n # across multiple threads for HOGWILD.\n # Probably the best way to do this is by moving the sample pushing\n # to a separate thread and then just sharing the data queue\n # but signalling the end is tricky without a non-blocking API\n raise NotImplementedError(""{} cannot be pickled"", self.__class__.__name__)\n\n\nclass _SingleProcessDataLoaderIter(_BaseDataLoaderIter):\n def __init__(self, loader):\n super().__init__(loader)\n assert self._timeout == 0\n assert self._num_workers == 0\n\n # Adds forward compatibilities so classic DataLoader can work with DataPipes:\n # Taking care of distributed sharding\n if isinstance(self._dataset, (IterDataPipe, MapDataPipe)):\n # For BC, use default SHARDING_PRIORITIES\n torch.utils.data.graph_settings.apply_sharding(\n self._dataset, self._world_size, self._rank\n )\n\n self._dataset_fetcher = _DatasetKind.create_fetcher(\n self._dataset_kind,\n self._dataset,\n self._auto_collation,\n self._collate_fn,\n self._drop_last,\n )\n\n def _next_data(self):\n index = self._next_index() # may raise StopIteration\n data = self._dataset_fetcher.fetch(index) # may raise StopIteration\n if self._pin_memory:\n data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)\n return data\n\n\nclass _MultiProcessingDataLoaderIter(_BaseDataLoaderIter):\n r""""""Iterates once over the DataLoader's dataset, as specified by the sampler.""""""\n\n # NOTE [ Data Loader Multiprocessing Shutdown Logic ]\n #\n # Preliminary:\n #\n # Our data model looks like this (queues are indicated with curly brackets):\n #\n # main process ||\n # | ||\n # {index_queue} ||\n # | ||\n # worker processes || DATA\n # | ||\n # {worker_result_queue} || FLOW\n # | ||\n # pin_memory_thread of main process || DIRECTION\n # | ||\n # {data_queue} ||\n # | ||\n # data output \/\n #\n # P.S. `worker_result_queue` and `pin_memory_thread` part may be omitted if\n # `pin_memory=False`.\n #\n #\n # Terminating multiprocessing logic requires very careful design. In\n # particular, we need to make sure that\n #\n # 1. The iterator gracefully exits the workers when its last reference is\n # gone or it is depleted.\n #\n # In this case, the workers should be gracefully exited because the\n # main process may still need to continue to run, and we want cleaning\n # up code in the workers to be executed (e.g., releasing GPU memory).\n # Naturally, we implement the shutdown logic in `__del__` of\n # DataLoaderIterator.\n #\n # We delay the discussion on the logic in this case until later.\n #\n # 2. The iterator exits the workers when the loader process and/or worker\n # processes exits normally or with error.\n #\n # We set all workers and `pin_memory_thread` to have `daemon=True`.\n #\n # You may ask, why can't we make the workers non-daemonic, and\n # gracefully exit using the same logic as we have in `__del__` when the\n # iterator gets deleted (see 1 above)?\n #\n # First of all, `__del__` is **not** guaranteed to be called when\n # interpreter exits. Even if it is called, by the time it executes,\n # many Python core library resources may already be freed, and even\n # simple things like acquiring an internal lock of a queue may hang.\n # Therefore, in this case, we actually need to prevent `__del__` from\n # being executed, and rely on the automatic termination of daemonic\n # children.\n #\n # Thus, we register an `atexit` hook that sets a global flag\n # `_utils.python_exit_status`. Since `atexit` hooks are executed in the\n # reverse order of registration, we are guaranteed that this flag is\n # set before library resources we use are freed (which, at least in\n # CPython, is done via an `atexit` handler defined in\n # `multiprocessing/util.py`\n # https://github.com/python/cpython/blob/c606624af8d4cb3b4a052fb263bb983b3f87585b/Lib/multiprocessing/util.py#L320-L362\n # registered when an object requiring this mechanism is first\n # created, e.g., `mp.Queue`\n # https://github.com/python/cpython/blob/c606624af8d4cb3b4a052fb263bb983b3f87585b/Lib/multiprocessing/context.py#L100-L103\n # https://github.com/python/cpython/blob/c606624af8d4cb3b4a052fb263bb983b3f87585b/Lib/multiprocessing/queues.py#L29\n # )\n #\n # So in `__del__`, we check if `_utils.python_exit_status` is set or\n # `None` (freed), and perform no-op if so.\n #\n # However, simply letting library clean-up codes run can also be bad,\n # because such codes (i.e., `multiprocessing.util._exit_function()`)\n # include join putting threads for `mp.Queue`, which can be blocking.\n # Hence, the main process putting threads are called with\n # `cancel_join_thread` at creation. See later section\n # [ 3b. A process won't hang when putting into a queue; ]\n # for more details.\n #\n # Here are two example cases where library clean-up codes can run\n # before `__del__` is called:\n #\n # 1. If we hold onto a reference to the iterator, it more often\n # than not tries to do `multiprocessing` library cleaning before\n # clearing the alive referenced objects (https://github.com/pytorch/pytorch/issues/48666)\n # and thus prevents our cleaning-up code to run first.\n #\n # 2. A similar issue araises when a `DataLoader` is used in a subprocess.\n # When a process ends, it shuts the all its daemonic children\n # down with a SIGTERM (instead of joining them without a timeout).\n # Simiarly for threads, but by a different mechanism. This fact,\n # together with a few implementation details of multiprocessing, forces\n # us to make workers daemonic. All of our problems arise when a\n # DataLoader is used in a subprocess, and are caused by multiprocessing\n # code which looks more or less like this:\n #\n # try:\n # your_function_using_a_dataloader()\n # finally:\n # multiprocessing.util._exit_function()\n #\n # The joining/termination mentioned above happens inside\n # `_exit_function()`. Now, if `your_function_using_a_dataloader()`\n # throws, the stack trace stored in the exception will prevent the\n # frame which uses `DataLoaderIter` to be freed. If the frame has any\n # reference to the `DataLoaderIter` (e.g., in a method of the iter),\n # its `__del__`, which starts the shutdown procedure, will not be\n # called. That, in turn, means that workers aren't notified. Attempting\n # to join in `_exit_function` will then result in a hang.\n #\n # For context, `_exit_function` is also registered as an `atexit` call.\n # So it is unclear to me (@ssnl) why this is needed in a finally block.\n # The code dates back to 2008 and there is no comment on the original\n # PEP 371 or patch https://bugs.python.org/issue3050 (containing both\n # the finally block and the `atexit` registration) that explains this.\n #\n #\n # Finally, another choice is to just shutdown workers with logic in 1\n # above whenever we see an error in `next`. This isn't ideal because\n # a. It prevents users from using try-catch to resume data loading.\n # b. It doesn't prevent hanging if users have references to the\n # iterator.\n #\n # 3. All processes exit if any of them die unexpectedly by fatal signals.\n #\n # As shown above, the workers are set as daemonic children of the main\n # process. However, automatic cleaning-up of such child processes only\n # happens if the parent process exits gracefully (e.g., not via fatal\n # signals like SIGKILL). So we must ensure that each process will exit\n # even the process that should send/receive data to/from it were\n # killed, i.e.,\n #\n # a. A process won't hang when getting from a queue.\n #\n # Even with carefully designed data dependencies (i.e., a `put()`\n # always corresponding to a `get()`), hanging on `get()` can still\n # happen when data in queue is corrupted (e.g., due to\n # `cancel_join_thread` or unexpected exit).\n #\n # For child exit, we set a timeout whenever we try to get data\n # from `data_queue`, and check the workers' status on each timeout\n # and error.\n # See `_DataLoaderiter._get_batch()` and\n # `_DataLoaderiter._try_get_data()` for details.\n #\n # Additionally, for child exit on non-Windows platforms, we also\n # register a SIGCHLD handler (which is supported on Windows) on\n # the main process, which checks if any of the workers fail in the\n # (Python) handler. This is more efficient and faster in detecting\n # worker failures, compared to only using the above mechanism.\n # See `DataLoader.cpp` and `_utils/signal_handling.py` for details.\n #\n # For `.get()` calls where the sender(s) is not the workers, we\n # guard them with timeouts, and check the status of the sender\n # when timeout happens:\n # + in the workers, the `_utils.worker.ManagerWatchdog` class\n # checks the status of the main process.\n # + if `pin_memory=True`, when getting from `pin_memory_thread`,\n # check `pin_memory_thread` status periodically until `.get()`\n # returns or see that `pin_memory_thread` died.\n #\n # b. A process won't hang when putting into a queue;\n #\n # We use `mp.Queue` which has a separate background thread to put\n # objects from an unbounded buffer array. The background thread is\n # daemonic and usually automatically joined when the process\n # *exits*.\n #\n # In case that the receiver has ended abruptly while\n # reading from the pipe, the join will hang forever. The usual\n # solution for this in Python is calling `q.cancel_join_thread`,\n # which prevents automatically joining it when finalizing\n # (exiting).\n #\n # Nonetheless, `cancel_join_thread` must only be called when the\n # queue is **not** going to be read from or write into by another\n # process, because it may hold onto a lock or leave corrupted data\n # in the queue, leading other readers/writers to hang.\n #\n # Hence,\n # + For worker processes, we only do so (for their output\n # queues, i.e., `worker_result_queue`) before exiting.\n # + For `pin_memory_thread`, its output queue `data_queue` is a\n # `queue.Queue` that does blocking `put` if the queue is full.\n # So there is no above problem, but as a result, in\n # `_pin_memory_loop`, we do need to wrap the `put` in a loop\n # that breaks not only upon success, but also when the main\n # process stops reading, i.e., is shutting down.\n # + For loader process, we `cancel_join_thread()` for all\n # `_index_queues` because the whole purpose of workers and\n # `pin_memory_thread` is to serve the loader process. If\n # loader process is already exiting, we don't really care if\n # the queues are corrupted.\n #\n #\n # Now let's get back to 1:\n # how we gracefully exit the workers when the last reference to the\n # iterator is gone.\n #\n # To achieve this, we implement the following logic along with the design\n # choices mentioned above:\n #\n # `workers_done_event`:\n # A `multiprocessing.Event` shared among the main process and all worker\n # processes. This is used to signal the workers that the iterator is\n # shutting down. After it is set, they will not send processed data to\n # queues anymore, and only wait for the final `None` before exiting.\n # `done_event` isn't strictly needed. I.e., we can just check for `None`\n # from the input queue, but it allows us to skip wasting resources\n # processing data if we are already shutting down.\n #\n # `pin_memory_thread_done_event`:\n # A `threading.Event` for a similar purpose to that of\n # `workers_done_event`, but is for the `pin_memory_thread`. The reason\n # that separate events are needed is that `pin_memory_thread` reads from\n # the output queue of the workers. But the workers, upon seeing that\n # `workers_done_event` is set, only wants to see the final `None`, and is\n # not required to flush all data in the output queue (e.g., it may call\n # `cancel_join_thread` on that queue if its `IterableDataset` iterator\n # happens to exhaust coincidentally, which is out of the control of the\n # main process). Thus, since we will exit `pin_memory_thread` before the\n # workers (see below), two separete events are used.\n #\n # NOTE: In short, the protocol is that the main process will set these\n # `done_event`s and then the corresponding processes/threads a `None`,\n # and that they may exit at any time after receiving the `None`.\n #\n # NOTE: Using `None` as the final signal is valid, since normal data will\n # always be a 2-tuple with the 1st element being the index of the data\n # transferred (different from dataset index/key), and the 2nd being\n # either the dataset key or the data sample (depending on which part\n # of the data model the queue is at).\n #\n # [ worker processes ]\n # While loader process is alive:\n # Get from `index_queue`.\n # If get anything else,\n # Check `workers_done_event`.\n # If set, continue to next iteration\n # i.e., keep getting until see the `None`, then exit.\n # Otherwise, process data:\n # If is fetching from an `IterableDataset` and the iterator\n # is exhausted, send an `_IterableDatasetStopIteration`\n # object to signal iteration end. The main process, upon\n # receiving such an object, will send `None` to this\n # worker and not use the corresponding `index_queue`\n # anymore.\n # If timed out,\n # No matter `workers_done_event` is set (still need to see `None`)\n # or not, must continue to next iteration.\n # (outside loop)\n # If `workers_done_event` is set, (this can be False with `IterableDataset`)\n # `data_queue.cancel_join_thread()`. (Everything is ending here:\n # main process won't read from it;\n # other workers will also call\n # `cancel_join_thread`.)\n #\n # [ pin_memory_thread ]\n # # No need to check main thread. If this thread is alive, the main loader\n # # thread must be alive, because this thread is set as daemonic.\n # While `pin_memory_thread_done_event` is not set:\n # Get from `worker_result_queue`.\n # If timed out, continue to get in the next iteration.\n # Otherwise, process data.\n # While `pin_memory_thread_done_event` is not set:\n # Put processed data to `data_queue` (a `queue.Queue` with blocking put)\n # If timed out, continue to put in the next iteration.\n # Otherwise, break, i.e., continuing to the out loop.\n #\n # NOTE: we don't check the status of the main thread because\n # 1. if the process is killed by fatal signal, `pin_memory_thread`\n # ends.\n # 2. in other cases, either the cleaning-up in __del__ or the\n # automatic exit of daemonic thread will take care of it.\n # This won't busy-wait either because `.get(timeout)` does not\n # busy-wait.\n #\n # [ main process ]\n # In the DataLoader Iter's `__del__`\n # b. Exit `pin_memory_thread`\n # i. Set `pin_memory_thread_done_event`.\n # ii Put `None` in `worker_result_queue`.\n # iii. Join the `pin_memory_thread`.\n # iv. `worker_result_queue.cancel_join_thread()`.\n #\n # c. Exit the workers.\n # i. Set `workers_done_event`.\n # ii. Put `None` in each worker's `index_queue`.\n # iii. Join the workers.\n # iv. Call `.cancel_join_thread()` on each worker's `index_queue`.\n #\n # NOTE: (c) is better placed after (b) because it may leave corrupted\n # data in `worker_result_queue`, which `pin_memory_thread`\n # reads from, in which case the `pin_memory_thread` can only\n # happen at timing out, which is slow. Nonetheless, same thing\n # happens if a worker is killed by signal at unfortunate times,\n # but in other cases, we are better off having a non-corrupted\n # `worker_result_queue` for `pin_memory_thread`.\n #\n # NOTE: If `pin_memory=False`, there is no `pin_memory_thread` and (b)\n # can be omitted\n #\n # NB: `done_event`s isn't strictly needed. E.g., we can just check for\n # `None` from `index_queue`, but it allows us to skip wasting resources\n # processing indices already in `index_queue` if we are already shutting\n # down.\n\n def __init__(self, loader):\n super().__init__(loader)\n\n self._prefetch_factor = loader.prefetch_factor\n self._in_order = loader.in_order\n\n assert self._num_workers > 0\n assert self._prefetch_factor > 0\n\n if loader.multiprocessing_context is None:\n multiprocessing_context = torch.multiprocessing\n else:\n multiprocessing_context = loader.multiprocessing_context\n\n self._worker_init_fn = loader.worker_init_fn\n\n # Adds forward compatibilities so classic DataLoader can work with DataPipes:\n # Additional worker init function will take care of sharding in MP and Distributed\n if isinstance(self._dataset, (IterDataPipe, MapDataPipe)):\n self._worker_init_fn = functools.partial(\n _sharding_worker_init_fn,\n self._worker_init_fn,\n self._world_size,\n self._rank,\n )\n\n # No certainty which module multiprocessing_context is\n self._worker_result_queue = multiprocessing_context.Queue() # type: ignore[var-annotated]\n self._worker_pids_set = False\n self._shutdown = False\n self._workers_done_event = multiprocessing_context.Event()\n\n self._index_queues = []\n self._workers = []\n for i in range(self._num_workers):\n # No certainty which module multiprocessing_context is\n index_queue = multiprocessing_context.Queue() # type: ignore[var-annotated]\n # Need to `cancel_join_thread` here!\n # See sections (2) and (3b) above.\n index_queue.cancel_join_thread()\n w = multiprocessing_context.Process(\n target=_utils.worker._worker_loop,\n args=(\n self._dataset_kind,\n self._dataset,\n index_queue,\n self._worker_result_queue,\n self._workers_done_event,\n self._auto_collation,\n self._collate_fn,\n self._drop_last,\n self._base_seed,\n self._worker_init_fn,\n i,\n self._num_workers,\n self._persistent_workers,\n self._shared_seed,\n ),\n )\n w.daemon = True\n # NB: Process.start() actually take some time as it needs to\n # start a process and pass the arguments over via a pipe.\n # Therefore, we only add a worker to self._workers list after\n # it started, so that we do not call .join() if program dies\n # before it starts, and __del__ tries to join but will get:\n # AssertionError: can only join a started process.\n w.start()\n self._index_queues.append(index_queue)\n self._workers.append(w)\n\n if self._pin_memory:\n self._pin_memory_thread_done_event = threading.Event()\n\n # Queue is not type-annotated\n self._data_queue = queue.Queue() # type: ignore[var-annotated]\n current_device = -1\n if self._pin_memory_device == ""cuda"":\n current_device = torch.cuda.current_device()\n elif self._pin_memory_device == ""xpu"":\n current_device = torch.xpu.current_device()\n elif self._pin_memory_device == torch._C._get_privateuse1_backend_name():\n custom_device_mod = getattr(\n torch, torch._C._get_privateuse1_backend_name()\n )\n current_device = custom_device_mod.current_device()\n elif self._pin_memory_device is None:\n current_device = torch.accelerator.current_device_index()\n pin_memory_thread = threading.Thread(\n target=_utils.pin_memory._pin_memory_loop,\n args=(\n self._worker_result_queue,\n self._data_queue,\n current_device,\n self._pin_memory_thread_done_event,\n self._pin_memory_device,\n ),\n )\n pin_memory_thread.daemon = True\n pin_memory_thread.start()\n # Similar to workers (see comment above), we only register\n # pin_memory_thread once it is started.\n self._pin_memory_thread = pin_memory_thread\n else:\n self._data_queue = self._worker_result_queue # type: ignore[assignment]\n\n # In some rare cases, persistent workers (daemonic processes)\n # would be terminated before `__del__` of iterator is invoked\n # when main process exits\n # It would cause failure when pin_memory_thread tries to read\n # corrupted data from worker_result_queue\n # atexit is used to shutdown thread and child processes in the\n # right sequence before main process exits\n if self._persistent_workers and self._pin_memory:\n import atexit\n\n for w in self._workers:\n atexit.register(_MultiProcessingDataLoaderIter._clean_up_worker, w)\n\n # .pid can be None only before process is spawned (not the case, so ignore)\n _utils.signal_handling._set_worker_pids(id(self), tuple(w.pid for w in self._workers)) # type: ignore[misc]\n _utils.signal_handling._set_SIGCHLD_handler()\n self._worker_pids_set = True\n self._reset(loader, first_iter=True)\n\n def _reset(self, loader, first_iter=False):\n super()._reset(loader, first_iter)\n self._send_idx = 0 # idx of the next task to be sent to workers\n self._rcvd_idx = 0 # idx of the next task to be returned in __next__\n # information about data not yet yielded, i.e., tasks w/ indices in range [rcvd_idx, send_idx).\n # map: task idx => - (worker_id,) if data isn't fetched (outstanding)\n # \ (worker_id, data) if data is already fetched (out-of-order)\n self._task_info = {}\n self._tasks_outstanding = (\n 0 # always equal to count(v for v in task_info.values() if len(v) == 1)\n )\n # A list of booleans representing whether each worker still has work to\n # do, i.e., not having exhausted its iterable dataset object. It always\n # contains all `True`s if not using an iterable-style dataset\n # (i.e., if kind != Iterable).\n # Not that this indicates that a worker still has work to do *for this epoch*.\n # It does not mean that a worker is dead. In case of `_persistent_workers`,\n # the worker will be reset to available in the next epoch.\n self._workers_status = [True for i in range(self._num_workers)]\n # A list of integers representing how many tasks are outstanding for each worker\n # Incremented when a task is dispatched to the worker\n # Decremented when that data has been given to the main thread\n # Each worker should have at most self._prefetch_factor tasks outstanding\n self._workers_num_tasks = [0 for i in range(self._num_workers)]\n # Reset the worker queue cycle so it resumes next epoch at worker 0\n self._worker_queue_idx_cycle = itertools.cycle(range(self._num_workers))\n # We resume the prefetching in case it was enabled\n if not first_iter:\n for idx in range(self._num_workers):\n self._index_queues[idx].put(\n _utils.worker._ResumeIteration(self._shared_seed)\n )\n resume_iteration_cnt = self._num_workers\n while resume_iteration_cnt > 0:\n return_idx, return_data = self._get_data()\n if isinstance(return_idx, _utils.worker._ResumeIteration):\n assert return_data is None\n resume_iteration_cnt -= 1\n # prime the prefetch loop\n for _ in range(self._prefetch_factor * self._num_workers):\n self._try_put_index()\n\n def _try_get_data(self, timeout=_utils.MP_STATUS_CHECK_INTERVAL):\n # Tries to fetch data from `self._data_queue` once for a given timeout.\n # This can also be used as inner loop of fetching without timeout, with\n # the sender status as the loop condition.\n #\n # This raises a `RuntimeError` if any worker died expectedly. This error\n # can come from either the SIGCHLD handler in `_utils/signal_handling.py`\n # (only for non-Windows platforms), or the manual check below on errors\n # and timeouts.\n #\n # Returns a 2-tuple:\n # (bool: whether successfully get data, any: data if successful else None)\n try:\n data = self._data_queue.get(timeout=timeout)\n return (True, data)\n except Exception as e:\n # At timeout and error, we manually check whether any worker has\n # failed. Note that this is the only mechanism for Windows to detect\n # worker failures.\n failed_workers = []\n for worker_id, w in enumerate(self._workers):\n if self._workers_status[worker_id] and not w.is_alive():\n failed_workers.append(w)\n self._mark_worker_as_unavailable(worker_id)\n if len(failed_workers) > 0:\n pids_str = "", "".join(str(w.pid) for w in failed_workers)\n raise RuntimeError(\n f""DataLoader worker (pid(s) {pids_str}) exited unexpectedly""\n ) from e\n if isinstance(e, queue.Empty):\n return (False, None)\n\n import errno\n import tempfile\n\n try:\n # Raise an exception if we are this close to the FDs limit.\n # Apparently, trying to open only one file is not a sufficient\n # test.\n # See NOTE [ DataLoader on Linux and open files limit ]\n fds_limit_margin = 10\n [tempfile.NamedTemporaryFile() for i in range(fds_limit_margin)]\n except OSError as e:\n if e.errno == errno.EMFILE:\n raise RuntimeError(\n ""Too many open files. Communication with the""\n "" workers is no longer possible. Please increase the""\n "" limit using `ulimit -n` in the shell or change the""\n "" sharing strategy by calling""\n "" `torch.multiprocessing.set_sharing_strategy('file_system')`""\n "" at the beginning of your code""\n ) from None\n raise\n\n # NOTE [ DataLoader on Linux and open files limit ]\n #\n # On Linux when DataLoader is used with multiprocessing we pass the data between\n # the root process and the workers through SHM files. We remove those files from\n # the filesystem as soon as they are created and keep them alive by\n # passing around their file descriptors through AF_UNIX sockets. (See\n # docs/source/multiprocessing.rst and 'Multiprocessing Technical Notes` in\n # the wiki (https://github.com/pytorch/pytorch/wiki).)\n #\n # This sometimes leads us to exceeding the open files limit. When that happens,\n # and the offending file descriptor is coming over a socket, the `socket` Python\n # package silently strips the file descriptor from the message, setting only the\n # `MSG_CTRUNC` flag (which might be a bit misleading since the manpage says that\n # it _indicates that some control data were discarded due to lack of space in\n # the buffer for ancillary data_). This might reflect the C implementation of\n # AF_UNIX sockets.\n #\n # This behaviour can be reproduced with the script and instructions at the\n # bottom of this note.\n #\n # When that happens, the standard Python `multiprocessing` (and not\n # `torch.multiprocessing`) raises a `RuntimeError: received 0 items of ancdata`\n #\n # Sometimes, instead of the FD being stripped, you may get an `OSError:\n # Too many open files`, both in the script below and in DataLoader. However,\n # this is rare and seems to be nondeterministic.\n #\n #\n # #!/usr/bin/env python3\n # import sys\n # import socket\n # import os\n # import array\n # import shutil\n # import socket\n #\n #\n # if len(sys.argv) != 4:\n # print(""Usage: "", sys.argv[0], "" tmp_dirname iteration (send|recv)"")\n # sys.exit(1)\n #\n # if __name__ == '__main__':\n # dirname = sys.argv[1]\n # sock_path = dirname + ""/sock""\n # iterations = int(sys.argv[2])\n # def dummy_path(i):\n # return dirname + ""/"" + str(i) + "".dummy""\n #\n #\n # if sys.argv[3] == 'send':\n # while not os.path.exists(sock_path):\n # pass\n # client = socket.socket(socket.AF_UNIX, socket.SOCK_DGRAM)\n # client.connect(sock_path)\n # for i in range(iterations):\n # fd = os.open(dummy_path(i), os.O_WRONLY | os.O_CREAT)\n # ancdata = array.array('i', [fd])\n # msg = bytes([i % 256])\n # print(""Sending fd "", fd, "" (iteration #"", i, "")"")\n # client.sendmsg([msg], [(socket.SOL_SOCKET, socket.SCM_RIGHTS, ancdata)])\n #\n #\n # else:\n # assert sys.argv[3] == 'recv'\n #\n # if os.path.exists(dirname):\n # raise Exception(""Directory exists"")\n #\n # os.mkdir(dirname)\n #\n # print(""Opening socket..."")\n # server = socket.socket(socket.AF_UNIX, socket.SOCK_DGRAM)\n # server.bind(sock_path)\n #\n # print(""Listening..."")\n # for i in range(iterations):\n # a = array.array('i')\n # msg, ancdata, flags, addr = server.recvmsg(1, socket.CMSG_SPACE(a.itemsize))\n # assert(len(ancdata) == 1)\n # cmsg_level, cmsg_type, cmsg_data = ancdata[0]\n # a.frombytes(cmsg_data)\n # print(""Received fd "", a[0], "" (iteration #"", i, "")"")\n #\n # shutil.rmtree(dirname)\n #\n # Steps to reproduce:\n #\n # 1. Run two shells and set lower file descriptor limit in the receiving one:\n # (shell1) ulimit -n 1020\n # (shell2) ulimit -n 1022\n #\n # 2. Run the script above with the `recv` option in the first shell\n # (shell1) ./test_socket.py sock_tmp 1017 recv\n #\n # 3. Run the script with the `send` option in the second shell:\n # (shell2) ./test_socket.py sock_tmp 1017 send\n\n def _get_data(self):\n # Fetches data from `self._data_queue`.\n #\n # We check workers' status every `MP_STATUS_CHECK_INTERVAL` seconds,\n # which we achieve by running `self._try_get_data(timeout=MP_STATUS_CHECK_INTERVAL)`\n # in a loop. This is the only mechanism to detect worker failures for\n # Windows. For other platforms, a SIGCHLD handler is also used for\n # worker failure detection.\n #\n # If `pin_memory=True`, we also need check if `pin_memory_thread` had\n # died at timeouts.\n if self._timeout > 0:\n success, data = self._try_get_data(self._timeout)\n if success:\n return data\n else:\n raise RuntimeError(\n f""DataLoader timed out after {self._timeout} seconds""\n )\n elif self._pin_memory:\n while self._pin_memory_thread.is_alive():\n success, data = self._try_get_data()\n if success:\n return data\n else:\n # while condition is false, i.e., pin_memory_thread died.\n raise RuntimeError(""Pin memory thread exited unexpectedly"")\n # In this case, `self._data_queue` is a `queue.Queue`,. But we don't\n # need to call `.task_done()` because we don't use `.join()`.\n else:\n while True:\n success, data = self._try_get_data()\n if success:\n return data\n\n def _next_data(self):\n while True:\n # If the worker responsible for `self._rcvd_idx` has already ended\n # and was unable to fulfill this task (due to exhausting an `IterableDataset`),\n # we try to advance `self._rcvd_idx` to find the next valid index.\n #\n # This part needs to run in the loop because both the `self._get_data()`\n # call and `_IterableDatasetStopIteration` check below can mark\n # extra worker(s) as dead.\n while self._rcvd_idx < self._send_idx:\n info = self._task_info.get(self._rcvd_idx, None)\n if info:\n worker_id = info[0]\n if (\n len(info) == 2 or self._workers_status[worker_id]\n ): # has data or is still active\n break\n del self._task_info[self._rcvd_idx]\n self._rcvd_idx += 1\n else:\n # no valid `self._rcvd_idx` is found (i.e., didn't break)\n if not self._persistent_workers:\n self._shutdown_workers()\n raise StopIteration\n\n # Now `self._rcvd_idx` is the batch index we want to fetch\n\n # Check if the next sample has already been generated\n if len(self._task_info[self._rcvd_idx]) == 2:\n worker_id, data = self._task_info.pop(self._rcvd_idx)\n self._rcvd_idx += 1\n return self._process_data(data, worker_id)\n\n assert not self._shutdown and self._tasks_outstanding > 0\n idx, data = self._get_data()\n self._tasks_outstanding -= 1\n if self._dataset_kind == _DatasetKind.Iterable:\n # Check for _IterableDatasetStopIteration\n if isinstance(data, _utils.worker._IterableDatasetStopIteration):\n if self._persistent_workers:\n self._workers_status[data.worker_id] = False\n else:\n self._mark_worker_as_unavailable(data.worker_id)\n self._try_put_index()\n continue\n\n if idx != self._rcvd_idx:\n if not self._in_order:\n # don't store it for later, process now\n # delete from self._task_info immediately\n # this keeps the object size manageable\n worker_id = self._task_info.pop(idx)[0]\n return self._process_data(data, worker_id)\n # store out-of-order samples\n self._task_info[idx] += (data,)\n else:\n worker_id = self._task_info.pop(idx)[0]\n self._rcvd_idx += 1\n return self._process_data(data, worker_id)\n\n def _try_put_index(self):\n max_tasks = self._prefetch_factor * self._num_workers\n assert self._tasks_outstanding < max_tasks\n\n try:\n index = self._next_index()\n except StopIteration:\n return\n for _ in range(self._num_workers): # find the next active worker, if any\n worker_queue_idx = next(self._worker_queue_idx_cycle)\n if self._workers_status[worker_queue_idx]:\n if self._in_order:\n break\n elif self._workers_num_tasks[worker_queue_idx] < max_tasks // sum(\n self._workers_status\n ):\n # when self._in_order is False, distribute work to a worker if it has capacity\n # _workers_status is updated only in this thread, so the sum is guaranteed > 0\n break\n else:\n # not found (i.e., didn't break)\n return\n\n self._index_queues[worker_queue_idx].put((self._send_idx, index)) # type: ignore[possibly-undefined]\n self._task_info[self._send_idx] = (worker_queue_idx,)\n self._workers_num_tasks[worker_queue_idx] += 1\n self._tasks_outstanding += 1\n self._send_idx += 1\n\n def _process_data(self, data, worker_idx):\n self._workers_num_tasks[worker_idx] -= 1\n self._try_put_index()\n if isinstance(data, ExceptionWrapper):\n data.reraise()\n return data\n\n def _mark_worker_as_unavailable(self, worker_id, shutdown=False):\n # Mark a worker as having finished its work e.g., due to\n # exhausting an `IterableDataset`. This should be used only when this\n # `_MultiProcessingDataLoaderIter` is going to continue running.\n\n assert self._workers_status[worker_id] or (\n self._persistent_workers and shutdown\n )\n\n # Signal termination to that specific worker.\n q = self._index_queues[worker_id]\n # Indicate that no more data will be put on this queue by the current\n # process.\n q.put(None)\n\n # Note that we don't actually join the worker here, nor do we remove the\n # worker's pid from C side struct because (1) joining may be slow, and\n # (2) since we don't join, the worker may still raise error, and we\n # prefer capturing those, rather than ignoring them, even though they\n # are raised after the worker has finished its job.\n # Joinning is deferred to `_shutdown_workers`, which it is called when\n # all workers finish their jobs (e.g., `IterableDataset` replicas) or\n # when this iterator is garbage collected.\n\n self._workers_status[worker_id] = False\n\n assert self._workers_done_event.is_set() == shutdown\n\n def _shutdown_workers(self):\n # Called when shutting down this `_MultiProcessingDataLoaderIter`.\n # See NOTE [ Data Loader Multiprocessing Shutdown Logic ] for details on\n # the logic of this function.\n if (\n _utils is None\n or _utils.python_exit_status is True\n or _utils.python_exit_status is None\n ):\n # See (2) of the note. If Python is shutting down, do no-op.\n return\n # Normal exit when last reference is gone / iterator is depleted.\n # See (1) and the second half of the note.\n if not self._shutdown:\n self._shutdown = True\n try:\n # Normal exit when last reference is gone / iterator is depleted.\n # See (1) and the second half of the note.\n\n # Exit `pin_memory_thread` first because exiting workers may leave\n # corrupted data in `worker_result_queue` which `pin_memory_thread`\n # reads from.\n if hasattr(self, ""_pin_memory_thread""):\n # Use hasattr in case error happens before we set the attribute.\n self._pin_memory_thread_done_event.set()\n # Send something to pin_memory_thread in case it is waiting\n # so that it can wake up and check `pin_memory_thread_done_event`\n self._worker_result_queue.put((None, None))\n self._pin_memory_thread.join()\n self._worker_result_queue.cancel_join_thread()\n self._worker_result_queue.close()\n\n # Exit workers now.\n self._workers_done_event.set()\n for worker_id in range(len(self._workers)):\n # Get number of workers from `len(self._workers)` instead of\n # `self._num_workers` in case we error before starting all\n # workers.\n # If we are using workers_status with persistent_workers\n # we have to shut it down because the worker is paused\n if self._persistent_workers or self._workers_status[worker_id]:\n self._mark_worker_as_unavailable(worker_id, shutdown=True)\n for w in self._workers:\n # We should be able to join here, but in case anything went\n # wrong, we set a timeout and if the workers fail to join,\n # they are killed in the `finally` block.\n w.join(timeout=_utils.MP_STATUS_CHECK_INTERVAL)\n for q in self._index_queues:\n q.cancel_join_thread()\n q.close()\n finally:\n # Even though all this function does is putting into queues that\n # we have called `cancel_join_thread` on, weird things can\n # happen when a worker is killed by a signal, e.g., hanging in\n # `Event.set()`. So we need to guard this with SIGCHLD handler,\n # and remove pids from the C side data structure only at the\n # end.\n #\n # FIXME: Unfortunately, for Windows, we are missing a worker\n # error detection mechanism here in this function, as it\n # doesn't provide a SIGCHLD handler.\n if self._worker_pids_set:\n _utils.signal_handling._remove_worker_pids(id(self))\n self._worker_pids_set = False\n for w in self._workers:\n if w.is_alive():\n # Existing mechanisms try to make the workers exit\n # peacefully, but in case that we unfortunately reach\n # here, which we shouldn't, (e.g., pytorch/pytorch#39570),\n # we kill the worker.\n w.terminate()\n\n # staticmethod is used to remove reference to `_MultiProcessingDataLoaderIter`\n @staticmethod\n def _clean_up_worker(w):\n try:\n w.join(timeout=_utils.MP_STATUS_CHECK_INTERVAL)\n finally:\n if w.is_alive():\n w.terminate()\n\n def __del__(self):\n self._shutdown_workers()\n",python,tab 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+1780,2957923,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19510,163," first_batch = next(dataloader_train)\n first_batch[""rng""] = rng # type: ignore\n compiled = train_step.lower(optimizer, first_batch).compile()",python,selection_command +1781,2958049,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19510,227," first_batch = next(dataloader_train)\n first_batch[""rng""] = rng # type: ignore\n compiled = train_step.lower(optimizer, first_batch).compile()\n print_compiled_memory_stats(compiled.memory_analysis())",python,selection_command +1782,2958202,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19510,290," first_batch = next(dataloader_train)\n first_batch[""rng""] = rng # type: ignore\n compiled = train_step.lower(optimizer, first_batch).compile()\n print_compiled_memory_stats(compiled.memory_analysis())\n print_compiled_cost_analysis(compiled.cost_analysis())",python,selection_command +1783,2958344,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19510,344," first_batch = next(dataloader_train)\n first_batch[""rng""] = rng # type: ignore\n compiled = train_step.lower(optimizer, first_batch).compile()\n print_compiled_memory_stats(compiled.memory_analysis())\n print_compiled_cost_analysis(compiled.cost_analysis())\n # Do not skip the first batch during training",python,selection_command +1784,2958721,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19510,420," first_batch = next(dataloader_train)\n first_batch[""rng""] = rng # type: ignore\n compiled = train_step.lower(optimizer, first_batch).compile()\n print_compiled_memory_stats(compiled.memory_analysis())\n print_compiled_cost_analysis(compiled.cost_analysis())\n # Do not skip the first batch during training\n dataloader_train = itertools.chain([first_batch], dataloader_train)",python,selection_command +1785,2959267,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19518,0,"",python,selection_command +1786,2961305,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19863,0,"ä",python,content +1787,2961305,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19809,0,"ä",python,content +1788,2961305,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19746,0,"ä",python,content +1789,2961305,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19682,0,"ä",python,content +1790,2961305,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19612,0,"ä",python,content 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next(dataloader_train)\n first_batch[""rng""] = rng # type: ignore",python,selection_command +1812,2965118,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19510,163," first_batch = next(dataloader_train)\n first_batch[""rng""] = rng # type: ignore\n compiled = train_step.lower(optimizer, first_batch).compile()",python,selection_command +1813,2965283,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19510,227," first_batch = next(dataloader_train)\n first_batch[""rng""] = rng # type: ignore\n compiled = train_step.lower(optimizer, first_batch).compile()\n print_compiled_memory_stats(compiled.memory_analysis())",python,selection_command +1814,2965422,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19510,290," first_batch = next(dataloader_train)\n first_batch[""rng""] = rng # type: ignore\n compiled = train_step.lower(optimizer, first_batch).compile()\n print_compiled_memory_stats(compiled.memory_analysis())\n print_compiled_cost_analysis(compiled.cost_analysis())",python,selection_command +1815,2965552,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19510,344," first_batch = next(dataloader_train)\n first_batch[""rng""] = rng # type: ignore\n compiled = train_step.lower(optimizer, first_batch).compile()\n print_compiled_memory_stats(compiled.memory_analysis())\n print_compiled_cost_analysis(compiled.cost_analysis())\n # Do not skip the first batch during training",python,selection_command +1816,2965902,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19510,420," first_batch = next(dataloader_train)\n first_batch[""rng""] = rng # type: ignore\n compiled = train_step.lower(optimizer, first_batch).compile()\n print_compiled_memory_stats(compiled.memory_analysis())\n print_compiled_cost_analysis(compiled.cost_analysis())\n # Do not skip the first batch during training\n dataloader_train = itertools.chain([first_batch], dataloader_train)",python,selection_command +1817,2966099,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19518,0,"",python,selection_command +1818,2966864,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19863,0,"#",python,content +1819,2966864,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19809,0,"#",python,content +1820,2966864,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19746,0,"#",python,content +1821,2966864,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19682,0,"#",python,content 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+1843,2968305,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19521,0,"s",python,content +1844,2968306,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19522,0,"",python,selection_keyboard +1845,2968607,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19521,0,"",python,selection_command +1846,2971363,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",,terminal_output +1847,2972593,"TERMINAL",0,0,"\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=4\r\n#SBATCH --time=00:20:00\r\n#SBATCH --partition=dev_accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --job-name=train_dyn_single_gpu\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python jasmine/train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=64 \\r\n --image_width=64 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=110 \\r\n --patch_size=4 \\r\n --init_lr=0 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=50 \\r\n --log_checkpoint_interval=2 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=coinrun-dyn-dev-$slurm_job_id \\r\n --tags dyn coinrun dev \\r\n --entity instant-uv \\r\n --project jafar \\r\n --warmup_steps 0 \\r\n --wsd_decay_steps 0 \\r\n --num_steps 10 \\r\n --data_dir $array_records_dir_train \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --val_data_dir $array_records_dir_val \\r\n --log_interval 1 \\r\n --val_interval 2 \\r\n --eval_full_frame \\r\n --val_steps 5\r\nSLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=1469415\r\nSLURM_JOB_GPUS=0\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\r\nSLURMD_NODENAME=hkn0401\r\nSLURM_JOB_START_TIME=1759399921\r\nSLURM_STEP_NODELIST=hkn0401\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1759403521\r\nSLURM_PMI2_SRUN_PORT=44089\r\nSLURM_CPUS_ON_NODE=8\r\nSLURM_JOB_CPUS_PER_NODE=8\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=dev_accelerated\r\nSLURM_TRES_PER_TASK=cpu=8\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=1\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3536670\r\nSLURM_PTY_PORT=41791\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.199\r\nSLURM_PTY_WIN_ROW=40\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0401\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.199\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=182\r\nSLURM_NODELIST=hkn0401\r\nSLURM_SRUN_COMM_PORT=44895\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1991.localdomain\r\nSLURM_JOB_ID=3536670\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0401\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_STEP_LAUNCHER_PORT=44895\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0401\r\n",,terminal_output +1848,2972654,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +1849,2976190,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +1850,2976442,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"",python,tab +1851,2977772,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19869,0,"",python,selection_mouse +1852,2978327,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19920,0,"",python,selection_mouse +1853,2978449,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19919,5,"chain",python,selection_mouse +1854,2980238,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20859,0,"",python,selection_mouse +1855,2981758,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20204,0,"",python,selection_mouse +1856,2982684,"TERMINAL",0,0,"Counting all components: ['dynamics', 'lam', 'tokenizer']\r\n",,terminal_output +1857,2983022,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20093,0,"",python,selection_mouse +1858,2983156,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +1859,2983217,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20086,16,"dataloader_train",python,selection_mouse +1860,2984010,"TERMINAL",0,0,"wandb: creating run\r\nwandb: Tracking run with wandb version 0.22.0\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_125016-6w71romt\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run coinrun-dyn-dev-3536670\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/6w71romt\r\n",,terminal_output +1861,2984170,"TERMINAL",0,0,"Parameter counts:\r\n{'dynamics': 26555904, 'lam': 17640416, 'tokenizer': 33750256, 'total': 77946576}\r\n",,terminal_output +1862,2986259,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +1863,2990640,"TERMINAL",0,0,"Starting training from step 0...\r\n",,terminal_output +1864,2990942,"TERMINAL",0,0,"Traceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 719, in \r\n main(args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 573, in main\r\n batch[""rng""] = _rng_mask\r\n ~~~~~^^^^^^^\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/numpy/array_methods.py"", line 617, in _unimplemented_setitem\r\n raise TypeError(msg.format(type(self)))\r\nTypeError: JAX arrays are immutable and do not support in-place item assignment. Instead of x[idx] = y, use x = x.at[idx].set(y) or another .at[] method: https://docs.jax.dev/en/latest/_autosummary/jax.numpy.ndarray.at.html\r\n",,terminal_output +1865,2991601,"TERMINAL",0,0,"wandb: \r\nwandb: 🚀 View run coinrun-dyn-dev-3536670 at: https://wandb.ai/instant-uv/jafar/runs/6w71romt\r\nwandb: Find logs at: ../../../../../hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_125016-6w71romt/logs\r\n",,terminal_output +1866,2991746,"TERMINAL",0,0,"W1002 12:50:24.899564 1480517 pjrt_client.cc:1469] WatchJobStateAsync failed for task goo.gle/debugonly job_name: ""jax_worker"": CANCELLED: CANCELLED\r\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/WatchJobState:\r\n:UNKNOWN:Error received from peer {grpc_message:""CANCELLED"", grpc_status:1} [type.googleapis.com/tensorflow.DerivedStatus='']\r\n",,terminal_output +1867,2992444,"TERMINAL",0,0,"srun: error: hkn0401: task 0: Exited with exit code 1\r\n]0;tum_cte0515@hkn0401:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +1868,2993477,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"",python,tab +1869,2994514,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19775,0,"",python,selection_mouse +1870,2994585,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19710,65,"ompiled_memory_stats(compiled.memory_analysis())\n # print_",python,selection_mouse +1871,2994831,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19977,0,"",python,selection_mouse +1872,2994945,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19899,78,"r_train = itertools.chain([first_batch], dataloader_train)\n print(f""Startin",python,selection_mouse +1873,2995147,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19844,0,"",python,selection_mouse +1874,2995272,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19843,4,"skip",python,selection_mouse +1875,2995564,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19900,0,"",python,selection_mouse +1876,2995744,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19890,16,"dataloader_train",python,selection_mouse +1877,2996215,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19897,0,"",python,selection_mouse +1878,2996284,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19890,16,"dataloader_train",python,selection_mouse +1879,2997437,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20355,0,"",python,selection_mouse +1880,2997570,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",20343,15,"print_mem_stats",python,selection_mouse +1881,3005749,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19522,0,"",python,selection_mouse +1882,3005753,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19521,0,"",python,selection_command +1883,3006528,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/utils/nn.py",0,0,"",python,tab +1884,3006862,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/models/tokenizer.py",0,0,"",python,tab +1885,3013204,"/home/hk-project-p0023960/tum_cte0515/Projects/jafar/generate_dataset.py",0,0,"",python,tab 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+1945,3068409,"jasmine/train_dynamics.py",19396,0,"",python,selection_command +1946,3068576,"jasmine/train_dynamics.py",19422,0,"",python,selection_command +1947,3068743,"jasmine/train_dynamics.py",19443,0,"",python,selection_command +1948,3068898,"jasmine/train_dynamics.py",19481,0,"",python,selection_command +1949,3069029,"jasmine/train_dynamics.py",19514,0,"",python,selection_command +1950,3069377,"jasmine/train_dynamics.py",19527,0,"",python,selection_command +1951,3070005,"jasmine/train_dynamics.py",19523,46," # first_batch = next(dataloader_train)",python,selection_command +1952,3070206,"jasmine/train_dynamics.py",19523,97," # first_batch = next(dataloader_train)\n # first_batch[""rng""] = rng # type: ignore",python,selection_command +1953,3070344,"jasmine/train_dynamics.py",19523,169," # first_batch = next(dataloader_train)\n # first_batch[""rng""] = rng # type: ignore\n # compiled = train_step.lower(optimizer, first_batch).compile()",python,selection_command +1954,3070500,"jasmine/train_dynamics.py",19523,235," # first_batch = next(dataloader_train)\n # first_batch[""rng""] = rng # type: ignore\n # compiled = train_step.lower(optimizer, first_batch).compile()\n # print_compiled_memory_stats(compiled.memory_analysis())",python,selection_command +1955,3070630,"jasmine/train_dynamics.py",19523,300," # first_batch = next(dataloader_train)\n # first_batch[""rng""] = rng # type: ignore\n # compiled = train_step.lower(optimizer, first_batch).compile()\n # print_compiled_memory_stats(compiled.memory_analysis())\n # print_compiled_cost_analysis(compiled.cost_analysis())",python,selection_command +1956,3071144,"jasmine/train_dynamics.py",19763,0,"",python,selection_command +1957,3071629,"jasmine/train_dynamics.py",19828,0,"",python,selection_command +1958,3071772,"jasmine/train_dynamics.py",19884,0,"",python,selection_command +1959,3071932,"jasmine/train_dynamics.py",19962,0,"",python,selection_command +1960,3072093,"jasmine/train_dynamics.py",20014,0,"",python,selection_command +1961,3072766,"jasmine/train_dynamics.py",20086,0,"",python,selection_command +1962,3075143,"jasmine/train_dynamics.py",20096,0,"",python,selection_mouse +1963,3075304,"jasmine/train_dynamics.py",20086,16,"dataloader_train",python,selection_mouse +1964,3082775,"jasmine/train_dynamics.py",20086,15,"dataloader_trai",python,selection_command +1965,3085422,"jasmine/train_dynamics.py",20086,16,"dataloader_train",python,selection_command +1966,3090809,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",,terminal_output +1967,3090937,"TERMINAL",0,0,"\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=4\r\n#SBATCH --time=00:20:00\r\n#SBATCH --partition=dev_accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --job-name=train_dyn_single_gpu\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python jasmine/train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=64 \\r\n --image_width=64 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=110 \\r\n --patch_size=4 \\r\n --init_lr=0 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=50 \\r\n --log_checkpoint_interval=2 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=coinrun-dyn-dev-$slurm_job_id \\r\n --tags dyn coinrun dev \\r\n --entity instant-uv \\r\n --project jafar \\r\n --warmup_steps 0 \\r\n --wsd_decay_steps 0 \\r\n --num_steps 10 \\r\n --data_dir $array_records_dir_train \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --val_data_dir $array_records_dir_val \\r\n --log_interval 1 \\r\n --val_interval 2 \\r\n --eval_full_frame \\r\n --val_steps 5\r\n",,terminal_output +1968,3091081,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=1469415\r\nSLURM_JOB_GPUS=0\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\r\nSLURMD_NODENAME=hkn0401\r\nSLURM_JOB_START_TIME=1759399921\r\nSLURM_STEP_NODELIST=hkn0401\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1759403521\r\nSLURM_PMI2_SRUN_PORT=44089\r\nSLURM_CPUS_ON_NODE=8\r\nSLURM_JOB_CPUS_PER_NODE=8\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=dev_accelerated\r\nSLURM_TRES_PER_TASK=cpu=8\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=1\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3536670\r\nSLURM_PTY_PORT=41791\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.199\r\nSLURM_PTY_WIN_ROW=40\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0401\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.199\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=182\r\nSLURM_NODELIST=hkn0401\r\nSLURM_SRUN_COMM_PORT=44895\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1991.localdomain\r\nSLURM_JOB_ID=3536670\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0401\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_STEP_LAUNCHER_PORT=44895\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0401\r\n",,terminal_output +1969,3091211,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +1970,3095104,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +1971,3101550,"TERMINAL",0,0,"Counting all components: ['dynamics', 'lam', 'tokenizer']\r\n",,terminal_output +1972,3101949,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +1973,3102719,"TERMINAL",0,0,"wandb: creating run\r\nwandb: Tracking run with wandb version 0.22.0\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_125215-uwl3tafv\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run coinrun-dyn-dev-3536670\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/uwl3tafv\r\n",,terminal_output +1974,3102893,"TERMINAL",0,0,"Parameter counts:\r\n{'dynamics': 26555904, 'lam': 17640416, 'tokenizer': 33750256, 'total': 77946576}\r\n",,terminal_output +1975,3105019,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +1976,3108751,"TERMINAL",0,0,"Starting training from step 0...\r\n",,terminal_output +1977,3161039,"jasmine/train_dynamics.py",0,0,"",python,tab +1978,3161157,"jasmine/train_dynamics.py",11744,0,"",python,selection_command +1979,3182255,"TERMINAL",0,0,"\r\nMemstats: After params initialized:\r\n\tUsing (GB) 1.18 / 38.7 (3.049096%) on cuda:0\r\nStep 1\r\n",,terminal_output +1980,3214316,"TERMINAL",0,0,"^Csrun: interrupt (one more within 1 sec to abort)\r\nsrun: StepId=3536670.11 task 0: running\r\n",,terminal_output +1981,3214414,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3536670.11\r\nsrun: forcing job termination\r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\nslurmstepd: error: *** STEP 3536670.11 ON hkn0401 CANCELLED AT 2025-10-02T12:54:07 ***\r\n",,terminal_output +1982,3214635,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3536670.11\r\nsrun: job abort in progress\r\n",,terminal_output +1983,3214787,"TERMINAL",0,0,"]0;tum_cte0515@hkn0401:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +1984,3217098,"jasmine/train_dynamics.py",0,0,"",python,tab +1985,3220866,"jasmine/utils/dataloader_torch.py",0,0,"",python,tab +1986,3222439,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",0,0,"",shellscript,tab +1987,3223601,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1897,0,"",shellscript,selection_mouse +1988,3224526,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1898,0,"",shellscript,selection_command +1989,3224656,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1899,0,"",shellscript,selection_command +1990,3224804,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1900,0,"",shellscript,selection_command +1991,3224962,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1901,0,"",shellscript,selection_command +1992,3225132,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1902,0,"",shellscript,selection_command +1993,3225512,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1901,1,"",shellscript,content +1994,3225998,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1901,0,"1",shellscript,content +1995,3225999,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1902,0,"",shellscript,selection_keyboard +1996,3226196,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1902,0,"0",shellscript,content +1997,3226197,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1903,0,"",shellscript,selection_keyboard +1998,3226406,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1902,0,"",shellscript,selection_command +1999,3230285,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",1926,0,"",shellscript,selection_command +2000,3234732,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",,terminal_output +2001,3235075,"TERMINAL",0,0,"\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=4\r\n#SBATCH --time=00:20:00\r\n#SBATCH --partition=dev_accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --job-name=train_dyn_single_gpu\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python jasmine/train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=64 \\r\n --image_width=64 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=110 \\r\n --patch_size=4 \\r\n --init_lr=0 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=50 \\r\n --log_checkpoint_interval=2 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=coinrun-dyn-dev-$slurm_job_id \\r\n --tags dyn coinrun dev \\r\n --entity instant-uv \\r\n --project jafar \\r\n --warmup_steps 0 \\r\n --wsd_decay_steps 0 \\r\n --num_steps 10 \\r\n --data_dir $array_records_dir_train \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --val_data_dir $array_records_dir_val \\r\n --log_interval 1 \\r\n --val_interval 10 \\r\n --eval_full_frame \\r\n --val_steps 5\r\nSLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=1469415\r\nSLURM_JOB_GPUS=0\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\r\nSLURMD_NODENAME=hkn0401\r\nSLURM_JOB_START_TIME=1759399921\r\nSLURM_STEP_NODELIST=hkn0401\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1759403521\r\nSLURM_PMI2_SRUN_PORT=44089\r\nSLURM_CPUS_ON_NODE=8\r\nSLURM_JOB_CPUS_PER_NODE=8\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=dev_accelerated\r\nSLURM_TRES_PER_TASK=cpu=8\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=1\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3536670\r\nSLURM_PTY_PORT=41791\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.199\r\nSLURM_PTY_WIN_ROW=40\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0401\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.199\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=182\r\nSLURM_NODELIST=hkn0401\r\nSLURM_SRUN_COMM_PORT=44895\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1991.localdomain\r\nSLURM_JOB_ID=3536670\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0401\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_STEP_LAUNCHER_PORT=44895\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0401\r\nGpuFreq=control_disabled\r\n",,terminal_output +2002,3238767,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +2003,3245227,"TERMINAL",0,0,"Counting all components: ['dynamics', 'lam', 'tokenizer']\r\n",,terminal_output +2004,3246004,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +2005,3246442,"TERMINAL",0,0,"wandb: creating run\r\nwandb: Tracking run with wandb version 0.22.0\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_125438-kp34ikdq\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run coinrun-dyn-dev-3536670\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/kp34ikdq\r\n",,terminal_output +2006,3246522,"TERMINAL",0,0,"Parameter counts:\r\n{'dynamics': 26555904, 'lam': 17640416, 'tokenizer': 33750256, 'total': 77946576}\r\n",,terminal_output +2007,3248567,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +2008,3252689,"TERMINAL",0,0,"Starting training from step 0...\r\n",,terminal_output +2009,3326412,"TERMINAL",0,0,"\r\nMemstats: After params initialized:\r\n\tUsing (GB) 1.21 / 38.7 (3.126615%) on cuda:0\r\nStep 1\r\n",,terminal_output +2010,3367348,"TERMINAL",0,0,"Step 2\r\n",,terminal_output +2011,3369963,"TERMINAL",0,0,"Saved checkpoint at step 2\r\n",,terminal_output +2012,3370595,"TERMINAL",0,0,"Step 3\r\n",,terminal_output +2013,3372590,"TERMINAL",0,0,"Step 4\r\n",,terminal_output +2014,3384157,"TERMINAL",0,0,"Saved checkpoint at step 4\r\n",,terminal_output +2015,3384518,"TERMINAL",0,0,"Step 5\r\n",,terminal_output +2016,3386572,"TERMINAL",0,0,"Step 6\r\n",,terminal_output +2017,3396776,"TERMINAL",0,0,"Saved checkpoint at step 6\r\n",,terminal_output +2018,3398116,"TERMINAL",0,0,"Step 7\r\n",,terminal_output +2019,3400142,"TERMINAL",0,0,"Step 8\r\n",,terminal_output +2020,3402482,"TERMINAL",0,0,"Saved checkpoint at step 8\r\n",,terminal_output +2021,3403521,"TERMINAL",0,0,"Step 9\r\n",,terminal_output +2022,3404048,"TERMINAL",0,0,"WARNING:absl:Path /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000008 could not be identified as a temporary checkpoint path using . Got error: Expected AtomicRenameTemporaryPath (/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000008) to end with "".orbax-checkpoint-tmp"".\r\nWARNING:absl:Path /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000002 could not be identified as a temporary checkpoint path using . Got error: Expected AtomicRenameTemporaryPath (/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000002) to end with "".orbax-checkpoint-tmp"".\r\nWARNING:absl:Path /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000006 could not be identified as a temporary checkpoint path using . Got error: Expected AtomicRenameTemporaryPath (/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000006) to end with "".orbax-checkpoint-tmp"".\r\nWARNING:absl:Path /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000004 could not be identified as a temporary checkpoint path using . Got error: Expected AtomicRenameTemporaryPath (/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000004) to end with "".orbax-checkpoint-tmp"".\r\n",,terminal_output +2023,3405788,"TERMINAL",0,0,"Step 10\r\nCalculating validation metrics...\r\n",,terminal_output +2024,3408857,"TERMINAL",0,0,"^Csrun: interrupt (one more within 1 sec to abort)\r\nsrun: StepId=3536670.12 task 0: running\r\n",,terminal_output +2025,3408967,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3536670.12\r\nsrun: forcing job termination\r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\nslurmstepd: error: *** STEP 3536670.12 ON hkn0401 CANCELLED AT 2025-10-02T12:57:22 ***\r\n",,terminal_output +2026,3409108,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3536670.12\r\nsrun: job abort in progress\r\n",,terminal_output +2027,3409351,"TERMINAL",0,0,"]0;tum_cte0515@hkn0401:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +2028,3412116,"jasmine/train_dynamics.py",0,0,"",python,tab +2029,3413272,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"",python,tab +2030,3415591,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19510,13,"",python,content +2031,3415617,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19518,0,"",python,selection_command +2032,3416371,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19510,46," # first_batch = next(dataloader_train)",python,selection_command +2033,3416559,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19510,97," # first_batch = next(dataloader_train)\n # first_batch[""rng""] = rng # type: ignore",python,selection_command +2034,3416693,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19510,169," # first_batch = next(dataloader_train)\n # first_batch[""rng""] = rng # type: ignore\n # compiled = train_step.lower(optimizer, first_batch).compile()",python,selection_command +2035,3416843,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19510,235," # first_batch = next(dataloader_train)\n # first_batch[""rng""] = rng # type: ignore\n # compiled = train_step.lower(optimizer, first_batch).compile()\n # print_compiled_memory_stats(compiled.memory_analysis())",python,selection_command +2036,3416970,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19510,300," # first_batch = next(dataloader_train)\n # first_batch[""rng""] = rng # type: ignore\n # compiled = train_step.lower(optimizer, first_batch).compile()\n # print_compiled_memory_stats(compiled.memory_analysis())\n # print_compiled_cost_analysis(compiled.cost_analysis())",python,selection_command +2037,3417132,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19510,356," # first_batch = next(dataloader_train)\n # first_batch[""rng""] = rng # type: ignore\n # compiled = train_step.lower(optimizer, first_batch).compile()\n # print_compiled_memory_stats(compiled.memory_analysis())\n # print_compiled_cost_analysis(compiled.cost_analysis())\n # # Do not skip the first batch during training",python,selection_command +2038,3417256,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19510,434," # first_batch = next(dataloader_train)\n # first_batch[""rng""] = rng # type: ignore\n # compiled = train_step.lower(optimizer, first_batch).compile()\n # print_compiled_memory_stats(compiled.memory_analysis())\n # print_compiled_cost_analysis(compiled.cost_analysis())\n # # Do not skip the first batch during training\n # dataloader_train = itertools.chain([first_batch], dataloader_train)",python,selection_command +2039,3417449,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19518,0,"",python,selection_command +2040,3417920,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19875,1,"",python,content +2041,3417920,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19819,1,"",python,content +2042,3417920,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19754,1,"",python,content +2043,3417920,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19688,1,"",python,content +2044,3417920,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19616,1,"",python,content +2045,3417920,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19565,1,"",python,content +2046,3417920,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19518,1,"",python,content +2047,3418056,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19869,1,"",python,content +2048,3418056,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19814,1,"",python,content +2049,3418056,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19750,1,"",python,content +2050,3418057,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19685,1,"",python,content +2051,3418057,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19614,1,"",python,content +2052,3418057,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19564,1,"",python,content +2053,3418057,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19518,1,"",python,content +2054,3418140,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19517,0,"",python,selection_command +2055,3421026,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",,terminal_output +2056,3421122,"TERMINAL",0,0,"\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=4\r\n#SBATCH --time=00:20:00\r\n#SBATCH --partition=dev_accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --job-name=train_dyn_single_gpu\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python jasmine/train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=64 \\r\n --image_width=64 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=110 \\r\n --patch_size=4 \\r\n --init_lr=0 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=50 \\r\n --log_checkpoint_interval=2 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=coinrun-dyn-dev-$slurm_job_id \\r\n --tags dyn coinrun dev \\r\n --entity instant-uv \\r\n --project jafar \\r\n --warmup_steps 0 \\r\n --wsd_decay_steps 0 \\r\n --num_steps 10 \\r\n --data_dir $array_records_dir_train \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --val_data_dir $array_records_dir_val \\r\n --log_interval 1 \\r\n --val_interval 10 \\r\n --eval_full_frame \\r\n --val_steps 5\r\n",,terminal_output +2057,3421264,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=1469415\r\nSLURM_JOB_GPUS=0\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\r\nSLURMD_NODENAME=hkn0401\r\nSLURM_JOB_START_TIME=1759399921\r\nSLURM_STEP_NODELIST=hkn0401\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1759403521\r\nSLURM_PMI2_SRUN_PORT=44089\r\nSLURM_CPUS_ON_NODE=8\r\nSLURM_JOB_CPUS_PER_NODE=8\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=dev_accelerated\r\nSLURM_TRES_PER_TASK=cpu=8\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=1\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3536670\r\nSLURM_PTY_PORT=41791\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.199\r\nSLURM_PTY_WIN_ROW=40\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0401\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.199\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=182\r\nSLURM_NODELIST=hkn0401\r\nSLURM_SRUN_COMM_PORT=44895\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1991.localdomain\r\nSLURM_JOB_ID=3536670\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0401\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_STEP_LAUNCHER_PORT=44895\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0401\r\n",,terminal_output +2058,3421398,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +2059,3422299,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"",python,tab +2060,3425473,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +2061,3431858,"TERMINAL",0,0,"Counting all components: ['dynamics', 'lam', 'tokenizer']\r\n",,terminal_output +2062,3432212,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +2063,3433123,"TERMINAL",0,0,"wandb: creating run\r\nwandb: Tracking run with wandb version 0.22.0\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_125745-7hvo8i22\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run coinrun-dyn-dev-3536670\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/7hvo8i22\r\n",,terminal_output +2064,3433181,"TERMINAL",0,0,"Parameter counts:\r\n{'dynamics': 26555904, 'lam': 17640416, 'tokenizer': 33750256, 'total': 77946576}\r\n",,terminal_output +2065,3434565,"TERMINAL",0,0,"WARNING:absl:Path /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000008 could not be identified as a temporary checkpoint path using . Got error: Expected AtomicRenameTemporaryPath (/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000008) to end with "".orbax-checkpoint-tmp"".\r\nWARNING:absl:Path /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000006 could not be identified as a temporary checkpoint path using . Got error: Expected AtomicRenameTemporaryPath (/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000006) to end with "".orbax-checkpoint-tmp"".\r\nWARNING:absl:Path /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000004 could not be identified as a temporary checkpoint path using . Got error: Expected AtomicRenameTemporaryPath (/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000004) to end with "".orbax-checkpoint-tmp"".\r\n",,terminal_output +2066,3435282,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +2067,3439505,"TERMINAL",0,0,"Traceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 718, in \r\n main(args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 559, in main\r\n first_batch = next(dataloader_train)\r\n ^^^^^^^^^^^^^^^^^^^^^^\r\nTypeError: 'DataLoader' object is not an iterator\r\n",,terminal_output +2068,3440683,"TERMINAL",0,0,"wandb: \r\nwandb: 🚀 View run coinrun-dyn-dev-3536670 at: https://wandb.ai/instant-uv/jafar/runs/7hvo8i22\r\nwandb: Find logs at: ../../../../../hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_125745-7hvo8i22/logs\r\n",,terminal_output +2069,3440818,"TERMINAL",0,0,"W1002 12:57:54.004564 1488753 pjrt_client.cc:1469] WatchJobStateAsync failed for task goo.gle/debugproto job_name: ""jax_worker"": CANCELLED: CANCELLED\r\nAdditional GRPC error information from remote target coordination_service while calling /tensorflow.CoordinationService/WatchJobState:\r\n:UNKNOWN:Error received from peer {grpc_status:1, grpc_message:""CANCELLED""} [type.googleapis.com/tensorflow.DerivedStatus='']\r\n",,terminal_output +2070,3441559,"TERMINAL",0,0,"srun: error: hkn0401: task 0: Exited with exit code 1\r\n]0;tum_cte0515@hkn0401:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +2071,3453144,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"",python,tab +2072,3453145,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19510,0,"",python,selection_command +2073,3454229,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19583,0,"",python,selection_mouse +2074,3455188,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19533,0,"",python,selection_mouse +2075,3455244,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19532,4,"next",python,selection_mouse +2076,3456355,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19533,0,"",python,selection_mouse +2077,3457185,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19532,4,"next",python,selection_mouse +2078,3464776,"TERMINAL",0,0,"\r(jasmine) [tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +2079,3488053,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"",python,tab +2080,3489140,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19504,0,"",python,selection_mouse +2081,3489972,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19537,0,"",python,selection_mouse +2082,3490944,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19537,0,"i",python,content +2083,3490945,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19538,0,"",python,selection_keyboard +2084,3491186,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19538,0,"t",python,content +2085,3491188,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19539,0,"",python,selection_keyboard +2086,3491386,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19539,0,"e",python,content +2087,3491388,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19540,0,"",python,selection_keyboard +2088,3491469,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19540,0,"r",python,content +2089,3491470,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19541,0,"",python,selection_keyboard +2090,3492052,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19541,0,"(",python,content +2091,3492053,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19542,0,"",python,selection_keyboard +2092,3493472,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19559,0,")",python,content +2093,3493473,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19560,0,"",python,selection_keyboard +2094,3493702,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19559,0,"",python,selection_command +2095,3497041,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",,terminal_output +2096,3497238,"TERMINAL",0,0,"\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=4\r\n#SBATCH --time=00:20:00\r\n#SBATCH --partition=dev_accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --job-name=train_dyn_single_gpu\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python jasmine/train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=64 \\r\n --image_width=64 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=110 \\r\n --patch_size=4 \\r\n --init_lr=0 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=50 \\r\n --log_checkpoint_interval=2 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=coinrun-dyn-dev-$slurm_job_id \\r\n --tags dyn coinrun dev \\r\n --entity instant-uv \\r\n --project jafar \\r\n --warmup_steps 0 \\r\n --wsd_decay_steps 0 \\r\n --num_steps 10 \\r\n --data_dir $array_records_dir_train \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --val_data_dir $array_records_dir_val \\r\n --log_interval 1 \\r\n --val_interval 10 \\r\n --eval_full_frame \\r\n --val_steps 5\r\n",,terminal_output +2097,3497330,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=1469415\r\nSLURM_JOB_GPUS=0\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\r\nSLURMD_NODENAME=hkn0401\r\nSLURM_JOB_START_TIME=1759399921\r\nSLURM_STEP_NODELIST=hkn0401\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1759403521\r\nSLURM_PMI2_SRUN_PORT=44089\r\nSLURM_CPUS_ON_NODE=8\r\nSLURM_JOB_CPUS_PER_NODE=8\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=dev_accelerated\r\nSLURM_TRES_PER_TASK=cpu=8\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=1\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3536670\r\nSLURM_PTY_PORT=41791\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.199\r\nSLURM_PTY_WIN_ROW=40\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0401\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.199\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=182\r\nSLURM_NODELIST=hkn0401\r\nSLURM_SRUN_COMM_PORT=44895\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1991.localdomain\r\nSLURM_JOB_ID=3536670\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0401\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_STEP_LAUNCHER_PORT=44895\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0401\r\n",,terminal_output +2098,3497461,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +2099,3500987,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +2100,3507567,"TERMINAL",0,0,"Counting all components: ['dynamics', 'lam', 'tokenizer']\r\n",,terminal_output +2101,3508178,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +2102,3508808,"TERMINAL",0,0,"wandb: creating run\r\nwandb: Tracking run with wandb version 0.22.0\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_125901-b39vz7mw\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run coinrun-dyn-dev-3536670\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/b39vz7mw\r\n",,terminal_output +2103,3508895,"TERMINAL",0,0,"Parameter counts:\r\n{'dynamics': 26555904, 'lam': 17640416, 'tokenizer': 33750256, 'total': 77946576}\r\n",,terminal_output +2104,3510300,"TERMINAL",0,0,"WARNING:absl:Path /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000008 could not be identified as a temporary checkpoint path using . Got error: Expected AtomicRenameTemporaryPath (/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000008) to end with "".orbax-checkpoint-tmp"".\r\nWARNING:absl:Path /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000006 could not be identified as a temporary checkpoint path using . Got error: Expected AtomicRenameTemporaryPath (/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000006) to end with "".orbax-checkpoint-tmp"".\r\nWARNING:absl:Path /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000004 could not be identified as a temporary checkpoint path using . Got error: Expected AtomicRenameTemporaryPath (/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000004) to end with "".orbax-checkpoint-tmp"".\r\n",,terminal_output +2105,3511063,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +2106,3588605,"TERMINAL",0,0,"Total memory size: 23.3 GB, Output size: 0.9 GB, Temp size: 22.3 GB, Argument size: 0.9 GB, Host temp size: 0.0 GB.\r\nFLOPs: 7.851e+11, Bytes: 6.639e+11 (618.3 GB), Intensity: 1.2 FLOPs/byte\r\nStarting training from step 0...\r\n",,terminal_output +2107,3589382,"TERMINAL",0,0,"\r\nMemstats: After params initialized:\r\n\tUsing (GB) 1.21 / 38.7 (3.126615%) on cuda:0\r\nStep 1\r\n",,terminal_output +2108,3630242,"TERMINAL",0,0,"Step 2\r\n",,terminal_output +2109,3632551,"TERMINAL",0,0,"Saved checkpoint at step 2\r\n",,terminal_output +2110,3711255,"TERMINAL",0,0,"^Csrun: interrupt (one more within 1 sec to abort)\r\nsrun: StepId=3536670.14 task 0: running\r\n",,terminal_output +2111,3711405,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3536670.14\r\nsrun: forcing job termination\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 718, in \r\n main(args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 568, in main\r\n while step < args.num_steps:\r\n ^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\nslurmstepd: error: *** STEP 3536670.14 ON hkn0401 CANCELLED AT 2025-10-02T13:02:24 ***\r\n",,terminal_output +2112,3711600,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3536670.14\r\nsrun: job abort in progress\r\n",,terminal_output +2113,3711761,"TERMINAL",0,0,"]0;tum_cte0515@hkn0401:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +2114,3713272,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"",python,tab +2115,3722854,"jasmine/utils/dataloader_torch.py",0,0,"",python,tab +2116,3722855,"jasmine/utils/dataloader_torch.py",851,0,"",python,selection_command +2117,3723292,"jasmine/train_dynamics.py",0,0,"",python,tab +2118,3723293,"jasmine/train_dynamics.py",11744,0,"",python,selection_command +2119,3734488,"jasmine/train_dynamics.py",19354,37," dataloader_train = (\n {\n ""videos"": jax.make_array_from_process_local_data(\n videos_sharding, local_data=elem[""videos""]\n ),\n ""actions"": (\n jax.make_array_from_process_local_data(\n actions_sharding, elem[""actions""]\n )\n if args.use_gt_actions\n else None\n ),\n }\n for elem in train_iterator\n )",python,content +2120,3736168,"jasmine/train_dynamics.py",19856,37," dataloader_val = (\n {\n ""videos"": jax.make_array_from_process_local_data(\n videos_sharding, elem[""videos""]\n ),\n ""actions"": (\n jax.make_array_from_process_local_data(\n actions_sharding, elem[""actions""]\n )\n if args.use_gt_actions\n else None\n ),\n }\n for elem in val_iterator\n )",python,content +2121,3743430,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",,terminal_output +2122,3744153,"TERMINAL",0,0,"\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=4\r\n#SBATCH --time=00:20:00\r\n#SBATCH --partition=dev_accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --job-name=train_dyn_single_gpu\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670\r\n\r\nenv | grep SLURM\r\n\r\nexport PYTHONUNBUFFERED=1\r\n\r\nsrun python jasmine/train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=64 \\r\n --image_width=64 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=110 \\r\n --patch_size=4 \\r\n --init_lr=0 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=50 \\r\n --log_checkpoint_interval=2 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=coinrun-dyn-dev-$slurm_job_id \\r\n --tags dyn coinrun dev \\r\n --entity instant-uv \\r\n --project jafar \\r\n --warmup_steps 0 \\r\n --wsd_decay_steps 0 \\r\n --num_steps 10 \\r\n --data_dir $array_records_dir_train \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --val_data_dir $array_records_dir_val \\r\n --log_interval 1 \\r\n --val_interval 10 \\r\n --eval_full_frame \\r\n --val_steps 5\r\n",,terminal_output +2123,3744283,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=1469415\r\nSLURM_JOB_GPUS=0\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\r\nSLURMD_NODENAME=hkn0401\r\nSLURM_JOB_START_TIME=1759399921\r\nSLURM_STEP_NODELIST=hkn0401\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1759403521\r\nSLURM_PMI2_SRUN_PORT=44089\r\nSLURM_CPUS_ON_NODE=8\r\nSLURM_JOB_CPUS_PER_NODE=8\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=dev_accelerated\r\nSLURM_TRES_PER_TASK=cpu=8\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=1\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3536670\r\nSLURM_PTY_PORT=41791\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.199\r\nSLURM_PTY_WIN_ROW=40\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0401\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.199\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=182\r\nSLURM_NODELIST=hkn0401\r\nSLURM_SRUN_COMM_PORT=44895\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NNODES=1\r\nSLURM_SUBMIT_HOST=hkn1991.localdomain\r\nSLURM_JOB_ID=3536670\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0401\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_STEP_LAUNCHER_PORT=44895\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0401\r\n",,terminal_output +2124,3744412,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +2125,3748197,"TERMINAL",0,0,"Running on 1 devices.\r\n",,terminal_output +2126,3754634,"TERMINAL",0,0,"Counting all components: ['dynamics', 'lam', 'tokenizer']\r\n",,terminal_output +2127,3755062,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +2128,3755818,"TERMINAL",0,0,"wandb: creating run\r\nwandb: Tracking run with wandb version 0.22.0\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_130308-826jhtsl\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run coinrun-dyn-dev-3536670\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/826jhtsl\r\n",,terminal_output +2129,3755996,"TERMINAL",0,0,"Parameter counts:\r\n{'dynamics': 26555904, 'lam': 17640416, 'tokenizer': 33750256, 'total': 77946576}\r\n",,terminal_output +2130,3757417,"TERMINAL",0,0,"WARNING:absl:Path /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000008 could not be identified as a temporary checkpoint path using . Got error: Expected AtomicRenameTemporaryPath (/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000008) to end with "".orbax-checkpoint-tmp"".\r\nWARNING:absl:Path /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000006 could not be identified as a temporary checkpoint path using . Got error: Expected AtomicRenameTemporaryPath (/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000006) to end with "".orbax-checkpoint-tmp"".\r\nWARNING:absl:Path /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000004 could not be identified as a temporary checkpoint path using . Got error: Expected AtomicRenameTemporaryPath (/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/interactive/3536670/000004) to end with "".orbax-checkpoint-tmp"".\r\n",,terminal_output +2131,3758135,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +2132,3835568,"TERMINAL",0,0,"Total memory size: 23.3 GB, Output size: 0.9 GB, Temp size: 22.3 GB, Argument size: 0.9 GB, Host temp size: 0.0 GB.\r\nFLOPs: 2.074e+12, Bytes: 6.664e+11 (620.6 GB), Intensity: 3.1 FLOPs/byte\r\nStarting training from step 0...\r\n",,terminal_output +2133,3836060,"TERMINAL",0,0,"\r\nMemstats: After params initialized:\r\n\tUsing (GB) 1.16 / 38.7 (2.997416%) on cuda:0\r\nStep 1\r\n",,terminal_output +2134,3896307,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"",python,tab +2135,3902983,"/home/hk-project-p0023960/tum_cte0515/Projects/jafar/generate_dataset.py",0,0,"",python,tab +2136,3921848,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",0,0,"""""""\nGenerates a dataset of random-action CoinRun episodes.\nEpisodes are saved individually as memory-mapped files for efficient loading.\n""""""\n\nfrom dataclasses import dataclass\n\nfrom gym3 import types_np\nimport numpy as np\nfrom procgen import ProcgenGym3Env\nimport tyro\nimport json\nimport os\nfrom jasmine_data.utils import save_chunks\n\n\n@dataclass\nclass Args:\n num_episodes_train: int = 10000\n num_episodes_val: int = 500\n num_episodes_test: int = 500\n output_dir: str = ""data/coinrun_episodes""\n min_episode_length: int = 1000\n max_episode_length: int = 1000\n chunk_size: int = 160\n chunks_per_file: int = 100\n seed: int = 0\n\n\nargs = tyro.cli(Args)\nassert (\n args.max_episode_length >= args.min_episode_length\n), ""Maximum episode length must be greater than or equal to minimum episode length.""\n\nif args.min_episode_length < args.chunk_size:\n print(\n ""Warning: Minimum episode length is smaller than chunk size. Note that episodes shorter than the chunk size will be discarded.""\n )\n\n\n# --- Generate episodes ---\ndef generate_episodes(num_episodes, split):\n episode_idx = 0\n episode_metadata = []\n obs_chunks = []\n act_chunks = []\n file_idx = 0\n output_dir_split = os.path.join(args.output_dir, split)\n while episode_idx < num_episodes:\n seed = np.random.randint(0, 10000)\n env = ProcgenGym3Env(num=1, env_name=""coinrun"", start_level=seed)\n\n observations_seq = []\n actions_seq = []\n episode_obs_chunks = []\n episode_act_chunks = []\n\n # --- Run episode ---\n step_t = 0\n first_obs = True\n for step_t in range(args.max_episode_length):\n _, obs, first = env.observe()\n action = types_np.sample(env.ac_space, bshape=(env.num,))\n env.act(action)\n observations_seq.append(obs[""rgb""])\n actions_seq.append(action)\n if len(observations_seq) == args.chunk_size:\n episode_obs_chunks.append(observations_seq)\n episode_act_chunks.append(actions_seq)\n observations_seq = []\n actions_seq = []\n if first and not first_obs:\n break\n first_obs = False\n\n # --- Save episode ---\n if step_t + 1 >= args.min_episode_length:\n if observations_seq:\n if len(observations_seq) < args.chunk_size:\n print(\n f""Warning: Inconsistent chunk_sizes. Episode has {len(observations_seq)} frames, ""\n f""which is smaller than the requested chunk_size: {args.chunk_size}. ""\n ""This might lead to performance degradation during training.""\n )\n episode_obs_chunks.append(observations_seq)\n episode_act_chunks.append(actions_seq)\n\n obs_chunks_data = [\n np.concatenate(seq, axis=0).astype(np.uint8)\n for seq in episode_obs_chunks\n ]\n act_chunks_data = [\n np.concatenate(act, axis=0) for act in episode_act_chunks\n ]\n obs_chunks.extend(obs_chunks_data)\n act_chunks.extend(act_chunks_data)\n\n ep_metadata, file_idx, obs_chunks, act_chunks = save_chunks(\n file_idx, args.chunks_per_file, output_dir_split, obs_chunks, act_chunks\n )\n episode_metadata.extend(ep_metadata)\n\n print(f""Episode {episode_idx} completed, length: {step_t + 1}."")\n episode_idx += 1\n else:\n print(f""Episode too short ({step_t + 1}), resampling..."")\n\n if len(obs_chunks) > 0:\n print(\n f""Warning: Dropping {len(obs_chunks)} chunks for consistent number of chunks per file."",\n ""Consider changing the chunk_size and chunks_per_file parameters to prevent data-loss."",\n )\n\n print(f""Done generating {split} split"")\n return episode_metadata\n\n\ndef get_action_space():\n env = ProcgenGym3Env(num=1, env_name=""coinrun"", start_level=0)\n return env.ac_space.eltype.n\n\n\ndef main():\n # Set random seed and create dataset directories\n np.random.seed(args.seed)\n # --- Generate episodes ---\n train_episode_metadata = generate_episodes(args.num_episodes_train, ""train"")\n val_episode_metadata = generate_episodes(args.num_episodes_val, ""val"")\n test_episode_metadata = generate_episodes(args.num_episodes_test, ""test"")\n\n # --- Save metadata ---\n metadata = {\n ""env"": ""coinrun"",\n ""num_actions"": get_action_space(),\n ""num_episodes_train"": args.num_episodes_train,\n ""num_episodes_val"": args.num_episodes_val,\n ""num_episodes_test"": args.num_episodes_test,\n ""avg_episode_len_train"": np.mean(\n [ep[""avg_seq_len""] for ep in train_episode_metadata]\n ),\n ""avg_episode_len_val"": np.mean(\n [ep[""avg_seq_len""] for ep in val_episode_metadata]\n ),\n ""avg_episode_len_test"": np.mean(\n [ep[""avg_seq_len""] for ep in test_episode_metadata]\n ),\n ""episode_metadata_train"": train_episode_metadata,\n ""episode_metadata_val"": val_episode_metadata,\n ""episode_metadata_test"": test_episode_metadata,\n }\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(f""Done generating dataset."")\n\n\nif __name__ == ""__main__"":\n main()\n",python,tab +2137,3925612,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",1365,0,"",python,selection_mouse +2138,3925884,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",1356,14,"ProcgenGym3Env",python,selection_mouse +2139,3927255,"data/jasmine_data/coinrun/generate_coinrun_dataset.py",1364,0,"",python,selection_mouse +2140,4298899,"TERMINAL",0,0,"^Csrun: interrupt (one more within 1 sec to abort)\r\nsrun: StepId=3536670.15 task 0: running\r\n",,terminal_output +2141,4299115,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3536670.15\r\nsrun: forcing job termination\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 746, in \r\n main(args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 596, in main\r\n while step < args.num_steps:\r\n ^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\nslurmstepd: error: *** STEP 3536670.15 ON hkn0401 CANCELLED AT 2025-10-02T13:12:12 ***\r\n",,terminal_output +2142,4299202,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3536670.15\r\nsrun: job abort in progress\r\n",,terminal_output +2143,4299447,"TERMINAL",0,0,"]0;tum_cte0515@hkn0401:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +2144,4299829,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",0,0,"",python,tab +2145,4307277,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19502,0,"",python,selection_mouse +2146,4307414,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19501,6,"videos",python,selection_mouse +2147,4308043,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19654,0,"",python,selection_mouse +2148,4308194,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19650,7,"actions",python,selection_mouse +2149,4308688,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19712,0,"",python,selection_mouse +2150,4308751,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19711,1,"t",python,selection_mouse +2151,4309322,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19711,1,"",python,content +2152,4309322,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19564,0,"t",python,content +2153,4310677,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19712,0,"t",python,content +2154,4310678,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py",19564,1,"",python,content +2155,4316863,"TERMINAL",0,0,"salloc: Job 3536670 has exceeded its time limit and its allocation has been revoked.\nslurmstepd: error: *** STEP 3536670.interactive ON hkn0401 CANCELLED AT 2025-10-02T13:12:30 DUE TO TIME LIMIT ***\r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\n",,terminal_output +2156,4327458,"TERMINAL",0,0,"^C[?2004l\r[?2004h[?2004l\r\r\n]0;tum_cte0515@hkn0401:~/Projects/jasmine[?2004h(jasmine) [tum_cte0515@hkn0401 jasmine]$ ",,terminal_output +2157,4333038,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",0,0,"",shellscript,tab +2158,4337585,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",86,0,"",shellscript,selection_mouse +2159,4340753,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",85,1,"",shellscript,content +2160,4340801,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",85,0,"4",shellscript,content +2161,4340802,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",86,0,"",shellscript,selection_keyboard +2162,4346510,"TERMINAL",0,0,"srun: error: hkn0401: task 0: Killed\r\n]0;tum_cte0515@hkn1991:~/Projects/jasmine",,terminal_output +2163,4354207,"TERMINAL",0,0,"source .venv/bin/activate",,terminal_command +2164,4354220,"TERMINAL",0,0,"]633;C]0;tum_cte0515@hkn1991:~/Projects/jasmine",,terminal_output +2165,4355631,"TERMINAL",0,0,"sbatch slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",,terminal_command +2166,4355661,"TERMINAL",0,0,"]633;CSubmitted batch job 3536940\r\n]0;tum_cte0515@hkn1991:~/Projects/jasmine",,terminal_output +2167,4357578,"TERMINAL",0,0,"queue",,terminal_command +2168,4357696,"TERMINAL",0,0,"]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1991.localdomain: Thu Oct 2 13:13:10 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3532383 accelerat train_to tum_cte0 R 1:33:53\t 1 hkn04253536940 dev_accel train_dy tum_cte0 PD\t0:00\t 1 (None)3533705 large generate tum_cte0 R 1-21:14:21\t 1 hkn1901",,terminal_output +2169,4358744,"TERMINAL",0,0,"142",,terminal_output +2170,4359742,"TERMINAL",0,0,"253",,terminal_output +2171,4360893,"TERMINAL",0,0,"364",,terminal_output +2172,4361812,"TERMINAL",0,0,"475",,terminal_output +2173,4363153,"TERMINAL",0,0,"597",,terminal_output +2174,4363883,"TERMINAL",0,0,"74:008",,terminal_output +2175,4364918,"TERMINAL",0,0,"81 Rhkn04019",,terminal_output +2176,4365989,"TERMINAL",0,0,"92130",,terminal_output +2177,4367109,"TERMINAL",0,0,"20321",,terminal_output +2178,4368067,"TERMINAL",0,0,"1432",,terminal_output +2179,4368878,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1991:~/Projects/jasmine",,terminal_output +2180,4524295,"TERMINAL",0,0,"queue",,terminal_command +2181,4524350,"TERMINAL",0,0,"]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1991.localdomain: Thu Oct 2 13:15:57 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3532383 accelerat train_to tum_cte0 R 1:36:40\t 1 hkn04253533705 large generate tum_cte0 R 1-21:17:08\t 1 hkn1901",,terminal_output +2182,4525337,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1991:~/Projects/jasmine",,terminal_output +2183,4531030,"TERMINAL",0,0,"logs",,terminal_command +2184,4536363,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",0,0,"",shellscript,tab +2185,4538673,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",0,0,"",shellscript,tab +2186,4553745,"TERMINAL",0,0,"cd coinrun/dynamics/",,terminal_command +2187,4554585,"TERMINAL",0,0,"ls",,terminal_command +2188,4554627,"TERMINAL",0,0,"]633;Ccausal maskgit train_dyn_single_gpu_3536940.log\r\n]0;tum_cte0515@hkn1991:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics",,terminal_output +2189,4560924,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/train_dyn_single_gpu_3536940.log",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=00:40:00\n#SBATCH --partition=dev_accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\n#SBATCH --job-name=train_dyn_single_gpu\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670\n\nenv | grep SLURM\n\nexport PYTHONUNBUFFERED=1\n\nsrun python jasmine/train_dynamics.py \\n --save_ckpt \\n --image_height=64 \\n --image_width=64 \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=110 \\n --patch_size=4 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=50 \\n --log_checkpoint_interval=2 \\n --dyna_type=maskgit \\n --log \\n --name=coinrun-dyn-dev-$slurm_job_id \\n --tags dyn coinrun dev \\n --entity instant-uv \\n --project jafar \\n --warmup_steps 0 \\n --wsd_decay_steps 0 \\n --num_steps 10 \\n --data_dir $array_records_dir_train \\n --tokenizer_checkpoint $tokenizer_checkpoint \\n --val_data_dir $array_records_dir_val \\n --log_interval 1 \\n --val_interval 10 \\n --eval_full_frame \\n --val_steps 5\nSLURM_JOB_USER=tum_cte0515\nSLURM_TASKS_PER_NODE=4\nSLURM_JOB_UID=999226\nSLURM_TASK_PID=1497666\nSLURM_JOB_GPUS=0\nSLURM_LOCALID=0\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine\nSLURMD_NODENAME=hkn0401\nSLURM_JOB_START_TIME=1759403598\nSLURM_CLUSTER_NAME=hk\nSLURM_JOB_END_TIME=1759405998\nSLURM_CPUS_ON_NODE=24\nSLURM_JOB_CPUS_PER_NODE=24\nSLURM_GPUS_ON_NODE=1\nSLURM_GTIDS=0\nSLURM_JOB_PARTITION=dev_accelerated\nSLURM_TRES_PER_TASK=cpu=5\nSLURM_OOM_KILL_STEP=0\nSLURM_JOB_NUM_NODES=1\nSLURM_JOBID=3536940\nSLURM_JOB_QOS=normal\nSLURM_PROCID=0\nSLURM_CPUS_PER_TASK=5\nSLURM_NTASKS=4\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0401\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\nSLURM_SCRIPT_CONTEXT=prolog_task\nSLURM_NODELIST=hkn0401\nSLURM_JOB_ACCOUNT=hk-project-p0023960\nSLURM_PRIO_PROCESS=0\nSLURM_NPROCS=4\nSLURM_NNODES=1\nSLURM_SUBMIT_HOST=hkn1991.localdomain\nSLURM_JOB_ID=3536940\nSLURM_NODEID=0\nSLURM_CONF=/etc/slurm/slurm.conf\nSLURM_JOB_NAME=train_dyn_single_gpu\nSLURM_NTASKS_PER_NODE=4\nSLURM_JOB_GID=502226\nSLURM_JOB_NODELIST=hkn0401\nGpuFreq=control_disabled\nW1002 13:13:50.189230 1497902 platform_util.cc:218] unable to create StreamExecutor for CUDA:1: CUDA error: Failed call to cuDeviceGet: CUDA_ERROR_INVALID_DEVICE: invalid device ordinal\nW1002 13:13:50.189230 1497901 platform_util.cc:218] unable to create StreamExecutor for CUDA:3: CUDA error: Failed call to cuDeviceGet: CUDA_ERROR_INVALID_DEVICE: invalid device ordinal\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 812, in backends\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 812, in backends\nW1002 13:13:50.189986 1497903 platform_util.cc:218] unable to create StreamExecutor for CUDA:2: CUDA error: Failed call to cuDeviceGet: CUDA_ERROR_INVALID_DEVICE: invalid device ordinal\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 812, in backends\n backend = _init_backend(platform)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 896, in _init_backend\n backend = _init_backend(platform)\n backend = _init_backend(platform)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 896, in _init_backend\n ^^^^^^^^^^^^^^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 896, in _init_backend\n backend = registration.factory()\n ^^^^^^^^^^^^^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 559, in make_pjrt_c_api_client\n backend = registration.factory()\n ^^^^^^^^^^^^^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 559, in make_pjrt_c_api_client\n return xla_client.make_c_api_client(\n backend = registration.factory()\n ^^^^^^^^^^^^^^^^^^^^^^\n return xla_client.make_c_api_client(\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 559, in make_pjrt_c_api_client\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jaxlib/xla_client.py"", line 156, in make_c_api_client\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jaxlib/xla_client.py"", line 156, in make_c_api_client\n return xla_client.make_c_api_client(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jaxlib/xla_client.py"", line 156, in make_c_api_client\n return _xla.get_c_api_client(\n return _xla.get_c_api_client(\n ^^^^^^^^^^^^^^^^^^^^^^\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 746, in \n main(args)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 350, in main\n return _xla.get_c_api_client(\n num_devices = jax.device_count()\n ^^^^^^^^^^^^^^^^^^^^^^\n ^^^^^^^^^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 975, in device_count\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 746, in \n ^^^^^^^^^^^^^^^^^^^^^^\njaxlib._jax.XlaRuntimeError: INTERNAL: no supported devices found for platform CUDA\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 746, in \n main(args)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 350, in main\n num_devices = jax.device_count()\n return int(get_backend(backend).device_count())\n main(args)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 350, in main\n ^^^^^^^^^^^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 944, in get_backend\n num_devices = jax.device_count()\n ^^^^^^^^^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 975, in device_count\n ^^^^^^^^^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 975, in device_count\n return _get_backend_uncached(platform)\n return int(get_backend(backend).device_count())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 923, in _get_backend_uncached\n ^^^^^^^^^^^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 944, in get_backend\n return int(get_backend(backend).device_count())\n ^^^^^^^^^^^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 944, in get_backend\n return _get_backend_uncached(platform)\n bs = backends()\n ^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 828, in backends\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 923, in _get_backend_uncached\n return _get_backend_uncached(platform)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n raise RuntimeError(err_msg)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 923, in _get_backend_uncached\n bs = backends()\nRuntimeError: Unable to initialize backend 'cuda': INTERNAL: no supported devices found for platform CUDA (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\n ^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 828, in backends\n bs = backends()\n ^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 828, in backends\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': INTERNAL: no supported devices found for platform CUDA (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': INTERNAL: no supported devices found for platform CUDA (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 812, in backends\n backend = _init_backend(platform)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 896, in _init_backend\n backend = registration.factory()\n ^^^^^^^^^^^^^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 559, in make_pjrt_c_api_client\n return xla_client.make_c_api_client(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jaxlib/xla_client.py"", line 156, in make_c_api_client\n return _xla.get_c_api_client(\n ^^^^^^^^^^^^^^^^^^^^^^\njaxlib._jax.XlaRuntimeError: INTERNAL: Getting local topologies failed: Error 1: GetKeyValue() timed out with key: cuda:local_topology/cuda/1 and duration: 2m\n\nError 2: GetKeyValue() timed out with key: cuda:local_topology/cuda/2 and duration: 2m\n\nError 3: GetKeyValue() timed out with key: cuda:local_topology/cuda/3 and duration: 2m\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 746, in \n main(args)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/jasmine/train_dynamics.py"", line 350, in main\n num_devices = jax.device_count()\n ^^^^^^^^^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 975, in device_count\n return int(get_backend(backend).device_count())\n ^^^^^^^^^^^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 944, in get_backend\n return _get_backend_uncached(platform)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 923, in _get_backend_uncached\n bs = backends()\n ^^^^^^^^^^\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/jax/_src/xla_bridge.py"", line 828, in backends\n raise RuntimeError(err_msg)\nRuntimeError: Unable to initialize backend 'cuda': INTERNAL: Getting local topologies failed: Error 1: GetKeyValue() timed out with key: cuda:local_topology/cuda/1 and duration: 2m\n\nError 2: GetKeyValue() timed out with key: cuda:local_topology/cuda/2 and duration: 2m\n\nError 3: GetKeyValue() timed out with key: cuda:local_topology/cuda/3 and duration: 2m (you may need to uninstall the failing plugin package, or set JAX_PLATFORMS=cpu to skip this backend.)\nsrun: error: hkn0401: tasks 1-3: Exited with exit code 1\nsrun: error: hkn0401: task 0: Exited with exit code 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--edit-todo"" to view and edit)\r\nYou are currently editing a commit while rebasing branch 'gt-actions' on 'c7522f2'.\r\n (use ""git commit --amend"" to amend the current commit)\r\n (use ""git rebase --continue"" once you are satisfied with your changes)\r\n\r\nChanges to be committed:\r\n (use ""git restore --staged ..."" to unstage)\r\n\tmodified: jasmine/train_dynamics.py\r\n\r\nChanges not staged for commit:\r\n (use ""git add ..."" to update what will be committed)\r\n (use ""git restore ..."" to discard changes in working directory)\r\n\tmodified: jasmine/utils/dataloader_torch.py\r\n\r\nUntracked files:\r\n (use ""git add ..."" to include in what will be committed)\r\n\tali-old-branch.diff\r\n\tdata/_vizdoom.ini\r\n\tdata/data/\r\n\tdata/jasmine_data/vizdoom/\r\n\tdata/uv.lock\r\n\tdiff.diff\r\n\tdiff2.diff\r\n\tinput_pipeline/\r\n\tkiller.sh\r\n\tkiller_partition.sh\r\n\tlog.log\r\n\tmessage.md\r\n\toverfit_dir.zip\r\n\trequirements-franz.txt\r\n\tsamples/\r\n\tscripts_cremers/\r\n\tslurm/\r\n\ttest.py\r\n\tutils/\r\n\tuv.lock\r\n\r\n]0;tum_cte0515@hkn1991:~/Projects/jasmine",,terminal_output +3098,5263245,"input_pipeline/generate_breakout_dataset_agent.py",0,0,"""""""\nTrain a DQN agent on MinAtar-Breakout and collect episodes to build a dataset.\nEpisodes are chunked and stored using save_chunks.\n""""""\n\nfrom dataclasses import dataclass\nimport numpy as np\nimport os\nimport json\nimport tyro\nfrom typing import List, Tuple\n\nfrom minatar import Environment # pip install minatar\nfrom gym import spaces\nimport gym\nfrom stable_baselines3 import DQN # pip install stable-baselines3[extra]\n\nfrom utils import save_chunks # reuse your existing utility\n\n\n# ---- Wrap MinAtar in a Gym API ----\nclass MinAtarBreakout(gym.Env):\n def __init__(self):\n super().__init__()\n self.env = Environment(""breakout"", sticky_action_prob=0.1)\n self.action_space = spaces.Discrete(self.env.num_actions())\n self.observation_space = spaces.Box(\n low=0, high=1, shape=self.env.state().shape, dtype=np.float32\n )\n\n def reset(self):\n self.env.reset()\n return self.env.state()\n\n def step(self, action):\n reward, done = self.env.act(action)\n return self.env.state(), reward, done, {}\n\n\n@dataclass\nclass Args:\n output_dir: str = ""data/breakout_episodes""\n num_episodes: int = 2000\n min_episode_length: int = 100\n max_episode_length: int = 500\n chunk_size: int = 50\n chunks_per_file: int = 100\n train_timesteps: int = 200_000 # DQN training timesteps\n collection_interval: int = 10_000 # Collect episodes every N training steps\n seed: int = 0\n\n\nargs = tyro.cli(Args)\n\n\ndef collect_episodes(model: DQN, num_eps: int) -> Tuple[List[np.ndarray], List[np.ndarray], List[dict]]:\n obs_chunks, act_chunks, ep_metadata = [], [], []\n for ep in range(num_eps):\n env = MinAtarBreakout()\n obs = env.reset()\n done = False\n ep_obs, ep_act = [], []\n steps = 0\n while not done and steps < args.max_episode_length:\n action, _ = model.predict(obs, deterministic=False)\n ep_obs.append(obs.astype(np.uint8))\n ep_act.append(action)\n obs, reward, done, _ = env.step(action)\n steps += 1\n\n if steps >= args.min_episode_length:\n # Chunk episode\n episode_obs_chunks, episode_act_chunks = [], []\n for i in range(0, len(ep_obs), args.chunk_size):\n o_chunk = np.stack(ep_obs[i:i + args.chunk_size])\n a_chunk = np.array(ep_act[i:i + args.chunk_size])\n episode_obs_chunks.append(o_chunk)\n episode_act_chunks.append(a_chunk)\n\n obs_chunks.extend(episode_obs_chunks)\n act_chunks.extend(episode_act_chunks)\n\n avg_len = np.mean([len(ep_obs)])\n ep_metadata.append({""avg_seq_len"": avg_len})\n else:\n print(f""Episode too short ({steps}), discarded."")\n\n return obs_chunks, act_chunks, ep_metadata\n\n\ndef main():\n np.random.seed(args.seed)\n os.makedirs(args.output_dir, exist_ok=True)\n\n # --- Create training environment and model ---\n env = MinAtarBreakout()\n model = DQN(\n ""MlpPolicy"",\n env,\n learning_starts=1_000,\n buffer_size=50_000,\n train_freq=4,\n verbose=1,\n seed=args.seed,\n )\n\n episode_metadata_all = []\n obs_chunks, act_chunks = [], []\n file_idx = 0\n\n # --- Train and periodically collect ---\n timesteps = 0\n while timesteps < args.train_timesteps:\n model.learn(total_timesteps=args.collection_interval, reset_num_timesteps=False)\n timesteps += args.collection_interval\n\n # Collect episodes from the current policy\n o_chunks, a_chunks, ep_meta = collect_episodes(model, num_eps=20)\n obs_chunks.extend(o_chunks)\n act_chunks.extend(a_chunks)\n episode_metadata_all.extend(ep_meta)\n\n # Save chunks to disk\n obs_chunks, act_chunks_meta, file_idx, act_chunks = save_chunks(\n obs_chunks, file_idx, args.chunks_per_file, args.output_dir, act_chunks\n )\n episode_metadata_all.extend(act_chunks_meta)\n\n print(f""Collected and saved dataset at {timesteps} timesteps."")\n\n # --- Save metadata ---\n metadata = {\n ""env"": ""MinAtar-Breakout"",\n ""num_actions"": env.action_space.n,\n ""train_timesteps"": args.train_timesteps,\n ""num_episodes"": args.num_episodes,\n ""avg_episode_len"": float(np.mean([ep[""avg_seq_len""] for ep in episode_metadata_all])),\n ""episode_metadata"": episode_metadata_all,\n }\n\n with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n json.dump(metadata, f)\n\n print(""Dataset generation complete."")\n\n\nif __name__ == ""__main__"":\n main()\n",python,tab +3099,5263791,"jasmine/utils/dataloader_torch.py",0,0,"",python,tab +3100,5263792,"jasmine/utils/dataloader_torch.py",851,0,"",python,selection_command +3101,5267810,"TERMINAL",0,0,"git status",,terminal_command +3102,5267872,"TERMINAL",0,0,"]633;C",,terminal_output +3103,5267972,"TERMINAL",0,0,"On branch ablation/use-pytorch-dataloader\r\nLast commands done (2 commands done):\r\n pick ba37453 feat: generate coinrun dataset with val split\r\n pick faadd10 feat: implemented validation loss for all three models\r\nNext commands to do (26 remaining commands):\r\n pick 9a17dbb fix: pass val data path to dataloader\r\n pick 6e69cdb fix typo in image logging\r\n (use ""git rebase --edit-todo"" to view and edit)\r\nYou are currently editing a commit while rebasing branch 'gt-actions' on 'c7522f2'.\r\n (use ""git commit --amend"" to amend the current commit)\r\n (use ""git rebase --continue"" once you are satisfied with your changes)\r\n\r\nChanges to be committed:\r\n (use ""git restore --staged ..."" to unstage)\r\n\tmodified: jasmine/train_dynamics.py\r\n\tmodified: jasmine/utils/dataloader_torch.py\r\n\r\nUntracked files:\r\n (use ""git add ..."" to include in what will be committed)\r\n\tali-old-branch.diff\r\n\tdata/_vizdoom.ini\r\n\tdata/data/\r\n\tdata/jasmine_data/vizdoom/\r\n\tdata/uv.lock\r\n\tdiff.diff\r\n\tdiff2.diff\r\n\tinput_pipeline/\r\n\tkiller.sh\r\n\tkiller_partition.sh\r\n\tlog.log\r\n\tmessage.md\r\n\toverfit_dir.zip\r\n\trequirements-franz.txt\r\n\tsamples/\r\n\tscripts_cremers/\r\n\tslurm/\r\n\ttest.py\r\n\tutils/\r\n\tuv.lock\r\n\r\n]0;tum_cte0515@hkn1991:~/Projects/jasmine",,terminal_output +3104,5288349,"TERMINAL",0,0,"git commit -m ""implemented dynamics model training with pytorch dataloader""",,terminal_command +3105,5288368,"TERMINAL",0,0,"]633;C",,terminal_output +3106,5289538,"TERMINAL",0,0,"black....................................................................",,terminal_output +3107,5290867,"TERMINAL",0,0,"Passed\r\n",,terminal_output +3108,5291014,"TERMINAL",0,0,"[ablation/use-pytorch-dataloader 174c42b] implemented dynamics model training with pytorch dataloader\r\n 2 files changed, 7 insertions(+), 39 deletions(-)\r\n]0;tum_cte0515@hkn1991:~/Projects/jasmine",,terminal_output +3109,5307169,"TERMINAL",0,0,"git push",,terminal_command +3110,5307204,"TERMINAL",0,0,"]633;Cfatal: The current branch ablation/use-pytorch-dataloader has no upstream branch.\r\nTo push the current branch and set the remote as upstream, use\r\n\r\n git push --set-upstream origin ablation/use-pytorch-dataloader\r\n\r\nTo have this happen automatically for branches without a tracking\r\nupstream, see 'push.autoSetupRemote' in 'git help config'.\r\n\r\n]0;tum_cte0515@hkn1991:~/Projects/jasmine",,terminal_output +3111,5311598,"TERMINAL",0,0,"git push --set-upstream origin ablation/use-pytorch-dataloader",,terminal_command +3112,5311672,"TERMINAL",0,0,"]633;C",,terminal_output +3113,5313210,"TERMINAL",0,0,"Enumerating objects: 22, done.\r\nCounting objects: 4% (1/22)\rCounting objects: 9% (2/22)\rCounting objects: 13% (3/22)\rCounting objects: 18% (4/22)\rCounting objects: 22% (5/22)\rCounting objects: 27% (6/22)\rCounting objects: 31% (7/22)\rCounting objects: 36% (8/22)\rCounting objects: 40% (9/22)\rCounting objects: 45% (10/22)\rCounting objects: 50% (11/22)\rCounting objects: 54% (12/22)\rCounting objects: 59% (13/22)\rCounting objects: 63% (14/22)\rCounting objects: 68% (15/22)\rCounting objects: 72% (16/22)\rCounting objects: 77% (17/22)\rCounting objects: 81% (18/22)\rCounting objects: 86% (19/22)\rCounting objects: 90% (20/22)\rCounting objects: 95% (21/22)\rCounting objects: 100% (22/22)\rCounting objects: 100% (22/22), done.\r\nDelta compression using up to 152 threads\r\nCompressing objects: 5% (1/17)\rCompressing objects: 11% (2/17)\rCompressing objects: 17% (3/17)\rCompressing objects: 23% (4/17)\rCompressing objects: 29% (5/17)\rCompressing objects: 35% (6/17)\rCompressing objects: 41% (7/17)\rCompressing objects: 47% (8/17)\rCompressing objects: 52% (9/17)\rCompressing objects: 58% (10/17)\rCompressing objects: 64% (11/17)\rCompressing objects: 70% (12/17)\rCompressing objects: 76% (13/17)\rCompressing objects: 82% (14/17)\rCompressing objects: 88% (15/17)\rCompressing objects: 94% (16/17)\rCompressing objects: 100% (17/17)\rCompressing objects: 100% (17/17), done.\r\nWriting objects: 5% (1/17)\rWriting objects: 11% (2/17)\rWriting objects: 17% (3/17)\rWriting objects: 23% (4/17)\rWriting objects: 29% (5/17)\rWriting objects: 47% (8/17)\rWriting objects: 52% (9/17)\rWriting objects: 58% (10/17)\rWriting objects: 64% (11/17)\rWriting objects: 70% (12/17)\rWriting objects: 76% (13/17)\rWriting objects: 82% (14/17)\rWriting objects: 88% (15/17)\rWriting objects: 94% (16/17)\rWriting objects: 100% (17/17)\rWriting objects: 100% (17/17), 2.28 KiB | 583.00 KiB/s, done.\r\nTotal 17 (delta 13), reused 0 (delta 0), pack-reused 0\r\n",,terminal_output +3114,5313273,"TERMINAL",0,0,"remote: Resolving deltas: 0% (0/13)\rremote: Resolving deltas: 7% (1/13)\rremote: Resolving deltas: 15% (2/13)\rremote: Resolving deltas: 23% (3/13)\rremote: Resolving deltas: 30% (4/13)\rremote: Resolving deltas: 38% (5/13)\rremote: Resolving deltas: 46% (6/13)\rremote: Resolving deltas: 53% (7/13)\rremote: Resolving deltas: 61% (8/13)\rremote: Resolving deltas: 69% (9/13)\rremote: Resolving deltas: 76% (10/13)\rremote: Resolving deltas: 84% (11/13)\rremote: Resolving deltas: 92% (12/13)\rremote: Resolving deltas: 100% (13/13)\rremote: Resolving deltas: 100% (13/13), completed with 5 local objects.\r\n",,terminal_output +3115,5313601,"TERMINAL",0,0,"remote: \r\nremote: Create a pull request for 'ablation/use-pytorch-dataloader' on GitHub by visiting:\r\nremote: https://github.com/p-doom/jasmine/pull/new/ablation/use-pytorch-dataloader\r\nremote: \r\nTo github.com:p-doom/jasmine.git\r\n * [new branch] ablation/use-pytorch-dataloader -> ablation/use-pytorch-dataloader\r\nbranch 'ablation/use-pytorch-dataloader' set up to track 'origin/ablation/use-pytorch-dataloader'.\r\n]0;tum_cte0515@hkn1991:~/Projects/jasmine",,terminal_output +3116,5322642,"TERMINAL",0,0,"queue",,terminal_command +3117,5322692,"TERMINAL",0,0,"]633;C",,terminal_output +3118,5322931,"TERMINAL",0,0,"[?1049h(B[?7hEvery 1.0s: squeue --mehkn1991.localdomain: Thu Oct 2 13:29:15 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3532383 accelerat train_to tum_cte0 R 1:49:59\t 1 hkn04253536978 dev_accel train_dy tum_cte0 R\t1:26\t 1 hkn04013533705 large generate tum_cte0 R 1-21:30:27\t 1 hkn1901",,terminal_output +3119,5323939,"TERMINAL",0,0,"750:0078",,terminal_output +3120,5325067,"TERMINAL",0,0,"8189",,terminal_output +3121,5326028,"TERMINAL",0,0,"92930",,terminal_output +3122,5327041,"TERMINAL",0,0,"203301",,terminal_output +3123,5328185,"TERMINAL",0,0,"1412",,terminal_output +3124,5329198,"TERMINAL",0,0,"2523",,terminal_output +3125,5330412,"TERMINAL",0,0,"3634",,terminal_output +3126,5331218,"TERMINAL",0,0,"4745",,terminal_output +3127,5332244,"TERMINAL",0,0,"5856",,terminal_output +3128,5333285,"TERMINAL",0,0,"6967",,terminal_output 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+3849,5905220,"TERMINAL",0,0,"slurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh",,terminal_output +3850,5905993,"TERMINAL",0,0,"92810",,terminal_output +3851,5907113,"TERMINAL",0,0,"9:00391",,terminal_output +3852,5908134,"TERMINAL",0,0,"14102",,terminal_output +3853,5908804,"TERMINAL",0,0,"\rslurm/jobs/mihir/horeka/coinrun/train_dyn_single_gpu.sh\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=1\r\n#SBATCH --ntasks-per-node=1\r\n#SBATCH --time=00:40:00\r\n#SBATCH --partition=dev_accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:1\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/dynamics/%x_%j.log\r\n#SBATCH --job-name=train_dyn_single_gpu\r\n#SBATCH --requeue\r\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/npy_test\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dyn/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\ntokenizer_checkpoint=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/interactive/3536670\r\n\r\nenv | grep SLURM\r\n\r\n\r\nsrun python jasmine/train_dynamics.py \\r\n --save_ckpt \\r\n --image_height=64 \\r\n --image_width=64 \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=110 \\r\n --patch_size=4 \\r\n --init_lr=0 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=50 \\r\n --log_checkpoint_interval=50 \\r\n --dyna_type=maskgit \\r\n --log \\r\n --name=coinrun-dyn-dev-$slurm_job_id \\r\n --tags dyn coinrun dev \\r\n --entity instant-uv \\r\n --project jafar \\r\n --warmup_steps 0 \\r\n --wsd_decay_steps 0 \\r\n --num_steps 10 \\r\n --data_dir $array_records_dir_train \\r\n --tokenizer_checkpoint $tokenizer_checkpoint \\r\n --val_data_dir $array_records_dir_val \\r\n --log_interval 1 \\r\n --val_interval 50 \\r\n --eval_full_frame \\r\n --val_steps 5\r\n",,terminal_output 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Use `wandb login --relogin` to force relogin\r\n",,terminal_output +3868,5920529,"TERMINAL",0,0,"3624",,terminal_output +3869,5920537,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.22.0\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/wandb/run-20251002_133912-juyysibs\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run coinrun-dyn-dev-3536993\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/juyysibs\r\n",,terminal_output +3870,5921552,"TERMINAL",0,0,"4735",,terminal_output +3871,5922589,"TERMINAL",0,0,"5846",,terminal_output +3872,5922890,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jasmine/.venv/lib64/python3.12/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py:1269: UserWarning: Sharding info not provided when restoring. Populating sharding info from sharding file. Please note restoration time will be slightly increased due to reading from file. Note also that this option is unsafe when restoring on a different topology than the checkpoint was saved with.\r\n warnings.warn(\r\n",,terminal_output +3873,5923628,"TERMINAL",0,0,"6957",,terminal_output +3874,5924960,"TERMINAL",0,0,"72:00:0068",,terminal_output +3875,5925971,"TERMINAL",0,0,"8179",,terminal_output +3876,5926789,"TERMINAL",0,0,"92830",,terminal_output +3877,5927780,"TERMINAL",0,0,"20391",,terminal_output +3878,5928922,"TERMINAL",0,0,"15313",,terminal_output +3879,5929940,"TERMINAL",0,0,"3624",,terminal_output +3880,5930972,"TERMINAL",0,0,"4735",,terminal_output +3881,5931999,"TERMINAL",0,0,"5846",,terminal_output +3882,5933021,"TERMINAL",0,0,"6957",,terminal_output +3883,5934023,"TERMINAL",0,0,"71068",,terminal_output +3884,5935047,"TERMINAL",0,0,"8179",,terminal_output +3885,5936197,"TERMINAL",0,0,"92840",,terminal_output +3886,5937124,"TERMINAL",0,0,"30391",,terminal_output +3887,5938245,"TERMINAL",0,0,"14402",,terminal_output 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maskgit-sampling-iterative-unmasking-fix\r\n metrics-logging-for-dynamics-model\r\n monkey-patch\r\n new-arch-sampling\r\n preprocess_video\r\n refactor-tmp\r\n revised-dataloader\r\n runner\r\n runner-grain\r\n sample-ali-branch\r\n sample-from-different-topologies\r\n sampling-startframe-indexing-fix\r\n speedup-tfrecord-preprocessing\r\n tmp\r\n train_lam_coinrun_ablation_wsd_3e-6_28747\r\n validation-loss\r\n\r[?1l>]0;tum_cte0515@hkn1990:~/Projects/jasmine",,terminal_output +38,407037,"TERMINAL",0,0,"git checkout action-mapper",,terminal_command +39,407117,"TERMINAL",0,0,"]633;C",,terminal_output +40,407832,"TERMINAL",0,0,"branch 'action-mapper' set up to track 'origin/action-mapper'.\r\nSwitched to a new branch 'action-mapper'\r\n]0;tum_cte0515@hkn1990:~/Projects/jasmine",,terminal_output +41,409795,"TERMINAL",0,0,"git pull",,terminal_command +42,409863,"TERMINAL",0,0,"]633;C",,terminal_output +43,410490,"",0,0,"Switched from branch 'input_pipeline/add-npy2array_record' to 'action-mapper'",,git_branch_checkout +44,411682,"TERMINAL",0,0,"remote: Enumerating objects: 4, done.\r\nremote: Counting objects: 25% (1/4)\rremote: Counting objects: 50% (2/4)\rremote: Counting objects: 75% (3/4)\rremote: Counting objects: 100% (4/4)\rremote: Counting objects: 100% (4/4), done.\r\nremote: Compressing objects: 50% (1/2)\rremote: Compressing objects: 100% (2/2)\rremote: Compressing objects: 100% (2/2), done.\r\nremote: Total 4 (delta 2), reused 3 (delta 2), pack-reused 0 (from 0)\r\nUnpacking objects: 25% (1/4)\rUnpacking objects: 50% (2/4)\rUnpacking objects: 75% (3/4)\rUnpacking objects: 100% (4/4)\rUnpacking objects: 100% (4/4), 1.28 KiB | 12.00 KiB/s, done.\r\n",,terminal_output +45,411881,"TERMINAL",0,0,"From github.com:p-doom/jasmine\r\n * [new branch] demo-notebook -> origin/demo-notebook\r\n 49fb80d..975ead7 main -> origin/main\r\n",,terminal_output +46,412050,"TERMINAL",0,0,"Already up to date.\r\n]0;tum_cte0515@hkn1990:~/Projects/jasmine",,terminal_output +47,442859,"TERMINAL",0,0,"git merge validation-loss",,terminal_command +48,442969,"TERMINAL",0,0,"]633;C",,terminal_output +49,443244,"TERMINAL",0,0,"hint: Waiting for your editor to close the file... ",,terminal_output +50,443411,"TERMINAL",0,0,"[?1049h[>4;2m[?1h=[?2004h[?1004h[?12h[?12l[?25l""~/Projects/jasmine/.git/MERGE_MSG"" 6L, 276B▽ Pzz\[0%m [>c]10;?]11;?Merge branch 'validation-loss' into action-mapper\r\n# Please enter a commit message to explain why this merge is necessary,# especially if it merges an updated upstream into a topic branch.#\r\n# Lines starting with '#' will be ignored, and an empty message aborts\r\n# the commit.\r\n~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 1,1All[?25h",,terminal_output +51,443456,"TERMINAL",0,0,"P+q436f\P+q6b75\P+q6b64\P+q6b72\P+q6b6c\P+q2332\P+q2334\P+q2569\P+q2a37\P+q6b31\[?12$p[?25l/3333/3333 [?25h[?25l/f6f6/e3e3 [?25h",,terminal_output +52,444973,"TERMINAL",0,0,"[?25l^[",,terminal_output +53,445109,"TERMINAL",0,0," ^[ [?25h",,terminal_output +54,445232,"TERMINAL",0,0,"[?25l::[?25h",,terminal_output +55,445361,"TERMINAL",0,0,"w",,terminal_output +56,445528,"TERMINAL",0,0,"q",,terminal_output +57,446046,"TERMINAL",0,0,"\r[?25l[?2004l[>4;m"".git/MERGE_MSG"" 6L, 276B written\r\r\r\n[?1004l[?2004l[?1l>[?25h[>4;m[?1049l\rMerge made by the 'ort' strategy.\r\n",,terminal_output +58,446207,"TERMINAL",0,0," README.md | 117 +++++--\r\n generate_dataset.py | 114 +++++--\r\n genie.py | 599 +++++++++++++++++++++-------------\r\n input_pipeline/download/download_array_records.sh | 2 +-\r\n input_pipeline/download/openai/download_actions_files.py | 93 ++++++\r\n input_pipeline/preprocess/npy_to_tfrecord.py | 125 --------\r\n input_pipeline/preprocess/pngs_to_array_records.py | 130 ++++++++\r\n input_pipeline/preprocess/video_to_array_records.py | 68 ++--\r\n input_pipeline/preprocess/video_to_npy.py | 117 -------\r\n models/dynamics.py | 176 ++++++++--\r\n models/lam.py | 146 ++++++---\r\n models/tokenizer.py | 125 +++++---\r\n requirements.txt | 12 +-\r\n sample.py | 303 +++++++++++-------\r\n tests/data/generate_dummy_data.py | 42 +--\r\n tests/test_dataloader.py | 18 +-\r\n train_dynamics.py | 352 +++++++++++++-------\r\n train_lam.py | 329 +++++++++++++------\r\n train_tokenizer.py | 310 ++++++++++++------\r\n utils/dataloader.py | 62 +++-\r\n utils/dataset_utils.py | 164 +++++-----\r\n utils/lr_utils.py | 35 +-\r\n utils/nn.py | 638 +++++++++++++++++++++++++++----------\r\n utils/parameter_utils.py | 12 +-\r\n utils/preprocess.py | 1 -\r\n 25 files changed, 2706 insertions(+), 1384 deletions(-)\r\n create mode 100644 input_pipeline/download/openai/download_actions_files.py\r\n delete mode 100644 input_pipeline/preprocess/npy_to_tfrecord.py\r\n create mode 100644 input_pipeline/preprocess/pngs_to_array_records.py\r\n delete mode 100644 input_pipeline/preprocess/video_to_npy.py\r\n]0;tum_cte0515@hkn1990:~/Projects/jasmine",,terminal_output +59,478151,"train_lamap.py",0,0,"from dataclasses import dataclass, field\nimport os\n\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.checkpoint as ocp\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\n\nfrom models.lamap import LatentActionMapper\nfrom utils.dataloader import get_dataloader\nfrom utils.lr_utils import get_lr_schedule\nfrom utils.parameter_utils import count_parameters_by_component\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n batch_size: int = 36\n init_lr: float = 0.0\n max_lr: float = 3e-5\n decay_end: float = 0.0\n wsd_decay_steps: int = 10000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n warmup_steps: int = 5000\n lr_schedule : str = ""wsd"" # supported options: wsd, cos\n # LAM\n model_dim: int = 512\n latent_dim: int = 32\n num_latents: int = 6\n patch_size: int = 16\n num_blocks: int = 8\n num_heads: int = 8\n dropout: float = 0.0\n codebook_dropout: float = 0.0\n param_dtype: jnp.dtype = jnp.float32\n dtype: jnp.dtype = jnp.bfloat16\n # LAMAP\n # FIXME (f.srambical): this assumes that the number of actions is the same as the number of latent actions\n action_dim: int = 32\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n name: str = ""train_lamap""\n tags: list[str] = field(default_factory=lambda: [""lamap""])\n log_interval: int = 5\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 10000\n log_checkpoint_keep_period: int = 20000\n wandb_id: str = """"\n use_flash_attention: bool = True\n\n\nargs = tyro.cli(Args)\n\ndef lamap_loss_fn(params, state, inputs):\n # --- Compute loss ---\n inputs[""videos""] = inputs[""videos""].astype(args.dtype) / 255.0\n outputs = state.apply_fn(\n params, inputs, training=True, rngs={""dropout"": inputs[""rng""]}\n )\n loss = optax.softmax_cross_entropy(outputs[""action_predictions""], inputs[""actions""]).mean()\n return loss\n\n@jax.jit\ndef train_step(state, inputs):\n grad_fn = jax.value_and_grad(lamap_loss_fn, allow_int=True)\n loss, grads = grad_fn(state.params, state, inputs)\n state = state.apply_gradients(grads=grads)\n return state, loss\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\n # --- Initialize model ---\n lamap = LatentActionMapper(\n in_dim=args.image_channels,\n model_dim=args.model_dim,\n latent_dim=args.latent_dim,\n action_dim=args.action_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 param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n )\n\n image_shape = (args.image_height, args.image_width, args.image_channels)\n rng, _rng = jax.random.split(rng)\n inputs = dict(\n videos=jnp.zeros(\n (per_device_batch_size_for_init, args.seq_len, *image_shape),\n dtype=args.dtype,\n ),\n rng=_rng,\n )\n rng, _rng = jax.random.split(rng)\n init_params = lamap.init(_rng, inputs)\n\n param_counts = count_parameters_by_component(init_params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\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 if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n lr_schedule = get_lr_schedule(args.lr_schedule, \n args.init_lr, \n args.max_lr, \n args.decay_end, \n args.num_steps, \n args.warmup_steps, \n args.wsd_decay_steps)\n tx = optax.adamw(learning_rate=lr_schedule, b1=0.9, b2=0.9, weight_decay=1e-4, mu_dtype=args.dtype)\n train_state = TrainState.create(apply_fn=lamap.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 videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n train_state = jax.device_put(train_state, replicated_sharding)\n\n # --- Initialize checkpoint manager ---\n step = 0\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.StandardSave, ocp.handlers.StandardCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.StandardRestore, ocp.handlers.StandardCheckpointHandler\n )\n handler_registry.add(""dataloader_state"", grain.checkpoint.CheckpointSave, grain.checkpoint.CheckpointHandler) # type: ignore\n handler_registry.add(""dataloader_state"", grain.checkpoint.CheckpointRestore, grain.checkpoint.CheckpointHandler) # type: ignore\n\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n\n # --- Create DataLoaderIterator from dataloader ---\n array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n\n # --- Restore checkpoint ---\n if args.restore_ckpt:\n abstract_train_state = jax.tree_util.tree_map(\n ocp.utils.to_shape_dtype_struct, train_state\n )\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.StandardRestore(abstract_train_state),\n dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator),\n ),\n )\n train_state = restored[""model_state""]\n grain_iterator = restored[""dataloader_state""]\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored dataloader and model state from step {step}"")\n\n # --- TRAIN LOOP ---\n dataloader = (jax.make_array_from_process_local_data(videos_sharding, elem) for elem in grain_iterator) # type: ignore\n print(f""Starting training from step {step}..."")\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n rng, _rng = jax.random.split(rng)\n\n # FIXME (f.srambical): use mock actions for now\n rng, _rng_actions = jax.random.split(rng)\n actions = jax.random.uniform(\n _rng_actions,\n (per_device_batch_size_for_init, args.action_dim),\n dtype=args.dtype,\n )\n\n inputs = dict(\n videos=videos,\n actions=actions,\n rng=_rng\n )\n train_state, loss = train_step(\n train_state, inputs\n )\n lr = lr_schedule(step)\n print(f""Step {step}, loss: {loss}"")\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 ""lr"": lr,\n }\n )\n # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n checkpoint_manager.save(\n step,\n args=ocp.args.Composite(\n model_state=ocp.args.StandardSave(train_state),\n dataloader_state=grain.checkpoint.CheckpointSave(\n grain_iterator\n ),\n ),\n )\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n checkpoint_manager.close()\n",python,tab +60,485581,"TERMINAL",0,0,"bash",,terminal_focus +61,490457,"train_lam.py",0,0,"from dataclasses import dataclass, field\nimport os\nfrom typing import cast\n\nimport einops\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\nimport flax.nnx as nnx\n\nfrom models.lam import LatentActionModel\nfrom utils.dataloader import create_dataloader_iterator\nfrom utils.lr_utils import get_lr_schedule\nfrom utils.parameter_utils import count_parameters_by_component\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n batch_size: int = 36\n vq_beta: float = 0.25\n init_lr: float = 0.0\n max_lr: float = 3e-5\n decay_end: float = 0.0\n wsd_decay_steps: int = (\n 10000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n )\n warmup_steps: int = 5000\n lr_schedule: str = ""wsd"" # supported options: wsd, cos\n vq_reset_thresh: int = 50\n # LAM\n model_dim: int = 512\n ffn_dim: int = 2048\n latent_dim: int = 32\n num_latents: int = 6\n patch_size: int = 16\n num_blocks: int = 4\n num_heads: int = 8\n dropout: float = 0.0\n codebook_dropout: float = 0.0\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n name: str = ""train_lam""\n tags: list[str] = field(default_factory=lambda: [""lam""])\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 10000\n log_checkpoint_keep_period: int = 20000\n val_data_dir: str = """"\n val_interval: int = 20_000\n val_steps: int = 50\n wandb_id: str = """"\n use_flash_attention: bool = True\n\n\nargs = tyro.cli(Args)\n\n\ndef lam_loss_fn(\n model: LatentActionModel, inputs: dict, training: bool = True\n) -> tuple[jax.Array, tuple[jax.Array, jax.Array, dict]]:\n # --- Compute loss ---\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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 = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt, recon)).mean()\n count_fn = jax.vmap(lambda i: (outputs[""indices""] == i).sum())\n index_counts = count_fn(jnp.arange(args.num_latents))\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=(index_counts != 0).mean(),\n )\n return loss, (outputs[""recon""], index_counts, metrics)\n\n\n@nnx.jit\ndef train_step(\n lam: LatentActionModel,\n optimizer: nnx.Optimizer,\n inputs: dict,\n action_last_active: jax.Array,\n rng: jax.Array,\n) -> tuple[jax.Array, jax.Array, jax.Array, dict]:\n def loss_fn(\n model: LatentActionModel,\n ) -> tuple[jax.Array, tuple[jax.Array, jax.Array, dict]]:\n model.train()\n return lam_loss_fn(model, inputs, training=True)\n\n # --- Update model ---\n (loss, (recon, idx_counts, metrics)), grads = nnx.value_and_grad(\n loss_fn, has_aux=True\n )(lam)\n optimizer.update(grads)\n\n # --- Reset inactive latent actions ---\n codebook = lam.vq.codebook\n num_codes = len(codebook)\n active_codes = idx_counts != 0.0\n action_last_active = jnp.where(active_codes, 0, action_last_active + 1)\n p_code = active_codes / active_codes.sum()\n reset_idxs = jax.random.choice(rng, num_codes, shape=(num_codes,), p=p_code)\n do_reset = action_last_active >= args.vq_reset_thresh\n new_codebook = jnp.where(\n jnp.expand_dims(do_reset, -1), codebook[reset_idxs], codebook.value\n )\n lam.vq.codebook.value = new_codebook\n action_last_active = jnp.where(do_reset, 0, action_last_active)\n return loss, recon, action_last_active, metrics\n\n@nnx.jit\ndef val_step(lam: LatentActionModel, inputs: dict) -> tuple[jax.Array, jax.Array, dict]:\n lam.eval()\n (loss, (recon, _, metrics)) = lam_loss_fn(lam, inputs, training=False)\n return loss, recon, metrics\n\ndef calculate_validation_metrics(val_dataloader, lam):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n for videos in val_dataloader:\n inputs = dict(videos=videos)\n loss, recon, metrics = val_step(lam, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\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.key(args.seed)\n\n # --- Initialize model ---\n rng, _rng = jax.random.split(rng)\n rngs = nnx.Rngs(_rng)\n lam = LatentActionModel(\n in_dim=args.image_channels,\n model_dim=args.model_dim,\n ffn_dim=args.ffn_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 param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n rngs=rngs,\n )\n\n # Count parameters\n _, params, _ = nnx.split(lam, nnx.Param, ...)\n param_counts = count_parameters_by_component(params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\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 if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n lr_schedule = get_lr_schedule(\n args.lr_schedule,\n args.init_lr,\n args.max_lr,\n args.decay_end,\n args.num_steps,\n args.warmup_steps,\n args.wsd_decay_steps,\n )\n tx = optax.adamw(\n learning_rate=lr_schedule,\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.dtype,\n )\n optimizer = nnx.Optimizer(lam, 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 videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n\n model_state = nnx.state(optimizer.model)\n model_sharded_state = jax.lax.with_sharding_constraint(\n model_state, replicated_sharding\n )\n nnx.update(optimizer.model, model_sharded_state)\n optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)\n optimizer_sharded_state = jax.lax.with_sharding_constraint(\n optimizer_state, replicated_sharding\n )\n nnx.update(optimizer, optimizer_sharded_state)\n\n # --- Initialize checkpoint manager ---\n step = 0\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n if args.val_data_dir:\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n\n # --- Create DataLoaderIterator from dataloader ---\n image_shape = (args.image_height, args.image_width, args.image_channels)\n train_iterator = create_dataloader_iterator(args.data_dir, image_shape, args.seq_len, args.batch_size, args.seed)\n if args.val_data_dir:\n val_iterator = create_dataloader_iterator(args.val_data_dir, image_shape, args.seq_len, args.batch_size, args.seed)\n\n # --- Restore checkpoint ---\n if args.restore_ckpt:\n abstract_optimizer = nnx.eval_shape(lambda: optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n if args.val_data_dir:\n restore_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointRestore(train_iterator), # type: ignore\n val_dataloader_state=grain.checkpoint.CheckpointRestore(val_iterator), # type: ignore\n )\n else: \n restore_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointRestore(train_iterator), # type: ignore\n )\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(),\n args=restore_args\n )\n restored_optimizer_state = restored[""model_state""]\n nnx.update(optimizer, restored_optimizer_state)\n train_iterator = restored[""train_dataloader_state""]\n if args.val_data_dir:\n val_iterator = restored[""val_dataloader_state""]\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored dataloader and model state from step {step}"")\n\n # --- TRAIN LOOP ---\n dataloader_train = (\n jax.make_array_from_process_local_data(videos_sharding, elem)\n for elem in train_iterator\n )\n\n if args.val_data_dir:\n dataloader_val = (\n jax.make_array_from_process_local_data(videos_sharding, elem)\n for elem in val_iterator\n )\n print(f""Starting training from step {step}..."")\n action_last_active = jnp.zeros(args.num_latents, dtype=jnp.int32)\n while step < args.num_steps:\n for videos in dataloader_train:\n # --- Train step ---\n rng, _rng = jax.random.split(rng)\n\n inputs = dict(videos=videos, rng=_rng)\n rng, _rng = jax.random.split(rng)\n loss, recon, action_last_active, metrics = train_step(\n lam, optimizer, inputs, action_last_active, _rng\n )\n metrics[""lr""] = lr_schedule(step)\n print(f""Step {step}, loss: {loss}"")\n step += 1\n\n # --- Validation loss ---\n if args.val_data_dir and step % args.val_interval == 0:\n print(f""Calculating validation metrics..."")\n val_loss, val_metrics, val_gt_batch, val_recon = calculate_validation_metrics(dataloader_val, lam)\n print(f""Step {step}, validation loss: {val_loss}"")\n\n # --- Logging ---\n if args.log:\n if step % args.log_interval == 0 and jax.process_index() == 0:\n log_dict = {\n ""loss"": loss,\n ""step"": step,\n **metrics\n }\n if args.val_data_dir and step % args.val_interval == 0:\n log_dict.update({\n ""val_loss"": val_loss,\n **val_metrics\n })\n wandb.log(log_dict)\n if step % args.log_image_interval == 0:\n gt_seq = inputs[""videos""][0, 1:].astype(jnp.float32) / 255.0\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 if args.val_data_dir and step % args.val_interval == 0:\n gt_seq_val = val_gt_batch[""videos""][0, 1:].astype(jnp.float32) / 255.0\n recon_seq_val = val_recon[0].clip(0, 1)\n val_comparison_seq = jnp.concatenate((gt_seq, recon_seq), axis=1)\n val_comparison_seq = einops.rearrange(\n val_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 if args.val_data_dir and step % args.val_interval == 0:\n log_images.update(\n dict(\n val_image=wandb.Image(np.asarray(gt_seq_val[0])),\n val_recon=wandb.Image(np.asarray(recon_seq_val[0])),\n val_true_vs_recon=wandb.Image(\n np.asarray(val_comparison_seq.astype(np.uint8))\n )\n )\n )\n wandb.log(log_images)\n # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n optimizer_state = nnx.state(optimizer)\n if args.val_data_dir:\n ckpt_manager_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n train_iterator # type: ignore\n ),\n val_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n val_iterator # type: ignore\n )\n )\n else: \n ckpt_manager_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n train_iterator # type: ignore\n )\n )\n checkpoint_manager.save(\n step,\n args=ckpt_manager_args\n )\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n checkpoint_manager.close()\n",python,tab +62,493091,"train_lam.py",0,0,"",python,tab +63,494819,"train_lamap.py",0,0,"",python,tab +64,494822,"train_lamap.py",5816,0,"",python,selection_mouse +65,497513,"train_lam.py",0,0,"",python,tab +66,497514,"train_lam.py",1561,0,"",python,selection_mouse +67,497571,"train_lam.py",1560,0,"",python,selection_command +68,503535,"train_lam.py",1953,0,"",python,selection_mouse +69,504298,"train_lam.py",1942,23," val_steps: int = 50",python,selection_command +70,504508,"train_lam.py",1911,54," val_interval: int = 20_000\n val_steps: int = 50",python,selection_command +71,504630,"train_lam.py",1884,81," val_data_dir: str = """"\n val_interval: int = 20_000\n val_steps: int = 50",python,selection_command +72,504990,"train_lam.py",1884,0,"",python,selection_command +73,506602,"train_lamap.py",0,0,"",python,tab +74,506603,"train_lamap.py",1900,0,"",python,selection_mouse +75,507410,"train_lamap.py",1878,0,"",python,selection_mouse +76,509138,"train_lamap.py",1866,0," val_data_dir: str = """"\n val_interval: int = 20_000\n val_steps: int = 50\n",python,content +77,509215,"train_lamap.py",1870,0,"",python,selection_command +78,557003,"train_lamap.py",2049,0,"",python,selection_mouse +79,566664,"train_lamap.py",2617,0,"",python,selection_mouse +80,566682,"train_lamap.py",2616,0,"",python,selection_command +81,622308,"train_lam.py",0,0,"",python,tab +82,622309,"train_lam.py",2051,0,"",python,selection_mouse +83,623899,"train_lam.py",2052,1,"d",python,selection_command +84,624024,"train_lam.py",2809,2,"da",python,selection_command +85,624088,"train_lam.py",2809,3,"dat",python,selection_command +86,624239,"train_lam.py",4993,4,"data",python,selection_command +87,624623,"train_lam.py",9650,5,"data_",python,selection_command +88,624851,"train_lam.py",9650,6,"data_d",python,selection_command +89,624981,"train_lam.py",9650,7,"data_di",python,selection_command +90,625092,"train_lam.py",9650,8,"data_dir",python,selection_command +91,626432,"train_lamap.py",0,0,"",python,tab +92,626433,"train_lamap.py",2676,0,"",python,selection_mouse +93,628274,"train_lamap.py",2688,1,"d",python,selection_command +94,628486,"train_lamap.py",4485,2,"da",python,selection_command +95,628544,"train_lamap.py",4485,3,"dat",python,selection_command +96,628635,"train_lamap.py",5564,4,"data",python,selection_command +97,629055,"train_lamap.py",6959,5,"data_",python,selection_command +98,629239,"train_lamap.py",6959,6,"data_d",python,selection_command +99,629390,"train_lamap.py",6959,7,"data_di",python,selection_command +100,629538,"train_lamap.py",6959,8,"data_dir",python,selection_command +101,632596,"train_lamap.py",7005,8,"data_dir",python,selection_command +102,637148,"train_lam.py",0,0,"",python,tab +103,637149,"train_lam.py",10076,0,"",python,selection_mouse +104,637150,"train_lam.py",9992,84," cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )",python,selection_mouse +105,637150,"train_lam.py",9862,214," )\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )",python,selection_mouse +106,637151,"train_lam.py",9729,347," grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )",python,selection_mouse +107,637151,"train_lam.py",9692,384," ""val_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )",python,selection_mouse +108,637151,"train_lam.py",9661,415," handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )",python,selection_mouse +109,637152,"train_lam.py",9635,441," if args.val_data_dir:\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )",python,selection_mouse +110,637152,"train_lam.py",9628,448," )\n if args.val_data_dir:\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )",python,selection_mouse +111,637241,"train_lam.py",9442,634," handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n if args.val_data_dir:\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )",python,selection_mouse +112,637242,"train_lam.py",10075,0,"",python,selection_command +113,637243,"train_lam.py",9992,84," cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )",python,selection_command +114,637287,"train_lam.py",9253,823," handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n if args.val_data_dir:\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )",python,selection_mouse +115,642282,"train_lamap.py",0,0,"",python,tab +116,642283,"train_lamap.py",6558,0,"",python,selection_mouse +117,642353,"train_lamap.py",6557,0,"",python,selection_command +118,643460,"train_lamap.py",6422,0,"",python,selection_mouse +119,644138,"train_lamap.py",6313,0,"",python,selection_mouse +120,644254,"train_lamap.py",6312,1,"d",python,selection_mouse +121,644366,"train_lamap.py",6300,13,"_registry.add",python,selection_mouse +122,644367,"train_lamap.py",6166,147,"dler_registry.add(""dataloader_state"", grain.checkpoint.CheckpointSave, grain.checkpoint.CheckpointHandler) # type: ignore\n handler_registry.add",python,selection_mouse +123,644367,"train_lamap.py",6163,150,"handler_registry.add(""dataloader_state"", grain.checkpoint.CheckpointSave, grain.checkpoint.CheckpointHandler) # type: ignore\n handler_registry.add",python,selection_mouse +124,644368,"train_lamap.py",6161,152," handler_registry.add(""dataloader_state"", grain.checkpoint.CheckpointSave, grain.checkpoint.CheckpointHandler) # type: ignore\n handler_registry.add",python,selection_mouse +125,644441,"train_lamap.py",6154,159," )\n handler_registry.add(""dataloader_state"", grain.checkpoint.CheckpointSave, grain.checkpoint.CheckpointHandler) # type: ignore\n handler_registry.add",python,selection_mouse +126,644474,"train_lamap.py",6153,160," )\n handler_registry.add(""dataloader_state"", grain.checkpoint.CheckpointSave, grain.checkpoint.CheckpointHandler) # type: ignore\n handler_registry.add",python,selection_mouse +127,644702,"train_lamap.py",6065,248," ""model_state"", ocp.args.StandardRestore, ocp.handlers.StandardCheckpointHandler\n )\n handler_registry.add(""dataloader_state"", grain.checkpoint.CheckpointSave, grain.checkpoint.CheckpointHandler) # type: ignore\n handler_registry.add",python,selection_mouse +128,645099,"train_lamap.py",6153,160," )\n handler_registry.add(""dataloader_state"", grain.checkpoint.CheckpointSave, grain.checkpoint.CheckpointHandler) # type: ignore\n handler_registry.add",python,selection_mouse +129,645961,"train_lamap.py",6422,0,"",python,selection_mouse +130,646402,"train_lamap.py",6292,130," handler_registry.add(""dataloader_state"", grain.checkpoint.CheckpointRestore, grain.checkpoint.CheckpointHandler) # type: ignore\n",python,selection_mouse +131,646570,"train_lamap.py",6291,131," handler_registry.add(""dataloader_state"", grain.checkpoint.CheckpointRestore, grain.checkpoint.CheckpointHandler) # type: ignore\n",python,selection_mouse +132,646571,"train_lamap.py",6290,132," handler_registry.add(""dataloader_state"", grain.checkpoint.CheckpointRestore, grain.checkpoint.CheckpointHandler) # type: ignore\n",python,selection_mouse +133,646632,"train_lamap.py",6160,262," handler_registry.add(""dataloader_state"", grain.checkpoint.CheckpointSave, grain.checkpoint.CheckpointHandler) # type: ignore\n handler_registry.add(""dataloader_state"", grain.checkpoint.CheckpointRestore, grain.checkpoint.CheckpointHandler) # type: ignore\n",python,selection_mouse +134,649763,"train_lamap.py",6159,263," handler_registry.add(""dataloader_state"", grain.checkpoint.CheckpointSave, grain.checkpoint.CheckpointHandler) # type: ignore\n handler_registry.add(""dataloader_state"", grain.checkpoint.CheckpointRestore, grain.checkpoint.CheckpointHandler) # type: ignore\n",python,selection_mouse +135,659330,"train_lamap.py",6159,264,"",python,content +136,660424,"train_lamap.py",6158,0,"\n ",python,content +137,661026,"train_lamap.py",6163,0," handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n if args.val_data_dir:\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )",python,content +138,662186,"train_lamap.py",6167,0,"",python,selection_mouse +139,662926,"train_lamap.py",6163,4,"",python,content +140,673087,"train_lam.py",0,0,"",python,tab +141,673088,"train_lam.py",9252,0,"",python,selection_mouse +142,673089,"train_lam.py",9165,87," ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )",python,selection_mouse +143,673089,"train_lam.py",9164,88," ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )",python,selection_mouse +144,673090,"train_lam.py",9163,89," ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )",python,selection_mouse +145,673198,"train_lam.py",9137,115," handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )",python,selection_mouse +146,673199,"train_lam.py",9251,0,"",python,selection_command +147,673199,"train_lam.py",9137,114," handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n ",python,selection_mouse +148,673200,"train_lam.py",9165,87," ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )",python,selection_command +149,673253,"train_lam.py",9137,115," handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )",python,selection_mouse +150,673304,"train_lam.py",9131,121," )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )",python,selection_mouse +151,673364,"train_lam.py",9050,202," ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )",python,selection_mouse +152,673458,"train_lam.py",9024,228," handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )",python,selection_mouse +153,679322,"train_lam.py",9252,0,"",python,selection_mouse +154,679373,"train_lam.py",9251,0,"",python,selection_command +155,679810,"train_lam.py",9252,0,"",python,selection_mouse +156,679827,"train_lam.py",9251,0,"",python,selection_command +157,680106,"train_lam.py",9251,1,")",python,selection_mouse +158,680107,"train_lam.py",9169,82," ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n ",python,selection_mouse +159,680108,"train_lam.py",9138,113," handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n ",python,selection_mouse +160,680108,"train_lam.py",9131,120," )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n ",python,selection_mouse +161,680108,"train_lam.py",9050,201," ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n ",python,selection_mouse +162,680113,"train_lam.py",9252,0,"",python,selection_command +163,680187,"train_lam.py",9024,228," handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )",python,selection_mouse +164,680270,"train_lam.py",8953,299," handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )",python,selection_mouse +165,680314,"train_lam.py",8940,312," step = 0\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )",python,selection_mouse +166,680814,"train_lam.py",8940,0,"",python,selection_command +167,682360,"train_lamap.py",0,0,"",python,tab +168,682361,"train_lamap.py",6158,0,"",python,selection_mouse +169,683400,"train_lamap.py",6157,0,"",python,selection_command +170,683668,"train_lamap.py",6158,0," step = 0\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )",python,content +171,683716,"train_lamap.py",6158,0,"",python,selection_command +172,684981,"train_lamap.py",6158,0,"\n ",python,content +173,686320,"train_lamap.py",6163,1,"",python,content +174,686479,"train_lamap.py",6163,1,"",python,content +175,686632,"train_lamap.py",6163,1,"",python,content +176,686782,"train_lamap.py",6163,1,"",python,content +177,687083,"train_lamap.py",6162,0,"",python,selection_command +178,687266,"train_lamap.py",6156,0,"",python,selection_command +179,688080,"train_lamap.py",6153,5," )",python,selection_command +180,688294,"train_lamap.py",6065,93," ""model_state"", ocp.args.StandardRestore, ocp.handlers.StandardCheckpointHandler\n )",python,selection_command +181,688412,"train_lamap.py",6039,119," handler_registry.add(\n ""model_state"", ocp.args.StandardRestore, ocp.handlers.StandardCheckpointHandler\n )",python,selection_command +182,688691,"train_lamap.py",6033,125," )\n handler_registry.add(\n ""model_state"", ocp.args.StandardRestore, ocp.handlers.StandardCheckpointHandler\n )",python,selection_command +183,688820,"train_lamap.py",5948,210," ""model_state"", ocp.args.StandardSave, ocp.handlers.StandardCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.StandardRestore, ocp.handlers.StandardCheckpointHandler\n )",python,selection_command +184,688863,"train_lamap.py",5922,236," handler_registry.add(\n ""model_state"", ocp.args.StandardSave, ocp.handlers.StandardCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.StandardRestore, ocp.handlers.StandardCheckpointHandler\n )",python,selection_command +185,688966,"train_lamap.py",5851,307," handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.StandardSave, ocp.handlers.StandardCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.StandardRestore, ocp.handlers.StandardCheckpointHandler\n )",python,selection_command +186,689143,"train_lamap.py",5838,320," step = 0\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.StandardSave, ocp.handlers.StandardCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.StandardRestore, ocp.handlers.StandardCheckpointHandler\n )",python,selection_command +187,689548,"train_lamap.py",5838,321,"",python,content +188,689594,"train_lamap.py",5842,0,"",python,selection_command +189,692721,"train_lam.py",0,0,"",python,tab +190,692722,"train_lam.py",9866,0,"",python,selection_mouse +191,692795,"train_lam.py",9865,0,"",python,selection_command +192,693430,"train_lam.py",9786,0,"",python,selection_mouse +193,693578,"train_lam.py",9783,4,"cast",python,selection_mouse +194,697986,"train_lam.py",58,0,"",python,selection_mouse +195,700395,"train_lamap.py",0,0,"",python,tab +196,700396,"train_lamap.py",694,0,"",python,selection_mouse +197,701952,"train_lamap.py",50,0,"",python,selection_mouse +198,701959,"train_lamap.py",49,0,"",python,selection_command +199,702792,"train_lamap.py",50,0,"\nfrom typing import cast",python,content +200,702874,"train_lamap.py",51,0,"",python,selection_command +201,706671,"train_lamap.py",70,0,"",python,selection_command +202,707148,"train_lamap.py",6284,0,"",python,selection_command +203,708985,"train_lamap.py",6362,0,"",python,selection_command +204,709180,"train_lamap.py",6372,0,"",python,selection_command +205,709324,"train_lamap.py",6398,0,"",python,selection_command +206,709825,"train_lamap.py",6432,0,"",python,selection_command +207,709881,"train_lamap.py",6476,0,"",python,selection_command +208,709939,"train_lamap.py",6554,0,"",python,selection_command +209,710044,"train_lamap.py",6564,0,"",python,selection_command +210,710045,"train_lamap.py",6590,0,"",python,selection_command +211,710157,"train_lamap.py",6620,0,"",python,selection_command +212,710158,"train_lamap.py",6656,0,"",python,selection_command +213,710158,"train_lamap.py",6701,0,"",python,selection_command +214,710210,"train_lamap.py",6787,0,"",python,selection_command +215,710211,"train_lamap.py",6797,0,"",python,selection_command +216,710212,"train_lamap.py",6827,0,"",python,selection_command +217,710212,"train_lamap.py",6863,0,"",python,selection_command +218,710667,"train_lamap.py",6911,0,"",python,selection_command +219,710995,"train_lamap.py",6915,0,"",python,selection_command +220,713774,"train_lamap.py",7535,8,"data_dir",python,selection_command +221,737932,"train_lam.py",0,0,"",python,tab +222,737933,"train_lam.py",10791,0,"",python,selection_mouse +223,738354,"train_lam.py",10906,0,"",python,selection_mouse +224,740805,"train_lam.py",10906,1,"\n",python,selection_command +225,741375,"train_lam.py",10782,124," val_iterator = create_dataloader_iterator(args.val_data_dir, image_shape, args.seq_len, args.batch_size, args.seed)\n",python,selection_command +226,741525,"train_lam.py",10756,150," if args.val_data_dir:\n val_iterator = create_dataloader_iterator(args.val_data_dir, image_shape, args.seq_len, args.batch_size, args.seed)\n",python,selection_command +227,741637,"train_lam.py",10638,268," train_iterator = create_dataloader_iterator(args.data_dir, image_shape, args.seq_len, args.batch_size, args.seed)\n if args.val_data_dir:\n val_iterator = create_dataloader_iterator(args.val_data_dir, image_shape, args.seq_len, args.batch_size, args.seed)\n",python,selection_command +228,741945,"train_lam.py",10561,345," image_shape = (args.image_height, args.image_width, args.image_channels)\n train_iterator = create_dataloader_iterator(args.data_dir, image_shape, args.seq_len, args.batch_size, args.seed)\n if args.val_data_dir:\n val_iterator = create_dataloader_iterator(args.val_data_dir, image_shape, args.seq_len, args.batch_size, args.seed)\n",python,selection_command +229,742167,"train_lam.py",10561,0,"",python,selection_command +230,744002,"train_lamap.py",0,0,"",python,tab +231,744003,"train_lamap.py",7439,0,"",python,selection_mouse +232,745744,"train_lamap.py",7440,0," image_shape = (args.image_height, args.image_width, args.image_channels)\n train_iterator = create_dataloader_iterator(args.data_dir, image_shape, args.seq_len, args.batch_size, args.seed)\n if args.val_data_dir:\n val_iterator = create_dataloader_iterator(args.val_data_dir, image_shape, args.seq_len, args.batch_size, args.seed)\n\n",python,content +233,745815,"train_lamap.py",7440,0,"",python,selection_command +234,747286,"train_lamap.py",7440,346,"",python,content +235,747349,"train_lamap.py",7439,0,"",python,selection_command +236,747615,"train_lamap.py",7481,0,"\n ",python,content +237,748311,"train_lamap.py",7482,4,"",python,content +238,749429,"train_lamap.py",7482,0," image_shape = (args.image_height, args.image_width, args.image_channels)\n train_iterator = create_dataloader_iterator(args.data_dir, image_shape, args.seq_len, args.batch_size, args.seed)\n if args.val_data_dir:\n val_iterator = create_dataloader_iterator(args.val_data_dir, image_shape, args.seq_len, args.batch_size, args.seed)\n\n",python,content +239,749446,"train_lamap.py",7482,0,"",python,selection_command +240,751092,"train_lamap.py",7559,0,"",python,selection_command +241,751650,"train_lamap.py",7677,0,"",python,selection_command +242,751947,"train_lamap.py",7703,0,"",python,selection_command +243,752233,"train_lamap.py",7827,0,"",python,selection_command +244,752543,"train_lamap.py",7828,0,"",python,selection_command +245,753009,"train_lamap.py",7829,0,"",python,selection_command +246,753969,"train_lamap.py",7829,26," array_record_files = [",python,selection_command +247,754293,"train_lamap.py",7829,65," array_record_files = [\n os.path.join(args.data_dir, x)",python,selection_command +248,754750,"train_lamap.py",7829,108," array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)",python,selection_command +249,754833,"train_lamap.py",7829,147," array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")",python,selection_command +250,755036,"train_lamap.py",7829,153," array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]",python,selection_command +251,755037,"train_lamap.py",7829,192," array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(",python,selection_command +252,755037,"train_lamap.py",7829,220," array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,",python,selection_command +253,755149,"train_lamap.py",7829,242," array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,",python,selection_command +254,755149,"train_lamap.py",7829,301," array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size",python,selection_command +255,755150,"train_lamap.py",7829,366," array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes",python,selection_command +256,755150,"train_lamap.py",7829,391," array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,",python,selection_command +257,755150,"train_lamap.py",7829,413," array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,",python,selection_command +258,755151,"train_lamap.py",7829,436," array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,",python,selection_command +259,755196,"train_lamap.py",7829,468," array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,",python,selection_command +260,755257,"train_lamap.py",7829,492," array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,",python,selection_command +261,755410,"train_lamap.py",7829,498," array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )",python,selection_command +262,755556,"train_lamap.py",7829,559," array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()",python,selection_command +263,755743,"train_lamap.py",7829,638," array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)",python,selection_command +264,756073,"train_lamap.py",7829,639,"",python,content +265,756482,"train_lamap.py",7828,0,"",python,selection_command +266,756933,"train_lamap.py",7828,1,"",python,content +267,757425,"train_lamap.py",7828,1,"",python,content +268,757450,"train_lamap.py",7832,0,"",python,selection_command +269,787826,"train_lamap.py",8750,0,"",python,selection_command +270,790489,"train_lamap.py",8138,0,"",python,selection_command +271,795489,"train_lamap.py",7827,0,"",python,selection_command +272,798749,"train_lamap.py",7736,0,"",python,selection_mouse +273,804016,"slurm/jobs/mihir/horeka/coinrun/train_tokenizer_single_gpu.sh",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=00:20:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/tokenizer/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/coinrun/tokenizer/%x_%j.log\n#SBATCH --job-name=train_tokenizer_1e-4\n#SBATCH --requeue\n#SBATCH --signal=b:usr1@300 # 5 min before timeout\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir_train=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_gt_actions_split/train\narray_records_dir_val=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m_gt_actions_split/val\n\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --save_ckpt \\n --image_height=64 \\n --image_width=64 \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=125 \\n --init_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=250 \\n --log_checkpoint_interval=250 \\n --log \\n --name=coinrun-tokenizer-dev-$slurm_job_id \\n --tags tokenizer coinrun dev \\n --entity instant-uv \\n --project jafar \\n --warmup_steps 0 \\n --wsd_decay_steps 0 \\n --num_steps 250 \\n --data_dir $array_records_dir_train\n",shellscript,tab +274,806158,"train_lamap.py",0,0,"",python,tab +275,810828,"train_lamap.py",7772,0,"",python,selection_command +276,812475,"train_lamap.py",7770,0,"",python,selection_command +277,812953,"train_lamap.py",7758,0,"",python,selection_command +278,813405,"train_lamap.py",7757,0,"",python,selection_command +279,813768,"train_lamap.py",7753,0,"",python,selection_command +280,814056,"train_lamap.py",7752,0,"",python,selection_command +281,820059,"train_lam.py",0,0,"",python,tab +282,820060,"train_lam.py",550,0,"",python,selection_command +283,821628,"train_lam.py",543,0,"",python,selection_mouse +284,821735,"train_lam.py",543,0," array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)",python,content +285,825090,"train_lam.py",543,638,"",python,content +286,825937,"train_lam.py",542,0,"",python,selection_command +287,830514,"train_lamap.py",0,0,"",python,tab +288,830515,"train_lamap.py",529,0,"",python,selection_command +289,834880,"train_lamap.py",569,0,"",python,selection_mouse +290,834886,"train_lamap.py",568,0,"",python,selection_command +291,835548,"train_lamap.py",558,0,"",python,selection_command +292,835711,"train_lamap.py",557,0,"",python,selection_command +293,835862,"train_lamap.py",502,0,"",python,selection_command +294,835986,"train_lamap.py",459,0,"",python,selection_command +295,836202,"train_lamap.py",415,0,"",python,selection_command +296,836476,"train_lamap.py",449,0,"",python,selection_command +297,836826,"train_lamap.py",435,14,"",python,content +298,837474,"train_lamap.py",435,0,"c",python,content +299,837475,"train_lamap.py",436,0,"",python,selection_keyboard +300,837666,"train_lamap.py",436,0,"r",python,content +301,837668,"train_lamap.py",437,0,"",python,selection_keyboard +302,837856,"train_lamap.py",437,0,"e",python,content +303,837858,"train_lamap.py",438,0,"",python,selection_keyboard +304,838067,"train_lamap.py",438,0,"a",python,content +305,838068,"train_lamap.py",439,0,"",python,selection_keyboard +306,839744,"train_lamap.py",439,0,"t",python,content +307,839746,"train_lamap.py",440,0,"",python,selection_keyboard +308,839974,"train_lamap.py",440,0,"e",python,content +309,839976,"train_lamap.py",441,0,"",python,selection_keyboard +310,841090,"train_lamap.py",435,6,"create_dataloader_iterator",python,content +311,841723,"train_lamap.py",460,0,"",python,selection_command +312,843412,"train_lamap.py",7592,0,"",python,selection_command +313,845762,"train_lamap.py",7710,0,"",python,selection_command +314,845952,"train_lamap.py",7736,0,"",python,selection_command +315,846109,"train_lamap.py",7839,0,"",python,selection_command +316,846262,"train_lamap.py",7861,0,"",python,selection_command +317,846399,"train_lamap.py",7894,0,"",python,selection_command +318,846542,"train_lamap.py",7920,0,"",python,selection_command +319,846682,"train_lamap.py",7975,0,"",python,selection_command +320,846836,"train_lamap.py",8019,0,"",python,selection_command +321,846996,"train_lamap.py",8042,0,"",python,selection_command +322,847150,"train_lamap.py",8089,0,"",python,selection_command +323,847272,"train_lamap.py",8135,0,"",python,selection_command +324,847420,"train_lamap.py",8172,0,"",python,selection_command +325,848054,"train_lamap.py",8248,0,"",python,selection_command +326,857115,"train_lamap.py",8260,1,"g",python,selection_command +327,857217,"train_lamap.py",8260,2,"gr",python,selection_command +328,857404,"train_lamap.py",8260,3,"gra",python,selection_command +329,857464,"train_lamap.py",8260,4,"grai",python,selection_command +330,857529,"train_lamap.py",8260,5,"grain",python,selection_command +331,857918,"train_lamap.py",8295,6,"grain_",python,selection_command +332,858329,"train_lamap.py",8295,7,"grain_i",python,selection_command +333,858489,"train_lamap.py",8295,8,"grain_it",python,selection_command +334,858678,"train_lamap.py",8295,9,"grain_ite",python,selection_command +335,858824,"train_lamap.py",8295,10,"grain_iter",python,selection_command +336,859139,"train_lamap.py",8295,11,"grain_itera",python,selection_command +337,859321,"train_lamap.py",8295,12,"grain_iterat",python,selection_command +338,859439,"train_lamap.py",8295,13,"grain_iterato",python,selection_command +339,859565,"train_lamap.py",8295,14,"grain_iterator",python,selection_command +340,873837,"train_lamap.py",8295,14,"train_iterator",python,content +341,873843,"train_lamap.py",8391,14,"grain_iterator",python,selection_command +342,875242,"train_lamap.py",8391,14,"train_iterator",python,content +343,875247,"train_lamap.py",8679,14,"grain_iterator",python,selection_command +344,877233,"train_lamap.py",8679,14,"train_iterator",python,content +345,877237,"train_lamap.py",10305,14,"grain_iterator",python,selection_command +346,878275,"train_lamap.py",10305,14,"train_iterator",python,content +347,934854,"train_lamap.py",2423,0,"",python,selection_mouse +348,934879,"train_lamap.py",2422,0,"",python,selection_command +349,935850,"train_lamap.py",2424,0,"",python,selection_command +350,936054,"train_lamap.py",2432,0,"",python,selection_command +351,936683,"train_lamap.py",2448,0,"",python,selection_command +352,936684,"train_lamap.py",2479,0,"",python,selection_command +353,936744,"train_lamap.py",2543,0,"",python,selection_command +354,936819,"train_lamap.py",2598,0,"",python,selection_command +355,936982,"train_lamap.py",2645,0,"",python,selection_command +356,937223,"train_lamap.py",2598,0,"",python,selection_command +357,937382,"train_lamap.py",2543,0,"",python,selection_command +358,937543,"train_lamap.py",2598,0,"",python,selection_command +359,937716,"train_lamap.py",2645,0,"",python,selection_command +360,937871,"train_lamap.py",2654,0,"",python,selection_command +361,938001,"train_lamap.py",2655,0,"",python,selection_command +362,938365,"train_lamap.py",2654,0,"",python,selection_command +363,938639,"train_lamap.py",2654,0,"\nfrom utils.parameter_utils import count_parameters_by_component",python,content +364,938665,"train_lamap.py",2655,0,"",python,selection_command +365,940470,"train_lamap.py",2655,64,"",python,content +366,940519,"train_lamap.py",10305,1,"g",python,content +367,940534,"train_lamap.py",8679,1,"g",python,content +368,940547,"train_lamap.py",8391,1,"g",python,content +369,940561,"train_lamap.py",8295,1,"g",python,content +370,940574,"train_lamap.py",2422,0,"",python,selection_command +371,940721,"train_lamap.py",2423,0,"\n ",python,content +372,941733,"train_lamap.py",2424,4,"",python,content +373,941931,"train_lamap.py",2425,1,"",python,content +374,941984,"train_lamap.py",2422,0,"",python,selection_command +375,942342,"train_lamap.py",2424,0,"",python,selection_command +376,942484,"train_lamap.py",2432,0,"",python,selection_command +377,942645,"train_lamap.py",2448,0,"",python,selection_command +378,942822,"train_lamap.py",2479,0,"",python,selection_command +379,942924,"train_lamap.py",2543,0,"",python,selection_command +380,943091,"train_lamap.py",2598,0,"",python,selection_command +381,943228,"train_lamap.py",2645,0,"",python,selection_command +382,943443,"train_lamap.py",2654,0,"",python,selection_command +383,943659,"train_lamap.py",2654,0,"\n",python,content +384,951546,"train_lam.py",0,0,"",python,tab +385,951547,"train_lam.py",5738,0,"",python,selection_mouse +386,953014,"train_lam.py",5690,48," return val_loss, val_metrics, inputs, recon\n",python,selection_command +387,953517,"train_lam.py",5684,54," }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +388,953576,"train_lam.py",5638,100," for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +389,953726,"train_lam.py",5572,166," f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +390,953727,"train_lam.py",5552,186," val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +391,953881,"train_lam.py",5514,224," val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +392,953882,"train_lam.py",5513,225,"\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +393,953882,"train_lam.py",5374,364," print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +394,953882,"train_lam.py",5344,394," if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +395,954115,"train_lam.py",5343,395,"\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +396,954115,"train_lam.py",5325,413," break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +397,954116,"train_lam.py",5291,447," if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +398,954116,"train_lam.py",5273,465," step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +399,954116,"train_lam.py",5232,506," metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +400,954319,"train_lam.py",5197,541," loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +401,954320,"train_lam.py",5144,594," loss, recon, metrics = val_step(lam, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +402,954321,"train_lam.py",5107,631," inputs = dict(videos=videos)\n loss, recon, metrics = val_step(lam, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +403,954321,"train_lam.py",5073,665," for videos in val_dataloader:\n inputs = dict(videos=videos)\n loss, recon, metrics = val_step(lam, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +404,954321,"train_lam.py",5047,691," metrics_per_step = []\n for videos in val_dataloader:\n inputs = dict(videos=videos)\n loss, recon, metrics = val_step(lam, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +405,954322,"train_lam.py",5024,714," loss_per_step = []\n metrics_per_step = []\n for videos in val_dataloader:\n inputs = dict(videos=videos)\n loss, recon, metrics = val_step(lam, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +406,954322,"train_lam.py",5011,727," step = 0\n loss_per_step = []\n metrics_per_step = []\n for videos in val_dataloader:\n inputs = dict(videos=videos)\n loss, recon, metrics = val_step(lam, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +407,954322,"train_lam.py",4956,782,"def calculate_validation_metrics(val_dataloader, lam):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n for videos in val_dataloader:\n inputs = dict(videos=videos)\n loss, recon, metrics = val_step(lam, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +408,954432,"train_lam.py",4955,783,"\ndef calculate_validation_metrics(val_dataloader, lam):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n for videos in val_dataloader:\n inputs = dict(videos=videos)\n loss, recon, metrics = val_step(lam, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +409,954433,"train_lam.py",4923,815," return loss, recon, metrics\n\ndef calculate_validation_metrics(val_dataloader, lam):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n for videos in val_dataloader:\n inputs = dict(videos=videos)\n loss, recon, metrics = val_step(lam, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +410,954433,"train_lam.py",4848,890," (loss, (recon, _, metrics)) = lam_loss_fn(lam, inputs, training=False)\n return loss, recon, metrics\n\ndef calculate_validation_metrics(val_dataloader, lam):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n for videos in val_dataloader:\n inputs = dict(videos=videos)\n loss, recon, metrics = val_step(lam, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +411,954434,"train_lam.py",4833,905," lam.eval()\n (loss, (recon, _, metrics)) = lam_loss_fn(lam, inputs, training=False)\n return loss, recon, metrics\n\ndef calculate_validation_metrics(val_dataloader, lam):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n for videos in val_dataloader:\n inputs = dict(videos=videos)\n loss, recon, metrics = val_step(lam, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +412,954434,"train_lam.py",4744,994,"def val_step(lam: LatentActionModel, inputs: dict) -> tuple[jax.Array, jax.Array, dict]:\n lam.eval()\n (loss, (recon, _, metrics)) = lam_loss_fn(lam, inputs, training=False)\n return loss, recon, metrics\n\ndef calculate_validation_metrics(val_dataloader, lam):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n for videos in val_dataloader:\n inputs = dict(videos=videos)\n loss, recon, metrics = val_step(lam, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +413,954453,"train_lam.py",4735,1003,"@nnx.jit\ndef val_step(lam: LatentActionModel, inputs: dict) -> tuple[jax.Array, jax.Array, dict]:\n lam.eval()\n (loss, (recon, _, metrics)) = lam_loss_fn(lam, inputs, training=False)\n return loss, recon, metrics\n\ndef calculate_validation_metrics(val_dataloader, lam):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n for videos in val_dataloader:\n inputs = dict(videos=videos)\n loss, recon, metrics = val_step(lam, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +414,954542,"train_lam.py",4734,1004,"\n@nnx.jit\ndef val_step(lam: LatentActionModel, inputs: dict) -> tuple[jax.Array, jax.Array, dict]:\n lam.eval()\n (loss, (recon, _, metrics)) = lam_loss_fn(lam, inputs, training=False)\n return loss, recon, metrics\n\ndef calculate_validation_metrics(val_dataloader, lam):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n for videos in val_dataloader:\n inputs = dict(videos=videos)\n loss, recon, metrics = val_step(lam, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +415,954695,"train_lam.py",4682,1056," return loss, recon, action_last_active, metrics\n\n@nnx.jit\ndef val_step(lam: LatentActionModel, inputs: dict) -> tuple[jax.Array, jax.Array, dict]:\n lam.eval()\n (loss, (recon, _, metrics)) = lam_loss_fn(lam, inputs, training=False)\n return loss, recon, metrics\n\ndef calculate_validation_metrics(val_dataloader, lam):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n for videos in val_dataloader:\n inputs = dict(videos=videos)\n loss, recon, metrics = val_step(lam, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +416,954938,"train_lam.py",4734,1004,"\n@nnx.jit\ndef val_step(lam: LatentActionModel, inputs: dict) -> tuple[jax.Array, jax.Array, dict]:\n lam.eval()\n (loss, (recon, _, metrics)) = lam_loss_fn(lam, inputs, training=False)\n return loss, recon, metrics\n\ndef calculate_validation_metrics(val_dataloader, lam):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n for videos in val_dataloader:\n inputs = dict(videos=videos)\n loss, recon, metrics = val_step(lam, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +417,955090,"train_lam.py",4735,1003,"@nnx.jit\ndef val_step(lam: LatentActionModel, inputs: dict) -> tuple[jax.Array, jax.Array, dict]:\n lam.eval()\n (loss, (recon, _, metrics)) = lam_loss_fn(lam, inputs, training=False)\n return loss, recon, metrics\n\ndef calculate_validation_metrics(val_dataloader, lam):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n for videos in val_dataloader:\n inputs = dict(videos=videos)\n loss, recon, metrics = val_step(lam, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +418,955317,"train_lam.py",4744,994,"def val_step(lam: LatentActionModel, inputs: dict) -> tuple[jax.Array, jax.Array, dict]:\n lam.eval()\n (loss, (recon, _, metrics)) = lam_loss_fn(lam, inputs, training=False)\n return loss, recon, metrics\n\ndef calculate_validation_metrics(val_dataloader, lam):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n for videos in val_dataloader:\n inputs = dict(videos=videos)\n loss, recon, metrics = val_step(lam, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +419,955613,"train_lam.py",4735,1003,"@nnx.jit\ndef val_step(lam: LatentActionModel, inputs: dict) -> tuple[jax.Array, jax.Array, dict]:\n lam.eval()\n (loss, (recon, _, metrics)) = lam_loss_fn(lam, inputs, training=False)\n return loss, recon, metrics\n\ndef calculate_validation_metrics(val_dataloader, lam):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n for videos in val_dataloader:\n inputs = dict(videos=videos)\n loss, recon, metrics = val_step(lam, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,selection_command +420,955850,"train_lam.py",4735,0,"",python,selection_command +421,958395,"train_lamap.py",0,0,"",python,tab +422,960422,"train_lamap.py",2655,0,"\n@nnx.jit\ndef val_step(lam: LatentActionModel, inputs: dict) -> tuple[jax.Array, jax.Array, dict]:\n lam.eval()\n (loss, (recon, _, metrics)) = lam_loss_fn(lam, inputs, training=False)\n return loss, recon, metrics\n\ndef calculate_validation_metrics(val_dataloader, lam):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n for videos in val_dataloader:\n inputs = dict(videos=videos)\n loss, recon, metrics = val_step(lam, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\n",python,content +423,960533,"train_lamap.py",2656,0,"",python,selection_command +424,961101,"train_lamap.py",2655,0,"",python,selection_command +425,961540,"train_lamap.py",2655,1,"",python,content +426,961894,"train_lamap.py",2664,0,"",python,selection_command +427,962124,"train_lamap.py",2753,0,"",python,selection_command +428,963116,"train_lamap.py",2768,0,"",python,selection_command +429,963309,"train_lamap.py",2753,0,"",python,selection_command +430,963466,"train_lamap.py",2664,0,"",python,selection_command +431,963629,"train_lamap.py",2655,0,"",python,selection_command +432,963736,"train_lamap.py",2654,0,"",python,selection_command +433,963884,"train_lamap.py",2631,0,"",python,selection_command 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+770,1286022,"train_lamap.py",4213,0,"",python,selection_command +771,1286188,"train_lamap.py",4244,0,"",python,selection_command +772,1286371,"train_lamap.py",4282,0,"",python,selection_command +773,1286563,"train_lamap.py",4308,0,"",python,selection_command +774,1286670,"train_lamap.py",4339,0,"",python,selection_command +775,1287842,"train_lamap.py",4371,0,"",python,selection_command +776,1288146,"train_lamap.py",4339,0,"",python,selection_command +777,1288641,"train_lamap.py",4371,0,"",python,selection_command +778,1290759,"train_lamap.py",4407,0,"",python,selection_command +779,1292907,"train_lamap.py",4441,0,"",python,selection_command +780,1300567,"train_lamap.py",4359,0,"",python,selection_mouse +781,1301038,"models/lamap.py",0,0,"from typing import Dict, Any\n\nimport jax\nimport jax.numpy as jnp\nimport flax.linen as nn\n\nfrom models.lam import LatentActionModel\n\n\nclass LatentActionMapper(nn.Module):\n """"""Latent Action Mapper""""""\n\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n # --- LAM ---\n in_dim: int\n model_dim: int\n latent_dim: int\n num_latents: int\n patch_size: int\n num_blocks: int\n num_heads: int\n dropout: float\n codebook_dropout: float\n use_flash_attention: bool\n\n # --- Mapper ---\n action_dim: int\n\n def setup(self):\n self.lam = LatentActionModel(\n in_dim=self.in_dim,\n model_dim=self.lam_dim,\n latent_dim=self.latent_dim,\n num_latents=self.num_latents,\n patch_size=self.lam_patch_size,\n num_blocks=self.lam_num_blocks,\n num_heads=self.lam_num_heads,\n dropout=self.lam_dropout,\n codebook_dropout=self.lam_codebook_dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n )\n self.action_map = nn.Dense(\n self.action_dim,\n use_bias=False,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )\n\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\n lam_outputs = self.lam.vq_encode(batch[""videos""], training=False)\n latent_actions = jax.lax.stop_gradient(lam_outputs[""z_q""])\n outputs = dict(\n action_predictions=self.action_map(latent_actions),\n )\n return outputs",python,tab +782,1301040,"models/lamap.py",139,0,"",python,selection_command +783,1309774,"models/lamap.py",1626,0,"",python,selection_mouse +784,1309796,"models/lamap.py",1625,0,"",python,selection_command +785,1309918,"models/lamap.py",1625,1,"s",python,selection_mouse +786,1309934,"models/lamap.py",1626,0,"",python,selection_command +787,1310053,"models/lamap.py",1270,356," )\n\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\n lam_outputs = self.lam.vq_encode(batch[""videos""], training=False)\n latent_actions = jax.lax.stop_gradient(lam_outputs[""z_q""])\n outputs = dict(\n action_predictions=self.action_map(latent_actions),\n )\n return outputs",python,selection_mouse +788,1310054,"models/lamap.py",524,1102," def setup(self):\n self.lam = LatentActionModel(\n in_dim=self.in_dim,\n model_dim=self.lam_dim,\n latent_dim=self.latent_dim,\n num_latents=self.num_latents,\n patch_size=self.lam_patch_size,\n num_blocks=self.lam_num_blocks,\n num_heads=self.lam_num_heads,\n dropout=self.lam_dropout,\n codebook_dropout=self.lam_codebook_dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n )\n self.action_map = nn.Dense(\n self.action_dim,\n use_bias=False,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )\n\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\n lam_outputs = self.lam.vq_encode(batch[""videos""], training=False)\n latent_actions = jax.lax.stop_gradient(lam_outputs[""z_q""])\n outputs = dict(\n action_predictions=self.action_map(latent_actions),\n )\n return outputs",python,selection_mouse +789,1310136,"models/lamap.py",304,1322," latent_dim: int\n num_latents: int\n patch_size: int\n num_blocks: int\n num_heads: int\n dropout: float\n codebook_dropout: float\n use_flash_attention: bool\n\n # --- Mapper ---\n action_dim: int\n\n def setup(self):\n self.lam = LatentActionModel(\n in_dim=self.in_dim,\n model_dim=self.lam_dim,\n latent_dim=self.latent_dim,\n num_latents=self.num_latents,\n patch_size=self.lam_patch_size,\n num_blocks=self.lam_num_blocks,\n num_heads=self.lam_num_heads,\n dropout=self.lam_dropout,\n codebook_dropout=self.lam_codebook_dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n )\n self.action_map = nn.Dense(\n self.action_dim,\n use_bias=False,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )\n\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\n lam_outputs = self.lam.vq_encode(batch[""videos""], training=False)\n latent_actions = jax.lax.stop_gradient(lam_outputs[""z_q""])\n outputs = dict(\n action_predictions=self.action_map(latent_actions),\n )\n return outputs",python,selection_mouse +790,1310243,"models/lamap.py",0,1626,"from typing import Dict, Any\n\nimport jax\nimport jax.numpy as jnp\nimport flax.linen as nn\n\nfrom models.lam import LatentActionModel\n\n\nclass LatentActionMapper(nn.Module):\n """"""Latent Action Mapper""""""\n\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n # --- LAM ---\n in_dim: int\n model_dim: int\n latent_dim: int\n num_latents: int\n patch_size: int\n num_blocks: int\n num_heads: int\n dropout: float\n codebook_dropout: float\n use_flash_attention: bool\n\n # --- Mapper ---\n action_dim: int\n\n def setup(self):\n self.lam = LatentActionModel(\n in_dim=self.in_dim,\n model_dim=self.lam_dim,\n latent_dim=self.latent_dim,\n num_latents=self.num_latents,\n patch_size=self.lam_patch_size,\n num_blocks=self.lam_num_blocks,\n num_heads=self.lam_num_heads,\n dropout=self.lam_dropout,\n codebook_dropout=self.lam_codebook_dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n use_flash_attention=self.use_flash_attention,\n )\n self.action_map = nn.Dense(\n self.action_dim,\n use_bias=False,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )\n\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\n lam_outputs = self.lam.vq_encode(batch[""videos""], training=False)\n latent_actions = jax.lax.stop_gradient(lam_outputs[""z_q""])\n outputs = dict(\n action_predictions=self.action_map(latent_actions),\n )\n return outputs",python,selection_mouse +791,1311660,"models/lamap.py",0,0,"",python,selection_command +792,1333365,"models/lamap.py",65,0,"import flax.nnx as nnx\n",python,content +793,1333367,"models/lamap.py",88,24,"",python,content +794,1334177,"models/lamap.py",132,0,"class LatentActionMapper(nnx.Module):\n",python,content +795,1334681,"models/lamap.py",170,37,"",python,content +796,1339268,"models/lamap.py",524,0," def __init__(self, rngs: nnx.Rngs):\n",python,content +797,1339895,"models/lamap.py",564,0," self.rngs = rngs\n",python,content +798,1340170,"models/lamap.py",589,21,"",python,content +799,1340395,"models/lamap.py",659,0," model_dim=self.model_dim,\n",python,content +800,1341077,"models/lamap.py",697,0," ffn_dim=self.model_dim * 4, # Assuming ffn_dim is 4 times model_dim\n",python,content +801,1341265,"models/lamap.py",778,36,"",python,content +802,1341606,"models/lamap.py",860,0," patch_size=self.patch_size,\n",python,content +803,1341801,"models/lamap.py",900,0," num_blocks=self.num_blocks,\n",python,content +804,1341996,"models/lamap.py",940,0," num_heads=self.num_heads,\n",python,content +805,1342161,"models/lamap.py",978,0," dropout=self.dropout,\n",python,content +806,1342653,"models/lamap.py",1012,0," codebook_dropout=self.codebook_dropout,\n",python,content +807,1343066,"models/lamap.py",1064,0," param_dtype=self.param_dtype,\n",python,content +808,1343524,"models/lamap.py",1106,0," dtype=self.dtype,\n",python,content +809,1343914,"models/lamap.py",1136,296,"",python,content +810,1344227,"models/lamap.py",1194,0," rngs=self.rngs,\n",python,content +811,1344430,"models/lamap.py",1232,0," self.action_map = nnx.Linear(\n",python,content +812,1344623,"models/lamap.py",1270,0," self.latent_dim,\n",python,content +813,1344765,"models/lamap.py",1299,36,"",python,content +814,1345973,"models/lamap.py",1428,0," rngs=self.rngs,\n",python,content +815,1346519,"models/lamap.py",1456,13," )\n )",python,content +816,1346520,"models/lamap.py",1466,14,"",python,content +817,1347905,"models/lamap.py",1555,0," lam_outputs = self.lam.vq_encode(batch[""videos""], training=training)\n",python,content +818,1348400,"models/lamap.py",1632,74,"",python,content +819,1348581,"models/lamap.py",1699,0," action_predictions = self.action_map(latent_actions)\n",python,content +820,1349663,"models/lamap.py",1784,0," action_predictions=action_predictions,\n",python,content +821,1350050,"models/lamap.py",1835,64,"",python,content +822,1382181,"models/lamap.py",1624,0,"",python,selection_mouse +823,1382399,"models/lamap.py",1622,8,"training",python,selection_mouse +824,1384326,"models/lamap.py",1622,8,"",python,content +825,1384804,"models/lamap.py",1622,0,"F",python,content +826,1384805,"models/lamap.py",1623,0,"",python,selection_keyboard +827,1385010,"models/lamap.py",1623,0,"a",python,content +828,1385012,"models/lamap.py",1624,0,"",python,selection_keyboard +829,1385069,"models/lamap.py",1624,0,"l",python,content +830,1385070,"models/lamap.py",1625,0,"",python,selection_keyboard +831,1385158,"models/lamap.py",1625,0,"s",python,content +832,1385159,"models/lamap.py",1626,0,"",python,selection_keyboard +833,1385340,"models/lamap.py",1626,0,"e",python,content +834,1385342,"models/lamap.py",1627,0,"",python,selection_keyboard +835,1385864,"models/lamap.py",1626,0,"",python,selection_command +836,1401133,"models/lamap.py",315,0,"",python,selection_mouse +837,1403452,"models/lamap.py",323,0,"\n latent_dim: int",python,content +838,1403478,"models/lamap.py",328,0,"",python,selection_command +839,1403845,"models/lamap.py",329,0,"",python,selection_command +840,1404360,"models/lamap.py",328,0,"",python,selection_command +841,1404645,"models/lamap.py",328,1,"",python,content +842,1404813,"models/lamap.py",328,1,"",python,content +843,1405016,"models/lamap.py",328,1,"",python,content +844,1405164,"models/lamap.py",328,1,"",python,content +845,1405360,"models/lamap.py",328,1,"",python,content +846,1405699,"models/lamap.py",328,1,"",python,content +847,1406156,"models/lamap.py",328,0,"f",python,content +848,1406157,"models/lamap.py",329,0,"",python,selection_keyboard +849,1406300,"models/lamap.py",329,0,"f",python,content +850,1406302,"models/lamap.py",330,0,"",python,selection_keyboard +851,1406368,"models/lamap.py",330,0,"n",python,content +852,1406369,"models/lamap.py",331,0,"",python,selection_keyboard +853,1406850,"models/lamap.py",330,0,"",python,selection_command +854,1407035,"models/lamap.py",347,0,"",python,selection_command +855,1407544,"models/lamap.py",368,0,"",python,selection_command +856,1407611,"models/lamap.py",388,0,"",python,selection_command +857,1407660,"models/lamap.py",408,0,"",python,selection_command +858,1407705,"models/lamap.py",427,0,"",python,selection_command +859,1407706,"models/lamap.py",446,0,"",python,selection_command +860,1407732,"models/lamap.py",474,0,"",python,selection_command +861,1407804,"models/lamap.py",498,0,"",python,selection_command +862,1407864,"models/lamap.py",505,0,"",python,selection_command +863,1407865,"models/lamap.py",526,0,"",python,selection_command +864,1407986,"models/lamap.py",540,0,"",python,selection_command +865,1407987,"models/lamap.py",547,0,"",python,selection_command +866,1407987,"models/lamap.py",587,0,"",python,selection_command +867,1408046,"models/lamap.py",612,0,"",python,selection_command +868,1408047,"models/lamap.py",650,0,"",python,selection_command +869,1408048,"models/lamap.py",682,0,"",python,selection_command +870,1408161,"models/lamap.py",720,0,"",python,selection_command +871,1408486,"models/lamap.py",721,0,"",python,selection_command +872,1408996,"models/lamap.py",722,0,"",python,selection_command +873,1409071,"models/lamap.py",723,0,"",python,selection_command +874,1409084,"models/lamap.py",724,0,"",python,selection_command +875,1409122,"models/lamap.py",725,0,"",python,selection_command +876,1409147,"models/lamap.py",726,0,"",python,selection_command +877,1409187,"models/lamap.py",727,0,"",python,selection_command +878,1409300,"models/lamap.py",728,0,"",python,selection_command +879,1409325,"models/lamap.py",729,0,"",python,selection_command +880,1409326,"models/lamap.py",730,0,"",python,selection_command +881,1409326,"models/lamap.py",731,0,"",python,selection_command +882,1409452,"models/lamap.py",732,0,"",python,selection_command +883,1409453,"models/lamap.py",733,0,"",python,selection_command +884,1409453,"models/lamap.py",734,0,"",python,selection_command 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+901,1414057,"models/lamap.py",740,0,"f",python,content +902,1414058,"models/lamap.py",741,0,"",python,selection_keyboard +903,1414538,"models/lamap.py",739,2,"ffn_dim",python,content +904,1414936,"models/lamap.py",745,0,"",python,selection_command +905,1415124,"models/lamap.py",746,0,"",python,selection_command +906,1415269,"models/lamap.py",747,0,"",python,selection_command +907,1415412,"models/lamap.py",748,0,"",python,selection_command +908,1415579,"models/lamap.py",749,0,"",python,selection_command +909,1416294,"models/lamap.py",749,39,"",python,content +910,1416329,"models/lamap.py",748,0,"",python,selection_command +911,1417720,"models/lamap.py",746,2,"",python,content +912,1418819,"models/lamap.py",747,0,"",python,selection_command +913,1418904,"models/lamap.py",747,0,",",python,content +914,1418905,"models/lamap.py",748,0,"",python,selection_keyboard +915,1419547,"models/lamap.py",747,1,"",python,content +916,1419694,"models/lamap.py",746,1,"",python,content +917,1419924,"models/lamap.py",746,0,",",python,content +918,1419925,"models/lamap.py",747,0,"",python,selection_keyboard +919,1420085,"models/lamap.py",746,0,"",python,selection_command +920,1425534,"models/lamap.py",332,0,"",python,selection_mouse +921,1427049,"models/lamap.py",324,17,"",python,content +922,1427072,"models/lamap.py",328,0,"",python,selection_command +923,1427072,"models/lamap.py",308,0,"",python,selection_command +924,1427307,"models/lamap.py",289,0,"",python,selection_command +925,1427552,"models/lamap.py",303,0,"\n ffn_dim: int",python,content +926,1427570,"models/lamap.py",308,0,"",python,selection_command +927,1428142,"models/lamap.py",325,0,"",python,selection_command +928,1441172,"train_lamap.py",0,0,"",python,tab +929,1443051,"train_lamap.py",0,0,"",python,selection_command +930,1443506,"train_lamap.py",0,40,"from dataclasses import dataclass, field",python,selection_command +931,1443906,"train_lamap.py",0,11655,"from dataclasses import dataclass, field\nimport os\nfrom typing import cast\n\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\nimport flax.nnx as nnx\n\nfrom models.lamap import LatentActionMapper\nfrom utils.dataloader import create_dataloader_iterator\nfrom utils.lr_utils import get_lr_schedule\nfrom utils.parameter_utils import count_parameters_by_component\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n batch_size: int = 36\n init_lr: float = 0.0\n max_lr: float = 3e-5\n decay_end: float = 0.0\n wsd_decay_steps: int = 10000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n warmup_steps: int = 5000\n lr_schedule : str = ""wsd"" # supported options: wsd, cos\n # LAM\n model_dim: int = 512\n latent_dim: int = 32\n num_latents: int = 6\n patch_size: int = 16\n num_blocks: int = 8\n num_heads: int = 8\n dropout: float = 0.0\n codebook_dropout: float = 0.0\n param_dtype: jnp.dtype = jnp.float32\n dtype: jnp.dtype = jnp.bfloat16\n # LAMAP\n # FIXME (f.srambical): this assumes that the number of actions is the same as the number of latent actions\n action_dim: int = 32\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n name: str = ""train_lamap""\n tags: list[str] = field(default_factory=lambda: [""lamap""])\n log_interval: int = 5\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 10000\n log_checkpoint_keep_period: int = 20000\n val_data_dir: str = """"\n val_interval: int = 20_000\n val_steps: int = 50\n wandb_id: str = """"\n use_flash_attention: bool = True\n\n\nargs = tyro.cli(Args)\n\ndef lamap_loss_fn(params, state, inputs):\n # --- Compute loss ---\n inputs[""videos""] = inputs[""videos""].astype(args.dtype) / 255.0\n outputs = state.apply_fn(\n params, inputs, training=True, rngs={""dropout"": inputs[""rng""]}\n )\n loss = optax.softmax_cross_entropy(outputs[""action_predictions""], inputs[""actions""]).mean()\n return loss\n\n@nnx.jit\ndef train_step(state, inputs):\n lamap.train()\n grad_fn = jax.value_and_grad(lamap_loss_fn, allow_int=True)\n loss, grads = grad_fn(state.params, state, inputs)\n state = state.apply_gradients(grads=grads)\n return state, loss\n\n@nnx.jit\ndef val_step(lamap: LatentActionMapper, inputs: dict) -> tuple[jax.Array, jax.Array, dict]:\n lamap.eval()\n (loss, (recon, _, metrics)) = lamap_loss_fn(lamap, inputs, allow_int=False)\n return loss, recon, metrics\n\ndef calculate_validation_metrics(val_dataloader, lam):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n for videos in val_dataloader:\n inputs = dict(videos=videos)\n loss, recon, metrics = val_step(lam, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\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.key(args.seed)\n\n # --- Initialize model ---\n rng, _rng = jax.random.split(rng)\n rngs = nnx.Rngs(_rng)\n # --- Initialize model ---\n lamap = LatentActionMapper(\n in_dim=args.image_channels,\n model_dim=args.model_dim,\n latent_dim=args.latent_dim,\n action_dim=args.action_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 param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n )\n\n image_shape = (args.image_height, args.image_width, args.image_channels)\n rng, _rng = jax.random.split(rng)\n inputs = dict(\n videos=jnp.zeros(\n (per_device_batch_size_for_init, args.seq_len, *image_shape),\n dtype=args.dtype,\n ),\n rng=_rng,\n )\n rng, _rng = jax.random.split(rng)\n init_params = lamap.init(_rng, inputs)\n\n param_counts = count_parameters_by_component(init_params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\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 if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n lr_schedule = get_lr_schedule(args.lr_schedule, \n args.init_lr, \n args.max_lr, \n args.decay_end, \n args.num_steps, \n args.warmup_steps, \n args.wsd_decay_steps)\n tx = optax.adamw(learning_rate=lr_schedule, b1=0.9, b2=0.9, weight_decay=1e-4, mu_dtype=args.dtype)\n train_state = TrainState.create(apply_fn=lamap.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 videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n train_state = jax.device_put(train_state, replicated_sharding)\n\n # --- Initialize checkpoint manager ---\n step = 0\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n if args.val_data_dir:\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n\n # --- Create DataLoaderIterator from dataloader ---\n image_shape = (args.image_height, args.image_width, args.image_channels)\n train_iterator = create_dataloader_iterator(args.data_dir, image_shape, args.seq_len, args.batch_size, args.seed)\n if args.val_data_dir:\n val_iterator = create_dataloader_iterator(args.val_data_dir, image_shape, args.seq_len, args.batch_size, args.seed)\n\n # --- Restore checkpoint ---\n if args.restore_ckpt:\n abstract_train_state = jax.tree_util.tree_map(\n ocp.utils.to_shape_dtype_struct, train_state\n )\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.StandardRestore(abstract_train_state),\n dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator),\n ),\n )\n train_state = restored[""model_state""]\n grain_iterator = restored[""dataloader_state""]\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored dataloader and model state from step {step}"")\n\n # --- TRAIN LOOP ---\n dataloader = (jax.make_array_from_process_local_data(videos_sharding, elem) for elem in grain_iterator) # type: ignore\n print(f""Starting training from step {step}..."")\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n rng, _rng = jax.random.split(rng)\n\n # FIXME (f.srambical): use mock actions for now\n rng, _rng_actions = jax.random.split(rng)\n actions = jax.random.uniform(\n _rng_actions,\n (per_device_batch_size_for_init, args.action_dim),\n dtype=args.dtype,\n )\n\n inputs = dict(\n videos=videos,\n actions=actions,\n rng=_rng\n )\n train_state, loss = train_step(\n train_state, inputs\n )\n lr = lr_schedule(step)\n print(f""Step {step}, loss: {loss}"")\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 ""lr"": lr,\n }\n )\n # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n checkpoint_manager.save(\n step,\n args=ocp.args.Composite(\n model_state=ocp.args.StandardSave(train_state),\n dataloader_state=grain.checkpoint.CheckpointSave(\n grain_iterator\n ),\n ),\n )\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n checkpoint_manager.close()\n",python,selection_command +932,1444836,"train_lamap.py",0,0,"",python,selection_command +933,1457182,"train_lamap.py",41,10,"",python,content +934,1457383,"train_lamap.py",66,0,"import jax\n",python,content +935,1457618,"train_lamap.py",77,0,"import jax.numpy as jnp\n",python,content +936,1457619,"train_lamap.py",101,0,"import numpy as np\n",python,content +937,1457835,"train_lamap.py",120,119,"",python,content +938,1457836,"train_lamap.py",133,85,"",python,content +939,1458770,"train_lamap.py",194,0,"from jax.sharding import Mesh, PartitionSpec, NamedSharding\n",python,content +940,1459340,"train_lamap.py",254,0,"from jax.experimental.mesh_utils import create_device_mesh\n",python,content +941,1459952,"train_lamap.py",313,0,"import orbax.checkpoint as ocp\n",python,content +942,1471080,"train_lamap.py",948,0," wsd_decay_steps: int = 10000\n",python,content +943,1471734,"train_lamap.py",981,101,"",python,content +944,1471945,"train_lamap.py",1010,0," lr_schedule: str = ""wsd""\n",python,content +945,1472119,"train_lamap.py",1039,60,"",python,content +946,1476147,"train_lamap.py",1344,111,"",python,content +947,1481530,"train_lamap.py",1842,0,"\n",python,content +948,1482333,"train_lamap.py",1843,0,"def lamap_loss_fn(model: LatentActionMapper, inputs: dict, training: bool = True) -> jax.Array:\n",python,content +949,1483011,"train_lamap.py",1939,69,"",python,content +950,1483273,"train_lamap.py",2006,0," outputs = model(inputs, training=training)\n",python,content +951,1484071,"train_lamap.py",2053,0," loss = optax.softmax_cross_entropy(outputs[""action_predictions""], inputs[""actions""]).mean()\n",python,content +952,1484333,"train_lamap.py",2149,203,"",python,content +953,1484334,"train_lamap.py",2166,0,"\n",python,content +954,1486592,"train_lamap.py",2207,18,"",python,content +955,1490248,"train_lamap.py",2397,0,"\n",python,content +956,1492184,"train_lamap.py",2499,17,"",python,content +957,1492643,"train_lamap.py",2612,0,"\n",python,content +958,1510609,"train_lam.py",0,0,"",python,tab +959,1515833,"train_lamap.py",0,0,"",python,tab +960,1519131,"train_lamap.py",4538,0," rngs=rngs,\n",python,content +961,1529447,"train_lamap.py",5679,0," lr_schedule = get_lr_schedule(\n",python,content +962,1529689,"train_lamap.py",5714,0," args.lr_schedule,\n",python,content +963,1529972,"train_lamap.py",5740,0," args.init_lr,\n",python,content +964,1530111,"train_lamap.py",5762,0," args.max_lr,\n",python,content +965,1530340,"train_lamap.py",5783,0," args.decay_end,\n",python,content +966,1530585,"train_lamap.py",5807,0," args.num_steps,\n",python,content +967,1535469,"train_lamap.py",5831,0," args.warmup_steps,\n",python,content +968,1535731,"train_lamap.py",5858,0," args.wsd_decay_steps,\n",python,content +969,1535985,"train_lamap.py",5888,0," )\n",python,content +970,1535986,"train_lamap.py",5894,0," tx = optax.adamw(\n",python,content +971,1536961,"train_lamap.py",5916,0," learning_rate=lr_schedule, b1=0.9, b2=0.9, weight_decay=1e-4, mu_dtype=args.dtype\n",python,content +972,1536962,"train_lamap.py",6006,0," )\n",python,content +973,1536963,"train_lamap.py",6012,0," train_state = nnx.TrainState.create(apply_fn=lamap.apply, params=init_params, tx=tx)\n",python,content +974,1536964,"train_lamap.py",6101,0,"\n",python,content +975,1536965,"train_lamap.py",6102,0," # FIXME: switch to create_hybrid_device_mesh for runs spanning multiple nodes\n",python,content +976,1536966,"train_lamap.py",6184,0," device_mesh_arr = create_device_mesh((num_devices,))\n",python,content +977,1537349,"train_lamap.py",6102,691,"",python,content +978,1553749,"train_lamap.py",8266,0," train_iterator = create_dataloader_iterator(\n",python,content +979,1554401,"train_lamap.py",8315,0," args.data_dir, image_shape, args.seq_len, args.batch_size, args.seed\n",python,content +980,1554559,"train_lamap.py",8392,0," )\n",python,content +981,1554875,"train_lamap.py",8398,118,"",python,content +982,1555121,"train_lamap.py",8424,0," val_iterator = create_dataloader_iterator(\n",python,content +983,1555861,"train_lamap.py",8475,0," args.val_data_dir, image_shape, args.seq_len, args.batch_size, args.seed\n",python,content +984,1556121,"train_lamap.py",8560,0," )\n",python,content +985,1556122,"train_lamap.py",8570,124,"",python,content +986,1558122,"train_lamap.py",8424,145," val_iterator = create_dataloader_iterator(args.val_data_dir, image_shape, args.seq_len, args.batch_size, args.seed)",python,content +987,1558123,"train_lamap.py",8266,131," train_iterator = create_dataloader_iterator(args.data_dir, image_shape, args.seq_len, args.batch_size, args.seed)",python,content +988,1558123,"train_lamap.py",5679,421," lr_schedule = get_lr_schedule(args.lr_schedule, \n args.init_lr, \n args.max_lr, \n args.decay_end, \n args.num_steps, \n args.warmup_steps, \n args.wsd_decay_steps)\n tx = optax.adamw(learning_rate=lr_schedule, b1=0.9, b2=0.9, weight_decay=1e-4, mu_dtype=args.dtype)\n train_state = TrainState.create(apply_fn=lamap.apply, params=init_params, tx=tx)",python,content +989,1558123,"train_lamap.py",4538,19,"",python,content +990,1558123,"train_lamap.py",4024,0," # --- Initialize model ---\n",python,content +991,1558123,"train_lamap.py",3888,39," rng = jax.random.key(args.seed)",python,content +992,1558123,"train_lamap.py",2611,1,"",python,content +993,1558123,"train_lamap.py",2499,0," lamap.eval()\n",python,content +994,1558123,"train_lamap.py",2396,1,"",python,content +995,1558123,"train_lamap.py",2207,0," lamap.train()\n",python,content +996,1558126,"train_lamap.py",2165,1,"",python,content +997,1558126,"train_lamap.py",2006,46," outputs = state.apply_fn(\n params, inputs, training=True, rngs={""dropout"": inputs[""rng""]}\n )",python,content +998,1558126,"train_lamap.py",1843,95,"def lamap_loss_fn(params, state, inputs):\n # --- Compute loss ---",python,content +999,1558126,"train_lamap.py",1841,1,"",python,content +1000,1558126,"train_lamap.py",1344,0," # FIXME (f.srambical): this assumes that the number of actions is the same as the number of latent actions\n",python,content +1001,1558126,"train_lamap.py",1010,28," lr_schedule : str = ""wsd"" # supported options: wsd, cos",python,content +1002,1558126,"train_lamap.py",948,32," wsd_decay_steps: int = 10000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule",python,content +1003,1558126,"train_lamap.py",194,150,"",python,content +1004,1558126,"train_lamap.py",101,32,"",python,content +1005,1558126,"train_lamap.py",66,0,"from jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\n",python,content +1006,1558127,"train_lamap.py",41,0,"import os\n",python,content +1007,1559389,"train_lamap.py",50,0,"",python,selection_mouse +1008,1559441,"train_lamap.py",49,0,"",python,selection_command +1009,1560142,"train_lamap.py",41,10,"",python,content +1010,1560294,"train_lamap.py",65,0,"",python,selection_command +1011,1560461,"train_lamap.py",66,0,"",python,selection_command +1012,1560992,"train_lamap.py",126,0,"",python,selection_command +1013,1561070,"train_lamap.py",185,0,"",python,selection_command +1014,1561126,"train_lamap.py",198,0,"",python,selection_command +1015,1561127,"train_lamap.py",229,0,"",python,selection_command +1016,1561292,"train_lamap.py",248,0,"",python,selection_command +1017,1561293,"train_lamap.py",259,0,"",python,selection_command +1018,1561294,"train_lamap.py",283,0,"",python,selection_command +1019,1561498,"train_lamap.py",295,0,"",python,selection_command +1020,1561498,"train_lamap.py",308,0,"",python,selection_command +1021,1561499,"train_lamap.py",321,0,"",python,selection_command +1022,1561499,"train_lamap.py",344,0,"",python,selection_command +1023,1561499,"train_lamap.py",345,0,"",python,selection_command +1024,1561677,"train_lamap.py",389,0,"",python,selection_command +1025,1561678,"train_lamap.py",445,0,"",python,selection_command +1026,1561678,"train_lamap.py",488,0,"",python,selection_command +1027,1561678,"train_lamap.py",552,0,"",python,selection_command +1028,1561679,"train_lamap.py",553,0,"",python,selection_command +1029,1561679,"train_lamap.py",554,0,"",python,selection_command +1030,1561845,"train_lamap.py",565,0,"",python,selection_command +1031,1561846,"train_lamap.py",577,0,"",python,selection_command +1032,1561846,"train_lamap.py",594,0,"",python,selection_command +1033,1561846,"train_lamap.py",623,0,"",python,selection_command +1034,1561847,"train_lamap.py",641,0,"",python,selection_command +1035,1561847,"train_lamap.py",663,0,"",python,selection_command +1036,1561893,"train_lamap.py",691,0,"",python,selection_command +1037,1561894,"train_lamap.py",718,0,"",python,selection_command +1038,1561895,"train_lamap.py",745,0,"",python,selection_command +1039,1561895,"train_lamap.py",768,0,"",python,selection_command +1040,1561895,"train_lamap.py",796,0,"",python,selection_command +1041,1561938,"train_lamap.py",827,0,"",python,selection_command +1042,1562024,"train_lamap.py",846,0,"",python,selection_command +1043,1562233,"train_lamap.py",871,0,"",python,selection_command +1044,1562358,"train_lamap.py",896,0,"",python,selection_command +1045,1562546,"train_lamap.py",921,0,"",python,selection_command +1046,1563031,"train_lamap.py",948,0,"",python,selection_command +1047,1563088,"train_lamap.py",1049,0,"",python,selection_command +1048,1563110,"train_lamap.py",1078,0,"",python,selection_command +1049,1563144,"train_lamap.py",1138,0,"",python,selection_command +1050,1563199,"train_lamap.py",1148,0,"",python,selection_command +1051,1563200,"train_lamap.py",1173,0,"",python,selection_command +1052,1563345,"train_lamap.py",1198,0,"",python,selection_command +1053,1563591,"train_lamap.py",1223,0,"",python,selection_command +1054,1563817,"train_lamap.py",1248,0,"",python,selection_command +1055,1564080,"train_lamap.py",1272,0,"",python,selection_command +1056,1578266,"train_lamap.py",1210,0,"",python,selection_mouse +1057,1579963,"train_lam.py",0,0,"",python,tab +1058,1580474,"train_lam.py",1339,0,"",python,selection_mouse +1059,1580947,"train_lam.py",1293,0,"",python,selection_mouse +1060,1582966,"train_lamap.py",0,0,"",python,tab +1061,1582967,"train_lamap.py",1155,0,"",python,selection_mouse +1062,1583344,"train_lamap.py",1172,0,"\n ffn_dim: int = 2048",python,content +1063,1583413,"train_lamap.py",1177,0,"",python,selection_command +1064,1584223,"train_lamap.py",1201,0,"",python,selection_command +1065,1584768,"train_lamap.py",1226,0,"",python,selection_command +1066,1584894,"train_lamap.py",1251,0,"",python,selection_command +1067,1584895,"train_lamap.py",1276,0,"",python,selection_command +1068,1584895,"train_lamap.py",1300,0,"",python,selection_command +1069,1585094,"train_lamap.py",1323,0,"",python,selection_command +1070,1585095,"train_lamap.py",1348,0,"",python,selection_command +1071,1585245,"train_lamap.py",1382,0,"",python,selection_command +1072,1585245,"train_lamap.py",1423,0,"",python,selection_command +1073,1585246,"train_lamap.py",1459,0,"",python,selection_command +1074,1585246,"train_lamap.py",1471,0,"",python,selection_command +1075,1585246,"train_lamap.py",1582,0,"",python,selection_command +1076,1585246,"train_lamap.py",1607,0,"",python,selection_command +1077,1585247,"train_lamap.py",1621,0,"",python,selection_command +1078,1585285,"train_lamap.py",1643,0,"",python,selection_command +1079,1585286,"train_lamap.py",1664,0,"",python,selection_command +1080,1585286,"train_lamap.py",1686,0,"",python,selection_command +1081,1585286,"train_lamap.py",1716,0,"",python,selection_command +1082,1585364,"train_lamap.py",1779,0,"",python,selection_command +1083,1585546,"train_lamap.py",1805,0,"",python,selection_command +1084,1586469,"train_lamap.py",1828,0,"",python,selection_command +1085,1586974,"train_lamap.py",1869,0,"",python,selection_command +1086,1587425,"train_lamap.py",1913,0,"",python,selection_command +1087,1587425,"train_lamap.py",1940,0,"",python,selection_command +1088,1587498,"train_lamap.py",1971,0,"",python,selection_command +1089,1587499,"train_lamap.py",1995,0,"",python,selection_command +1090,1587499,"train_lamap.py",2018,0,"",python,selection_command +1091,1587499,"train_lamap.py",2051,0,"",python,selection_command +1092,1587499,"train_lamap.py",2052,0,"",python,selection_command +1093,1587500,"train_lamap.py",2057,0,"",python,selection_command +1094,1587676,"train_lamap.py",2075,0,"",python,selection_command +1095,1587974,"train_lamap.py",2080,0,"",python,selection_command +1096,1588645,"train_lamap.py",2122,0,"",python,selection_command +1097,1588995,"train_lamap.py",2149,0,"",python,selection_command +1098,1589232,"train_lamap.py",2216,0,"",python,selection_command +1099,1589408,"train_lamap.py",2246,0,"",python,selection_command +1100,1589562,"train_lamap.py",2317,0,"",python,selection_command +1101,1589736,"train_lamap.py",2323,0,"",python,selection_command +1102,1589924,"train_lamap.py",2419,0,"",python,selection_command +1103,1590097,"train_lamap.py",2431,0,"",python,selection_command +1104,1590223,"train_lamap.py",2436,0,"",python,selection_command +1105,1590391,"train_lamap.py",2445,0,"",python,selection_command +1106,1590533,"train_lamap.py",2476,0,"",python,selection_command +1107,1590683,"train_lamap.py",2494,0,"",python,selection_command +1108,1590816,"train_lamap.py",2558,0,"",python,selection_command +1109,1590990,"train_lamap.py",2613,0,"",python,selection_command +1110,1591124,"train_lamap.py",2660,0,"",python,selection_command +1111,1591229,"train_lamap.py",2679,0,"",python,selection_command +1112,1597132,"train_lamap.py",2135,0,"",python,selection_mouse +1113,1598802,"train_lamap.py",2166,0,"",python,selection_mouse +1114,1617047,"train_lam.py",0,0,"",python,tab +1115,1617048,"train_lam.py",2165,0,"",python,selection_mouse +1116,1619677,"train_lam.py",2135,57,") -> tuple[jax.Array, tuple[jax.Array, jax.Array, dict]]:",python,selection_command +1117,1619840,"train_lam.py",2069,123," model: LatentActionModel, inputs: dict, training: bool = True\n) -> tuple[jax.Array, tuple[jax.Array, jax.Array, dict]]:",python,selection_command +1118,1620052,"train_lam.py",2052,140,"def lam_loss_fn(\n model: LatentActionModel, inputs: dict, training: bool = True\n) -> tuple[jax.Array, tuple[jax.Array, jax.Array, dict]]:",python,selection_command +1119,1621414,"train_lam.py",2052,0,"",python,selection_command +1120,1623419,"train_lamap.py",0,0,"",python,tab +1121,1623420,"train_lamap.py",2075,0,"",python,selection_mouse +1122,1624059,"train_lamap.py",2075,0,"\ndef lam_loss_fn(\n model: LatentActionModel, inputs: dict, training: bool = True\n) -> tuple[jax.Array, tuple[jax.Array, jax.Array, dict]]:",python,content +1123,1624132,"train_lamap.py",2076,0,"",python,selection_command +1124,1624751,"train_lamap.py",2075,0,"",python,selection_command +1125,1625279,"train_lamap.py",2076,0,"",python,selection_command +1126,1625585,"train_lamap.py",2077,0,"",python,selection_command +1127,1625735,"train_lamap.py",2078,0,"",python,selection_command +1128,1625908,"train_lamap.py",2079,0,"",python,selection_command +1129,1626047,"train_lamap.py",2080,0,"",python,selection_command +1130,1626161,"train_lamap.py",2081,0,"",python,selection_command +1131,1626331,"train_lamap.py",2082,0,"",python,selection_command +1132,1626610,"train_lamap.py",2083,0,"",python,selection_command +1133,1626909,"train_lamap.py",2083,0,"a",python,content +1134,1626910,"train_lamap.py",2084,0,"",python,selection_keyboard +1135,1627233,"train_lamap.py",2084,0,"p",python,content +1136,1627234,"train_lamap.py",2085,0,"",python,selection_keyboard +1137,1627806,"train_lamap.py",2084,0,"",python,selection_command +1138,1628150,"train_lamap.py",2103,0,"",python,selection_command +1139,1628788,"train_lamap.py",2104,0,"",python,selection_command +1140,1629317,"train_lamap.py",2105,0,"",python,selection_command +1141,1629355,"train_lamap.py",2106,0,"",python,selection_command +1142,1629355,"train_lamap.py",2107,0,"",python,selection_command +1143,1629475,"train_lamap.py",2108,0,"",python,selection_command +1144,1629475,"train_lamap.py",2109,0,"",python,selection_command +1145,1629560,"train_lamap.py",2110,0,"",python,selection_command +1146,1629561,"train_lamap.py",2111,0,"",python,selection_command +1147,1629561,"train_lamap.py",2112,0,"",python,selection_command +1148,1629659,"train_lamap.py",2113,0,"",python,selection_command +1149,1629660,"train_lamap.py",2114,0,"",python,selection_command +1150,1629660,"train_lamap.py",2115,0,"",python,selection_command +1151,1629727,"train_lamap.py",2116,0,"",python,selection_command +1152,1629728,"train_lamap.py",2117,0,"",python,selection_command +1153,1629785,"train_lamap.py",2118,0,"",python,selection_command +1154,1630472,"train_lamap.py",2118,5,"",python,content +1155,1631191,"train_lamap.py",2118,0,"M",python,content +1156,1631192,"train_lamap.py",2119,0,"",python,selection_keyboard +1157,1632055,"train_lamap.py",2119,0,"a",python,content +1158,1632056,"train_lamap.py",2120,0,"",python,selection_keyboard +1159,1632154,"train_lamap.py",2120,0,"p",python,content +1160,1632156,"train_lamap.py",2121,0,"",python,selection_keyboard +1161,1632360,"train_lamap.py",2121,0,"p",python,content +1162,1632363,"train_lamap.py",2122,0,"",python,selection_keyboard +1163,1632437,"train_lamap.py",2122,0,"e",python,content +1164,1632438,"train_lamap.py",2123,0,"",python,selection_keyboard +1165,1632564,"train_lamap.py",2123,0,"r",python,content +1166,1632564,"train_lamap.py",2124,0,"",python,selection_keyboard +1167,1634093,"train_lamap.py",2123,0,"",python,selection_command +1168,1635530,"train_lamap.py",2190,0,"",python,selection_command +1169,1636940,"train_lamap.py",2219,0,"\n",python,content +1170,1638532,"train_lamap.py",2220,0,"\n",python,content +1171,1638670,"train_lamap.py",2221,0,"\n",python,content +1172,1639123,"train_lamap.py",2221,0,"",python,selection_command +1173,1639410,"train_lamap.py",2220,0,"",python,selection_command +1174,1640118,"train_lamap.py",2220,0," ",python,content +1175,1641360,"train_lam.py",0,0,"",python,tab +1176,1641360,"train_lam.py",2302,0,"",python,selection_mouse +1177,1641731,"train_lam.py",2239,0,"",python,selection_mouse +1178,1642574,"train_lam.py",2220,65," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0",python,selection_command +1179,1642850,"train_lam.py",2220,110," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)",python,selection_command +1180,1643441,"train_lam.py",2220,157," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)",python,selection_command +1181,1643592,"train_lam.py",2220,217," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)",python,selection_command +1182,1643593,"train_lam.py",2220,250," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]",python,selection_command +1183,1643593,"train_lam.py",2220,315," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - outputs[""recon""]).mean()",python,selection_command +1184,1643593,"train_lam.py",2220,400," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - outputs[""recon""]).mean()\n q_loss = jnp.square(jax.lax.stop_gradient(outputs[""emb""]) - outputs[""z""]).mean()",python,selection_command +1185,1643971,"train_lam.py",2220,434," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - outputs[""recon""]).mean()\n q_loss = jnp.square(jax.lax.stop_gradient(outputs[""emb""]) - outputs[""z""]).mean()\n commitment_loss = jnp.square(",python,selection_command +1186,1643971,"train_lam.py",2220,495," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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""])",python,selection_command +1187,1643972,"train_lam.py",2220,508," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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()",python,selection_command +1188,1643972,"train_lam.py",2220,565," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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",python,selection_command +1189,1643972,"train_lam.py",2220,566," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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",python,selection_command +1190,1643972,"train_lam.py",2220,607," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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 ---",python,selection_command +1191,1643973,"train_lam.py",2220,685," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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 = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])",python,selection_command +1192,1644114,"train_lam.py",2220,766," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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 = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])",python,selection_command +1193,1644115,"train_lam.py",2220,817," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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 = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()",python,selection_command +1194,1644115,"train_lam.py",2220,868," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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 = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt, recon)).mean()",python,selection_command +1195,1644115,"train_lam.py",2220,935," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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 = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt, recon)).mean()\n count_fn = jax.vmap(lambda i: (outputs[""indices""] == i).sum())",python,selection_command +1196,1644116,"train_lam.py",2220,993," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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 = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt, recon)).mean()\n count_fn = jax.vmap(lambda i: (outputs[""indices""] == i).sum())\n index_counts = count_fn(jnp.arange(args.num_latents))",python,selection_command +1197,1644116,"train_lam.py",2220,1013," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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 = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt, recon)).mean()\n count_fn = jax.vmap(lambda i: (outputs[""indices""] == i).sum())\n index_counts = count_fn(jnp.arange(args.num_latents))\n metrics = dict(",python,selection_command +1198,1644116,"train_lam.py",2220,1032," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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 = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt, recon)).mean()\n count_fn = jax.vmap(lambda i: (outputs[""indices""] == i).sum())\n index_counts = count_fn(jnp.arange(args.num_latents))\n metrics = dict(\n loss=loss,",python,selection_command +1199,1644116,"train_lam.py",2220,1049," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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 = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt, recon)).mean()\n count_fn = jax.vmap(lambda i: (outputs[""indices""] == i).sum())\n index_counts = count_fn(jnp.arange(args.num_latents))\n metrics = dict(\n loss=loss,\n mse=mse,",python,selection_command +1200,1644117,"train_lam.py",2220,1072," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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 = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt, recon)).mean()\n count_fn = jax.vmap(lambda i: (outputs[""indices""] == i).sum())\n index_counts = count_fn(jnp.arange(args.num_latents))\n metrics = dict(\n loss=loss,\n mse=mse,\n q_loss=q_loss,",python,selection_command +1201,1644176,"train_lam.py",2220,1113," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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 = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt, recon)).mean()\n count_fn = jax.vmap(lambda i: (outputs[""indices""] == i).sum())\n index_counts = count_fn(jnp.arange(args.num_latents))\n metrics = dict(\n loss=loss,\n mse=mse,\n q_loss=q_loss,\n commitment_loss=commitment_loss,",python,selection_command +1202,1644291,"train_lam.py",2220,1132," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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 = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt, recon)).mean()\n count_fn = jax.vmap(lambda i: (outputs[""indices""] == i).sum())\n index_counts = count_fn(jnp.arange(args.num_latents))\n metrics = dict(\n loss=loss,\n mse=mse,\n q_loss=q_loss,\n commitment_loss=commitment_loss,\n psnr=psnr,",python,selection_command +1203,1644397,"train_lam.py",2220,1151," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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 = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt, recon)).mean()\n count_fn = jax.vmap(lambda i: (outputs[""indices""] == i).sum())\n index_counts = count_fn(jnp.arange(args.num_latents))\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,",python,selection_command +1204,1644533,"train_lam.py",2220,1202," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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 = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt, recon)).mean()\n count_fn = jax.vmap(lambda i: (outputs[""indices""] == i).sum())\n index_counts = count_fn(jnp.arange(args.num_latents))\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=(index_counts != 0).mean(),",python,selection_command +1205,1644674,"train_lam.py",2220,1208," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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 = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt, recon)).mean()\n count_fn = jax.vmap(lambda i: (outputs[""indices""] == i).sum())\n index_counts = count_fn(jnp.arange(args.num_latents))\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=(index_counts != 0).mean(),\n )",python,selection_command +1206,1644940,"train_lam.py",2220,1267," gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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 = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt, recon)).mean()\n count_fn = jax.vmap(lambda i: (outputs[""indices""] == i).sum())\n index_counts = count_fn(jnp.arange(args.num_latents))\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=(index_counts != 0).mean(),\n )\n return loss, (outputs[""recon""], index_counts, metrics)",python,selection_command +1207,1645368,"train_lam.py",2220,0,"",python,selection_command +1208,1647003,"train_lamap.py",0,0,"",python,tab +1209,1647875,"train_lamap.py",2223,0,"",python,selection_command +1210,1648952,"train_lamap.py",2224,0,"\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=training)\n outputs[""recon""] = outputs[""recon""].astype(jnp.float32)\n gt_future_frames = gt[:, 1:]\n mse = jnp.square(gt_future_frames - 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 = gt_future_frames.clip(0, 1).reshape(-1, *gt_future_frames.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt, recon)).mean()\n count_fn = jax.vmap(lambda i: (outputs[""indices""] == i).sum())\n index_counts = count_fn(jnp.arange(args.num_latents))\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=(index_counts != 0).mean(),\n )\n return loss, (outputs[""recon""], index_counts, metrics)",python,content +1211,1648952,"train_lamap.py",2220,4,"",python,content +1212,1649029,"train_lamap.py",2225,0,"",python,selection_command +1213,1649888,"train_lamap.py",2220,0,"",python,selection_command +1214,1650644,"train_lamap.py",2220,1,"",python,content +1215,1650680,"train_lamap.py",2224,0,"",python,selection_command +1216,1650804,"train_lamap.py",2220,0,"\n",python,content +1217,1650873,"train_lamap.py",2220,0,"",python,selection_command +1218,1651237,"train_lamap.py",2225,1263,"",python,content +1219,1651292,"train_lamap.py",2220,1,"",python,content +1220,1651344,"train_lamap.py",2223,0,"",python,selection_command +1221,1654366,"train_lam.py",0,0,"",python,tab +1222,1654367,"train_lam.py",2301,0,"",python,selection_mouse +1223,1654980,"train_lam.py",2239,0,"",python,selection_mouse +1224,1657653,"train_lam.py",2302,0,"",python,selection_mouse +1225,1661166,"train_lam.py",2236,0,"",python,selection_command +1226,1665582,"train_lamap.py",0,0,"",python,tab +1227,1665582,"train_lamap.py",2224,0,"",python,selection_mouse +1228,1666437,"train_lamap.py",2223,0,"",python,selection_command +1229,1666803,"train_lamap.py",2224,0,"\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)",python,content +1230,1666841,"train_lamap.py",2229,0,"",python,selection_command +1231,1667482,"train_lamap.py",2223,0,"",python,selection_command +1232,1667834,"train_lamap.py",2220,5,"",python,content +1233,1667903,"train_lamap.py",2224,0,"",python,selection_command +1234,1668191,"train_lamap.py",2290,0,"",python,selection_command +1235,1669781,"train_lam.py",0,0,"",python,tab +1236,1669782,"train_lam.py",2300,0,"",python,selection_mouse +1237,1670209,"train_lam.py",2392,0,"",python,selection_mouse +1238,1671047,"train_lam.py",2345,0,"",python,selection_mouse +1239,1678112,"train_lamap.py",0,0,"",python,tab +1240,1678113,"train_lamap.py",2311,0,"",python,selection_mouse +1241,1678712,"train_lamap.py",2330,0,"\n outputs = model(inputs, training=training)",python,content +1242,1678771,"train_lamap.py",2335,0,"",python,selection_command +1243,1682117,"models/lamap.py",0,0,"",python,tab +1244,1685747,"train_lamap.py",0,0,"",python,tab +1245,1688744,"train_lam.py",0,0,"",python,tab +1246,1688745,"train_lam.py",2452,0,"",python,selection_mouse +1247,1690655,"train_lam.py",2476,0,"",python,selection_mouse +1248,1694648,"train_lamap.py",0,0,"",python,tab +1249,1694649,"train_lamap.py",2636,0,"",python,selection_mouse +1250,1695145,"train_lamap.py",2734,0,"",python,selection_mouse +1251,1695177,"train_lamap.py",2733,0,"",python,selection_command +1252,1695906,"train_lamap.py",2648,0,"",python,selection_mouse +1253,1696952,"train_lamap.py",2621,0,"",python,selection_command +1254,1697094,"train_lamap.py",2571,0,"",python,selection_command +1255,1697261,"train_lamap.py",2541,0,"",python,selection_command +1256,1697400,"train_lamap.py",2474,0,"",python,selection_command +1257,1697574,"train_lamap.py",2447,0,"",python,selection_command +1258,1697718,"train_lamap.py",2405,0,"",python,selection_command +1259,1697838,"train_lamap.py",2379,0,"",python,selection_command +1260,1698006,"train_lamap.py",2378,0,"",python,selection_command +1261,1698137,"train_lamap.py",2356,0,"",python,selection_command +1262,1698355,"train_lamap.py",2377,0,"\n loss = optax.softmax_cross_entropy(outputs[""action_predictions""], inputs[""actions""]).mean()",python,content +1263,1698404,"train_lamap.py",2382,0,"",python,selection_command +1264,1698946,"train_lamap.py",2474,0,"",python,selection_command +1265,1699268,"train_lamap.py",2382,0,"",python,selection_command +1266,1699497,"train_lamap.py",2473,0,"\n ",python,content +1267,1699961,"train_lamap.py",2478,0,"r",python,content +1268,1699961,"train_lamap.py",2479,0,"",python,selection_keyboard +1269,1700144,"train_lamap.py",2479,0,"e",python,content +1270,1700145,"train_lamap.py",2480,0,"",python,selection_keyboard +1271,1700344,"train_lamap.py",2480,0,"t",python,content +1272,1700346,"train_lamap.py",2481,0,"",python,selection_keyboard +1273,1700946,"train_lamap.py",2481,0,"u",python,content +1274,1700947,"train_lamap.py",2482,0,"",python,selection_keyboard +1275,1701123,"train_lamap.py",2482,0,"r",python,content +1276,1701125,"train_lamap.py",2483,0,"",python,selection_keyboard +1277,1701240,"train_lamap.py",2483,0,"n",python,content +1278,1701241,"train_lamap.py",2484,0,"",python,selection_keyboard +1279,1701293,"train_lamap.py",2484,0," ",python,content +1280,1701294,"train_lamap.py",2485,0,"",python,selection_keyboard +1281,1701408,"train_lamap.py",2485,0,"l",python,content +1282,1701408,"train_lamap.py",2486,0,"",python,selection_keyboard +1283,1701581,"train_lamap.py",2486,0,"o",python,content +1284,1701582,"train_lamap.py",2487,0,"",python,selection_keyboard +1285,1701679,"train_lamap.py",2487,0,"s",python,content +1286,1701680,"train_lamap.py",2488,0,"",python,selection_keyboard +1287,1701789,"train_lamap.py",2488,0,"s",python,content +1288,1701790,"train_lamap.py",2489,0,"",python,selection_keyboard +1289,1702149,"train_lamap.py",2488,0,"",python,selection_command +1290,1702309,"train_lamap.py",2490,0,"",python,selection_command +1291,1702467,"train_lamap.py",2491,0,"",python,selection_command +1292,1702856,"train_lamap.py",2506,0,"",python,selection_command +1293,1713469,"train_lamap.py",2491,0,"",python,selection_command +1294,1713699,"train_lamap.py",2490,0,"",python,selection_command +1295,1713846,"train_lamap.py",2488,0,"",python,selection_command +1296,1713997,"train_lamap.py",2392,0,"",python,selection_command +1297,1714382,"train_lamap.py",2345,0,"",python,selection_command +1298,1732884,"train_dynamics.py",0,0,"from dataclasses import dataclass, field\nimport os\nfrom typing import cast\n\nimport einops\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\nimport flax.nnx as nnx\n\nfrom genie import Genie, restore_genie_components\nfrom utils.dataloader import create_dataloader_iterator\nfrom utils.lr_utils import get_lr_schedule\nfrom utils.parameter_utils import count_parameters_by_component\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n batch_size: int = 36\n init_lr: float = 0.0\n max_lr: float = 3e-5\n decay_end: float = 0.0\n wsd_decay_steps: int = (\n 10000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n )\n warmup_steps: int = 5000\n lr_schedule: str = ""wsd"" # supported options: wsd, cos\n # Tokenizer\n tokenizer_dim: int = 512\n tokenizer_ffn_dim: int = 2048\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 4\n tokenizer_num_heads: int = 8\n tokenizer_checkpoint: str = """"\n # LAM\n lam_dim: int = 512\n lam_ffn_dim: int = 2048\n latent_action_dim: int = 32\n num_latent_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 4\n lam_num_heads: int = 8\n lam_checkpoint: str = """"\n # Dynamics\n dyna_type: str = ""maskgit"" # supported options: maskgit, causal\n dyna_dim: int = 512\n dyna_ffn_dim: int = 2048\n dyna_num_blocks: int = 6\n dyna_num_heads: int = 8\n dropout: float = 0.0\n mask_limit: float = 0.5\n param_dtype = jnp.float32\n dtype = jnp.bfloat16\n use_flash_attention: bool = True\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n name: str = ""train_dynamics""\n tags: list[str] = field(default_factory=lambda: [""dynamics""])\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 25000\n log_checkpoint_keep_period: int = 20000\n log_gradients: bool = False\n val_data_dir: str = """"\n val_interval: int = 20_000\n val_steps: int = 50\n wandb_id: str = """"\n\n\nargs = tyro.cli(Args)\n\n\ndef dynamics_loss_fn(\n model: Genie, inputs: dict\n) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n """"""Compute masked dynamics loss""""""\n gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n inputs[""videos""] = gt.astype(args.dtype)\n outputs = model(inputs, training=True)\n mask = outputs[""mask""]\n outputs[""token_logits""] = outputs[""token_logits""].astype(jnp.float32)\n ce_loss = optax.softmax_cross_entropy_with_integer_labels(\n outputs[""token_logits""], outputs[""video_tokens""]\n )\n ce_loss = (mask * ce_loss).sum() / mask.sum()\n acc = outputs[""token_logits""].argmax(-1) == outputs[""video_tokens""]\n acc = (mask * acc).sum() / mask.sum()\n select_probs = jax.nn.softmax(outputs[""token_logits""])\n gt = gt.clip(0, 1).reshape(-1, *gt.shape[2:])\n recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n psnr = jnp.asarray(pix.psnr(gt, recon)).mean()\n ssim = jnp.asarray(pix.ssim(gt, recon)).mean()\n _, index_counts_lam = jnp.unique_counts(\n jnp.ravel(outputs[""lam_indices""]), size=args.num_latent_actions, fill_value=0\n )\n _, index_counts_tokenizer = jnp.unique_counts(\n jnp.ravel(outputs[""video_tokens""]), size=args.num_patch_latents, fill_value=0\n )\n codebook_usage_lam = (index_counts_lam != 0).mean()\n codebook_usage_tokenizer = (index_counts_tokenizer != 0).mean()\n metrics = dict(\n cross_entropy_loss=ce_loss,\n masked_token_accuracy=acc,\n select_logit=outputs[""token_logits""].max(-1).mean(),\n select_p=select_probs.max(-1).mean(),\n entropy=jax.scipy.special.entr(select_probs).sum(-1).mean(),\n psnr=psnr,\n ssim=ssim,\n codebook_usage_lam=codebook_usage_lam,\n codebook_usage_tokenizer=codebook_usage_tokenizer,\n )\n return ce_loss, (outputs[""recon""], metrics)\n\n\n@nnx.jit\ndef train_step(\n model: Genie, optimizer: nnx.Optimizer, inputs: dict\n) -> tuple[jax.Array, jax.Array, dict]:\n """"""Update state and compute metrics""""""\n\n def loss_fn(model: Genie) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n model.train()\n return dynamics_loss_fn(model, inputs)\n\n (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(model)\n optimizer.update(grads)\n if args.log_gradients:\n metrics[""gradients_std/""] = jax.tree.map(\n lambda x: x.std(), grads[""params""][""dynamics""]\n )\n return loss, recon, metrics\n\n@nnx.jit\ndef val_step(genie: Genie, inputs: dict) -> tuple[jax.Array, jax.Array, dict]:\n """"""Evaluate model and compute metrics""""""\n genie.eval()\n (loss, (recon, metrics)) = dynamics_loss_fn(genie, inputs)\n return loss, recon, metrics\n\n\ndef calculate_validation_metrics(val_dataloader, genie, rng):\n step = 0\n loss_per_step = []\n metrics_per_step = []\n for videos in val_dataloader:\n rng, _rng_mask = jax.random.split(rng, 2)\n inputs = dict(videos=videos, mask_rng=_rng_mask)\n loss, recon, metrics = val_step(genie, inputs)\n loss_per_step.append(loss)\n metrics_per_step.append(metrics)\n step += 1\n if step > args.val_steps:\n break\n\n if step < args.val_steps:\n print(f""Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"")\n\n val_loss = np.mean(loss_per_step)\n val_metrics = {\n f""val_{key}"": np.mean([m[key] for m in metrics_per_step])\n for key in metrics_per_step[0].keys()\n }\n return val_loss, val_metrics, inputs, recon\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.key(args.seed)\n\n # --- Initialize model ---\n rng, _rng = jax.random.split(rng)\n rngs = nnx.Rngs(_rng)\n genie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n tokenizer_ffn_dim=args.tokenizer_ffn_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n lam_ffn_dim=args.lam_ffn_dim,\n latent_action_dim=args.latent_action_dim,\n num_latent_actions=args.num_latent_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n lam_co_train=not args.lam_checkpoint,\n # Dynamics\n dyna_type=args.dyna_type,\n dyna_dim=args.dyna_dim,\n dyna_ffn_dim=args.dyna_ffn_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n dropout=args.dropout,\n mask_limit=args.mask_limit,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n use_flash_attention=args.use_flash_attention,\n decode=False,\n rngs=rngs,\n )\n\n _, params, _ = nnx.split(genie, nnx.Param, ...)\n param_counts = count_parameters_by_component(params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\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 if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n lr_schedule = get_lr_schedule(\n args.lr_schedule,\n args.init_lr,\n args.max_lr,\n args.decay_end,\n args.num_steps,\n args.warmup_steps,\n args.wsd_decay_steps,\n )\n tx = optax.adamw(\n learning_rate=lr_schedule,\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n mu_dtype=args.dtype,\n )\n optimizer = nnx.Optimizer(genie, tx)\n del genie\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 videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n\n model_state = nnx.state(optimizer.model)\n model_sharded_state = jax.lax.with_sharding_constraint(\n model_state, replicated_sharding\n )\n nnx.update(optimizer.model, model_sharded_state)\n optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)\n optimizer_sharded_state = jax.lax.with_sharding_constraint(\n optimizer_state, replicated_sharding\n )\n nnx.update(optimizer, optimizer_sharded_state)\n\n # --- Initialize checkpoint manager ---\n step = 0\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n )\n handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""train_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n if args.val_data_dir:\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointSave,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n handler_registry.add(\n ""val_dataloader_state"",\n grain.checkpoint.CheckpointRestore,\n cast(ocp.handlers.CheckpointHandler, grain.checkpoint.CheckpointHandler),\n )\n\n\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n\n # --- Create DataLoaderIterator from dataloader ---\n image_shape = (args.image_height, args.image_width, args.image_channels)\n train_iterator = create_dataloader_iterator(args.data_dir, image_shape, args.seq_len, args.batch_size, args.seed)\n if args.val_data_dir:\n val_iterator = create_dataloader_iterator(args.val_data_dir, image_shape, args.seq_len, args.batch_size, args.seed)\n\n # --- Restore checkpoint ---\n if args.restore_ckpt:\n abstract_optimizer = nnx.eval_shape(lambda: optimizer)\n abstract_optimizer_state = nnx.state(abstract_optimizer)\n if args.val_data_dir:\n restore_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointRestore(train_iterator), # type: ignore\n val_dataloader_state=grain.checkpoint.CheckpointRestore(val_iterator), # type: ignore\n )\n else: \n restore_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeRestore(abstract_optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointRestore(train_iterator), # type: ignore\n )\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(),\n args=restore_args\n )\n restored_optimizer_state = restored[""model_state""]\n nnx.update(optimizer, restored_optimizer_state)\n train_iterator = restored[""train_dataloader_state""]\n if args.val_data_dir:\n val_iterator = restored[""val_dataloader_state""]\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored dataloader and model state from step {step}"")\n else:\n # Restore from pre-trained tokenizer (and LAM)\n optimizer = restore_genie_components(optimizer, replicated_sharding, rng, args)\n # NOTE: We have to remove the (unused) tokenizer vq dropout due flax.nnx lazily initializing modules.\n # Specifically, the first dynamics model checkpoint will contain the vq dropout module,\n # but the first full restore will fail due to nnx not initializing the module when\n # dropout is set to 0.0.\n del optimizer.model.tokenizer.vq.drop\n\n # --- TRAIN LOOP ---\n dataloader_train = (\n jax.make_array_from_process_local_data(videos_sharding, elem)\n for elem in train_iterator\n )\n if args.val_data_dir:\n dataloader_val = (\n jax.make_array_from_process_local_data(videos_sharding, elem)\n for elem in val_iterator\n )\n print(f""Starting training from step {step}..."")\n while step < args.num_steps:\n for videos in dataloader_train:\n # --- Train step ---\n rng, _rng_mask = jax.random.split(rng, 2)\n inputs = dict(videos=videos, mask_rng=_rng_mask)\n loss, recon, metrics = train_step(optimizer.model, optimizer, inputs)\n metrics[""lr""] = lr_schedule(step)\n print(f""Step {step}, loss: {loss}"")\n step += 1\n\n # --- Validation loss ---\n if args.val_data_dir and step % args.val_interval == 0:\n rng, _rng_mask_val = jax.random.split(rng, 2)\n print(f""Calculating validation metrics..."")\n val_loss, val_metrics, val_gt_batch, val_recon = calculate_validation_metrics(dataloader_val, optimizer.model, _rng_mask_val)\n print(f""Step {step}, validation loss: {val_loss}"")\n\n # --- Logging ---\n if args.log:\n if step % args.log_interval == 0 and jax.process_index() == 0:\n log_dict = {\n ""loss"": loss,\n ""step"": step,\n **metrics\n }\n if args.val_data_dir and step % args.val_interval == 0:\n log_dict.update({\n ""val_loss"": val_loss,\n **val_metrics\n })\n wandb.log(log_dict)\n if step % args.log_image_interval == 0:\n gt_seq = inputs[""videos""][0].astype(jnp.float32) / 255.0\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 if args.val_data_dir and step % args.val_interval == 0:\n gt_seq_val = val_gt_batch[""videos""][0].astype(jnp.float32) / 255.0\n recon_seq_val = val_recon[0].clip(0, 1)\n val_comparison_seq = jnp.concatenate((gt_seq, recon_seq), axis=1)\n val_comparison_seq = einops.rearrange(\n val_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 if args.val_data_dir and step % args.val_interval == 0:\n log_images.update(\n dict(\n val_image=wandb.Image(np.asarray(gt_seq_val[0])),\n val_recon=wandb.Image(np.asarray(recon_seq_val[0])),\n val_true_vs_recon=wandb.Image(\n np.asarray(val_comparison_seq.astype(np.uint8))\n )\n )\n )\n wandb.log(log_images)\n # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n optimizer_state = nnx.state(optimizer)\n if args.val_data_dir:\n ckpt_manager_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n train_iterator # type: ignore\n ),\n val_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n val_iterator # type: ignore\n )\n )\n else: \n ckpt_manager_args = ocp.args.Composite(\n model_state=ocp.args.PyTreeSave(optimizer_state), # type: ignore\n train_dataloader_state=grain.checkpoint.CheckpointSave( # type: ignore\n train_iterator # type: ignore\n )\n )\n checkpoint_manager.save(\n step,\n args=ckpt_manager_args\n )\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n checkpoint_manager.close()\n",python,tab +1299,1737148,"train_dynamics.py",3555,0,"",python,selection_mouse +1300,1744873,"train_dynamics.py",3311,0,"",python,selection_mouse +1301,1749765,"train_lamap.py",0,0,"",python,tab 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+1397,1772477,"train_lamap.py",2601,0,"",python,selection_command +1398,1774945,"train_lamap.py",2574,0,"",python,selection_command +1399,1776861,"train_lamap.py",2534,41,"def lamap_loss_fn(params, state, inputs):",python,selection_command +1400,1777088,"train_lamap.py",2534,68,"def lamap_loss_fn(params, state, inputs):\n # --- Compute loss ---",python,selection_command +1401,1777197,"train_lamap.py",2534,135,"def lamap_loss_fn(params, state, inputs):\n # --- Compute loss ---\n inputs[""videos""] = inputs[""videos""].astype(args.dtype) / 255.0",python,selection_command +1402,1777347,"train_lamap.py",2534,165,"def lamap_loss_fn(params, state, inputs):\n # --- Compute loss ---\n inputs[""videos""] = inputs[""videos""].astype(args.dtype) / 255.0\n outputs = state.apply_fn(",python,selection_command +1403,1777508,"train_lamap.py",2534,236,"def lamap_loss_fn(params, state, inputs):\n # --- Compute loss ---\n inputs[""videos""] = inputs[""videos""].astype(args.dtype) / 255.0\n outputs = state.apply_fn(\n params, inputs, training=True, rngs={""dropout"": inputs[""rng""]}",python,selection_command +1404,1777756,"train_lamap.py",2534,242,"def lamap_loss_fn(params, state, inputs):\n # --- Compute loss ---\n inputs[""videos""] = inputs[""videos""].astype(args.dtype) / 255.0\n outputs = state.apply_fn(\n params, inputs, training=True, rngs={""dropout"": inputs[""rng""]}\n )",python,selection_command +1405,1777841,"train_lamap.py",2534,338,"def lamap_loss_fn(params, state, inputs):\n # --- Compute loss ---\n inputs[""videos""] = inputs[""videos""].astype(args.dtype) / 255.0\n outputs = state.apply_fn(\n params, inputs, training=True, rngs={""dropout"": inputs[""rng""]}\n )\n loss = optax.softmax_cross_entropy(outputs[""action_predictions""], inputs[""actions""]).mean()",python,selection_command +1406,1777955,"train_lamap.py",2534,354,"def lamap_loss_fn(params, state, inputs):\n # --- Compute loss ---\n inputs[""videos""] = inputs[""videos""].astype(args.dtype) / 255.0\n outputs = state.apply_fn(\n params, inputs, training=True, rngs={""dropout"": inputs[""rng""]}\n )\n loss = optax.softmax_cross_entropy(outputs[""action_predictions""], inputs[""actions""]).mean()\n return loss",python,selection_command +1407,1778302,"train_lamap.py",2534,355,"",python,content +1408,1778641,"train_lamap.py",2533,0,"",python,selection_command +1409,1779122,"train_lamap.py",2533,1,"",python,content +1410,1779683,"train_lamap.py",2533,1,"",python,content +1411,1785176,"train_dynamics.py",0,0,"",python,tab +1412,1787945,"train_dynamics.py",3272,0,"",python,selection_mouse +1413,1791540,"train_dynamics.py",3230,0,"",python,selection_command +1414,1791973,"train_dynamics.py",3272,0,"",python,selection_command +1415,1792108,"train_dynamics.py",3230,0,"",python,selection_command +1416,1792306,"train_dynamics.py",3158,0,"",python,selection_command +1417,1793444,"train_dynamics.py",3230,0,"",python,selection_command +1418,1797273,"train_dynamics.py",3158,0,"",python,selection_command +1419,1797534,"train_dynamics.py",3108,0,"",python,selection_command +1420,1797770,"train_dynamics.py",3080,0,"",python,selection_command +1421,1799247,"models/lamap.py",0,0,"",python,tab +1422,1801338,"train_lamap.py",0,0,"",python,tab +1423,1804450,"train_dynamics.py",0,0,"",python,tab +1424,1812043,"train_lamap.py",0,0,"",python,tab +1425,1813712,"train_lamap.py",2404,0,"",python,selection_mouse +1426,1815642,".venv/lib/python3.10/site-packages/optax/__init__.py",0,0,"# Copyright 2019 DeepMind Technologies Limited. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n""""""Optax: composable gradient processing and optimization, in JAX.""""""\n\n# pylint: disable=wrong-import-position\n# pylint: disable=g-importing-member\n\nimport typing as _typing\n\nfrom optax import assignment\nfrom optax import contrib\nfrom optax import losses\nfrom optax import monte_carlo\nfrom optax import perturbations\nfrom optax import projections\nfrom optax import schedules\nfrom optax import second_order\nfrom optax import transforms\nfrom optax import tree\nfrom optax import tree_utils\nfrom optax._src.alias import adabelief\nfrom optax._src.alias import adadelta\nfrom optax._src.alias import adafactor\nfrom optax._src.alias import adagrad\nfrom optax._src.alias import adam\nfrom optax._src.alias import adamax\nfrom optax._src.alias import adamaxw\nfrom optax._src.alias import adamw\nfrom optax._src.alias import adan\nfrom optax._src.alias import amsgrad\nfrom optax._src.alias import fromage\nfrom optax._src.alias import lamb\nfrom optax._src.alias import lars\nfrom optax._src.alias import lbfgs\nfrom optax._src.alias import lion\nfrom optax._src.alias import MaskOrFn\nfrom optax._src.alias import nadam\nfrom optax._src.alias import nadamw\nfrom optax._src.alias import noisy_sgd\nfrom optax._src.alias import novograd\nfrom optax._src.alias import optimistic_adam\nfrom optax._src.alias import optimistic_gradient_descent\nfrom optax._src.alias import polyak_sgd\nfrom optax._src.alias import radam\nfrom optax._src.alias import rmsprop\nfrom optax._src.alias import rprop\nfrom optax._src.alias import sgd\nfrom optax._src.alias import sign_sgd\nfrom optax._src.alias import sm3\nfrom optax._src.alias import yogi\nfrom optax._src.base import EmptyState\nfrom optax._src.base import GradientTransformation\nfrom optax._src.base import GradientTransformationExtraArgs\nfrom optax._src.base import identity\nfrom optax._src.base import OptState\nfrom optax._src.base import Params\nfrom optax._src.base import ScalarOrSchedule\nfrom optax._src.base import Schedule\nfrom optax._src.base import set_to_zero\nfrom optax._src.base import stateless\nfrom optax._src.base import stateless_with_tree_map\nfrom optax._src.base import TransformInitFn\nfrom optax._src.base import TransformUpdateExtraArgsFn\nfrom optax._src.base import TransformUpdateFn\nfrom optax._src.base import Updates\nfrom optax._src.base import with_extra_args_support\nfrom optax._src.factorized import FactoredState\nfrom optax._src.factorized import scale_by_factored_rms\nfrom optax._src.linear_algebra import global_norm\nfrom optax._src.linear_algebra import matrix_inverse_pth_root\nfrom optax._src.linear_algebra import nnls\nfrom optax._src.linear_algebra import power_iteration\nfrom optax._src.linesearch import scale_by_backtracking_linesearch\nfrom optax._src.linesearch import scale_by_zoom_linesearch\nfrom optax._src.linesearch import ScaleByBacktrackingLinesearchState\nfrom optax._src.linesearch import ScaleByZoomLinesearchState\nfrom optax._src.linesearch import ZoomLinesearchInfo\nfrom optax._src.lookahead import lookahead\nfrom optax._src.lookahead import LookaheadParams\nfrom optax._src.lookahead import LookaheadState\nfrom optax._src.numerics import safe_increment\nfrom optax._src.numerics import safe_int32_increment\nfrom optax._src.numerics import safe_norm\nfrom optax._src.numerics import safe_root_mean_squares\nfrom optax._src.transform import apply_every\nfrom optax._src.transform import ApplyEvery\nfrom optax._src.transform import centralize\nfrom optax._src.transform import normalize_by_update_norm\nfrom optax._src.transform import scale\nfrom optax._src.transform import scale_by_adadelta\nfrom optax._src.transform import scale_by_adam\nfrom optax._src.transform import scale_by_adamax\nfrom optax._src.transform import scale_by_adan\nfrom optax._src.transform import scale_by_amsgrad\nfrom optax._src.transform import scale_by_belief\nfrom optax._src.transform import scale_by_distance_over_gradients\nfrom optax._src.transform import scale_by_lbfgs\nfrom optax._src.transform import scale_by_learning_rate\nfrom optax._src.transform import scale_by_lion\nfrom optax._src.transform import scale_by_novograd\nfrom optax._src.transform import scale_by_optimistic_gradient\nfrom optax._src.transform import scale_by_param_block_norm\nfrom optax._src.transform import scale_by_param_block_rms\nfrom optax._src.transform import scale_by_polyak\nfrom optax._src.transform import scale_by_radam\nfrom optax._src.transform import scale_by_rms\nfrom optax._src.transform import scale_by_rprop\nfrom optax._src.transform import scale_by_rss\nfrom optax._src.transform import scale_by_schedule\nfrom optax._src.transform import scale_by_sign\nfrom optax._src.transform import scale_by_sm3\nfrom optax._src.transform import scale_by_stddev\nfrom optax._src.transform import scale_by_trust_ratio\nfrom optax._src.transform import scale_by_yogi\nfrom optax._src.transform import ScaleByAdaDeltaState\nfrom optax._src.transform import ScaleByAdamState\nfrom optax._src.transform import ScaleByAdanState\nfrom optax._src.transform import ScaleByAmsgradState\nfrom optax._src.transform import ScaleByBeliefState\nfrom optax._src.transform import ScaleByLBFGSState\nfrom optax._src.transform import ScaleByLionState\nfrom optax._src.transform import ScaleByNovogradState\nfrom optax._src.transform import ScaleByRmsState\nfrom optax._src.transform import ScaleByRpropState\nfrom optax._src.transform import ScaleByRssState\nfrom optax._src.transform import ScaleByRStdDevState\nfrom optax._src.transform import ScaleByScheduleState\nfrom optax._src.transform import ScaleBySM3State\nfrom optax._src.update import apply_updates\nfrom optax._src.update import incremental_update\nfrom optax._src.update import periodic_update\nfrom optax._src.utils import multi_normal\nfrom optax._src.utils import scale_gradient\nfrom optax._src.utils import value_and_grad_from_state\n\n# TODO(mtthss): remove contrib aliases from flat namespace once users updated.\n# Deprecated modules\nfrom optax.contrib import differentially_private_aggregate as _deprecated_differentially_private_aggregate\nfrom optax.contrib import DifferentiallyPrivateAggregateState as _deprecated_DifferentiallyPrivateAggregateState\nfrom optax.contrib import dpsgd as _deprecated_dpsgd\n\n\n# TODO(mtthss): remove aliases after updates.\nadaptive_grad_clip = transforms.adaptive_grad_clip\nAdaptiveGradClipState = EmptyState\nclip = transforms.clip\nclip_by_block_rms = transforms.clip_by_block_rms\nclip_by_global_norm = transforms.clip_by_global_norm\nClipByGlobalNormState = EmptyState\nClipState = EmptyState\nper_example_global_norm_clip = transforms.per_example_global_norm_clip\nper_example_layer_norm_clip = transforms.per_example_layer_norm_clip\nkeep_params_nonnegative = transforms.keep_params_nonnegative\nNonNegativeParamsState = transforms.NonNegativeParamsState\nzero_nans = transforms.zero_nans\nZeroNansState = transforms.ZeroNansState\nchain = transforms.chain\npartition = transforms.partition\nPartitionState = transforms.PartitionState\nmulti_transform = transforms.partition # for backwards compatibility\nMultiTransformState = transforms.PartitionState # for backwards compatibility\nnamed_chain = transforms.named_chain\ntrace = transforms.trace\nTraceState = transforms.TraceState\nema = transforms.ema\nEmaState = transforms.EmaState\nadd_noise = transforms.add_noise\nAddNoiseState = transforms.AddNoiseState\nadd_decayed_weights = transforms.add_decayed_weights\nAddDecayedWeightsState = EmptyState\nScaleByTrustRatioState = EmptyState\nScaleState = EmptyState\napply_if_finite = transforms.apply_if_finite\nApplyIfFiniteState = transforms.ApplyIfFiniteState\nconditionally_mask = transforms.conditionally_mask\nconditionally_transform = transforms.conditionally_transform\nConditionallyMaskState = transforms.ConditionallyMaskState\nConditionallyTransformState = transforms.ConditionallyTransformState\nflatten = transforms.flatten\nmasked = transforms.masked\nMaskedNode = transforms.MaskedNode\nMaskedState = transforms.MaskedState\nMultiSteps = transforms.MultiSteps\nMultiStepsState = transforms.MultiStepsState\nShouldSkipUpdateFunction = transforms.ShouldSkipUpdateFunction\nskip_large_updates = transforms.skip_large_updates\nskip_not_finite = transforms.skip_not_finite\n\n# TODO(mtthss): remove tree_utils aliases after updates.\ntree_map_params = tree_utils.tree_map_params\nbias_correction = tree_utils.tree_bias_correction\nupdate_infinity_moment = tree_utils.tree_update_infinity_moment\nupdate_moment = tree_utils.tree_update_moment\nupdate_moment_per_elem_norm = tree_utils.tree_update_moment_per_elem_norm\n\n# TODO(mtthss): remove schedules aliases from flat namespaces after user updates\nconstant_schedule = schedules.constant_schedule\ncosine_decay_schedule = schedules.cosine_decay_schedule\ncosine_onecycle_schedule = schedules.cosine_onecycle_schedule\nexponential_decay = schedules.exponential_decay\ninject_hyperparams = schedules.inject_hyperparams\nInjectHyperparamsState = schedules.InjectHyperparamsState\njoin_schedules = schedules.join_schedules\nlinear_onecycle_schedule = schedules.linear_onecycle_schedule\nlinear_schedule = schedules.linear_schedule\npiecewise_constant_schedule = schedules.piecewise_constant_schedule\npiecewise_interpolate_schedule = schedules.piecewise_interpolate_schedule\npolynomial_schedule = schedules.polynomial_schedule\nsgdr_schedule = schedules.sgdr_schedule\nwarmup_constant_schedule = schedules.warmup_constant_schedule\nwarmup_cosine_decay_schedule = schedules.warmup_cosine_decay_schedule\nwarmup_exponential_decay_schedule = schedules.warmup_exponential_decay_schedule\ninject_stateful_hyperparams = schedules.inject_stateful_hyperparams\nInjectStatefulHyperparamsState = schedules.InjectStatefulHyperparamsState\nWrappedSchedule = schedules.WrappedSchedule\n\n# TODO(mtthss): remove loss aliases from flat namespace once users have updated.\nconvex_kl_divergence = losses.convex_kl_divergence\ncosine_distance = losses.cosine_distance\ncosine_similarity = losses.cosine_similarity\nctc_loss = losses.ctc_loss\nctc_loss_with_forward_probs = losses.ctc_loss_with_forward_probs\nhinge_loss = losses.hinge_loss\nhuber_loss = losses.huber_loss\nkl_divergence = losses.kl_divergence\nl2_loss = losses.l2_loss\nlog_cosh = losses.log_cosh\nntxent = losses.ntxent\nsigmoid_binary_cross_entropy = losses.sigmoid_binary_cross_entropy\nsmooth_labels = losses.smooth_labels\nsafe_softmax_cross_entropy = losses.safe_softmax_cross_entropy\nsoftmax_cross_entropy = losses.softmax_cross_entropy\nsoftmax_cross_entropy_with_integer_labels = (\n losses.softmax_cross_entropy_with_integer_labels\n)\nsquared_error = losses.squared_error\nsigmoid_focal_loss = losses.sigmoid_focal_loss\n\n_deprecations = {\n # Added Apr 2024\n ""differentially_private_aggregate"": (\n (\n ""optax.differentially_private_aggregate is deprecated: use""\n "" optax.contrib.differentially_private_aggregate (optax v0.1.8 or""\n "" newer).""\n ),\n _deprecated_differentially_private_aggregate,\n ),\n ""DifferentiallyPrivateAggregateState"": (\n (\n ""optax.DifferentiallyPrivateAggregateState is deprecated: use""\n "" optax.contrib.DifferentiallyPrivateAggregateState (optax v0.1.8""\n "" or newer).""\n ),\n _deprecated_DifferentiallyPrivateAggregateState,\n ),\n ""dpsgd"": (\n (\n ""optax.dpsgd is deprecated: use optax.contrib.dpsgd (optax v0.1.8""\n "" or newer).""\n ),\n _deprecated_dpsgd,\n ),\n}\n# pylint: disable=g-import-not-at-top\n# pylint: disable=g-bad-import-order\nif _typing.TYPE_CHECKING:\n # pylint: disable=reimported\n from optax.contrib import differentially_private_aggregate\n from optax.contrib import DifferentiallyPrivateAggregateState\n from optax.contrib import dpsgd\n # pylint: enable=reimported\n\nelse:\n from optax._src.deprecations import deprecation_getattr as _deprecation_getattr\n\n __getattr__ = _deprecation_getattr(__name__, _deprecations)\n del _deprecation_getattr\ndel _typing\n# pylint: enable=g-bad-import-order\n# pylint: enable=g-import-not-at-top\n# pylint: enable=g-importing-member\n\n\n__version__ = ""0.2.5""\n\n__all__ = (\n ""adabelief"",\n ""adadelta"",\n ""adafactor"",\n ""adagrad"",\n ""adam"",\n ""adamax"",\n ""adamaxw"",\n ""adamw"",\n ""adan"",\n ""adaptive_grad_clip"",\n ""AdaptiveGradClipState"",\n ""add_decayed_weights"",\n ""add_noise"",\n ""AddDecayedWeightsState"",\n ""AddNoiseState"",\n ""amsgrad"",\n ""apply_every"",\n ""apply_if_finite"",\n ""apply_updates"",\n ""ApplyEvery"",\n ""ApplyIfFiniteState"",\n ""assignment"",\n ""centralize"",\n ""chain"",\n ""clip_by_block_rms"",\n ""clip_by_global_norm"",\n ""clip"",\n ""ClipByGlobalNormState"",\n ""ClipState"",\n ""conditionally_mask"",\n ""ConditionallyMaskState"",\n ""conditionally_transform"",\n ""ConditionallyTransformState"",\n ""constant_schedule"",\n ""ctc_loss"",\n ""ctc_loss_with_forward_probs"",\n ""convex_kl_divergence"",\n ""cosine_decay_schedule"",\n ""cosine_distance"",\n ""cosine_onecycle_schedule"",\n ""cosine_similarity"",\n ""differentially_private_aggregate"",\n ""DifferentiallyPrivateAggregateState"",\n ""dpsgd"",\n ""ema"",\n ""EmaState"",\n ""EmptyState"",\n ""exponential_decay"",\n ""FactoredState"",\n ""flatten"",\n ""fromage"",\n ""global_norm"",\n ""GradientTransformation"",\n ""GradientTransformationExtraArgs"",\n ""hinge_loss"",\n ""huber_loss"",\n ""identity"",\n ""incremental_update"",\n ""inject_hyperparams"",\n ""InjectHyperparamsState"",\n ""join_schedules"",\n ""keep_params_nonnegative"",\n ""kl_divergence"",\n ""l2_loss"",\n ""lamb"",\n ""lars"",\n ""lbfgs"",\n ""lion"",\n ""linear_onecycle_schedule"",\n ""linear_schedule"",\n ""log_cosh"",\n ""lookahead"",\n ""LookaheadParams"",\n ""LookaheadState"",\n ""masked"",\n ""MaskOrFn"",\n ""MaskedState"",\n ""matrix_inverse_pth_root"",\n ""multi_normal"",\n ""multi_transform"", # for backwards compatibility\n ""MultiSteps"",\n ""MultiStepsState"",\n ""MultiTransformState"", # for backwards compatibility\n ""nadam"",\n ""nadamw"",\n ""nnls"",\n ""noisy_sgd"",\n ""novograd"",\n ""NonNegativeParamsState"",\n ""ntxent"",\n ""OptState"",\n ""Params"",\n ""partition"",\n ""PartitionState"",\n ""periodic_update"",\n ""per_example_global_norm_clip"",\n ""per_example_layer_norm_clip"",\n ""piecewise_constant_schedule"",\n ""piecewise_interpolate_schedule"",\n ""polynomial_schedule"",\n ""power_iteration"",\n ""polyak_sgd"",\n ""radam"",\n ""rmsprop"",\n ""rprop"",\n ""safe_increment"",\n ""safe_int32_increment"",\n ""safe_norm"",\n ""safe_root_mean_squares"",\n ""ScalarOrSchedule"",\n ""scale_by_adadelta"",\n ""scale_by_adam"",\n ""scale_by_adamax"",\n ""scale_by_adan"",\n ""scale_by_amsgrad"",\n ""scale_by_backtracking_linesearch"",\n ""scale_by_belief"",\n ""scale_by_lbfgs"",\n ""scale_by_lion"",\n ""scale_by_factored_rms"",\n ""scale_by_novograd"",\n ""scale_by_param_block_norm"",\n ""scale_by_param_block_rms"",\n ""scale_by_polyak"",\n ""scale_by_radam"",\n ""scale_by_rms"",\n ""scale_by_rprop"",\n ""scale_by_rss"",\n ""scale_by_schedule"",\n ""scale_by_sign"",\n ""scale_by_sm3"",\n ""scale_by_stddev"",\n ""scale_by_trust_ratio"",\n ""scale_by_yogi"",\n ""scale_by_zoom_linesearch"",\n ""scale_gradient"",\n ""scale"",\n ""ScaleByAdaDeltaState"",\n ""ScaleByAdamState"",\n ""ScaleByAdanState"",\n ""ScaleByAmsgradState"",\n ""ScaleByBacktrackingLinesearchState"",\n ""ScaleByBeliefState"",\n ""ScaleByLBFGSState"",\n ""ScaleByLionState"",\n ""ScaleByNovogradState"",\n ""ScaleByRmsState"",\n ""ScaleByRpropState"",\n ""ScaleByRssState"",\n ""ScaleByRStdDevState"",\n ""ScaleByScheduleState"",\n ""ScaleBySM3State"",\n ""ScaleByTrustRatioState"",\n ""ScaleByZoomLinesearchState"",\n ""ScaleState"",\n ""Schedule"",\n ""set_to_zero"",\n ""sgd"",\n ""sgdr_schedule"",\n ""ShouldSkipUpdateFunction"",\n ""sigmoid_binary_cross_entropy"",\n ""sign_sgd"",\n ""skip_large_updates"",\n ""skip_not_finite"",\n ""sm3"",\n ""smooth_labels"",\n ""softmax_cross_entropy"",\n ""softmax_cross_entropy_with_integer_labels"",\n ""stateless"",\n ""stateless_with_tree_map"",\n ""trace"",\n ""TraceState"",\n ""TransformInitFn"",\n ""TransformUpdateFn"",\n ""TransformUpdateExtraArgsFn"",\n ""Updates"",\n ""value_and_grad_from_state"",\n ""warmup_cosine_decay_schedule"",\n ""warmup_exponential_decay_schedule"",\n ""yogi"",\n ""zero_nans"",\n ""ZeroNansState"",\n ""ZoomLinesearchInfo"",\n)\n\n# _________________________________________\n# / Please don't use symbols in `_src` they \\n# \ are not part of the Optax public API. /\n# -----------------------------------------\n# \ ^__^\n# \ (oo)\_______\n# (__)\ )\/\\n# ||----w |\n# || ||\n#\n",python,tab +1427,1815643,".venv/lib/python3.10/site-packages/optax/__init__.py",10982,0,"",python,selection_command +1428,1818301,"train_lamap.py",0,0,"",python,tab +1429,1819370,"train_lamap.py",2364,0,"",python,selection_mouse +1430,1820645,"train_lamap.py",2377,0,"\n ",python,content +1431,1821075,"train_lamap.py",2382,0,"#",python,content +1432,1821076,"train_lamap.py",2383,0,"",python,selection_keyboard +1433,1821302,"train_lamap.py",2383,0," ",python,content 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nnx.value_and_grad(\n loss_fn, has_aux=True\n )(lam)\n optimizer.update(grads)",python,selection_command +1710,1954198,"train_lam.py",3889,166,"\n # --- Update model ---\n (loss, (recon, idx_counts, metrics)), grads = nnx.value_and_grad(\n loss_fn, has_aux=True\n )(lam)\n optimizer.update(grads)",python,selection_command +1711,1954830,"train_lam.py",3832,223," return lam_loss_fn(model, inputs, training=True)\n\n # --- Update model ---\n (loss, (recon, idx_counts, metrics)), grads = nnx.value_and_grad(\n loss_fn, has_aux=True\n )(lam)\n optimizer.update(grads)",python,selection_command +1712,1954987,"train_lam.py",3810,245," model.train()\n return lam_loss_fn(model, inputs, training=True)\n\n # --- Update model ---\n (loss, (recon, idx_counts, metrics)), grads = nnx.value_and_grad(\n loss_fn, has_aux=True\n )(lam)\n optimizer.update(grads)",python,selection_command +1713,1955145,"train_lam.py",3748,307," ) -> tuple[jax.Array, tuple[jax.Array, jax.Array, dict]]:\n model.train()\n return lam_loss_fn(model, inputs, training=True)\n\n # --- Update model ---\n (loss, (recon, idx_counts, metrics)), grads = nnx.value_and_grad(\n loss_fn, has_aux=True\n )(lam)\n optimizer.update(grads)",python,selection_command +1714,1955559,"train_lam.py",3810,245," model.train()\n return lam_loss_fn(model, inputs, training=True)\n\n # --- Update model ---\n (loss, (recon, idx_counts, metrics)), grads = nnx.value_and_grad(\n loss_fn, has_aux=True\n )(lam)\n optimizer.update(grads)",python,selection_command +1715,1956718,"train_lam.py",3748,307," ) -> tuple[jax.Array, tuple[jax.Array, jax.Array, dict]]:\n model.train()\n return lam_loss_fn(model, inputs, training=True)\n\n # --- Update model ---\n (loss, (recon, idx_counts, metrics)), grads = nnx.value_and_grad(\n loss_fn, has_aux=True\n )(lam)\n optimizer.update(grads)",python,selection_command +1716,1956876,"train_lam.py",3714,341," model: LatentActionModel,\n ) -> tuple[jax.Array, tuple[jax.Array, jax.Array, dict]]:\n model.train()\n return lam_loss_fn(model, inputs, training=True)\n\n # --- Update model ---\n (loss, (recon, idx_counts, metrics)), grads = nnx.value_and_grad(\n loss_fn, has_aux=True\n )(lam)\n optimizer.update(grads)",python,selection_command +1717,1957051,"train_lam.py",3697,358," def loss_fn(\n model: LatentActionModel,\n ) -> tuple[jax.Array, tuple[jax.Array, jax.Array, dict]]:\n model.train()\n return lam_loss_fn(model, inputs, training=True)\n\n # --- Update model ---\n (loss, (recon, idx_counts, metrics)), grads = nnx.value_and_grad(\n loss_fn, has_aux=True\n )(lam)\n optimizer.update(grads)",python,selection_command +1718,1957428,"train_lam.py",3697,0,"",python,selection_command +1719,1959895,"train_lamap.py",0,0,"",python,tab +1720,1960952,"train_lamap.py",2777,0,"\n def loss_fn(\n model: LatentActionModel,\n ) -> tuple[jax.Array, tuple[jax.Array, jax.Array, dict]]:\n model.train()\n return lam_loss_fn(model, inputs, training=True)\n\n # --- Update model ---\n (loss, (recon, idx_counts, metrics)), grads = nnx.value_and_grad(\n loss_fn, has_aux=True\n )(lam)\n optimizer.update(grads)",python,content +1721,1961014,"train_lamap.py",2782,0,"",python,selection_command +1722,1961419,"train_lamap.py",2777,0,"",python,selection_command +1723,1961833,"train_lamap.py",2777,1,"",python,content +1724,1961965,"train_lamap.py",2781,0,"",python,selection_command +1725,1961965,"train_lamap.py",2730,0,"",python,selection_command +1726,1962043,"train_lamap.py",2710,0,"",python,selection_command +1727,1962221,"train_lamap.py",2675,0,"",python,selection_command +1728,1962397,"train_lamap.py",2657,0,"",python,selection_command +1729,1962590,"train_lamap.py",2675,0,"",python,selection_command +1730,1962946,"train_lamap.py",2671,35,"",python,content +1731,1963097,"train_lamap.py",2675,0,"",python,selection_command +1732,1963098,"train_lamap.py",2695,0,"",python,selection_command +1733,1963367,"train_lamap.py",2746,0,"",python,selection_command 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+1900,2094643,"train_lamap.py",3089,49,"@nnx.jit\ndef train_step(\n lamap, \n inputs):",python,selection_command +1901,2094792,"train_lamap.py",3089,67,"@nnx.jit\ndef train_step(\n lamap, \n inputs):\n lamap.train()",python,selection_command +1902,2094957,"train_lamap.py",3089,131,"@nnx.jit\ndef train_step(\n lamap, \n inputs):\n lamap.train()\n grad_fn = jax.value_and_grad(lamap_loss_fn, allow_int=True)",python,selection_command +1903,2095103,"train_lamap.py",3089,186,"@nnx.jit\ndef train_step(\n lamap, \n inputs):\n lamap.train()\n grad_fn = jax.value_and_grad(lamap_loss_fn, allow_int=True)\n loss, grads = grad_fn(state.params, state, inputs)",python,selection_command +1904,2095244,"train_lamap.py",3089,233,"@nnx.jit\ndef train_step(\n lamap, \n inputs):\n lamap.train()\n grad_fn = jax.value_and_grad(lamap_loss_fn, allow_int=True)\n loss, grads = grad_fn(state.params, state, inputs)\n state = state.apply_gradients(grads=grads)",python,selection_command +1905,2095458,"train_lamap.py",3089,256,"@nnx.jit\ndef train_step(\n lamap, \n inputs):\n lamap.train()\n grad_fn = jax.value_and_grad(lamap_loss_fn, allow_int=True)\n loss, grads = grad_fn(state.params, state, inputs)\n state = state.apply_gradients(grads=grads)\n return state, loss",python,selection_command +1906,2095780,"train_lamap.py",3089,257,"",python,content +1907,2096540,"train_lamap.py",3089,1,"",python,content +1908,2097276,"train_lamap.py",3098,0,"",python,selection_command +1909,2097618,"train_lamap.py",3190,0,"",python,selection_command +1910,2097829,"train_lamap.py",3207,0,"",python,selection_command +1911,2097989,"train_lamap.py",3190,0,"",python,selection_command +1912,2098133,"train_lamap.py",3098,0,"",python,selection_command +1913,2098289,"train_lamap.py",3089,0,"",python,selection_command +1914,2098462,"train_lamap.py",3088,0,"",python,selection_command +1915,2098590,"train_lamap.py",3072,0,"",python,selection_command +1916,2098748,"train_lamap.py",3044,0,"",python,selection_command +1917,2099058,"train_lamap.py",3072,0,"",python,selection_command +1918,2104044,"train_lam.py",0,0,"",python,tab +1919,2104044,"train_lam.py",4861,0,"",python,selection_mouse +1920,2104477,"train_lam.py",4946,0,"",python,selection_mouse +1921,2106808,"train_lam.py",4923,31," return loss, recon, metrics",python,selection_command +1922,2106994,"train_lam.py",4848,106," (loss, (recon, _, metrics)) = lam_loss_fn(lam, inputs, training=False)\n return loss, recon, metrics",python,selection_command +1923,2107172,"train_lam.py",4833,121," lam.eval()\n (loss, (recon, _, metrics)) = lam_loss_fn(lam, inputs, training=False)\n return loss, recon, metrics",python,selection_command +1924,2108048,"train_lam.py",4846,0,"",python,selection_command +1925,2110069,"train_lamap.py",0,0,"",python,tab +1926,2110070,"train_lamap.py",3226,0,"",python,selection_mouse +1927,2111349,"train_lamap.py",3210,0,"",python,selection_mouse 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+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +2,58754,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"12:30:43 PM [info] Activating crowd-code\n12:30:43 PM [info] Recording started\n12:30:43 PM [info] Initializing git provider using file system watchers...\n12:30:45 PM [info] Retrying git provider initialization...\n12:30:46 PM [error] Not a git repository: EntryNotFound (FileSystemError): Error: ENOENT: no such file or directory, stat '/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/train_tokenizer_lr_sweep_1e-4/.git'\n12:30:47 PM [error] Not a git repository: EntryNotFound (FileSystemError): Error: ENOENT: no such file or directory, stat '/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/train_tokenizer_lr_sweep_1e-4/.git'\n",Log,tab diff --git a/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-a5aad456-d021-49a4-8368-8264b401bd3e1757237946327-2025_09_07-11.39.28.68/source.csv b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-a5aad456-d021-49a4-8368-8264b401bd3e1757237946327-2025_09_07-11.39.28.68/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..5ac558ded6036a0357c498cccddfcd4ac645f105 --- /dev/null +++ b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-a5aad456-d021-49a4-8368-8264b401bd3e1757237946327-2025_09_07-11.39.28.68/source.csv @@ -0,0 +1,2352 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +1,5,"train_tokenizer.py",0,0,"from dataclasses import dataclass, field\n\nimport os\nimport time\n\nimport einops\nfrom flax.training import orbax_utils\nfrom flax.training.train_state import TrainState\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\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_resolution: int = 64\n data_dir: str = ""data/coinrun_episodes""\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 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 name: str = """"\n tags: list[str] = field(default_factory=lambda: [""tokenizer""])\n wandb_id: str = """"\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, inputs, training=True, rngs={""dropout"": inputs[""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 rng = jax.random.PRNGKey(args.seed)\n if args.log:\n wandb_init_kwargs = {\n ""entity"": args.entity,\n ""project"": args.project,\n ""name"": args.name,\n ""tags"": args.tags,\n ""group"": ""debug"",\n ""config"": args,\n }\n if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\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_resolution, args.image_resolution, args.image_channels)\n inputs = dict(\n videos=jnp.zeros(\n (args.batch_size, args.seq_len, *image_shape), dtype=jnp.float32\n ),\n )\n init_params = tokenizer.init(_rng, inputs)\n\n # --- Load checkpoint ---\n step = 0\n if args.checkpoint:\n init_params[""params""].update(\n PyTreeCheckpointer().restore(args.checkpoint)[""model""][""params""][""params""]\n )\n # Assume checkpoint is of the form tokenizer__\n step += int(args.checkpoint.split(""_"")[-1])\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 # --- TRAIN LOOP ---\n dataloader = get_dataloader(args.data_dir, args.seq_len, args.batch_size)\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n rng, _rng = jax.random.split(rng)\n inputs = dict(videos=videos, rng=_rng)\n train_state, loss, recon, metrics = train_step(train_state, inputs)\n print(f""Step {step}, loss: {loss}"")\n step += 1\n\n # --- Logging ---\n if args.log:\n if step % args.log_interval == 0:\n wandb.log({""loss"": loss, ""step"": step, **metrics})\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 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(\n os.getcwd(), args.ckpt_dir, f""tokenizer_{ts}_{step}""\n ),\n ckpt,\n save_args=save_args,\n )\n if step >= args.num_steps:\n break\n",python,tab +2,1122,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"11:39:28 AM [info] Activating crowd-code\n11:39:28 AM [info] Recording started\n11:39:28 AM [info] Initializing git provider using file system watchers...\n",Log,tab +3,1421,"extension-output-pdoom-org.crowd-code-#1-crowd-code",153,0,"11:39:28 AM [info] Git repository found\n11:39:28 AM [info] Git provider initialized successfully\n11:39:28 AM [info] Initial git state: [object Object]\n",Log,content +4,2367,"extension-output-pdoom-org.crowd-code-#1-crowd-code",33,0,"",Log,selection_mouse +5,3206,"train_tokenizer.py",0,0,"",python,tab +6,7097,"TERMINAL",0,0,"undefined[tum_cte0515@hkn1991 jafar]$ source .venv/bin/activate",,terminal_command +7,10927,"train_dynamics.py",0,0,"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\nimport optax\nimport orbax\nimport numpy as np\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\n\nfrom genie import Genie, restore_genie_components\nfrom utils.dataloader import get_dataloader\n\nts = int(time.time())\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_000\n seed: int = 0\n seq_len: int = 16\n image_channels: int = 3\n image_resolution: int = 64\n data_dir: str = ""data/coinrun_episodes""\n # Optimization\n batch_size: int = 36\n min_lr: float = 3e-6\n max_lr: float = 3e-5\n warmup_steps: int = 5000\n # Tokenizer\n tokenizer_dim: int = 512\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 8\n tokenizer_num_heads: int = 8\n tokenizer_checkpoint: str = """"\n # LAM\n lam_dim: int = 512\n latent_action_dim: int = 32\n num_latent_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 8\n lam_num_heads: int = 8\n lam_checkpoint: str = """"\n # Dynamics\n dyna_dim: int = 512\n dyna_num_blocks: int = 12\n dyna_num_heads: int = 8\n dropout: float = 0.0\n mask_limit: float = 0.5\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 25000\n log_gradients: bool = False\n name: str = """"\n tags: list[str] = field(default_factory=lambda: [""dynamics""])\n wandb_id: str = """"\n\nargs = tyro.cli(Args)\n\n\ndef dynamics_loss_fn(params, state, inputs):\n """"""Compute masked dynamics loss""""""\n outputs = state.apply_fn(\n params, inputs, training=True, rngs={""dropout"": inputs[""dropout_rng""]}\n )\n mask = outputs[""mask""]\n ce_loss = optax.softmax_cross_entropy_with_integer_labels(\n outputs[""token_logits""], outputs[""video_tokens""]\n )\n ce_loss = (mask * ce_loss).sum() / mask.sum()\n acc = outputs[""token_logits""].argmax(-1) == outputs[""video_tokens""]\n acc = (mask * acc).sum() / mask.sum()\n select_probs = jax.nn.softmax(outputs[""token_logits""])\n metrics = dict(\n cross_entropy_loss=ce_loss,\n masked_token_accuracy=acc,\n select_logit=outputs[""token_logits""].max(-1).mean(),\n select_p=select_probs.max(-1).mean(),\n entropy=jax.scipy.special.entr(select_probs).sum(-1).mean(),\n )\n return ce_loss, (outputs[""recon""], metrics)\n\n\n@jax.jit\ndef train_step(state, inputs):\n """"""Update state and compute metrics""""""\n grad_fn = jax.value_and_grad(dynamics_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[""gradients_std/""] = jax.tree.map(\n lambda x: x.std(), grads[""params""][""dynamics""]\n )\n return state, loss, recon, metrics\n\n\nif __name__ == ""__main__"":\n rng = jax.random.PRNGKey(args.seed)\n if args.log:\n wandb_init_kwargs = {\n ""entity"": args.entity,\n ""project"": args.project,\n ""name"": args.name,\n ""tags"": args.tags,\n ""group"": ""debug"",\n ""config"": args,\n }\n if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n # --- Initialize model ---\n genie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n latent_action_dim=args.latent_action_dim,\n num_latent_actions=args.num_latent_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n # Dynamics\n dyna_dim=args.dyna_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n dropout=args.dropout,\n mask_limit=args.mask_limit,\n )\n rng, _rng = jax.random.split(rng)\n image_shape = (args.image_resolution, args.image_resolution, args.image_channels)\n dummy_inputs = dict(\n videos=jnp.zeros(\n (args.batch_size, args.seq_len, *image_shape), dtype=jnp.float32\n ),\n mask_rng=_rng,\n )\n rng, _rng = jax.random.split(rng)\n init_params = genie.init(_rng, dummy_inputs)\n init_params = restore_genie_components(\n init_params, args.tokenizer_checkpoint, args.lam_checkpoint\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=genie.apply, params=init_params, tx=tx)\n\n # --- TRAIN LOOP ---\n dataloader = get_dataloader(args.data_dir, args.seq_len, args.batch_size)\n step = 0\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n rng, _rng, _mask_rng = jax.random.split(rng, 3)\n inputs = dict(\n videos=videos,\n action=jnp.zeros((args.batch_size, args.seq_len), dtype=jnp.float32),\n dropout_rng=_rng,\n mask_rng=_mask_rng,\n )\n train_state, loss, recon, metrics = train_step(train_state, inputs)\n print(f""Step {step}, loss: {loss}"")\n step += 1\n\n # --- Logging ---\n if args.log:\n if step % args.log_interval == 0:\n wandb.log({""loss"": loss, ""step"": step, **metrics})\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 log_images = dict(\n image=wandb.Image(np.asarray(gt_seq[15])),\n recon=wandb.Image(np.asarray(recon_seq[15])),\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""genie_{ts}_{step}""),\n ckpt,\n save_args=save_args,\n )\n if step >= args.num_steps:\n break\n",python,tab +8,30606,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=2-00:00:00\n#SBATCH --cpus-per-task=8\n#SBATCH --gres=gpu:1\n#SBATCH --partition=accelerated\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/jafar_og_reproduction/dynamics/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/jafar_og_reproduction/dynamics/%x_%j.log\n#SBATCH --job-name=train_dynamics_coinrun_og_reproduction\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\ntags=""coinrun_og dynanmics 10m_dataset repro_mihir""\n\nnpy_records_dir=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m""\n\n# TODO mihir: update the tokenizer and lam checkpoints\ntokenizer_ckpt_dir=""""\nlam_ckpt_dir=""""\n\nCHECKPOINT_DIR=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/dynamics/${job_name}/${slurm_job_id}""\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --tokenizer_checkpoint=""${tokenizer_ckpt_dir}"" \\n --lam_checkpoint=""${lam_ckpt_dir}"" \\n --log_image_interval=1000 \\n --log \\n --name=""${job_name}_${slurm_job_id}"" \\n --tags ${tags} \\n --entity instant-uv \\n --project jafar \\n --data_dir $npy_records_dir \\n --wandb_id $slurm_job_id\n",shellscript,tab +9,49394,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_lam_reproduction.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=2-00:00:00\n#SBATCH --cpus-per-task=8\n#SBATCH --gres=gpu:1\n#SBATCH --partition=accelerated\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/jafar_og_reproduction/lam/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/jafar_og_reproduction/lam/%x_%j.log\n#SBATCH --job-name=train_lam_coinrun_og_reproduction\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\ntags=""coinrun_og reproduction 10m_dataset lam repro_mihir""\n\nnpy_records_dir=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m""\n\nCHECKPOINT_DIR=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/lam/$job_name/$slurm_job_id""\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_lam.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --log_image_interval=1000 \\n --log \\n --name=""${job_name}_${slurm_job_id}"" \\n --tags $tags \\n --entity instant-uv \\n --project jafar \\n --data_dir $npy_records_dir \\n --wandb_id $slurm_job_id\n",shellscript,tab +10,52177,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_tokenizer_repoduction.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=2-00:00:00\n#SBATCH --cpus-per-task=8\n#SBATCH --gres=gpu:1\n#SBATCH --partition=accelerated\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/jafar_og_reproduction/tokenizer/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/jafar_og_reproduction/tokenizer/%x_%j.log\n#SBATCH --job-name=train_tokenizer_coinrun_og_reproduction\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource 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idle\rPartition cpuonly: 190 nodes idle\rPartition dev_accelerated:\t 2 nodes idle\rPartition accelerated: 78 nodes idle\rPartition dev_accelerated-h100 :\t 1 nodes idle\rPartition accelerated-h100:\t 2 nodes idle\rPartition large:\t 3 nodes idle\rPartition accelerated-h200:\t 0 nodes idle",,terminal_output +89,256684,"TERMINAL",0,0,"4\t",,terminal_output +90,257677,"TERMINAL",0,0,"5\t",,terminal_output +91,258724,"TERMINAL",0,0,"6\t",,terminal_output +92,259791,"TERMINAL",0,0,"7\t",,terminal_output +93,260817,"TERMINAL",0,0,"8\t",,terminal_output +94,261819,"TERMINAL",0,0,"9\t",,terminal_output +95,262928,"TERMINAL",0,0,"50\t",,terminal_output +96,263893,"TERMINAL",0,0,"1\t",,terminal_output +97,264926,"TERMINAL",0,0,"2\t",,terminal_output +98,265954,"TERMINAL",0,0,"3\t",,terminal_output +99,267035,"TERMINAL",0,0,"4\t",,terminal_output +100,268064,"TERMINAL",0,0,"5\t",,terminal_output +101,268628,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",190,0,"",shellscript,selection_mouse +102,269150,"TERMINAL",0,0,"6\t",,terminal_output +103,270158,"TERMINAL",0,0,"8\t",,terminal_output +104,271195,"TERMINAL",0,0,"9\t",,terminal_output +105,271997,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction copy.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=2-00:00:00\n#SBATCH --cpus-per-task=8\n#SBATCH --gres=gpu:1\n#SBATCH --partition=accelerated\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/jafar_og_reproduction/dynamics/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/jafar_og_reproduction/dynamics/%x_%j.log\n#SBATCH --job-name=train_dynamics_coinrun_og_reproduction\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\ntags=""coinrun_og dynanmics 10m_dataset repro_mihir""\n\nnpy_records_dir=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m""\n\n# TODO mihir: update the tokenizer and lam checkpoints\ntokenizer_ckpt_dir=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/tokenizer/train_tokenizer_coinrun_og_reproduction/3466286""\nlam_ckpt_dir=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/lam/train_lam_coinrun_og_reproduction/3466287""\n\nCHECKPOINT_DIR=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/dynamics/${job_name}/${slurm_job_id}""\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --tokenizer_checkpoint=""${tokenizer_ckpt_dir}"" \\n --patch_size=16 \\n --lam_checkpoint=""${lam_ckpt_dir}"" \\n --log_image_interval=1000 \\n --log \\n --name=""${job_name}_${slurm_job_id}"" \\n --tags ${tags} \\n --entity instant-uv \\n --project jafar \\n --data_dir $npy_records_dir \\n --wandb_id $slurm_job_id\n",shellscript,tab +106,272305,"TERMINAL",0,0,"4:00\t",,terminal_output +107,273223,"TERMINAL",0,0,"1\t",,terminal_output +108,274254,"TERMINAL",0,0,"2\t",,terminal_output +109,275290,"TERMINAL",0,0,"3\t",,terminal_output +110,276326,"TERMINAL",0,0,"4\t",,terminal_output +111,277364,"TERMINAL",0,0,"5\t",,terminal_output +112,278536,"TERMINAL",0,0,"6\t",,terminal_output +113,279441,"TERMINAL",0,0,"7\t",,terminal_output +114,280560,"TERMINAL",0,0,"8\t",,terminal_output +115,281516,"TERMINAL",0,0,"9\t",,terminal_output +116,283252,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction-h100.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=2-00:00:00\n#SBATCH --cpus-per-task=8\n#SBATCH --gres=gpu:1\n#SBATCH --partition=accelerated\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/jafar_og_reproduction/dynamics/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/jafar_og_reproduction/dynamics/%x_%j.log\n#SBATCH --job-name=train_dynamics_coinrun_og_reproduction\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\ntags=""coinrun_og dynanmics 10m_dataset repro_mihir""\n\nnpy_records_dir=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m""\n\n# TODO mihir: update the tokenizer and lam checkpoints\ntokenizer_ckpt_dir=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/tokenizer/train_tokenizer_coinrun_og_reproduction/3466286""\nlam_ckpt_dir=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/lam/train_lam_coinrun_og_reproduction/3466287""\n\nCHECKPOINT_DIR=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/dynamics/${job_name}/${slurm_job_id}""\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --tokenizer_checkpoint=""${tokenizer_ckpt_dir}"" \\n --patch_size=16 \\n --lam_checkpoint=""${lam_ckpt_dir}"" \\n --log_image_interval=1000 \\n --log \\n --name=""${job_name}_${slurm_job_id}"" \\n --tags ${tags} \\n --entity instant-uv \\n --project jafar \\n --data_dir $npy_records_dir \\n --wandb_id $slurm_job_id\n",shellscript,tab 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+145,306239,"TERMINAL",0,0,"idling",,terminal_command +146,306343,"TERMINAL",0,0,"]633;C[?1049h(B[?7hEvery 1.0s: sinfo_t_idlehkn1991.localdomain: Sun Sep 7 11:44:34 2025Partition dev_cpuonly: 11 nodes idle\rPartition cpuonly: 190 nodes idle\rPartition dev_accelerated:\t 2 nodes idle\rPartition accelerated: 78 nodes idle\rPartition dev_accelerated-h100 :\t 1 nodes idle\rPartition accelerated-h100:\t 2 nodes idle\rPartition large:\t 3 nodes idle\rPartition accelerated-h200:\t 0 nodes idle",,terminal_output +147,307348,"TERMINAL",0,0,"5\t",,terminal_output +148,308371,"TERMINAL",0,0,"6\t",,terminal_output +149,309401,"TERMINAL",0,0,"7\t",,terminal_output +150,309711,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1991:~/Projects/jafar",,terminal_output +151,310753,"TERMINAL",0,0,"queue",,terminal_command +152,310850,"TERMINAL",0,0,"]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1991.localdomain: Sun Sep 7 11:44:38 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3472612 accelerat train_dy tum_cte0 R\t1:13\t 1 hkn08043472614 accelerat train_dy tum_cte0 PD\t0:00\t 1 (Priority)",,terminal_output +153,311885,"TERMINAL",0,0,"94",,terminal_output +154,313017,"TERMINAL",0,0,"405",,terminal_output +155,313949,"TERMINAL",0,0,"16",,terminal_output +156,315051,"TERMINAL",0,0,"27",,terminal_output +157,316176,"TERMINAL",0,0,"39",,terminal_output +158,317040,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1991:~/Projects/jafar",,terminal_output +159,318671,"TERMINAL",0,0,"fqueue",,terminal_command +160,318734,"TERMINAL",0,0,"]633;C[?1049h(B[?7hEvery 1.0s: squeue -o ""%.10i %.16P %.30j %.8u %.8T %.10M %.9l %.6D %R""hkn1991.localdomain: Sun Sep 7 11:44:46 2025JOBIDPARTITIONNAME USER STATE\t TIME TIME_LIMI NODES NODELIST(REASON)3472612\taccelerated train_dynamics_coinrun_og_repr tum_cte0 RUNNING\t 1:21 2-00:00:00\t1 hkn08043472614 accelerated-h100 train_dynamics_coinrun_og_repr tum_cte0 PENDING\t 0:00 2-00:00:00\t1 (Priority)\t\t",,terminal_output +161,319864,"TERMINAL",0,0,"72",,terminal_output +162,320814,"TERMINAL",0,0,"83",,terminal_output +163,321804,"TERMINAL",0,0,"94",,terminal_output +164,322838,"TERMINAL",0,0,"505",,terminal_output +165,323966,"TERMINAL",0,0,"16",,terminal_output +166,324996,"TERMINAL",0,0,"27",,terminal_output +167,325708,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1991:~/Projects/jafar",,terminal_output +168,404460,"TERMINAL",0,0,"logs",,terminal_command +169,406286,"TERMINAL",0,0,"cd jafar_og_reproduction/",,terminal_command +170,406324,"TERMINAL",0,0,"]633;C]0;tum_cte0515@hkn1991:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/jafar_og_reproduction",,terminal_output +171,408616,"TERMINAL",0,0,"cd dynamics/",,terminal_command +172,410467,"TERMINAL",0,0,"ls",,terminal_command +173,412256,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/jafar_og_reproduction/dynamics/train_dynamics_coinrun_og_reproduction_3472612.log",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=2-00:00:00\n#SBATCH --cpus-per-task=8\n#SBATCH --gres=gpu:1\n#SBATCH --partition=accelerated\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/jafar_og_reproduction/dynamics/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/jafar_og_reproduction/dynamics/%x_%j.log\n#SBATCH --job-name=train_dynamics_coinrun_og_reproduction\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\ntags=""coinrun_og dynanmics 10m_dataset repro_mihir""\n\nnpy_records_dir=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_coinrun/coinrun_episodes_10m""\n\n# TODO mihir: update the tokenizer and lam checkpoints\ntokenizer_ckpt_dir=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/tokenizer/train_tokenizer_coinrun_og_reproduction/3466286""\nlam_ckpt_dir=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/lam/train_lam_coinrun_og_reproduction/3466287""\n\nCHECKPOINT_DIR=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/dynamics/${job_name}/${slurm_job_id}""\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --tokenizer_checkpoint=""${tokenizer_ckpt_dir}"" \\n --patch_size=16 \\n --lam_checkpoint=""${lam_ckpt_dir}"" \\n --log_image_interval=1000 \\n --log \\n --name=""${job_name}_${slurm_job_id}"" \\n --tags ${tags} \\n --entity instant-uv \\n --project jafar \\n --data_dir $npy_records_dir \\n --wandb_id $slurm_job_id\nSLURM_JOB_USER=tum_cte0515\nSLURM_TASKS_PER_NODE=1\nSLURM_JOB_UID=999226\nSLURM_TASK_PID=3909444\nSLURM_JOB_GPUS=3\nSLURM_LOCALID=0\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar\nSLURMD_NODENAME=hkn0804\nSLURM_JOB_START_TIME=1757238205\nSLURM_CLUSTER_NAME=hk\nSLURM_JOB_END_TIME=1757411005\nSLURM_CPUS_ON_NODE=8\nSLURM_JOB_CPUS_PER_NODE=8\nSLURM_GPUS_ON_NODE=1\nSLURM_GTIDS=0\nSLURM_JOB_PARTITION=accelerated\nSLURM_TRES_PER_TASK=cpu=8\nSLURM_OOM_KILL_STEP=0\nSLURM_JOB_NUM_NODES=1\nSLURM_JOBID=3472612\nSLURM_JOB_QOS=normal\nSLURM_PROCID=0\nSLURM_CPUS_PER_TASK=8\nSLURM_NTASKS=1\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e27.hkn0804\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\nSLURM_SCRIPT_CONTEXT=prolog_task\nSLURM_NODELIST=hkn0804\nSLURM_JOB_ACCOUNT=hk-project-p0023960\nSLURM_PRIO_PROCESS=0\nSLURM_NPROCS=1\nSLURM_NNODES=1\nSLURM_SUBMIT_HOST=hkn1991.localdomain\nSLURM_JOB_ID=3472612\nSLURM_NODEID=0\nSLURM_CONF=/etc/slurm/slurm.conf\nSLURM_JOB_NAME=train_dynamics_coinrun_og_reproduction\nSLURM_NTASKS_PER_NODE=1\nSLURM_JOB_GID=502226\nSLURM_JOB_NODELIST=hkn0804\nGpuFreq=control_disabled\nwandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\nwandb: creating run\nwandb: Tracking run with wandb version 0.21.3\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/wandb/run-20250907_114419-3472612\nwandb: Run `wandb offline` to turn off syncing.\nwandb: Syncing run train_dynamics_coinrun_og_reproduction_3472612\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/3472612\n2025-09-07 11:44:35.257771: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\n2025-09-07 11:44:48.740866: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\n2025-09-07 11:44:52.925493: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 164, in \n init_params = restore_genie_components(\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 202, in restore_genie_components\n PyTreeCheckpointer().restore(tokenizer)[""model""][""params""][""params""]\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 306, in restore\n restored = self._restore(directory, args=ckpt_args)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 328, in _restore\n return self._handler.restore(directory, args=args)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 842, in restore\n structure, use_zarr3_metadata = self._get_internal_metadata(directory)\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 1000, in _get_internal_metadata\n raise FileNotFoundError(\nFileNotFoundError: No structure could be identified for the checkpoint at /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/tokenizer/train_tokenizer_coinrun_og_reproduction/3466286.\nwandb: \nwandb: 🚀 View run train_dynamics_coinrun_og_reproduction_3472612 at: https://wandb.ai/instant-uv/jafar/runs/3472612\nwandb: Find logs at: ../../../../../hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/wandb/run-20250907_114419-3472612/logs\nsrun: error: hkn0804: task 0: Exited with exit code 1\n\n============================= JOB FEEDBACK =============================\n\nJob ID: 3472612\nCluster: hk\nUser/Group: tum_cte0515/hk-project-p0023960\nAccount: hk-project-p0023960\nState: FAILED (exit code 1)\nPartition: accelerated\nNodes: 1\nCores per node: 8\nNodelist: hkn0804\nCPU Utilized: 00:01:25\nCPU Efficiency: 11.42% of 00:12:24 core-walltime\nJob Wall-clock time: 00:01:33\nStarttime: Sun Sep 7 11:43:25 2025\nEndtime: Sun Sep 7 11:44:58 2025\nMemory Utilized: 1.54 GB\nMemory Efficiency: 0.00% of 0.00 MB\nEnergy Consumed: 65527 Joule / 18.2019444444444 Watthours\nAverage node power draw: 704.591397849462 Watt\n",log,tab +174,421706,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",0,0,"",shellscript,tab +175,423484,"TERMINAL",0,0,"queue",,terminal_command +176,423546,"TERMINAL",0,0,"]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1991.localdomain: Sun Sep 7 11:46:31 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3472614 accelerat train_dy tum_cte0 PD\t0:00\t 1 (Priority)",,terminal_output +177,424584,"TERMINAL",0,0,"2\t",,terminal_output +178,425677,"TERMINAL",0,0,"3\t",,terminal_output +179,426424,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1991:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/jafar_og_reproduction/dynamics",,terminal_output +180,428599,"TERMINAL",0,0,"scancel 3472614",,terminal_command +181,428621,"TERMINAL",0,0,"]633;C]0;tum_cte0515@hkn1991:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/jafar_og_reproduction/dynamics",,terminal_output +182,434653,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",899,0,"",shellscript,selection_mouse 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+204,435326,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",899,272,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/tokenizer/train_tokenizer_coinrun_og_reproduction/3466286""\nlam_ckpt_dir=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/lam/tra",shellscript,selection_mouse +205,435351,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",899,311,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/tokenizer/train_tokenizer_coinrun_og_reproduction/3466286""\nlam_ckpt_dir=""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/lam/train_lam_coinrun_og_reproduction/3466287""",shellscript,selection_mouse +206,435518,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",899,154,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/tokenizer/train_tokenizer_coinrun_og_reproduction/3466286""",shellscript,selection_mouse +207,436032,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",899,153,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/tokenizer/train_tokenizer_coinrun_og_reproduction/3466286",shellscript,selection_mouse +208,442975,"TERMINAL",0,0,"cd /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/tokenizer/train_tokenizer_coinrun_og_reproduction/3466286",,terminal_command +209,443576,"TERMINAL",0,0,"ls",,terminal_command +210,443648,"TERMINAL",0,0,"]633;C",,terminal_output +211,443825,"TERMINAL",0,0,"tokenizer_1757013407_10000 tokenizer_1757013407_160000 tokenizer_1757013407_220000 tokenizer_1757013407_30000\r\ntokenizer_1757013407_100000 tokenizer_1757013407_170000 tokenizer_1757013407_230000 tokenizer_1757013407_40000\r\ntokenizer_1757013407_110000 tokenizer_1757013407_180000 tokenizer_1757013407_240000 tokenizer_1757013407_50000\r\ntokenizer_1757013407_120000 tokenizer_1757013407_190000 tokenizer_1757013407_250000 tokenizer_1757013407_60000\r\ntokenizer_1757013407_130000 tokenizer_1757013407_20000 tokenizer_1757013407_260000 tokenizer_1757013407_70000\r\ntokenizer_1757013407_140000 tokenizer_1757013407_200000 tokenizer_1757013407_270000 tokenizer_1757013407_80000\r\ntokenizer_1757013407_150000 tokenizer_1757013407_210000 tokenizer_1757013407_280000 tokenizer_1757013407_90000\r\n]0;tum_cte0515@hkn1991:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/tokenizer/train_tokenizer_coinrun_og_reproduction/3466286",,terminal_output 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lam_1757013407_70000\r\nlam_1757013407_110000 lam_1757013407_150000 lam_1757013407_190000 lam_1757013407_40000 lam_1757013407_80000\r\nlam_1757013407_120000 lam_1757013407_160000 lam_1757013407_20000 lam_1757013407_50000 lam_1757013407_90000\r\n]0;tum_cte0515@hkn1991:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/mihir/jafar_og_reproduction/lam/train_lam_coinrun_og_reproduction/3466287",,terminal_output +228,485679,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",1237,0,"",shellscript,selection_mouse +229,486642,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",1237,0,"/",shellscript,content +230,486643,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",1238,0,"",shellscript,selection_keyboard +231,486888,"slurm/jobs/mihir/horeka/jafar_og_reproduction/og_coinrun_dynamics_reproduction.sbatch",1238,0,"lam_1757013407_200000",shellscript,content 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b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-b85ae4be-e003-4b5f-b92d-85f73081a0e71757580020189-2025_09_11-10.41.19.400/source.csv @@ -0,0 +1,4 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +1,15,"train_dynamics.py",0,0,"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\nimport optax\nimport orbax\nimport numpy as np\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\n\nfrom genie import Genie, restore_genie_components\nfrom utils.dataloader import get_dataloader\n\nts = int(time.time())\n\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_000\n seed: int = 0\n seq_len: int = 16\n image_channels: int = 3\n image_resolution: int = 64\n data_dir: str = ""data/coinrun_episodes""\n # Optimization\n batch_size: int = 36\n min_lr: float = 3e-6\n max_lr: float = 3e-5\n warmup_steps: int = 5000\n # Tokenizer\n tokenizer_dim: int = 512\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 8\n tokenizer_num_heads: int = 8\n tokenizer_checkpoint: str = """"\n # LAM\n lam_dim: int = 512\n latent_action_dim: int = 32\n num_latent_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 8\n lam_num_heads: int = 8\n lam_checkpoint: str = """"\n # Dynamics\n dyna_dim: int = 512\n dyna_num_blocks: int = 12\n dyna_num_heads: int = 8\n dropout: float = 0.0\n mask_limit: float = 0.5\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 25000\n log_gradients: bool = False\n name: str = """"\n tags: list[str] = field(default_factory=lambda: [""dynamics""])\n wandb_id: str = """"\n\nargs = tyro.cli(Args)\n\n\ndef dynamics_loss_fn(params, state, inputs):\n """"""Compute masked dynamics loss""""""\n outputs = state.apply_fn(\n params, inputs, training=True, rngs={""dropout"": inputs[""dropout_rng""]}\n )\n mask = outputs[""mask""]\n ce_loss = optax.softmax_cross_entropy_with_integer_labels(\n outputs[""token_logits""], outputs[""video_tokens""]\n )\n ce_loss = (mask * ce_loss).sum() / mask.sum()\n acc = outputs[""token_logits""].argmax(-1) == outputs[""video_tokens""]\n acc = (mask * acc).sum() / mask.sum()\n select_probs = jax.nn.softmax(outputs[""token_logits""])\n metrics = dict(\n cross_entropy_loss=ce_loss,\n masked_token_accuracy=acc,\n select_logit=outputs[""token_logits""].max(-1).mean(),\n select_p=select_probs.max(-1).mean(),\n entropy=jax.scipy.special.entr(select_probs).sum(-1).mean(),\n )\n return ce_loss, (outputs[""recon""], metrics)\n\n\n@jax.jit\ndef train_step(state, inputs):\n """"""Update state and compute metrics""""""\n grad_fn = jax.value_and_grad(dynamics_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[""gradients_std/""] = jax.tree.map(\n lambda x: x.std(), grads[""params""][""dynamics""]\n )\n return state, loss, recon, metrics\n\n\nif __name__ == ""__main__"":\n rng = jax.random.PRNGKey(args.seed)\n if args.log:\n wandb_init_kwargs = {\n ""entity"": args.entity,\n ""project"": args.project,\n ""name"": args.name,\n ""tags"": args.tags,\n ""group"": ""debug"",\n ""config"": args,\n }\n if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n # --- Initialize model ---\n genie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n latent_action_dim=args.latent_action_dim,\n num_latent_actions=args.num_latent_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n # Dynamics\n dyna_dim=args.dyna_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n dropout=args.dropout,\n mask_limit=args.mask_limit,\n )\n rng, _rng = jax.random.split(rng)\n image_shape = (args.image_resolution, args.image_resolution, args.image_channels)\n dummy_inputs = dict(\n videos=jnp.zeros(\n (args.batch_size, args.seq_len, *image_shape), dtype=jnp.float32\n ),\n mask_rng=_rng,\n )\n rng, _rng = jax.random.split(rng)\n init_params = genie.init(_rng, dummy_inputs)\n init_params = restore_genie_components(\n init_params, args.tokenizer_checkpoint, args.lam_checkpoint\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=genie.apply, params=init_params, tx=tx)\n\n # --- TRAIN LOOP ---\n dataloader = get_dataloader(args.data_dir, args.seq_len, args.batch_size)\n step = 0\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n rng, _rng, _mask_rng = jax.random.split(rng, 3)\n inputs = dict(\n videos=videos,\n action=jnp.zeros((args.batch_size, args.seq_len), dtype=jnp.float32),\n dropout_rng=_rng,\n mask_rng=_mask_rng,\n )\n train_state, loss, recon, metrics = train_step(train_state, inputs)\n print(f""Step {step}, loss: {loss}"")\n step += 1\n\n # --- Logging ---\n if args.log:\n if step % args.log_interval == 0:\n wandb.log({""loss"": loss, ""step"": step, **metrics})\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 log_images = dict(\n image=wandb.Image(np.asarray(gt_seq[15])),\n recon=wandb.Image(np.asarray(recon_seq[15])),\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""genie_{ts}_{step}""),\n ckpt,\n save_args=save_args,\n )\n if step >= args.num_steps:\n break\n",python,tab +2,910,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"10:41:19 AM [info] Activating crowd-code\n10:41:19 AM [info] Recording started\n10:41:19 AM [info] Initializing git provider using file system watchers...\n",Log,tab +3,1419,"extension-output-pdoom-org.crowd-code-#1-crowd-code",153,0,"10:41:19 AM [info] Git repository found\n10:41:19 AM [info] Git provider initialized successfully\n10:41:19 AM [info] Initial git state: [object Object]\n",Log,content diff --git a/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-c0873732-2064-4df8-be5c-5c4fe03049aa1751367864693-2025_07_01-13.05.12.330/source.csv b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-c0873732-2064-4df8-be5c-5c4fe03049aa1751367864693-2025_07_01-13.05.12.330/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..87d6c9f3402b5409a1e3fbabe52632c561f040e8 --- /dev/null +++ b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-c0873732-2064-4df8-be5c-5c4fe03049aa1751367864693-2025_07_01-13.05.12.330/source.csv @@ -0,0 +1,204 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +2,663,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"1:05:12 PM [info] Activating crowd-code\n1:05:12 PM [info] Recording started\n1:05:12 PM [info] Initializing git provider using file system watchers...\n1:05:12 PM [info] Git repository found\n1:05:12 PM [info] Git provider initialized successfully\n1:05:12 PM [info] Initial git state: [object Object]\n",Log,tab +3,3981,"TERMINAL",0,0,"/bin/python3 /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/printEnvVariablesToFile.py /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash/envVars.txt",,terminal_command +4,4032,"TERMINAL",0,0,"]633;E;2025-07-01 13:05:16 /bin/python3 /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/printEnvVariablesToFile.py /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash/envVars.txt;9ca2de01-018b-4814-8d7c-cf21dc51d48c]633;C",,terminal_output +5,4078,"TERMINAL",0,0,"]0;tum_cte0515@hkn1991:/hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash]633;D;0",,terminal_output +6,140668,"TERMINAL",0,0,"ls $ws_dir",,terminal_command +7,140713,"TERMINAL",0,0,"]633;E;2025-07-01 13:07:32 ls $ws_dir;4d11dbdc-690b-4257-b927-bbd493ebfa56]633;C",,terminal_output +8,140977,"TERMINAL",0,0,"checkpoints knoms_arrayrecords_500_shards knoms_tfrecords knoms_tfrecords_500_shards_overfit_1 open_ai_minecraft_first_try_npy overfit_dir\r\ncoinrun knoms_mp4 knoms_tfrecords_200_shards knoms_tfrecords_500_shards_overfit_10 open_ai_minecraft_first_try_tfrecord procgen_env_16_episodes_20000\r\ndata_knoms knoms_mp4_clips knoms_tfrecords_2_shards_overfit open_ai_minecraft open_ai_minecraft_npy\r\ndummy knoms_npy knoms_tfrecords_500_shards open_ai_minecraft_first_try open_ai_minecraft_tfrecord\r\n]0;tum_cte0515@hkn1991:~/Projects/jafar]633;D;0",,terminal_output +9,145213,"TERMINAL",0,0,"ls $ws_dir/../",,terminal_command +10,145252,"TERMINAL",0,0,"]633;E;2025-07-01 13:07:37 ls $ws_dir/../;4d11dbdc-690b-4257-b927-bbd493ebfa56]633;Ccheckpoints count_items.sh data logs scripts\r\n]0;tum_cte0515@hkn1991:~/Projects/jafar]633;D;0",,terminal_output +11,147530,"TERMINAL",0,0,"ls $ws_dir/../logs",,terminal_command +12,147592,"TERMINAL",0,0,"]633;E;2025-07-01 13:07:39 ls $ws_dir/../logs;4d11dbdc-690b-4257-b927-bbd493ebfa56]633;C3306965 logs_alfred\r\n]0;tum_cte0515@hkn1991:~/Projects/jafar]633;D;0",,terminal_output +13,153045,"TERMINAL",0,0,"ls $ws_dir/../logs/logs_alfred",,terminal_command +14,153102,"TERMINAL",0,0,"]633;E;2025-07-01 13:07:45 ls $ws_dir/../logs/logs_alfred;4d11dbdc-690b-4257-b927-bbd493ebfa56]633;Clogs_training_dyn logs_training_tokenizer\r\n]0;tum_cte0515@hkn1991:~/Projects/jafar]633;D;0",,terminal_output +15,162155,"TERMINAL",0,0,"ls $ws_dir/../logs/logs_alfred/logs_training_tokenizer",,terminal_command +16,162198,"TERMINAL",0,0,"]633;E;2025-07-01 13:07:54 ls $ws_dir/../logs/logs_alfred/logs_training_tokenizer;4d11dbdc-690b-4257-b927-bbd493ebfa56]633;C",,terminal_output +17,162274,"TERMINAL",0,0,"train_tokenizer_batch_size_scaling_16_node_3307475.log train_tokenizer_batch_size_scaling_2_node_3307422.log train_tokenizer_overfit_sample_size_0_5_21mio_3297706.log\r\ntrain_tokenizer_batch_size_scaling_1_node_3307372.log train_tokenizer_batch_size_scaling_2_node_3307472.log train_tokenizer_overfit_sample_size_0_5_3297582.log\r\ntrain_tokenizer_batch_size_scaling_1_node_3307421.log train_tokenizer_batch_size_scaling_4_node_3307473.log train_tokenizer_overfit_sample_size_0_5_3297586.log\r\ntrain_tokenizer_batch_size_scaling_1_node_3307470.log train_tokenizer_batch_size_scaling_4_node_3307688.log train_tokenizer_overfit_sample_size_0_5_9mio_3297693.log\r\ntrain_tokenizer_batch_size_scaling_2_node_3307416.log train_tokenizer_batch_size_scaling_8_node_3307474.log\r\n]0;tum_cte0515@hkn1991:~/Projects/jafar]633;D;0",,terminal_output +18,174522,"TERMINAL",0,0,"ls $ws_dir/../logs/logs_alfred/logs_training_tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474.log",,terminal_command +19,174550,"TERMINAL",0,0,"]633;E;2025-07-01 13:08:06 ls $ws_dir/../logs/logs_alfred/logs_training_tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474.log;4d11dbdc-690b-4257-b927-bbd493ebfa56]633;C/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/../logs/logs_alfred/logs_training_tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474.log\r\n]0;tum_cte0515@hkn1991:~/Projects/jafar]633;D;0",,terminal_output +20,177362,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_alfred/logs_training_tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474.log",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=10:00:00\n#SBATCH --account=hk-project-p0023960\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=8\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_alfred/logs_training_tokenizer/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_alfred/logs_training_tokenizer/%x_%j.log\n#SBATCH --mail-user=avocadoaling@gmail.com\n#SBATCH --mail-type=ALL\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv_jafar/bin/activate\n\nJOB_NAME=$SLURM_JOB_NAME\nSLURM_JOB_ID=$SLURM_JOB_ID\nTAGS=(tokenizer batch-size-scaling 8-node time-step adjusted-lr)\n\nWS_DIR='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\nTF_RECORDS_DIR=$WS_DIR/data/open_ai_minecraft_tfrecord\nCHECKPOINT_DIR=$WS_DIR/checkpoints/tokenizer/${JOB_NAME}_${SLURM_JOB_ID}\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=8.49e-4 \\n --max_lr=8.49e-4 \\n --log_image_interval=250 \\n --log \\n --name=${JOB_NAME}-${SLURM_JOB_ID} \\n --tags $TAGS \\n --entity instant-uv \\n --project jafar \\n --log_checkpoint_interval=1000 \\n --data_dir $TF_RECORDS_DIR\nSLURM_JOB_USER=tum_ind3695\nSLURM_TASKS_PER_NODE=4(x8)\nSLURM_JOB_UID=991285\nSLURM_TASK_PID=634526\nSLURM_JOB_GPUS=0,1,2,3\nSLURM_LOCALID=0\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling\nSLURMD_NODENAME=hkn0420\nSLURM_JOB_START_TIME=1751318063\nSLURM_CLUSTER_NAME=hk\nSLURM_JOB_END_TIME=1751354063\nSLURM_CPUS_ON_NODE=32\nSLURM_JOB_CPUS_PER_NODE=32(x8)\nSLURM_GPUS_ON_NODE=4\nSLURM_GTIDS=0\nSLURM_JOB_PARTITION=accelerated\nSLURM_TRES_PER_TASK=cpu=8\nSLURM_OOM_KILL_STEP=0\nSLURM_JOB_NUM_NODES=8\nSLURM_JOBID=3307474\nSLURM_JOB_QOS=normal\nSLURM_PROCID=0\nSLURM_CPUS_PER_TASK=8\nSLURM_NTASKS=32\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e12.hkn0420\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\nSLURM_SCRIPT_CONTEXT=prolog_task\nSLURM_NODELIST=hkn[0420,0516,0520,0523,0704,0717,0810,0820]\nSLURM_JOB_ACCOUNT=hk-project-p0023960\nSLURM_PRIO_PROCESS=0\nSLURM_NPROCS=32\nSLURM_NNODES=8\nSLURM_SUBMIT_HOST=hkn1991.localdomain\nSLURM_JOB_ID=3307474\nSLURM_NODEID=0\nSLURM_CONF=/etc/slurm/slurm.conf\nSLURM_JOB_NAME=train_tokenizer_batch_size_scaling_8_node\nSLURM_NTASKS_PER_NODE=4\nSLURM_JOB_GID=502289\nSLURM_JOB_NODELIST=hkn[0420,0516,0520,0523,0704,0717,0810,0820]\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\nGpuFreq=control_disabled\n2025-06-30 23:14:53.456775: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:14:53.456867: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:14:53.457066: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:14:53.457602: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318093.469657 2856352 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318093.469881 2856354 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318093.470016 2856351 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318093.470869 2856353 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nE0000 00:00:1751318093.474025 2856352 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318093.474537 2856354 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318093.474636 2856351 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318093.475228 2856353 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nW0000 00:00:1751318093.487901 2856353 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318093.487916 2856353 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318093.487918 2856353 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318093.487919 2856353 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318093.488292 2856352 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318093.488308 2856352 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318093.488310 2856352 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318093.488312 2856352 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318093.488485 2856351 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318093.488502 2856351 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318093.488503 2856351 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318093.488505 2856351 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318093.488598 2856354 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318093.488615 2856354 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318093.488617 2856354 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318093.488619 2856354 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n2025-06-30 23:14:54.547413: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:14:54.547731: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:14:54.547735: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:14:54.547955: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318094.560520 2160604 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318094.560830 2160602 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318094.560803 2160603 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318094.561295 2160605 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nE0000 00:00:1751318094.565025 2160604 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318094.565476 2160602 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318094.565428 2160603 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318094.566045 2160605 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n2025-06-30 23:14:54.566659: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:14:54.566657: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:14:54.566878: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:14:54.567170: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\nW0000 00:00:1751318094.578832 2160604 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.578847 2160604 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.578849 2160604 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.578850 2160604 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.579318 2160602 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.579333 2160602 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.579335 2160602 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.579337 2160602 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.579319 2160603 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.579333 2160603 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.579336 2160603 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.579337 2160603 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.579563 2160605 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.579580 2160605 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.579582 2160605 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.579583 2160605 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318094.579697 2427863 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318094.579746 2427864 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318094.579766 2427865 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318094.580755 2427862 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nE0000 00:00:1751318094.584398 2427863 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318094.584385 2427864 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318094.584400 2427865 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318094.585575 2427862 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nW0000 00:00:1751318094.598710 2427863 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.598724 2427863 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.598725 2427863 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.598727 2427863 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.598695 2427864 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.598711 2427864 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.598713 2427864 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.598715 2427864 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.598698 2427865 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.598713 2427865 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.598716 2427865 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.598717 2427865 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.599187 2427862 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.599205 2427862 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.599207 2427862 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.599208 2427862 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n2025-06-30 23:14:54.661486: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:14:54.661507: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:14:54.661685: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:14:54.661718: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318094.674450 634594 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318094.674449 634595 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318094.674517 634596 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318094.674538 634593 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nE0000 00:00:1751318094.678694 634596 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318094.678813 634594 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318094.679115 634593 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318094.679028 634595 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nW0000 00:00:1751318094.692678 634593 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.692692 634593 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.692694 634593 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.692696 634593 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.692652 634594 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.692665 634594 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.692667 634594 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.692668 634594 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.692666 634595 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.692682 634595 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.692683 634595 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.692685 634595 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.692627 634596 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.692643 634596 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.692645 634596 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318094.692646 634596 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318099.464209 2856354 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318099.464573 2856353 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318099.464844 2856351 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318099.467389 2856352 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318099.527601 2160602 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318099.527693 2160603 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318099.527574 2160604 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318099.527854 2160605 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318099.612797 2427862 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318099.612805 2427863 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318099.612785 2427864 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318099.612795 2427865 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318100.065098 634594 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318100.065192 634595 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318100.065238 634593 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318100.065260 634596 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\n2025-06-30 23:15:01.213957: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:15:01.213951: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:15:01.213959: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:15:01.213955: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318101.384363 1578014 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318101.384257 1578015 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318101.384268 1578017 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318101.384491 1578016 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nE0000 00:00:1751318101.391716 1578014 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318101.391711 1578015 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318101.391718 1578016 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318101.391711 1578017 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nW0000 00:00:1751318101.879162 1578014 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318101.879190 1578014 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318101.879193 1578014 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318101.879194 1578014 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318101.879159 1578015 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318101.879189 1578015 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318101.879190 1578015 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318101.879192 1578015 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318101.879165 1578016 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318101.879191 1578016 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318101.879193 1578016 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318101.879195 1578016 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318101.879160 1578017 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318101.879185 1578017 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318101.879187 1578017 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318101.879188 1578017 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n2025-06-30 23:15:02.184361: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:15:02.184363: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:15:02.184357: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:15:02.184357: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318102.214725 1012654 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318102.214631 1012655 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318102.214813 1012656 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318102.214638 1012657 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nE0000 00:00:1751318102.234122 1012655 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318102.234118 1012657 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318102.234789 1012654 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318102.234772 1012656 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nW0000 00:00:1751318102.361690 1012654 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.361715 1012654 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.361717 1012654 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.361719 1012654 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.361694 1012655 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.361718 1012655 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.361720 1012655 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.361721 1012655 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.361687 1012656 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.361712 1012656 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.361715 1012656 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.361716 1012656 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.361696 1012657 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.361714 1012657 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.361717 1012657 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.361718 1012657 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n2025-06-30 23:15:02.368657: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:15:02.368656: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:15:02.368655: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:15:02.368654: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318102.398233 3005151 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318102.398299 3005153 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318102.398548 3005152 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318102.398657 3005154 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nE0000 00:00:1751318102.404511 3005151 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318102.404506 3005152 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318102.404509 3005153 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318102.404508 3005154 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n2025-06-30 23:15:02.421302: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:15:02.421417: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:15:02.421303: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2025-06-30 23:15:02.421425: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318102.455392 3894030 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318102.455330 3894031 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318102.455513 3894032 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1751318102.455482 3894033 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nE0000 00:00:1751318102.461184 3894031 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318102.461184 3894032 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318102.461181 3894033 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nE0000 00:00:1751318102.462009 3894030 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nW0000 00:00:1751318102.504381 3005151 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.504408 3005151 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.504410 3005151 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.504412 3005151 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.504377 3005152 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.504402 3005152 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.504405 3005152 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.504407 3005152 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.504382 3005153 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.504409 3005153 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.504412 3005153 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.504414 3005153 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.504380 3005154 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.504405 3005154 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.504407 3005154 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.504409 3005154 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.534858 3894030 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.534885 3894030 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.534887 3894030 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.534889 3894030 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.534860 3894031 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.534886 3894031 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.534888 3894031 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.534890 3894031 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.534857 3894032 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.534883 3894032 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.534885 3894032 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.534887 3894032 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.534855 3894033 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.534878 3894033 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.534881 3894033 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318102.534883 3894033 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1751318118.615248 1578017 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318118.615450 1578015 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318118.615611 1578014 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318118.615637 1578016 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318120.944360 3894031 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318120.944433 3894032 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318120.944359 3894033 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318120.944517 3894030 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318122.638730 3005151 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318122.638731 3005153 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318122.638863 3005154 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318122.638879 3005152 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318123.581820 1012654 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318123.581743 1012655 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318123.581753 1012657 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nW0000 00:00:1751318123.581744 1012656 gpu_device.cc:2341] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\nwandb: Currently logged in as: avocadoali (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\nwandb: Tracking run with wandb version 0.19.11\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/wandb/run-20250630_231525-hzw9mfvm\nwandb: Run `wandb offline` to turn off syncing.\nwandb: Syncing run train_tokenizer_batch_size_scaling_8_node-3307474\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/hzw9mfvm\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 0.13509593904018402\nStep 6, loss: 0.15321968495845795\nStep 7, loss: 0.14519496262073517\nStep 8, loss: 0.14099560678005219\nStep 9, loss: 0.1463150680065155\nStep 10, loss: 0.14669667184352875\nStep 11, loss: 0.1430281549692154\nStep 12, loss: 0.13978496193885803\nStep 13, loss: 0.13720174133777618\nStep 14, loss: 0.14147496223449707\nStep 15, loss: 0.12998127937316895\nStep 16, loss: 0.12920445203781128\nStep 17, loss: 0.13227719068527222\nStep 18, loss: 0.125120609998703\nStep 19, loss: 0.13470731675624847\nStep 20, loss: 0.12474726885557175\nStep 21, loss: 0.1333005726337433\nStep 22, loss: 0.1423025131225586\nStep 23, loss: 0.1377020627260208\nStep 24, loss: 0.1437397450208664\nStep 25, loss: 0.13958904147148132\nStep 26, loss: 0.13821439445018768\nStep 27, loss: 0.14022579789161682\nStep 28, loss: 0.13478153944015503\nStep 29, loss: 0.1434846669435501\nStep 30, loss: 0.13330970704555511\nStep 31, loss: 0.14295107126235962\nStep 32, loss: 0.14157991111278534\nStep 33, loss: 0.13778111338615417\nStep 34, loss: 0.1367509663105011\nStep 35, loss: 0.14291462302207947\nStep 36, loss: 0.13713470101356506\nStep 37, loss: 0.1495264619588852\nStep 38, loss: 0.15450292825698853\nStep 39, loss: 0.1477455347776413\nStep 40, loss: 0.14942966401576996\nStep 41, loss: 0.15012970566749573\nStep 42, loss: 0.14514152705669403\nStep 43, loss: 0.1656753122806549\nStep 44, loss: 0.15873289108276367\nStep 45, loss: 0.17154277861118317\nStep 46, loss: 0.17314167320728302\nStep 47, loss: 0.17424646019935608\nStep 48, loss: 0.17465342581272125\nStep 49, loss: 0.17834438383579254\nStep 50, loss: 0.1750008463859558\nStep 51, loss: 0.17383235692977905\nStep 52, loss: 0.1738968789577484\nStep 53, loss: 0.1788439154624939\nStep 54, loss: 0.17455320060253143\nStep 55, loss: 0.17899788916110992\nStep 56, loss: 0.17514869570732117\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 0.13509593904018402\nStep 6, loss: 0.15321968495845795\nStep 7, loss: 0.14519496262073517\nStep 8, loss: 0.14099560678005219\nStep 9, loss: 0.1463150680065155\nStep 10, loss: 0.14669667184352875\nStep 11, loss: 0.1430281549692154\nStep 12, loss: 0.13978496193885803\nStep 13, loss: 0.13720174133777618\nStep 14, loss: 0.14147496223449707\nStep 15, loss: 0.12998127937316895\nStep 16, loss: 0.12920445203781128\nStep 17, loss: 0.13227719068527222\nStep 18, loss: 0.125120609998703\nStep 19, loss: 0.13470731675624847\nStep 20, loss: 0.12474726885557175\nStep 21, loss: 0.1333005726337433\nStep 22, loss: 0.1423025131225586\nStep 23, loss: 0.1377020627260208\nStep 24, loss: 0.1437397450208664\nStep 25, loss: 0.13958904147148132\nStep 26, loss: 0.13821439445018768\nStep 27, loss: 0.14022579789161682\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 0.13509593904018402\nStep 6, loss: 0.15321968495845795\nStep 7, loss: 0.14519496262073517\nStep 8, loss: 0.14099560678005219\nStep 9, loss: 0.1463150680065155\nStep 10, loss: 0.14669667184352875\nStep 11, loss: 0.1430281549692154\nStep 12, loss: 0.13978496193885803\nStep 13, loss: 0.13720174133777618\nStep 14, loss: 0.14147496223449707\nStep 15, loss: 0.12998127937316895\nStep 16, loss: 0.12920445203781128\nStep 17, loss: 0.13227719068527222\nStep 18, loss: 0.125120609998703\nStep 19, loss: 0.13470731675624847\nStep 20, loss: 0.12474726885557175\nStep 21, loss: 0.1333005726337433\nStep 22, loss: 0.1423025131225586\nStep 23, loss: 0.1377020627260208\nStep 24, loss: 0.1437397450208664\nStep 25, loss: 0.13958904147148132\nStep 26, loss: 0.13821439445018768\nStep 27, loss: 0.14022579789161682\nStep 57, loss: 0.17526665329933167\nStep 58, loss: 0.17442293465137482\nStep 59, loss: 0.1755731999874115\nStep 60, loss: 0.17686791718006134\nStep 61, loss: 0.1774514615535736\nStep 62, loss: 0.17734190821647644\nStep 63, loss: 0.17445750534534454\nStep 64, loss: 0.1791164129972458\nStep 65, loss: 0.17443639039993286\nStep 66, loss: 0.1721915602684021\nStep 67, loss: 0.17724011838436127\nStep 68, loss: 0.1726222038269043\nStep 69, loss: 0.17514818906784058\nStep 70, loss: 0.17387129366397858\nStep 71, loss: 0.17663298547267914\nStep 72, loss: 0.17917156219482422\nStep 73, loss: 0.17480577528476715\nStep 74, loss: 0.17429769039154053\nStep 75, loss: 0.17326530814170837\nStep 76, loss: 0.17588405311107635\nStep 77, loss: 0.16884107887744904\nStep 78, loss: 0.17379479110240936\nStep 79, loss: 0.17513428628444672\nStep 80, loss: 0.17434917390346527\nStep 81, loss: 0.17318695783615112\nStep 82, loss: 0.17380864918231964\nStep 83, loss: 0.17578117549419403\nStep 84, loss: 0.1710706204175949\nStep 85, loss: 0.17886455357074738\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 0.13509593904018402\nStep 6, loss: 0.15321968495845795\nStep 7, loss: 0.14519496262073517\nStep 8, loss: 0.14099560678005219\nStep 9, loss: 0.1463150680065155\nStep 10, loss: 0.14669667184352875\nStep 11, loss: 0.1430281549692154\nStep 12, loss: 0.13978496193885803\nStep 13, loss: 0.13720174133777618\nStep 14, loss: 0.14147496223449707\nStep 15, loss: 0.12998127937316895\nStep 16, loss: 0.12920445203781128\nStep 17, loss: 0.13227719068527222\nStep 18, loss: 0.125120609998703\nStep 19, loss: 0.13470731675624847\nStep 20, loss: 0.12474726885557175\nStep 21, loss: 0.1333005726337433\nStep 22, loss: 0.1423025131225586\nStep 23, loss: 0.1377020627260208\nStep 24, loss: 0.1437397450208664\nStep 25, loss: 0.13958904147148132\nStep 26, loss: 0.13821439445018768\nStep 27, loss: 0.14022579789161682\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 0.13509593904018402\nStep 6, loss: 0.15321968495845795\nStep 7, loss: 0.14519496262073517\nStep 8, loss: 0.14099560678005219\nStep 9, loss: 0.1463150680065155\nStep 10, loss: 0.14669667184352875\nStep 11, loss: 0.1430281549692154\nStep 12, loss: 0.13978496193885803\nStep 13, loss: 0.13720174133777618\nStep 14, loss: 0.14147496223449707\nStep 15, loss: 0.12998127937316895\nStep 16, loss: 0.12920445203781128\nStep 17, loss: 0.13227719068527222\nStep 18, loss: 0.125120609998703\nStep 19, loss: 0.13470731675624847\nStep 20, loss: 0.12474726885557175\nStep 21, loss: 0.1333005726337433\nStep 22, loss: 0.1423025131225586\nStep 23, loss: 0.1377020627260208\nStep 24, loss: 0.1437397450208664\nStep 25, loss: 0.13958904147148132\nStep 26, loss: 0.13821439445018768\nStep 27, loss: 0.14022579789161682\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 0.13509593904018402\nStep 6, loss: 0.15321968495845795\nStep 7, loss: 0.14519496262073517\nStep 8, loss: 0.14099560678005219\nStep 9, loss: 0.1463150680065155\nStep 10, loss: 0.14669667184352875\nStep 11, loss: 0.1430281549692154\nStep 12, loss: 0.13978496193885803\nStep 13, loss: 0.13720174133777618\nStep 14, loss: 0.14147496223449707\nStep 15, loss: 0.12998127937316895\nStep 16, loss: 0.12920445203781128\nStep 17, loss: 0.13227719068527222\nStep 18, loss: 0.125120609998703\nStep 19, loss: 0.13470731675624847\nStep 20, loss: 0.12474726885557175\nStep 21, loss: 0.1333005726337433\nStep 22, loss: 0.1423025131225586\nStep 23, loss: 0.1377020627260208\nStep 24, loss: 0.1437397450208664\nStep 25, loss: 0.13958904147148132\nStep 26, loss: 0.13821439445018768\nStep 27, loss: 0.14022579789161682\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 0.13509593904018402\nStep 6, loss: 0.15321968495845795\nStep 7, loss: 0.14519496262073517\nStep 8, loss: 0.14099560678005219\nStep 9, loss: 0.1463150680065155\nStep 10, loss: 0.14669667184352875\nStep 11, loss: 0.1430281549692154\nStep 12, loss: 0.13978496193885803\nStep 13, loss: 0.13720174133777618\nStep 14, loss: 0.14147496223449707\nStep 15, loss: 0.12998127937316895\nStep 16, loss: 0.12920445203781128\nStep 17, loss: 0.13227719068527222\nStep 18, loss: 0.125120609998703\nStep 19, loss: 0.13470731675624847\nStep 20, loss: 0.12474726885557175\nStep 21, loss: 0.1333005726337433\nStep 22, loss: 0.1423025131225586\nStep 23, loss: 0.1377020627260208\nStep 24, loss: 0.1437397450208664\nStep 25, loss: 0.13958904147148132\nStep 26, loss: 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0.17590537667274475\nStep 89, loss: 0.1696556806564331\nStep 90, loss: 0.1746663749217987\nStep 91, loss: 0.1718718260526657\nStep 92, loss: 0.17620952427387238\nStep 93, loss: 0.17497602105140686\nStep 94, loss: 0.17112667858600616\nStep 95, loss: 0.17390812933444977\nStep 96, loss: 0.17191694676876068\nStep 97, loss: 0.17629854381084442\nStep 98, loss: 0.17539462447166443\nStep 99, loss: 0.173387348651886\nStep 100, loss: 0.17558738589286804\nStep 101, loss: 0.17344261705875397\nStep 102, loss: 0.17063868045806885\nStep 103, loss: 0.17445780336856842\nStep 104, loss: 0.17477689683437347\nStep 105, loss: 0.1743089109659195\nStep 106, loss: 0.17661947011947632\nStep 107, loss: 0.17260797321796417\nStep 108, loss: 0.17181360721588135\nStep 109, loss: 0.17290376126766205\nStep 110, loss: 0.17456933856010437\nStep 111, loss: 0.17321565747261047\nStep 112, loss: 0.17227257788181305\nStep 113, loss: 0.1743168830871582\nStep 114, loss: 0.16588141024112701\nStep 86, loss: 0.17388099431991577\nStep 87, loss: 0.17131978273391724\nStep 88, loss: 0.17590537667274475\nStep 89, loss: 0.1696556806564331\nStep 90, loss: 0.1746663749217987\nStep 91, loss: 0.1718718260526657\nStep 92, loss: 0.17620952427387238\nStep 93, loss: 0.17497602105140686\nStep 94, loss: 0.17112667858600616\nStep 95, loss: 0.17390812933444977\nStep 96, loss: 0.17191694676876068\nStep 97, loss: 0.17629854381084442\nStep 98, loss: 0.17539462447166443\nStep 99, loss: 0.173387348651886\nStep 100, loss: 0.17558738589286804\nStep 101, loss: 0.17344261705875397\nStep 102, loss: 0.17063868045806885\nStep 103, loss: 0.17445780336856842\nStep 104, loss: 0.17477689683437347\nStep 105, loss: 0.1743089109659195\nStep 106, loss: 0.17661947011947632\nStep 107, loss: 0.17260797321796417\nStep 108, loss: 0.17181360721588135\nStep 109, loss: 0.17290376126766205\nStep 110, loss: 0.17456933856010437\nStep 111, loss: 0.17321565747261047\nStep 112, loss: 0.17227257788181305\nStep 113, loss: 0.1743168830871582\nStep 114, loss: 0.16588141024112701\nStep 86, loss: 0.17388099431991577\nStep 87, loss: 0.17131978273391724\nStep 88, loss: 0.17590537667274475\nStep 89, loss: 0.1696556806564331\nStep 90, loss: 0.1746663749217987\nStep 91, loss: 0.1718718260526657\nStep 92, loss: 0.17620952427387238\nStep 93, loss: 0.17497602105140686\nStep 94, loss: 0.17112667858600616\nStep 95, loss: 0.17390812933444977\nStep 96, loss: 0.17191694676876068\nStep 97, loss: 0.17629854381084442\nStep 98, loss: 0.17539462447166443\nStep 99, loss: 0.173387348651886\nStep 100, loss: 0.17558738589286804\nStep 101, loss: 0.17344261705875397\nStep 102, loss: 0.17063868045806885\nStep 103, loss: 0.17445780336856842\nStep 104, loss: 0.17477689683437347\nStep 105, loss: 0.1743089109659195\nStep 106, loss: 0.17661947011947632\nStep 107, loss: 0.17260797321796417\nStep 108, loss: 0.17181360721588135\nStep 109, loss: 0.17290376126766205\nStep 110, loss: 0.17456933856010437\nStep 111, loss: 0.17321565747261047\nStep 112, loss: 0.17227257788181305\nStep 113, loss: 0.1743168830871582\nStep 114, loss: 0.16588141024112701\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 0.13509593904018402\nStep 6, loss: 0.15321968495845795\nStep 7, loss: 0.14519496262073517\nStep 8, loss: 0.14099560678005219\nStep 9, loss: 0.1463150680065155\nStep 10, loss: 0.14669667184352875\nStep 11, loss: 0.1430281549692154\nStep 12, loss: 0.13978496193885803\nStep 13, loss: 0.13720174133777618\nStep 14, loss: 0.14147496223449707\nStep 15, loss: 0.12998127937316895\nStep 16, loss: 0.12920445203781128\nStep 17, loss: 0.13227719068527222\nStep 18, loss: 0.125120609998703\nStep 19, loss: 0.13470731675624847\nStep 20, loss: 0.12474726885557175\nStep 21, loss: 0.1333005726337433\nStep 22, loss: 0.1423025131225586\nStep 23, loss: 0.1377020627260208\nStep 24, loss: 0.1437397450208664\nStep 25, loss: 0.13958904147148132\nStep 26, loss: 0.13821439445018768\nStep 27, loss: 0.14022579789161682\nStep 115, loss: 0.17339496314525604\nStep 115, loss: 0.17339496314525604\nStep 144, loss: 0.17160776257514954\nStep 145, loss: 0.17339970171451569\nStep 146, loss: 0.17081758379936218\nStep 147, loss: 0.16918820142745972\nStep 148, loss: 0.1746884435415268\nStep 149, loss: 0.17227376997470856\nStep 150, loss: 0.17421938478946686\nStep 151, loss: 0.1723541021347046\nStep 152, loss: 0.17495852708816528\nStep 153, loss: 0.1736605316400528\nStep 154, loss: 0.1731863170862198\nStep 155, loss: 0.173353374004364\nStep 156, loss: 0.17461292445659637\nStep 157, loss: 0.17833106219768524\nStep 158, loss: 0.1696862280368805\nStep 159, loss: 0.17354346811771393\nStep 160, loss: 0.17869757115840912\nStep 161, loss: 0.16943944990634918\nStep 162, loss: 0.17502373456954956\nStep 163, loss: 0.17490755021572113\nStep 164, loss: 0.1724388152360916\nStep 165, loss: 0.1717279553413391\nStep 166, loss: 0.1696639358997345\nStep 167, loss: 0.1763082593679428\nStep 168, loss: 0.17012852430343628\nStep 169, loss: 0.1756332814693451\nStep 170, loss: 0.17349863052368164\nStep 171, loss: 0.17180608212947845\nStep 115, loss: 0.17339496314525604\nStep 115, loss: 0.17339496314525604\nStep 115, loss: 0.17339496314525604\nStep 115, loss: 0.17339496314525604\nStep 28, loss: 0.13478153944015503\nStep 29, loss: 0.1434846669435501\nStep 30, loss: 0.13330970704555511\nStep 31, loss: 0.14295107126235962\nStep 32, loss: 0.14157991111278534\nStep 33, loss: 0.13778111338615417\nStep 34, loss: 0.1367509663105011\nStep 35, loss: 0.14291462302207947\nStep 36, loss: 0.13713470101356506\nStep 37, loss: 0.1495264619588852\nStep 38, loss: 0.15450292825698853\nStep 39, loss: 0.1477455347776413\nStep 40, loss: 0.14942966401576996\nStep 41, loss: 0.15012970566749573\nStep 42, loss: 0.14514152705669403\nStep 43, loss: 0.1656753122806549\nStep 44, loss: 0.15873289108276367\nStep 45, loss: 0.17154277861118317\nStep 46, loss: 0.17314167320728302\nStep 47, loss: 0.17424646019935608\nStep 48, loss: 0.17465342581272125\nStep 49, loss: 0.17834438383579254\nStep 50, loss: 0.1750008463859558\nStep 51, loss: 0.17383235692977905\nStep 52, loss: 0.1738968789577484\nStep 53, loss: 0.1788439154624939\nStep 54, loss: 0.17455320060253143\nStep 55, loss: 0.17899788916110992\nStep 56, loss: 0.17514869570732117\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 0.13509593904018402\nStep 6, loss: 0.15321968495845795\nStep 7, loss: 0.14519496262073517\nStep 8, loss: 0.14099560678005219\nStep 9, loss: 0.1463150680065155\nStep 10, loss: 0.14669667184352875\nStep 11, loss: 0.1430281549692154\nStep 12, loss: 0.13978496193885803\nStep 13, loss: 0.13720174133777618\nStep 14, loss: 0.14147496223449707\nStep 15, loss: 0.12998127937316895\nStep 16, loss: 0.12920445203781128\nStep 17, loss: 0.13227719068527222\nStep 18, loss: 0.125120609998703\nStep 19, loss: 0.13470731675624847\nStep 20, loss: 0.12474726885557175\nStep 21, loss: 0.1333005726337433\nStep 22, loss: 0.1423025131225586\nStep 23, loss: 0.1377020627260208\nStep 24, loss: 0.1437397450208664\nStep 25, loss: 0.13958904147148132\nStep 26, loss: 0.13821439445018768\nStep 27, loss: 0.14022579789161682\nStep 116, loss: 0.17650632560253143\nStep 117, loss: 0.1721123307943344\nStep 118, loss: 0.17484426498413086\nStep 119, loss: 0.17425554990768433\nStep 120, loss: 0.17355427145957947\nStep 121, loss: 0.17475546896457672\nStep 122, loss: 0.1722601056098938\nStep 123, loss: 0.17617866396903992\nStep 124, loss: 0.1784822940826416\nStep 125, loss: 0.17228849232196808\nStep 126, loss: 0.1726805716753006\nStep 127, loss: 0.17028960585594177\nStep 128, loss: 0.1759348213672638\nStep 129, loss: 0.1740720421075821\nStep 130, loss: 0.17981602251529694\nStep 131, loss: 0.1703324317932129\nStep 132, loss: 0.17444254457950592\nStep 133, loss: 0.17495402693748474\nStep 134, loss: 0.1774214208126068\nStep 135, loss: 0.16589730978012085\nStep 136, loss: 0.17302198708057404\nStep 137, loss: 0.17555880546569824\nStep 138, loss: 0.1742158681154251\nStep 139, loss: 0.172081857919693\nStep 140, loss: 0.17138473689556122\nStep 141, loss: 0.17552287876605988\nStep 142, loss: 0.17444556951522827\nStep 143, loss: 0.1737712323665619\nStep 172, loss: 0.1735956072807312\nStep 173, loss: 0.16950492560863495\nStep 174, loss: 0.1710001528263092\nStep 175, loss: 0.1720818281173706\nStep 176, loss: 0.17734099924564362\nStep 177, loss: 0.17544645071029663\nStep 178, loss: 0.17164328694343567\nStep 179, loss: 0.17365553975105286\nStep 180, loss: 0.17731669545173645\nStep 181, loss: 0.16690389811992645\nStep 182, loss: 0.1742502599954605\nStep 183, loss: 0.1716139167547226\nStep 184, loss: 0.1698140799999237\nStep 185, loss: 0.17759019136428833\nStep 186, loss: 0.17442889511585236\nStep 187, loss: 0.17123228311538696\nStep 188, loss: 0.17149075865745544\nStep 189, loss: 0.17389832437038422\nStep 190, loss: 0.1708046942949295\nStep 191, loss: 0.171993225812912\nStep 192, loss: 0.17431600391864777\nStep 193, loss: 0.17453940212726593\nStep 194, loss: 0.1709374040365219\nStep 195, loss: 0.17524591088294983\nStep 196, loss: 0.17337879538536072\nStep 197, loss: 0.1743336170911789\nStep 198, loss: 0.1775231659412384\nStep 199, loss: 0.16916300356388092\nStep 116, loss: 0.17650632560253143\nStep 117, loss: 0.1721123307943344\nStep 118, loss: 0.17484426498413086\nStep 119, loss: 0.17425554990768433\nStep 120, loss: 0.17355427145957947\nStep 121, loss: 0.17475546896457672\nStep 122, loss: 0.1722601056098938\nStep 123, loss: 0.17617866396903992\nStep 124, loss: 0.1784822940826416\nStep 125, loss: 0.17228849232196808\nStep 126, loss: 0.1726805716753006\nStep 127, loss: 0.17028960585594177\nStep 128, loss: 0.1759348213672638\nStep 129, loss: 0.1740720421075821\nStep 130, loss: 0.17981602251529694\nStep 131, loss: 0.1703324317932129\nStep 132, loss: 0.17444254457950592\nStep 133, loss: 0.17495402693748474\nStep 134, loss: 0.1774214208126068\nStep 135, loss: 0.16589730978012085\nStep 136, loss: 0.17302198708057404\nStep 137, loss: 0.17555880546569824\nStep 138, loss: 0.1742158681154251\nStep 139, loss: 0.172081857919693\nStep 140, loss: 0.17138473689556122\nStep 141, loss: 0.17552287876605988\nStep 142, loss: 0.17444556951522827\nStep 143, loss: 0.1737712323665619\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 0.13509593904018402\nStep 6, loss: 0.15321968495845795\nStep 7, loss: 0.14519496262073517\nStep 8, loss: 0.14099560678005219\nStep 9, loss: 0.1463150680065155\nStep 10, loss: 0.14669667184352875\nStep 11, loss: 0.1430281549692154\nStep 12, loss: 0.13978496193885803\nStep 13, loss: 0.13720174133777618\nStep 14, loss: 0.14147496223449707\nStep 15, loss: 0.12998127937316895\nStep 16, loss: 0.12920445203781128\nStep 17, loss: 0.13227719068527222\nStep 18, loss: 0.125120609998703\nStep 19, loss: 0.13470731675624847\nStep 20, loss: 0.12474726885557175\nStep 21, loss: 0.1333005726337433\nStep 22, loss: 0.1423025131225586\nStep 23, loss: 0.1377020627260208\nStep 24, loss: 0.1437397450208664\nStep 25, loss: 0.13958904147148132\nStep 26, loss: 0.13821439445018768\nStep 27, loss: 0.14022579789161682\nStep 116, loss: 0.17650632560253143\nStep 117, loss: 0.1721123307943344\nStep 118, loss: 0.17484426498413086\nStep 119, loss: 0.17425554990768433\nStep 120, loss: 0.17355427145957947\nStep 121, loss: 0.17475546896457672\nStep 122, loss: 0.1722601056098938\nStep 123, loss: 0.17617866396903992\nStep 124, 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0.173387348651886\nStep 100, loss: 0.17558738589286804\nStep 101, loss: 0.17344261705875397\nStep 102, loss: 0.17063868045806885\nStep 103, loss: 0.17445780336856842\nStep 104, loss: 0.17477689683437347\nStep 105, loss: 0.1743089109659195\nStep 106, loss: 0.17661947011947632\nStep 107, loss: 0.17260797321796417\nStep 108, loss: 0.17181360721588135\nStep 109, loss: 0.17290376126766205\nStep 110, loss: 0.17456933856010437\nStep 111, loss: 0.17321565747261047\nStep 112, loss: 0.17227257788181305\nStep 113, loss: 0.1743168830871582\nStep 114, loss: 0.16588141024112701\nStep 200, loss: 0.17302356660366058\nStep 201, loss: 0.17107906937599182\nStep 202, loss: 0.17300015687942505\nStep 203, loss: 0.1730821281671524\nStep 204, loss: 0.17554880678653717\nStep 205, loss: 0.1745760142803192\nStep 206, loss: 0.17443722486495972\nStep 207, loss: 0.17716984450817108\nStep 208, loss: 0.178369402885437\nStep 209, loss: 0.17524774372577667\nStep 210, loss: 0.17759115993976593\nStep 211, loss: 0.17509549856185913\nStep 212, loss: 0.1736840456724167\nStep 213, loss: 0.1769413948059082\nStep 214, loss: 0.17503388226032257\nStep 215, loss: 0.17699705064296722\nStep 216, loss: 0.17280948162078857\nStep 217, loss: 0.17379453778266907\nStep 218, loss: 0.17551490664482117\nStep 219, loss: 0.1724730134010315\nStep 220, loss: 0.17256437242031097\nStep 221, loss: 0.17449802160263062\nStep 222, loss: 0.1751682460308075\nStep 223, loss: 0.17546094954013824\nStep 224, loss: 0.17379115521907806\nStep 225, loss: 0.1778661012649536\nStep 226, loss: 0.17416249215602875\nStep 227, loss: 0.17225857079029083\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 0.13509593904018402\nStep 6, loss: 0.15321968495845795\nStep 7, loss: 0.14519496262073517\nStep 8, loss: 0.14099560678005219\nStep 9, loss: 0.1463150680065155\nStep 10, loss: 0.14669667184352875\nStep 11, loss: 0.1430281549692154\nStep 12, loss: 0.13978496193885803\nStep 13, loss: 0.13720174133777618\nStep 14, loss: 0.14147496223449707\nStep 15, loss: 0.12998127937316895\nStep 16, loss: 0.12920445203781128\nStep 17, loss: 0.13227719068527222\nStep 18, loss: 0.125120609998703\nStep 19, loss: 0.13470731675624847\nStep 20, loss: 0.12474726885557175\nStep 21, loss: 0.1333005726337433\nStep 22, loss: 0.1423025131225586\nStep 23, loss: 0.1377020627260208\nStep 24, loss: 0.1437397450208664\nStep 25, loss: 0.13958904147148132\nStep 26, loss: 0.13821439445018768\nStep 27, loss: 0.14022579789161682\nStep 200, loss: 0.17302356660366058\nStep 201, loss: 0.17107906937599182\nStep 202, loss: 0.17300015687942505\nStep 203, loss: 0.1730821281671524\nStep 204, loss: 0.17554880678653717\nStep 205, loss: 0.1745760142803192\nStep 206, loss: 0.17443722486495972\nStep 207, loss: 0.17716984450817108\nStep 208, loss: 0.178369402885437\nStep 209, loss: 0.17524774372577667\nStep 210, loss: 0.17759115993976593\nStep 211, loss: 0.17509549856185913\nStep 212, loss: 0.1736840456724167\nStep 213, loss: 0.1769413948059082\nStep 214, loss: 0.17503388226032257\nStep 215, loss: 0.17699705064296722\nStep 216, loss: 0.17280948162078857\nStep 217, loss: 0.17379453778266907\nStep 218, loss: 0.17551490664482117\nStep 219, loss: 0.1724730134010315\nStep 220, loss: 0.17256437242031097\nStep 221, loss: 0.17449802160263062\nStep 222, loss: 0.1751682460308075\nStep 223, loss: 0.17546094954013824\nStep 224, loss: 0.17379115521907806\nStep 225, loss: 0.1778661012649536\nStep 226, loss: 0.17416249215602875\nStep 227, loss: 0.17225857079029083\nStep 86, loss: 0.17388099431991577\nStep 87, loss: 0.17131978273391724\nStep 88, loss: 0.17590537667274475\nStep 89, loss: 0.1696556806564331\nStep 90, loss: 0.1746663749217987\nStep 91, loss: 0.1718718260526657\nStep 92, loss: 0.17620952427387238\nStep 93, loss: 0.17497602105140686\nStep 94, loss: 0.17112667858600616\nStep 95, loss: 0.17390812933444977\nStep 96, loss: 0.17191694676876068\nStep 97, loss: 0.17629854381084442\nStep 98, loss: 0.17539462447166443\nStep 99, loss: 0.173387348651886\nStep 100, loss: 0.17558738589286804\nStep 101, loss: 0.17344261705875397\nStep 102, loss: 0.17063868045806885\nStep 103, loss: 0.17445780336856842\nStep 104, loss: 0.17477689683437347\nStep 105, loss: 0.1743089109659195\nStep 106, loss: 0.17661947011947632\nStep 107, loss: 0.17260797321796417\nStep 108, loss: 0.17181360721588135\nStep 109, loss: 0.17290376126766205\nStep 110, loss: 0.17456933856010437\nStep 111, loss: 0.17321565747261047\nStep 112, loss: 0.17227257788181305\nStep 113, loss: 0.1743168830871582\nStep 114, loss: 0.16588141024112701\nStep 200, loss: 0.17302356660366058\nStep 201, loss: 0.17107906937599182\nStep 202, loss: 0.17300015687942505\nStep 203, loss: 0.1730821281671524\nStep 204, loss: 0.17554880678653717\nStep 205, loss: 0.1745760142803192\nStep 206, loss: 0.17443722486495972\nStep 207, loss: 0.17716984450817108\nStep 208, loss: 0.178369402885437\nStep 209, loss: 0.17524774372577667\nStep 210, loss: 0.17759115993976593\nStep 211, loss: 0.17509549856185913\nStep 212, loss: 0.1736840456724167\nStep 213, loss: 0.1769413948059082\nStep 214, loss: 0.17503388226032257\nStep 215, loss: 0.17699705064296722\nStep 216, loss: 0.17280948162078857\nStep 217, loss: 0.17379453778266907\nStep 218, loss: 0.17551490664482117\nStep 219, loss: 0.1724730134010315\nStep 220, loss: 0.17256437242031097\nStep 221, loss: 0.17449802160263062\nStep 222, loss: 0.1751682460308075\nStep 223, loss: 0.17546094954013824\nStep 224, loss: 0.17379115521907806\nStep 225, loss: 0.1778661012649536\nStep 226, loss: 0.17416249215602875\nStep 227, loss: 0.17225857079029083\nStep 86, loss: 0.17388099431991577\nStep 87, loss: 0.17131978273391724\nStep 88, loss: 0.17590537667274475\nStep 89, loss: 0.1696556806564331\nStep 90, loss: 0.1746663749217987\nStep 91, loss: 0.1718718260526657\nStep 92, loss: 0.17620952427387238\nStep 93, loss: 0.17497602105140686\nStep 94, loss: 0.17112667858600616\nStep 95, loss: 0.17390812933444977\nStep 96, loss: 0.17191694676876068\nStep 97, loss: 0.17629854381084442\nStep 98, loss: 0.17539462447166443\nStep 99, loss: 0.173387348651886\nStep 100, loss: 0.17558738589286804\nStep 101, loss: 0.17344261705875397\nStep 102, loss: 0.17063868045806885\nStep 103, loss: 0.17445780336856842\nStep 104, loss: 0.17477689683437347\nStep 105, loss: 0.1743089109659195\nStep 106, loss: 0.17661947011947632\nStep 107, loss: 0.17260797321796417\nStep 108, loss: 0.17181360721588135\nStep 109, loss: 0.17290376126766205\nStep 110, loss: 0.17456933856010437\nStep 111, loss: 0.17321565747261047\nStep 112, loss: 0.17227257788181305\nStep 113, loss: 0.1743168830871582\nStep 114, loss: 0.16588141024112701\nStep 116, loss: 0.17650632560253143\nStep 117, loss: 0.1721123307943344\nStep 118, loss: 0.17484426498413086\nStep 119, loss: 0.17425554990768433\nStep 120, loss: 0.17355427145957947\nStep 121, loss: 0.17475546896457672\nStep 122, loss: 0.1722601056098938\nStep 123, loss: 0.17617866396903992\nStep 124, loss: 0.1784822940826416\nStep 125, loss: 0.17228849232196808\nStep 126, loss: 0.1726805716753006\nStep 127, loss: 0.17028960585594177\nStep 128, loss: 0.1759348213672638\nStep 129, loss: 0.1740720421075821\nStep 130, loss: 0.17981602251529694\nStep 131, loss: 0.1703324317932129\nStep 132, loss: 0.17444254457950592\nStep 133, loss: 0.17495402693748474\nStep 134, loss: 0.1774214208126068\nStep 135, loss: 0.16589730978012085\nStep 136, loss: 0.17302198708057404\nStep 137, loss: 0.17555880546569824\nStep 138, loss: 0.1742158681154251\nStep 139, loss: 0.172081857919693\nStep 140, loss: 0.17138473689556122\nStep 141, loss: 0.17552287876605988\nStep 142, loss: 0.17444556951522827\nStep 143, loss: 0.1737712323665619\nStep 115, loss: 0.17339496314525604\nStep 228, loss: 0.17188940942287445\nStep 229, loss: 0.17189325392246246\nStep 230, loss: 0.17526964843273163\nStep 28, loss: 0.13478153944015503\nStep 29, loss: 0.1434846669435501\nStep 30, loss: 0.13330970704555511\nStep 31, loss: 0.14295107126235962\nStep 32, loss: 0.14157991111278534\nStep 33, loss: 0.13778111338615417\nStep 34, loss: 0.1367509663105011\nStep 35, loss: 0.14291462302207947\nStep 36, loss: 0.13713470101356506\nStep 37, loss: 0.1495264619588852\nStep 38, loss: 0.15450292825698853\nStep 39, loss: 0.1477455347776413\nStep 40, loss: 0.14942966401576996\nStep 41, loss: 0.15012970566749573\nStep 42, loss: 0.14514152705669403\nStep 43, loss: 0.1656753122806549\nStep 44, loss: 0.15873289108276367\nStep 45, loss: 0.17154277861118317\nStep 46, loss: 0.17314167320728302\nStep 47, loss: 0.17424646019935608\nStep 48, loss: 0.17465342581272125\nStep 49, loss: 0.17834438383579254\nStep 50, loss: 0.1750008463859558\nStep 51, loss: 0.17383235692977905\nStep 52, loss: 0.1738968789577484\nStep 53, loss: 0.1788439154624939\nStep 54, loss: 0.17455320060253143\nStep 55, loss: 0.17899788916110992\nStep 56, loss: 0.17514869570732117\nStep 228, loss: 0.17188940942287445\nStep 229, loss: 0.17189325392246246\nStep 230, loss: 0.17526964843273163\nStep 115, loss: 0.17339496314525604\nStep 228, loss: 0.17188940942287445\nStep 229, loss: 0.17189325392246246\nStep 230, loss: 0.17526964843273163\nStep 115, loss: 0.17339496314525604\nStep 144, loss: 0.17160776257514954\nStep 145, loss: 0.17339970171451569\nStep 146, loss: 0.17081758379936218\nStep 147, loss: 0.16918820142745972\nStep 148, loss: 0.1746884435415268\nStep 149, loss: 0.17227376997470856\nStep 150, loss: 0.17421938478946686\nStep 151, loss: 0.1723541021347046\nStep 152, loss: 0.17495852708816528\nStep 153, loss: 0.1736605316400528\nStep 154, loss: 0.1731863170862198\nStep 155, loss: 0.173353374004364\nStep 156, loss: 0.17461292445659637\nStep 157, loss: 0.17833106219768524\nStep 158, loss: 0.1696862280368805\nStep 159, loss: 0.17354346811771393\nStep 160, loss: 0.17869757115840912\nStep 161, loss: 0.16943944990634918\nStep 162, loss: 0.17502373456954956\nStep 163, loss: 0.17490755021572113\nStep 164, loss: 0.1724388152360916\nStep 165, loss: 0.1717279553413391\nStep 166, loss: 0.1696639358997345\nStep 167, loss: 0.1763082593679428\nStep 168, loss: 0.17012852430343628\nStep 169, loss: 0.1756332814693451\nStep 170, loss: 0.17349863052368164\nStep 171, loss: 0.17180608212947845\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 0.13509593904018402\nStep 6, loss: 0.15321968495845795\nStep 7, loss: 0.14519496262073517\nStep 8, loss: 0.14099560678005219\nStep 9, loss: 0.1463150680065155\nStep 10, loss: 0.14669667184352875\nStep 11, loss: 0.1430281549692154\nStep 12, loss: 0.13978496193885803\nStep 13, loss: 0.13720174133777618\nStep 14, loss: 0.14147496223449707\nStep 15, loss: 0.12998127937316895\nStep 16, loss: 0.12920445203781128\nStep 17, loss: 0.13227719068527222\nStep 18, loss: 0.125120609998703\nStep 19, loss: 0.13470731675624847\nStep 20, loss: 0.12474726885557175\nStep 21, loss: 0.1333005726337433\nStep 22, loss: 0.1423025131225586\nStep 23, loss: 0.1377020627260208\nStep 24, loss: 0.1437397450208664\nStep 25, loss: 0.13958904147148132\nStep 26, loss: 0.13821439445018768\nStep 27, loss: 0.14022579789161682\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 0.13509593904018402\nStep 6, loss: 0.15321968495845795\nStep 7, loss: 0.14519496262073517\nStep 8, loss: 0.14099560678005219\nStep 9, loss: 0.1463150680065155\nStep 10, loss: 0.14669667184352875\nStep 11, loss: 0.1430281549692154\nStep 12, loss: 0.13978496193885803\nStep 13, loss: 0.13720174133777618\nStep 14, loss: 0.14147496223449707\nStep 15, loss: 0.12998127937316895\nStep 16, loss: 0.12920445203781128\nStep 17, loss: 0.13227719068527222\nStep 18, loss: 0.125120609998703\nStep 19, loss: 0.13470731675624847\nStep 20, loss: 0.12474726885557175\nStep 21, loss: 0.1333005726337433\nStep 22, loss: 0.1423025131225586\nStep 23, loss: 0.1377020627260208\nStep 24, loss: 0.1437397450208664\nStep 25, loss: 0.13958904147148132\nStep 26, loss: 0.13821439445018768\nStep 27, loss: 0.14022579789161682\nStep 57, loss: 0.17526665329933167\nStep 58, loss: 0.17442293465137482\nStep 59, loss: 0.1755731999874115\nStep 60, loss: 0.17686791718006134\nStep 61, loss: 0.1774514615535736\nStep 62, loss: 0.17734190821647644\nStep 63, loss: 0.17445750534534454\nStep 64, loss: 0.1791164129972458\nStep 65, loss: 0.17443639039993286\nStep 66, loss: 0.1721915602684021\nStep 67, loss: 0.17724011838436127\nStep 68, loss: 0.1726222038269043\nStep 69, loss: 0.17514818906784058\nStep 70, loss: 0.17387129366397858\nStep 71, loss: 0.17663298547267914\nStep 72, loss: 0.17917156219482422\nStep 73, loss: 0.17480577528476715\nStep 74, loss: 0.17429769039154053\nStep 75, loss: 0.17326530814170837\nStep 76, loss: 0.17588405311107635\nStep 77, loss: 0.16884107887744904\nStep 78, loss: 0.17379479110240936\nStep 79, loss: 0.17513428628444672\nStep 80, loss: 0.17434917390346527\nStep 81, loss: 0.17318695783615112\nStep 82, loss: 0.17380864918231964\nStep 83, loss: 0.17578117549419403\nStep 84, loss: 0.1710706204175949\nStep 85, loss: 0.17886455357074738\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 0.13509593904018402\nStep 6, loss: 0.15321968495845795\nStep 7, loss: 0.14519496262073517\nStep 8, loss: 0.14099560678005219\nStep 9, loss: 0.1463150680065155\nStep 10, loss: 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0.17390812933444977\nStep 96, loss: 0.17191694676876068\nStep 97, loss: 0.17629854381084442\nStep 98, loss: 0.17539462447166443\nStep 99, loss: 0.173387348651886\nStep 100, loss: 0.17558738589286804\nStep 101, loss: 0.17344261705875397\nStep 102, loss: 0.17063868045806885\nStep 103, loss: 0.17445780336856842\nStep 104, loss: 0.17477689683437347\nStep 105, loss: 0.1743089109659195\nStep 106, loss: 0.17661947011947632\nStep 107, loss: 0.17260797321796417\nStep 108, loss: 0.17181360721588135\nStep 109, loss: 0.17290376126766205\nStep 110, loss: 0.17456933856010437\nStep 111, loss: 0.17321565747261047\nStep 112, loss: 0.17227257788181305\nStep 113, loss: 0.1743168830871582\nStep 114, loss: 0.16588141024112701\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 0.13509593904018402\nStep 6, loss: 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0.178369402885437\nStep 209, loss: 0.17524774372577667\nStep 210, loss: 0.17759115993976593\nStep 211, loss: 0.17509549856185913\nStep 212, loss: 0.1736840456724167\nStep 213, loss: 0.1769413948059082\nStep 214, loss: 0.17503388226032257\nStep 215, loss: 0.17699705064296722\nStep 216, loss: 0.17280948162078857\nStep 217, loss: 0.17379453778266907\nStep 218, loss: 0.17551490664482117\nStep 219, loss: 0.1724730134010315\nStep 220, loss: 0.17256437242031097\nStep 221, loss: 0.17449802160263062\nStep 222, loss: 0.1751682460308075\nStep 223, loss: 0.17546094954013824\nStep 224, loss: 0.17379115521907806\nStep 225, loss: 0.1778661012649536\nStep 226, loss: 0.17416249215602875\nStep 227, loss: 0.17225857079029083\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 0.13509593904018402\nStep 6, loss: 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0.15873289108276367\nStep 45, loss: 0.17154277861118317\nStep 46, loss: 0.17314167320728302\nStep 47, loss: 0.17424646019935608\nStep 48, loss: 0.17465342581272125\nStep 49, loss: 0.17834438383579254\nStep 50, loss: 0.1750008463859558\nStep 51, loss: 0.17383235692977905\nStep 52, loss: 0.1738968789577484\nStep 53, loss: 0.1788439154624939\nStep 54, loss: 0.17455320060253143\nStep 55, loss: 0.17899788916110992\nStep 56, loss: 0.17514869570732117\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 0.13509593904018402\nStep 6, loss: 0.15321968495845795\nStep 7, loss: 0.14519496262073517\nStep 8, loss: 0.14099560678005219\nStep 9, loss: 0.1463150680065155\nStep 10, loss: 0.14669667184352875\nStep 11, loss: 0.1430281549692154\nStep 12, loss: 0.13978496193885803\nStep 13, loss: 0.13720174133777618\nStep 14, loss: 0.14147496223449707\nStep 15, loss: 0.12998127937316895\nStep 16, loss: 0.12920445203781128\nStep 17, loss: 0.13227719068527222\nStep 18, loss: 0.125120609998703\nStep 19, loss: 0.13470731675624847\nStep 20, loss: 0.12474726885557175\nStep 21, loss: 0.1333005726337433\nStep 22, loss: 0.1423025131225586\nStep 23, loss: 0.1377020627260208\nStep 24, loss: 0.1437397450208664\nStep 25, loss: 0.13958904147148132\nStep 26, loss: 0.13821439445018768\nStep 27, loss: 0.14022579789161682\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 0.13509593904018402\nStep 6, loss: 0.15321968495845795\nStep 7, loss: 0.14519496262073517\nStep 8, loss: 0.14099560678005219\nStep 9, loss: 0.1463150680065155\nStep 10, loss: 0.14669667184352875\nStep 11, loss: 0.1430281549692154\nStep 12, loss: 0.13978496193885803\nStep 13, loss: 0.13720174133777618\nStep 14, loss: 0.14147496223449707\nStep 15, loss: 0.12998127937316895\nStep 16, loss: 0.12920445203781128\nStep 17, loss: 0.13227719068527222\nStep 18, loss: 0.125120609998703\nStep 19, loss: 0.13470731675624847\nStep 20, loss: 0.12474726885557175\nStep 21, loss: 0.1333005726337433\nStep 22, loss: 0.1423025131225586\nStep 23, loss: 0.1377020627260208\nStep 24, loss: 0.1437397450208664\nStep 25, loss: 0.13958904147148132\nStep 26, loss: 0.13821439445018768\nStep 27, loss: 0.14022579789161682\nStep 172, loss: 0.1735956072807312\nStep 173, loss: 0.16950492560863495\nStep 174, loss: 0.1710001528263092\nStep 175, loss: 0.1720818281173706\nStep 176, loss: 0.17734099924564362\nStep 177, loss: 0.17544645071029663\nStep 178, loss: 0.17164328694343567\nStep 179, loss: 0.17365553975105286\nStep 180, loss: 0.17731669545173645\nStep 181, loss: 0.16690389811992645\nStep 182, loss: 0.1742502599954605\nStep 183, loss: 0.1716139167547226\nStep 184, loss: 0.1698140799999237\nStep 185, loss: 0.17759019136428833\nStep 186, loss: 0.17442889511585236\nStep 187, loss: 0.17123228311538696\nStep 188, loss: 0.17149075865745544\nStep 189, loss: 0.17389832437038422\nStep 190, loss: 0.1708046942949295\nStep 191, loss: 0.171993225812912\nStep 192, loss: 0.17431600391864777\nStep 193, loss: 0.17453940212726593\nStep 194, loss: 0.1709374040365219\nStep 195, loss: 0.17524591088294983\nStep 196, loss: 0.17337879538536072\nStep 197, loss: 0.1743336170911789\nStep 198, loss: 0.1775231659412384\nStep 199, loss: 0.16916300356388092\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 0.13509593904018402\nStep 6, loss: 0.15321968495845795\nStep 7, loss: 0.14519496262073517\nStep 8, loss: 0.14099560678005219\nStep 9, loss: 0.1463150680065155\nStep 10, loss: 0.14669667184352875\nStep 11, loss: 0.1430281549692154\nStep 12, loss: 0.13978496193885803\nStep 13, loss: 0.13720174133777618\nStep 14, loss: 0.14147496223449707\nStep 15, loss: 0.12998127937316895\nStep 16, loss: 0.12920445203781128\nStep 17, loss: 0.13227719068527222\nStep 18, loss: 0.125120609998703\nStep 19, loss: 0.13470731675624847\nStep 20, loss: 0.12474726885557175\nStep 21, loss: 0.1333005726337433\nStep 22, loss: 0.1423025131225586\nStep 23, loss: 0.1377020627260208\nStep 24, loss: 0.1437397450208664\nStep 25, loss: 0.13958904147148132\nStep 26, loss: 0.13821439445018768\nStep 27, loss: 0.14022579789161682\nStep 116, loss: 0.17650632560253143\nStep 117, loss: 0.1721123307943344\nStep 118, loss: 0.17484426498413086\nStep 119, loss: 0.17425554990768433\nStep 120, loss: 0.17355427145957947\nStep 121, loss: 0.17475546896457672\nStep 122, loss: 0.1722601056098938\nStep 123, loss: 0.17617866396903992\nStep 124, loss: 0.1784822940826416\nStep 125, loss: 0.17228849232196808\nStep 126, loss: 0.1726805716753006\nStep 127, loss: 0.17028960585594177\nStep 128, loss: 0.1759348213672638\nStep 129, loss: 0.1740720421075821\nStep 130, loss: 0.17981602251529694\nStep 131, loss: 0.1703324317932129\nStep 132, loss: 0.17444254457950592\nStep 133, loss: 0.17495402693748474\nStep 134, loss: 0.1774214208126068\nStep 135, loss: 0.16589730978012085\nStep 136, loss: 0.17302198708057404\nStep 137, loss: 0.17555880546569824\nStep 138, loss: 0.1742158681154251\nStep 139, loss: 0.172081857919693\nStep 140, loss: 0.17138473689556122\nStep 141, loss: 0.17552287876605988\nStep 142, loss: 0.17444556951522827\nStep 143, loss: 0.1737712323665619\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 0.13509593904018402\nStep 6, loss: 0.15321968495845795\nStep 7, loss: 0.14519496262073517\nStep 8, loss: 0.14099560678005219\nStep 9, loss: 0.1463150680065155\nStep 10, loss: 0.14669667184352875\nStep 11, loss: 0.1430281549692154\nStep 12, loss: 0.13978496193885803\nStep 13, loss: 0.13720174133777618\nStep 14, loss: 0.14147496223449707\nStep 15, loss: 0.12998127937316895\nStep 16, loss: 0.12920445203781128\nStep 17, loss: 0.13227719068527222\nStep 18, loss: 0.125120609998703\nStep 19, loss: 0.13470731675624847\nStep 20, loss: 0.12474726885557175\nStep 21, loss: 0.1333005726337433\nStep 22, loss: 0.1423025131225586\nStep 23, loss: 0.1377020627260208\nStep 24, loss: 0.1437397450208664\nStep 25, loss: 0.13958904147148132\nStep 26, loss: 0.13821439445018768\nStep 27, loss: 0.14022579789161682\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 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0.17899788916110992\nStep 56, loss: 0.17514869570732117\nStep 28, loss: 0.13478153944015503\nStep 29, loss: 0.1434846669435501\nStep 30, loss: 0.13330970704555511\nStep 31, loss: 0.14295107126235962\nStep 32, loss: 0.14157991111278534\nStep 33, loss: 0.13778111338615417\nStep 34, loss: 0.1367509663105011\nStep 35, loss: 0.14291462302207947\nStep 36, loss: 0.13713470101356506\nStep 37, loss: 0.1495264619588852\nStep 38, loss: 0.15450292825698853\nStep 39, loss: 0.1477455347776413\nStep 40, loss: 0.14942966401576996\nStep 41, loss: 0.15012970566749573\nStep 42, loss: 0.14514152705669403\nStep 43, loss: 0.1656753122806549\nStep 44, loss: 0.15873289108276367\nStep 45, loss: 0.17154277861118317\nStep 46, loss: 0.17314167320728302\nStep 47, loss: 0.17424646019935608\nStep 48, loss: 0.17465342581272125\nStep 49, loss: 0.17834438383579254\nStep 50, loss: 0.1750008463859558\nStep 51, loss: 0.17383235692977905\nStep 52, loss: 0.1738968789577484\nStep 53, loss: 0.1788439154624939\nStep 54, loss: 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0.17981602251529694\nStep 131, loss: 0.1703324317932129\nStep 132, loss: 0.17444254457950592\nStep 133, loss: 0.17495402693748474\nStep 134, loss: 0.1774214208126068\nStep 135, loss: 0.16589730978012085\nStep 136, loss: 0.17302198708057404\nStep 137, loss: 0.17555880546569824\nStep 138, loss: 0.1742158681154251\nStep 139, loss: 0.172081857919693\nStep 140, loss: 0.17138473689556122\nStep 141, loss: 0.17552287876605988\nStep 142, loss: 0.17444556951522827\nStep 143, loss: 0.1737712323665619\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 0.13509593904018402\nStep 6, loss: 0.15321968495845795\nStep 7, loss: 0.14519496262073517\nStep 8, loss: 0.14099560678005219\nStep 9, loss: 0.1463150680065155\nStep 10, loss: 0.14669667184352875\nStep 11, loss: 0.1430281549692154\nStep 12, loss: 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0.17514818906784058\nStep 70, loss: 0.17387129366397858\nStep 71, loss: 0.17663298547267914\nStep 72, loss: 0.17917156219482422\nStep 73, loss: 0.17480577528476715\nStep 74, loss: 0.17429769039154053\nStep 75, loss: 0.17326530814170837\nStep 76, loss: 0.17588405311107635\nStep 77, loss: 0.16884107887744904\nStep 78, loss: 0.17379479110240936\nStep 79, loss: 0.17513428628444672\nStep 80, loss: 0.17434917390346527\nStep 81, loss: 0.17318695783615112\nStep 82, loss: 0.17380864918231964\nStep 83, loss: 0.17578117549419403\nStep 84, loss: 0.1710706204175949\nStep 85, loss: 0.17886455357074738\nRunning on 32 devices.\nStarting training from step 0...\nStep 0, loss: 0.3471579849720001\nStep 1, loss: 0.26770803332328796\nStep 2, loss: 0.14104889333248138\nStep 3, loss: 0.13309144973754883\nStep 4, loss: 0.13091082870960236\nStep 5, loss: 0.13509593904018402\nStep 6, loss: 0.15321968495845795\nStep 7, loss: 0.14519496262073517\nStep 8, loss: 0.14099560678005219\nStep 9, loss: 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0.1971161663532257\nStep 939, loss: 0.19739972054958344\nStep 940, loss: 0.19577215611934662\nStep 941, loss: 0.19569700956344604\nStep 942, loss: 0.1939944624900818\nStep 943, loss: 0.1971275806427002\nStep 944, loss: 0.19501261413097382\nStep 945, loss: 0.19413445889949799\nStep 946, loss: 0.1940343976020813\nStep 947, loss: 0.19624318182468414\nStep 948, loss: 0.19629356265068054\nStep 949, loss: 0.19340308010578156\nStep 950, loss: 0.193202942609787\nStep 951, loss: 0.19428031146526337\nStep 952, loss: 0.19374477863311768\nStep 953, loss: 0.1919095665216446\nStep 954, loss: 0.19433891773223877\nStep 955, loss: 0.19187457859516144\nStep 956, loss: 0.19621175527572632\nStep 957, loss: 0.19424453377723694\nStep 958, loss: 0.19666950404644012\nStep 959, loss: 0.19401375949382782\nStep 960, loss: 0.19302953779697418\nStep 961, loss: 0.19898679852485657\nStep 962, loss: 0.19154222309589386\nStep 963, loss: 0.1965298056602478\nStep 964, loss: 0.19366717338562012\nStep 965, loss: 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0.19323404133319855\nStep 993, loss: 0.1923423707485199\nStep 994, loss: 0.1945382058620453\nStep 995, loss: 0.19345350563526154\nStep 996, loss: 0.1991584599018097\nStep 997, loss: 0.19994023442268372\nStep 998, loss: 0.1919410675764084\nStep 999, loss: 0.19577297568321228\nStep 918, loss: 0.19523441791534424\nStep 919, loss: 0.19546496868133545\nStep 920, loss: 0.19409704208374023\nStep 921, loss: 0.19166922569274902\nStep 922, loss: 0.1975981593132019\nStep 923, loss: 0.19526618719100952\nStep 924, loss: 0.19312119483947754\nStep 925, loss: 0.19502730667591095\nStep 926, loss: 0.19699089229106903\nStep 927, loss: 0.19339591264724731\nStep 928, loss: 0.19219785928726196\nStep 929, loss: 0.19684770703315735\nStep 930, loss: 0.1927916556596756\nStep 931, loss: 0.1985294222831726\nStep 932, loss: 0.1954779475927353\nStep 933, loss: 0.19568152725696564\nStep 934, loss: 0.19383743405342102\nStep 935, loss: 0.19561338424682617\nStep 936, loss: 0.19460172951221466\nStep 937, loss: 0.19666717946529388\nStep 938, loss: 0.1971161663532257\nStep 939, loss: 0.19739972054958344\nStep 940, loss: 0.19577215611934662\nStep 941, loss: 0.19569700956344604\nStep 942, loss: 0.1939944624900818\nStep 943, loss: 0.1971275806427002\nStep 944, loss: 0.19501261413097382\nStep 945, loss: 0.19413445889949799\nStep 946, loss: 0.1940343976020813\nStep 947, loss: 0.19624318182468414\nStep 948, loss: 0.19629356265068054\nStep 949, loss: 0.19340308010578156\nStep 950, loss: 0.193202942609787\nStep 951, loss: 0.19428031146526337\nStep 952, loss: 0.19374477863311768\nStep 953, loss: 0.1919095665216446\nStep 954, loss: 0.19433891773223877\nStep 955, loss: 0.19187457859516144\nStep 956, loss: 0.19621175527572632\nStep 957, loss: 0.19424453377723694\nStep 958, loss: 0.19666950404644012\nStep 959, loss: 0.19401375949382782\nStep 960, loss: 0.19302953779697418\nStep 961, loss: 0.19898679852485657\nStep 962, loss: 0.19154222309589386\nStep 963, loss: 0.1965298056602478\nStep 964, loss: 0.19366717338562012\nStep 965, loss: 0.19513016939163208\nStep 966, loss: 0.19310520589351654\nStep 967, loss: 0.19546397030353546\nStep 968, loss: 0.19591833651065826\nStep 969, loss: 0.19176341593265533\nStep 970, loss: 0.19398139417171478\nStep 971, loss: 0.19647134840488434\nStep 972, loss: 0.1946771889925003\nStep 973, loss: 0.19320639967918396\nStep 974, loss: 0.19349446892738342\nStep 975, loss: 0.19277448952198029\nStep 976, loss: 0.19457297027111053\nStep 977, loss: 0.19542548060417175\nStep 978, loss: 0.19564610719680786\nStep 979, loss: 0.19077078998088837\nStep 980, loss: 0.19871804118156433\nStep 981, loss: 0.1972716599702835\nStep 982, loss: 0.19280266761779785\nStep 983, loss: 0.19526249170303345\nStep 984, loss: 0.19640924036502838\nStep 985, loss: 0.19558152556419373\nStep 986, loss: 0.19950821995735168\nStep 987, loss: 0.19390521943569183\nStep 988, loss: 0.19487619400024414\nStep 989, loss: 0.19438578188419342\nStep 990, loss: 0.19506306946277618\nStep 991, loss: 0.19303816556930542\nStep 992, loss: 0.19323404133319855\nStep 993, loss: 0.1923423707485199\nStep 994, loss: 0.1945382058620453\nStep 995, loss: 0.19345350563526154\nStep 996, loss: 0.1991584599018097\nStep 997, loss: 0.19994023442268372\nStep 998, loss: 0.1919410675764084\nStep 999, loss: 0.19577297568321228\nStep 918, loss: 0.19523441791534424\nStep 919, loss: 0.19546496868133545\nStep 920, loss: 0.19409704208374023\nStep 921, loss: 0.19166922569274902\nStep 922, loss: 0.1975981593132019\nStep 923, loss: 0.19526618719100952\nStep 924, loss: 0.19312119483947754\nStep 925, loss: 0.19502730667591095\nStep 926, loss: 0.19699089229106903\nStep 927, loss: 0.19339591264724731\nStep 928, loss: 0.19219785928726196\nStep 929, loss: 0.19684770703315735\nStep 930, loss: 0.1927916556596756\nStep 931, loss: 0.1985294222831726\nStep 932, loss: 0.1954779475927353\nStep 933, loss: 0.19568152725696564\nStep 934, loss: 0.19383743405342102\nStep 935, loss: 0.19561338424682617\nStep 936, loss: 0.19460172951221466\nStep 937, loss: 0.19666717946529388\nStep 938, loss: 0.1971161663532257\nStep 939, loss: 0.19739972054958344\nStep 940, loss: 0.19577215611934662\nStep 941, loss: 0.19569700956344604\nStep 942, loss: 0.1939944624900818\nStep 943, loss: 0.1971275806427002\nStep 944, loss: 0.19501261413097382\nStep 945, loss: 0.19413445889949799\nStep 946, loss: 0.1940343976020813\nStep 947, loss: 0.19624318182468414\nStep 948, loss: 0.19629356265068054\nStep 949, loss: 0.19340308010578156\nStep 950, loss: 0.193202942609787\nStep 951, loss: 0.19428031146526337\nStep 952, loss: 0.19374477863311768\nStep 953, loss: 0.1919095665216446\nStep 954, loss: 0.19433891773223877\nStep 955, loss: 0.19187457859516144\nStep 956, loss: 0.19621175527572632\nStep 957, loss: 0.19424453377723694\nStep 958, loss: 0.19666950404644012\nStep 959, loss: 0.19401375949382782\nStep 960, loss: 0.19302953779697418\nStep 961, loss: 0.19898679852485657\nStep 962, loss: 0.19154222309589386\nStep 963, loss: 0.1965298056602478\nStep 964, loss: 0.19366717338562012\nStep 965, loss: 0.19513016939163208\nStep 966, loss: 0.19310520589351654\nStep 967, loss: 0.19546397030353546\nStep 968, loss: 0.19591833651065826\nStep 969, loss: 0.19176341593265533\nStep 970, loss: 0.19398139417171478\nStep 971, loss: 0.19647134840488434\nStep 972, loss: 0.1946771889925003\nStep 973, loss: 0.19320639967918396\nStep 974, loss: 0.19349446892738342\nStep 975, loss: 0.19277448952198029\nStep 976, loss: 0.19457297027111053\nStep 977, loss: 0.19542548060417175\nStep 978, loss: 0.19564610719680786\nStep 979, loss: 0.19077078998088837\nStep 980, loss: 0.19871804118156433\nStep 981, loss: 0.1972716599702835\nStep 982, loss: 0.19280266761779785\nStep 983, loss: 0.19526249170303345\nStep 984, loss: 0.19640924036502838\nStep 985, loss: 0.19558152556419373\nStep 986, loss: 0.19950821995735168\nStep 987, loss: 0.19390521943569183\nStep 988, loss: 0.19487619400024414\nStep 989, loss: 0.19438578188419342\nStep 990, loss: 0.19506306946277618\nStep 991, loss: 0.19303816556930542\nStep 992, loss: 0.19323404133319855\nStep 993, loss: 0.1923423707485199\nStep 994, loss: 0.1945382058620453\nStep 995, loss: 0.19345350563526154\nStep 996, loss: 0.1991584599018097\nStep 997, loss: 0.19994023442268372\nStep 998, loss: 0.1919410675764084\nStep 999, loss: 0.19577297568321228\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\nValueError: [process_index=11] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318099_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\nValueError: [process_index=9] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318099_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\nValueError: [process_index=8] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318099_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\nValueError: [process_index=10] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318099_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\nValueError: [process_index=25] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318099_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\nValueError: [process_index=5] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318099_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\nValueError: [process_index=27] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318099_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\nValueError: [process_index=7] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318099_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\nValueError: [process_index=4] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318099_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\nValueError: [process_index=24] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318099_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\nValueError: [process_index=6] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318099_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\nValueError: [process_index=26] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318099_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\nStep 918, loss: 0.19523441791534424\nStep 919, loss: 0.19546496868133545\nStep 920, loss: 0.19409704208374023\nStep 921, loss: 0.19166922569274902\nStep 922, loss: 0.1975981593132019\nStep 923, loss: 0.19526618719100952\nStep 924, loss: 0.19312119483947754\nStep 925, loss: 0.19502730667591095\nStep 926, loss: 0.19699089229106903\nStep 927, loss: 0.19339591264724731\nStep 928, loss: 0.19219785928726196\nStep 929, loss: 0.19684770703315735\nStep 930, loss: 0.1927916556596756\nStep 931, loss: 0.1985294222831726\nStep 932, loss: 0.1954779475927353\nStep 933, loss: 0.19568152725696564\nStep 934, loss: 0.19383743405342102\nStep 935, loss: 0.19561338424682617\nStep 936, loss: 0.19460172951221466\nStep 937, loss: 0.19666717946529388\nStep 938, loss: 0.1971161663532257\nStep 939, loss: 0.19739972054958344\nStep 940, loss: 0.19577215611934662\nStep 941, loss: 0.19569700956344604\nStep 942, loss: 0.1939944624900818\nStep 943, loss: 0.1971275806427002\nStep 944, loss: 0.19501261413097382\nStep 945, loss: 0.19413445889949799\nStep 946, loss: 0.1940343976020813\nStep 947, loss: 0.19624318182468414\nStep 948, loss: 0.19629356265068054\nStep 949, loss: 0.19340308010578156\nStep 950, loss: 0.193202942609787\nStep 951, loss: 0.19428031146526337\nStep 952, loss: 0.19374477863311768\nStep 953, loss: 0.1919095665216446\nStep 954, loss: 0.19433891773223877\nStep 955, loss: 0.19187457859516144\nStep 956, loss: 0.19621175527572632\nStep 957, loss: 0.19424453377723694\nStep 958, loss: 0.19666950404644012\nStep 959, loss: 0.19401375949382782\nStep 960, loss: 0.19302953779697418\nStep 961, loss: 0.19898679852485657\nStep 962, loss: 0.19154222309589386\nStep 963, loss: 0.1965298056602478\nStep 964, loss: 0.19366717338562012\nStep 965, loss: 0.19513016939163208\nStep 966, loss: 0.19310520589351654\nStep 967, loss: 0.19546397030353546\nStep 968, loss: 0.19591833651065826\nStep 969, loss: 0.19176341593265533\nStep 970, loss: 0.19398139417171478\nStep 971, loss: 0.19647134840488434\nStep 972, loss: 0.1946771889925003\nStep 973, loss: 0.19320639967918396\nStep 918, loss: 0.19523441791534424\nStep 919, loss: 0.19546496868133545\nStep 920, loss: 0.19409704208374023\nStep 921, loss: 0.19166922569274902\nStep 922, loss: 0.1975981593132019\nStep 923, loss: 0.19526618719100952\nStep 924, loss: 0.19312119483947754\nStep 925, loss: 0.19502730667591095\nStep 926, loss: 0.19699089229106903\nStep 927, loss: 0.19339591264724731\nStep 928, loss: 0.19219785928726196\nStep 929, loss: 0.19684770703315735\nStep 930, loss: 0.1927916556596756\nStep 931, loss: 0.1985294222831726\nStep 932, loss: 0.1954779475927353\nStep 933, loss: 0.19568152725696564\nStep 934, loss: 0.19383743405342102\nStep 935, loss: 0.19561338424682617\nStep 936, loss: 0.19460172951221466\nStep 937, loss: 0.19666717946529388\nStep 938, loss: 0.1971161663532257\nStep 939, loss: 0.19739972054958344\nStep 940, loss: 0.19577215611934662\nStep 941, loss: 0.19569700956344604\nStep 942, loss: 0.1939944624900818\nStep 943, loss: 0.1971275806427002\nStep 944, loss: 0.19501261413097382\nStep 945, loss: 0.19413445889949799\nStep 974, loss: 0.19349446892738342\nStep 975, loss: 0.19277448952198029\nStep 976, loss: 0.19457297027111053\nStep 977, loss: 0.19542548060417175\nStep 978, loss: 0.19564610719680786\nStep 979, loss: 0.19077078998088837\nStep 980, loss: 0.19871804118156433\nStep 981, loss: 0.1972716599702835\nStep 982, loss: 0.19280266761779785\nStep 983, loss: 0.19526249170303345\nStep 984, loss: 0.19640924036502838\nStep 985, loss: 0.19558152556419373\nStep 986, loss: 0.19950821995735168\nStep 987, loss: 0.19390521943569183\nStep 988, loss: 0.19487619400024414\nStep 989, loss: 0.19438578188419342\nStep 990, loss: 0.19506306946277618\nStep 991, loss: 0.19303816556930542\nStep 992, loss: 0.19323404133319855\nStep 993, loss: 0.1923423707485199\nStep 994, loss: 0.1945382058620453\nStep 995, loss: 0.19345350563526154\nStep 996, loss: 0.1991584599018097\nStep 997, loss: 0.19994023442268372\nStep 998, loss: 0.1919410675764084\nStep 999, loss: 0.19577297568321228\nStep 946, loss: 0.1940343976020813\nStep 947, loss: 0.19624318182468414\nStep 948, loss: 0.19629356265068054\nStep 949, loss: 0.19340308010578156\nStep 950, loss: 0.193202942609787\nStep 951, loss: 0.19428031146526337\nStep 952, loss: 0.19374477863311768\nStep 953, loss: 0.1919095665216446\nStep 954, loss: 0.19433891773223877\nStep 955, loss: 0.19187457859516144\nStep 956, loss: 0.19621175527572632\nStep 957, loss: 0.19424453377723694\nStep 958, loss: 0.19666950404644012\nStep 959, loss: 0.19401375949382782\nStep 960, loss: 0.19302953779697418\nStep 961, loss: 0.19898679852485657\nStep 962, loss: 0.19154222309589386\nStep 963, loss: 0.1965298056602478\nStep 964, loss: 0.19366717338562012\nStep 965, loss: 0.19513016939163208\nStep 966, loss: 0.19310520589351654\nStep 967, loss: 0.19546397030353546\nStep 968, loss: 0.19591833651065826\nStep 969, loss: 0.19176341593265533\nStep 970, loss: 0.19398139417171478\nStep 971, loss: 0.19647134840488434\nStep 972, loss: 0.1946771889925003\nStep 973, loss: 0.19320639967918396\nStep 918, loss: 0.19523441791534424\nStep 919, loss: 0.19546496868133545\nStep 920, loss: 0.19409704208374023\nStep 921, loss: 0.19166922569274902\nStep 922, loss: 0.1975981593132019\nStep 923, loss: 0.19526618719100952\nStep 924, loss: 0.19312119483947754\nStep 925, loss: 0.19502730667591095\nStep 926, loss: 0.19699089229106903\nStep 927, loss: 0.19339591264724731\nStep 928, loss: 0.19219785928726196\nStep 929, loss: 0.19684770703315735\nStep 930, loss: 0.1927916556596756\nStep 931, loss: 0.1985294222831726\nStep 932, loss: 0.1954779475927353\nStep 933, loss: 0.19568152725696564\nStep 934, loss: 0.19383743405342102\nStep 935, loss: 0.19561338424682617\nStep 936, loss: 0.19460172951221466\nStep 937, loss: 0.19666717946529388\nStep 938, loss: 0.1971161663532257\nStep 939, loss: 0.19739972054958344\nStep 940, loss: 0.19577215611934662\nStep 941, loss: 0.19569700956344604\nStep 942, loss: 0.1939944624900818\nStep 943, loss: 0.1971275806427002\nStep 944, loss: 0.19501261413097382\nStep 945, loss: 0.19413445889949799\nStep 974, loss: 0.19349446892738342\nStep 975, loss: 0.19277448952198029\nStep 976, loss: 0.19457297027111053\nStep 977, loss: 0.19542548060417175\nStep 978, loss: 0.19564610719680786\nStep 979, loss: 0.19077078998088837\nStep 980, loss: 0.19871804118156433\nStep 981, loss: 0.1972716599702835\nStep 982, loss: 0.19280266761779785\nStep 983, loss: 0.19526249170303345\nStep 984, loss: 0.19640924036502838\nStep 985, loss: 0.19558152556419373\nStep 986, loss: 0.19950821995735168\nStep 987, loss: 0.19390521943569183\nStep 988, loss: 0.19487619400024414\nStep 989, loss: 0.19438578188419342\nStep 990, loss: 0.19506306946277618\nStep 991, loss: 0.19303816556930542\nStep 992, loss: 0.19323404133319855\nStep 993, loss: 0.1923423707485199\nStep 994, loss: 0.1945382058620453\nStep 995, loss: 0.19345350563526154\nStep 996, loss: 0.1991584599018097\nStep 997, loss: 0.19994023442268372\nStep 998, loss: 0.1919410675764084\nStep 999, loss: 0.19577297568321228\nStep 918, loss: 0.19523441791534424\nStep 919, loss: 0.19546496868133545\nStep 920, loss: 0.19409704208374023\nStep 921, loss: 0.19166922569274902\nStep 922, loss: 0.1975981593132019\nStep 923, loss: 0.19526618719100952\nStep 924, loss: 0.19312119483947754\nStep 925, loss: 0.19502730667591095\nStep 926, loss: 0.19699089229106903\nStep 927, loss: 0.19339591264724731\nStep 928, loss: 0.19219785928726196\nStep 929, loss: 0.19684770703315735\nStep 930, loss: 0.1927916556596756\nStep 931, loss: 0.1985294222831726\nStep 932, loss: 0.1954779475927353\nStep 933, loss: 0.19568152725696564\nStep 934, loss: 0.19383743405342102\nStep 935, loss: 0.19561338424682617\nStep 936, loss: 0.19460172951221466\nStep 937, loss: 0.19666717946529388\nStep 938, loss: 0.1971161663532257\nStep 939, loss: 0.19739972054958344\nStep 940, loss: 0.19577215611934662\nStep 941, loss: 0.19569700956344604\nStep 942, loss: 0.1939944624900818\nStep 943, loss: 0.1971275806427002\nStep 944, loss: 0.19501261413097382\nStep 945, loss: 0.19413445889949799\nStep 946, loss: 0.1940343976020813\nStep 947, loss: 0.19624318182468414\nStep 948, loss: 0.19629356265068054\nStep 949, loss: 0.19340308010578156\nStep 950, loss: 0.193202942609787\nStep 951, loss: 0.19428031146526337\nStep 952, loss: 0.19374477863311768\nStep 953, loss: 0.1919095665216446\nStep 954, loss: 0.19433891773223877\nStep 955, loss: 0.19187457859516144\nStep 956, loss: 0.19621175527572632\nStep 957, loss: 0.19424453377723694\nStep 958, loss: 0.19666950404644012\nStep 959, loss: 0.19401375949382782\nStep 960, loss: 0.19302953779697418\nStep 961, loss: 0.19898679852485657\nStep 962, loss: 0.19154222309589386\nStep 963, loss: 0.1965298056602478\nStep 964, loss: 0.19366717338562012\nStep 965, loss: 0.19513016939163208\nStep 966, loss: 0.19310520589351654\nStep 967, loss: 0.19546397030353546\nStep 968, loss: 0.19591833651065826\nStep 969, loss: 0.19176341593265533\nStep 970, loss: 0.19398139417171478\nStep 971, loss: 0.19647134840488434\nStep 972, loss: 0.1946771889925003\nStep 973, loss: 0.19320639967918396\nStep 918, loss: 0.19523441791534424\nStep 919, loss: 0.19546496868133545\nStep 920, loss: 0.19409704208374023\nStep 921, loss: 0.19166922569274902\nStep 922, loss: 0.1975981593132019\nStep 923, loss: 0.19526618719100952\nStep 924, loss: 0.19312119483947754\nStep 925, loss: 0.19502730667591095\nStep 926, loss: 0.19699089229106903\nStep 927, loss: 0.19339591264724731\nStep 928, loss: 0.19219785928726196\nStep 929, loss: 0.19684770703315735\nStep 930, loss: 0.1927916556596756\nStep 931, loss: 0.1985294222831726\nStep 932, loss: 0.1954779475927353\nStep 933, loss: 0.19568152725696564\nStep 934, loss: 0.19383743405342102\nStep 935, loss: 0.19561338424682617\nStep 936, loss: 0.19460172951221466\nStep 937, loss: 0.19666717946529388\nStep 938, loss: 0.1971161663532257\nStep 939, loss: 0.19739972054958344\nStep 940, loss: 0.19577215611934662\nStep 941, loss: 0.19569700956344604\nStep 942, loss: 0.1939944624900818\nStep 943, loss: 0.1971275806427002\nStep 944, loss: 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0.19154222309589386\nStep 963, loss: 0.1965298056602478\nStep 964, loss: 0.19366717338562012\nStep 965, loss: 0.19513016939163208\nStep 966, loss: 0.19310520589351654\nStep 967, loss: 0.19546397030353546\nStep 968, loss: 0.19591833651065826\nStep 969, loss: 0.19176341593265533\nStep 970, loss: 0.19398139417171478\nStep 971, loss: 0.19647134840488434\nStep 972, loss: 0.1946771889925003\nStep 973, loss: 0.19320639967918396\nStep 974, loss: 0.19349446892738342\nStep 975, loss: 0.19277448952198029\nStep 976, loss: 0.19457297027111053\nStep 977, loss: 0.19542548060417175\nStep 978, loss: 0.19564610719680786\nStep 979, loss: 0.19077078998088837\nStep 980, loss: 0.19871804118156433\nStep 981, loss: 0.1972716599702835\nStep 982, loss: 0.19280266761779785\nStep 983, loss: 0.19526249170303345\nStep 984, loss: 0.19640924036502838\nStep 985, loss: 0.19558152556419373\nStep 986, loss: 0.19950821995735168\nStep 987, loss: 0.19390521943569183\nStep 988, loss: 0.19487619400024414\nStep 989, loss: 0.19438578188419342\nStep 990, loss: 0.19506306946277618\nStep 991, loss: 0.19303816556930542\nStep 992, loss: 0.19323404133319855\nStep 993, loss: 0.1923423707485199\nStep 994, loss: 0.1945382058620453\nStep 995, loss: 0.19345350563526154\nStep 996, loss: 0.1991584599018097\nStep 997, loss: 0.19994023442268372\nStep 998, loss: 0.1919410675764084\nStep 999, loss: 0.19577297568321228\nStep 918, loss: 0.19523441791534424\nStep 919, loss: 0.19546496868133545\nStep 920, loss: 0.19409704208374023\nStep 921, loss: 0.19166922569274902\nStep 922, loss: 0.1975981593132019\nStep 923, loss: 0.19526618719100952\nStep 924, loss: 0.19312119483947754\nStep 925, loss: 0.19502730667591095\nStep 926, loss: 0.19699089229106903\nStep 927, loss: 0.19339591264724731\nStep 928, loss: 0.19219785928726196\nStep 929, loss: 0.19684770703315735\nStep 930, loss: 0.1927916556596756\nStep 931, loss: 0.1985294222831726\nStep 932, loss: 0.1954779475927353\nStep 933, loss: 0.19568152725696564\nStep 934, loss: 0.19383743405342102\nStep 935, loss: 0.19561338424682617\nStep 936, loss: 0.19460172951221466\nStep 937, loss: 0.19666717946529388\nStep 938, loss: 0.1971161663532257\nStep 939, loss: 0.19739972054958344\nStep 940, loss: 0.19577215611934662\nStep 941, loss: 0.19569700956344604\nStep 942, loss: 0.1939944624900818\nStep 943, loss: 0.1971275806427002\nStep 944, loss: 0.19501261413097382\nStep 945, loss: 0.19413445889949799\nStep 946, loss: 0.1940343976020813\nStep 947, loss: 0.19624318182468414\nStep 948, loss: 0.19629356265068054\nStep 949, loss: 0.19340308010578156\nStep 950, loss: 0.193202942609787\nStep 951, loss: 0.19428031146526337\nStep 952, loss: 0.19374477863311768\nStep 953, loss: 0.1919095665216446\nStep 954, loss: 0.19433891773223877\nStep 955, loss: 0.19187457859516144\nStep 956, loss: 0.19621175527572632\nStep 957, loss: 0.19424453377723694\nStep 958, loss: 0.19666950404644012\nStep 959, loss: 0.19401375949382782\nStep 960, loss: 0.19302953779697418\nStep 961, loss: 0.19898679852485657\nStep 962, loss: 0.19154222309589386\nStep 963, loss: 0.1965298056602478\nStep 964, loss: 0.19366717338562012\nStep 965, loss: 0.19513016939163208\nStep 966, loss: 0.19310520589351654\nStep 967, loss: 0.19546397030353546\nStep 968, loss: 0.19591833651065826\nStep 969, loss: 0.19176341593265533\nStep 970, loss: 0.19398139417171478\nStep 971, loss: 0.19647134840488434\nStep 972, loss: 0.1946771889925003\nStep 973, loss: 0.19320639967918396\nStep 974, loss: 0.19349446892738342\nStep 975, loss: 0.19277448952198029\nStep 976, loss: 0.19457297027111053\nStep 977, loss: 0.19542548060417175\nStep 978, loss: 0.19564610719680786\nStep 979, loss: 0.19077078998088837\nStep 980, loss: 0.19871804118156433\nStep 981, loss: 0.1972716599702835\nStep 982, loss: 0.19280266761779785\nStep 983, loss: 0.19526249170303345\nStep 984, loss: 0.19640924036502838\nStep 985, loss: 0.19558152556419373\nStep 986, loss: 0.19950821995735168\nStep 987, loss: 0.19390521943569183\nStep 988, loss: 0.19487619400024414\nStep 989, loss: 0.19438578188419342\nStep 990, loss: 0.19506306946277618\nStep 991, loss: 0.19303816556930542\nStep 992, loss: 0.19323404133319855\nStep 993, loss: 0.1923423707485199\nStep 994, loss: 0.1945382058620453\nStep 995, loss: 0.19345350563526154\nStep 996, loss: 0.1991584599018097\nStep 997, loss: 0.19994023442268372\nStep 998, loss: 0.1919410675764084\nStep 999, loss: 0.19577297568321228\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\nValueError: [process_index=23] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318122_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\nValueError: [process_index=16] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318118_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\nValueError: [process_index=18] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318118_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\nValueError: [process_index=31] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318120_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\nValueError: [process_index=21] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318122_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\nValueError: [process_index=14] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318123_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\nValueError: [process_index=12] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318123_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\nValueError: [process_index=17] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318118_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\nValueError: [process_index=28] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318120_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\nValueError: [process_index=20] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318122_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\nValueError: [process_index=19] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318118_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\nValueError: [process_index=22] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318122_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\nValueError: [process_index=13] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318123_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\nValueError: [process_index=15] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318123_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\nValueError: [process_index=30] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318120_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\nValueError: [process_index=29] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318120_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n2025-07-01 00:12:31.357123: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service.cc:1689] Shutdown barrier in coordination service has failed:\nDEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n2025-07-01 00:12:31.357635: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\nThis suggests that the workers are out of sync. Either at least one worker (a) crashed early due to program error or scheduler events (e.g. preemption, eviction), (b) was too fast in its execution, or (c) too slow / hanging. Check the logs (both the program and scheduler events) for an earlier error to identify the root cause.\n2025-07-01 00:12:31.357150: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service.cc:1730] Use error polling to propagate the following error to all tasks: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n2025-07-01 00:12:31.357272: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.357408: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n2025-07-01 00:12:31.357511: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.357537: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.357838: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n2025-07-01 00:12:31.357879: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.357487: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.357789: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.358027: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\n2025-07-01 00:12:31.357624: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\n2025-07-01 00:12:31.357937: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\n2025-07-01 00:12:31.357864: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.357965: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\n2025-07-01 00:12:31.358050: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.357759: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.357856: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.358181: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\n2025-07-01 00:12:31.357705: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.358251: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.357880: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\n2025-07-01 00:12:31.357838: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.357950: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n2025-07-01 00:12:31.358247: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n2025-07-01 00:12:31.358561: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.358682: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.357944: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.358160: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\n2025-07-01 00:12:31.358277: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.358191: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.388132: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.389752: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.387984: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.389659: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.388310: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.389838: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.388302: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.389938: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.388225: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.390670: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\n2025-07-01 00:12:31.374109: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.390981: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.388191: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.390626: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\n2025-07-01 00:12:31.374262: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.391021: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.388298: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.390927: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\n2025-07-01 00:12:31.374546: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.391311: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.387946: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.390776: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.388298: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.390889: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.374395: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.391196: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.388167: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.390957: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.388074: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.390919: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.388180: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.391065: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\nsrun: error: hkn0520: task 10: Aborted (core dumped)\nsrun: error: hkn0523: task 15: Aborted (core dumped)\nsrun: error: hkn0820: task 31: Aborted (core dumped)\nsrun: error: hkn0810: task 27: Aborted (core dumped)\nsrun: error: hkn0420: tasks 1-3: Aborted (core dumped)\nsrun: error: hkn0516: tasks 5,7: Aborted (core dumped)\nsrun: error: hkn0717: tasks 20,22: Aborted (core dumped)\nsrun: error: hkn0520: task 11: Aborted (core dumped)\nsrun: error: hkn0704: tasks 17,19: Aborted (core dumped)\nsrun: error: hkn0820: task 29: Aborted (core dumped)\nsrun: error: hkn0810: task 24: Aborted (core dumped)\nsrun: error: hkn0520: task 9: Aborted (core dumped)\nsrun: error: hkn0820: task 28: Aborted (core dumped)\nsrun: error: hkn0516: task 4: Aborted (core dumped)\nsrun: error: hkn0704: task 16: Aborted (core dumped)\nsrun: error: hkn0523: tasks 13-14: Aborted (core dumped)\nsrun: error: hkn0717: task 21: Aborted (core dumped)\nsrun: error: hkn0717: task 23: Aborted (core dumped)\nsrun: error: hkn0820: task 30: Aborted (core dumped)\nsrun: error: hkn0810: task 26: Aborted (core dumped)\nsrun: error: hkn0520: task 8: Aborted (core dumped)\nsrun: error: hkn0516: task 6: Aborted (core dumped)\nsrun: error: hkn0810: task 25: Aborted (core dumped)\nsrun: error: hkn0523: task 12: Aborted (core dumped)\nsrun: error: hkn0704: task 18: Aborted (core dumped)\nsrun: error: hkn0420: task 0: Aborted (core dumped)\n\n============================= JOB FEEDBACK =============================\n\nJob ID: 3307474\nCluster: hk\nUser/Group: tum_ind3695/hk-project-pai00039\nAccount: hk-project-p0023960\nState: FAILED (exit code 134)\nPartition: accelerated\nNodes: 8\nCores per node: 32\nNodelist: hkn[0420,0516,0520,0523,0704,0717,0810,0820]\nCPU Utilized: 22:56:17\nCPU Efficiency: 9.24% of 10-08:10:40 core-walltime\nJob Wall-clock time: 00:58:10\nStarttime: Mon Jun 30 23:14:23 2025\nEndtime: Tue Jul 1 00:12:33 2025\nMemory Utilized: 423.14 GB (estimated maximum)\nMemory Efficiency: 0.00% of 0.00 MB (0.00 MB/node)\nEnergy Consumed: 35390279 Joule / 9830.63305555556 Watthours\nAverage node power draw: 10140.4810888252 Watt\n",log,tab +21,177754,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_alfred/logs_training_tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474.log",645,0,"",log,selection_mouse +22,177789,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_alfred/logs_training_tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474.log",644,0,"",log,selection_command +23,178578,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_alfred/logs_training_tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474.log",1441133,0,"",log,selection_command +24,184664,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_alfred/logs_training_tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474.log",1438043,0,"",log,selection_mouse +25,184801,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_alfred/logs_training_tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474.log",1438030,17,"DEADLINE_EXCEEDED",log,selection_mouse +26,194680,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_alfred/logs_training_tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474.log",1384536,0,"",log,selection_mouse +27,194841,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_alfred/logs_training_tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474.log",1384530,15,"array_metadatas",log,selection_mouse +28,216890,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_alfred/logs_training_tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474.log",1273273,0,"",log,selection_mouse +29,216914,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_alfred/logs_training_tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474.log",1273272,0,"",log,selection_command +30,217576,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_alfred/logs_training_tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474.log",1273308,0,"",log,selection_mouse +31,217590,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_alfred/logs_training_tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474.log",1273307,0,"",log,selection_command +32,225590,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_alfred/logs_training_tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474.log",1273274,34,"Traceback (most recent call last):",log,selection_command +33,227875,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_alfred/logs_training_tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474.log",1273274,167859,"Traceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\nValueError: [process_index=23] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318122_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\nValueError: [process_index=16] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318118_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\nValueError: [process_index=18] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318118_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\nValueError: [process_index=31] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318120_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\nValueError: [process_index=21] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318122_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\nValueError: [process_index=14] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318123_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\nValueError: [process_index=12] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318123_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\nValueError: [process_index=17] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318118_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\nValueError: [process_index=28] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318120_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\nValueError: [process_index=20] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318122_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 159, in wait_for_base_dir_creation\n await asyncio.sleep(0.25)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 605, in sleep\n return await future\nasyncio.exceptions.CancelledError\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 456, in wait_for\n return fut.result()\nasyncio.exceptions.CancelledError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 162, in _maybe_create_base_dir\n await asyncio.wait_for(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/tasks.py"", line 458, in wait_for\n raise exceptions.TimeoutError() from exc\nasyncio.exceptions.TimeoutError\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/train_tokenizer.py"", line 262, in \n orbax_checkpointer.save(\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/checkpointers/checkpointer.py"", line 255, in save\n self._handler.save(tmpdir.get(), args=ckpt_args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\nValueError: [process_index=19] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318118_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/pytree_checkpoint_handler.py"", line 594, in save\n self._handler_impl.save(directory, args=args)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 554, in save\n asyncio_utils.run_sync(async_save(directory, *args, **kwargs))\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/handlers/base_pytree_checkpoint_handler.py"", line 552, in async_save\n f.result() # Block on result.\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 174, in result\n f.result(timeout=time_remaining)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 397, in result\n return self._f.result(timeout=timeout)\nValueError: [process_index=22] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318122_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\nValueError: [process_index=13] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318123_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 348, in result\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\nValueError: [process_index=15] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318123_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\nValueError: [process_index=30] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318120_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n return self._t.result(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 297, in result\n self.join(timeout=timeout)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 294, in join\n raise self._exception\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 281, in run\n super().run()\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/threading.py"", line 953, in run\n self._target(*self._args, **self._kwargs)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 230, in _target_setting_result\n self._result = target()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/futures/future.py"", line 341, in \n target=lambda: asyncio_utils.run_sync(coro),\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/asyncio_utils.py"", line 50, in run_sync\n return asyncio.run(coro)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/runners.py"", line 44, in run\n return loop.run_until_complete(main)\n File ""/home/hk-project-pai00039/tum_ind3695/.local/share/uv/python/cpython-3.10.17-linux-x86_64-gnu/lib/python3.10/asyncio/base_events.py"", line 649, in run_until_complete\n return future.result()\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/serialization/type_handlers.py"", line 1105, in _background_serialize\n await asyncio.gather(*write_coros)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 194, in write\n await self._maybe_create_base_dir(file_path.parent)\n File ""/hkfs/home/project/hk-project-pai00039/tum_ind3695/projects/jafar_batch_size_scaling/.venv_jafar/lib/python3.10/site-packages/orbax/checkpoint/_src/metadata/array_metadata_store.py"", line 169, in _maybe_create_base_dir\n raise ValueError(\nValueError: [process_index=29] Timed out waiting for array_metadatas base directory creation: /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474/tokenizer_1751318120_1000.orbax-checkpoint-tmp-0/array_metadatas. timeout=600 seconds. primary_process=0\n2025-07-01 00:12:31.357123: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service.cc:1689] Shutdown barrier in coordination service has failed:\nDEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n2025-07-01 00:12:31.357635: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\nThis suggests that the workers are out of sync. Either at least one worker (a) crashed early due to program error or scheduler events (e.g. preemption, eviction), (b) was too fast in its execution, or (c) too slow / hanging. Check the logs (both the program and scheduler events) for an earlier error to identify the root cause.\n2025-07-01 00:12:31.357150: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service.cc:1730] Use error polling to propagate the following error to all tasks: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n2025-07-01 00:12:31.357272: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.357408: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n2025-07-01 00:12:31.357511: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380'] [type.googleapis.com/tensorflow.CoordinationServiceError='']\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.357537: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.357838: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n2025-07-01 00:12:31.357879: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.357487: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.357789: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.358027: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\n2025-07-01 00:12:31.357624: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\n2025-07-01 00:12:31.357937: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\n2025-07-01 00:12:31.357864: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.357965: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\n2025-07-01 00:12:31.358050: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.357759: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.357856: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.358181: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\n2025-07-01 00:12:31.357705: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.358251: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.357880: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\n2025-07-01 00:12:31.357838: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.357950: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n2025-07-01 00:12:31.358247: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n2025-07-01 00:12:31.358561: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.358682: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.357944: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.358160: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\n2025-07-01 00:12:31.358277: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.358191: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/PollForError [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.388132: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.389752: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.387984: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.389659: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.388310: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.389838: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.388302: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.389938: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.388225: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.390670: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\n2025-07-01 00:12:31.374109: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.390981: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.388191: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.390626: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\n2025-07-01 00:12:31.374262: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.391021: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.388298: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.390927: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\n2025-07-01 00:12:31.374546: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.391311: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.387946: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.390776: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.388298: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.390889: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.374395: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.391196: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.388167: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.390957: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.388074: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.390919: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\n2025-07-01 00:12:31.388180: E external/xla/xla/tsl/distributed_runtime/coordination/coordination_service_agent.cc:427] Polled an error from coordination service (this can be an error from this or another task).\n2025-07-01 00:12:31.391065: F external/xla/xla/pjrt/distributed/client.h:80] Terminating process because the JAX distributed service detected fatal errors. This most likely indicates that another task died; see the other task logs for more details. Disable Python buffering, i.e. `python -u`, to be sure to see all the previous output. absl::Status: INTERNAL: Failed to disconnect from coordination service with status: INTERNAL: Shutdown barrier has failed.\nBarrier result: 'DEADLINE_EXCEEDED: Barrier timed out. Id: Shutdown::16634615291091153380::0. This usually happens because a task triggered the barrier too early or too slowly. Please look at the task logs (both timed out and first task) to debug further.\n# of tasks that reached the barrier: 28/32.\nThe first task at the barrier: /job:jax_worker/replica:0/task:5. Some timed out task names:\n/job:jax_worker/replica:0/task:2\n/job:jax_worker/replica:0/task:0\n [type.googleapis.com/tensorflow.CoordinationServiceError=''] [type.googleapis.com/tensorflow.BarrierError='\n\x1eShutdown::16634615291091153380']\n\nRPC: /tensorflow.CoordinationService/ShutdownTask [type.googleapis.com/tensorflow.CoordinationServiceError='']Proceeding with agent shutdown anyway. This is usually caused by an earlier error during execution. Check the logs of (a) this task, (b) the leader (usually slice 0 task 0) and (c) the scheduler (e.g. preemption, eviction) for an earlier error to debug further. [type.googleapis.com/tensorflow.CoordinationServiceError='']\nsrun: error: hkn0520: task 10: Aborted (core dumped)\nsrun: error: hkn0523: task 15: Aborted (core dumped)\nsrun: error: hkn0820: task 31: Aborted (core dumped)\nsrun: error: hkn0810: task 27: Aborted (core dumped)\nsrun: error: hkn0420: tasks 1-3: Aborted (core dumped)\nsrun: error: hkn0516: tasks 5,7: Aborted (core dumped)\nsrun: error: hkn0717: tasks 20,22: Aborted (core dumped)\nsrun: error: hkn0520: task 11: Aborted (core dumped)\nsrun: error: hkn0704: tasks 17,19: Aborted (core dumped)\nsrun: error: hkn0820: task 29: Aborted (core dumped)\nsrun: error: hkn0810: task 24: Aborted (core dumped)\nsrun: error: hkn0520: task 9: Aborted (core dumped)\nsrun: error: hkn0820: task 28: Aborted (core dumped)\nsrun: error: hkn0516: task 4: Aborted (core dumped)\nsrun: error: hkn0704: task 16: Aborted (core dumped)\nsrun: error: hkn0523: tasks 13-14: Aborted (core dumped)\nsrun: error: hkn0717: task 21: Aborted (core dumped)\nsrun: error: hkn0717: task 23: Aborted (core dumped)\nsrun: error: hkn0820: task 30: Aborted (core dumped)\nsrun: error: hkn0810: task 26: Aborted (core dumped)\nsrun: error: hkn0520: task 8: Aborted (core dumped)\nsrun: error: hkn0516: task 6: Aborted (core dumped)\nsrun: error: hkn0810: task 25: Aborted (core dumped)\nsrun: error: hkn0523: task 12: Aborted (core dumped)\nsrun: error: hkn0704: task 18: Aborted (core dumped)\nsrun: error: hkn0420: task 0: Aborted (core dumped)\n\n============================= JOB FEEDBACK =============================\n\nJob ID: 3307474\nCluster: hk\nUser/Group: tum_ind3695/hk-project-pai00039\nAccount: hk-project-p0023960\nState: FAILED (exit code 134)\nPartition: accelerated\nNodes: 8\nCores per node: 32\nNodelist: hkn[0420,0516,0520,0523,0704,0717,0810,0820]\nCPU Utilized: 22:56:17\nCPU Efficiency: 9.24% of 10-08:10:40 core-walltime\nJob Wall-clock time: 00:58:10\nStarttime: Mon Jun 30 23:14:23 2025\nEndtime: Tue Jul 1 00:12:33 2025\nMemory Utilized: 423.14 GB (estimated maximum)\nMemory Efficiency: 0.00% of 0.00 MB (0.00 MB/node)\nEnergy Consumed: 35390279 Joule / 9830.63305555556 Watthours\nAverage node power draw: 10140.4810888252 Watt\n",log,selection_command +34,299049,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_alfred/logs_training_tokenizer/train_tokenizer_batch_size_scaling_8_node_3307474.log",1441133,0,"",log,selection_mouse +35,326035,"scripts_horeka/train_tokenizer.sh",0,0,"#!/usr/bin/env bash\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=""debug""\nslurm_job_id=""0000""\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=16 \\n --min_lr=4.24e-4 \\n --max_lr=4.24e-4 \\n --log_image_interval=100 \\n --log \\n --name=test-wandb-tags-$slurm_job_id \\n --tags test tokenizer debug \\n --entity instant-uv \\n --project jafar \\n --data_dir $tf_records_dir",shellscript,tab +36,337973,"train_tokenizer.py",0,0,"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\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 = 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 # --- 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__\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 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",python,tab +37,340875,"TERMINAL",0,0,"bash",,terminal_focus +38,342066,"TERMINAL",0,0,"idling",,terminal_command +39,342153,"TERMINAL",0,0,"]633;E;2025-07-01 13:10:54 idling;d7d28c43-3fca-451b-bc76-771109467dda]633;C[?1049h(B[?7hEvery 1.0s: sinfo_t_idlehkn1991.localdomain: Tue Jul 1 13:10:54 2025Partition dev_cpuonly: 11 nodes idle\rPartition cpuonly: 32 nodes idle\rPartition dev_accelerated:\t 2 nodes idle\rPartition accelerated: 10 nodes idle\rPartition dev_accelerated-h100 :\t 0 nodes idle\rPartition accelerated-h100:\t 0 nodes idle\rPartition large:\t 7 nodes idle",,terminal_output +40,343235,"TERMINAL",0,0,"5",,terminal_output +41,343633,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1991:~/Projects/jafar]633;D;0",,terminal_output +42,355197,"train_tokenizer.py",2394,0,"",python,selection_mouse +43,355208,"train_tokenizer.py",2393,0,"",python,selection_command +44,355807,"train_tokenizer.py",1743,0,"",python,selection_mouse +45,360688,"train_tokenizer.py",9298,0,"",python,selection_mouse +46,361292,"train_tokenizer.py",9290,0,"",python,selection_mouse +47,361455,"train_tokenizer.py",9289,4,"ckpt",python,selection_mouse +48,361735,"train_tokenizer.py",9289,5,"ckpt ",python,selection_mouse +49,361736,"train_tokenizer.py",9289,8,"ckpt = {",python,selection_mouse +50,361736,"train_tokenizer.py",9289,14,"ckpt = {""model",python,selection_mouse +51,361799,"train_tokenizer.py",9289,17,"ckpt = {""model"": ",python,selection_mouse +52,361800,"train_tokenizer.py",9289,28,"ckpt = {""model"": train_state",python,selection_mouse +53,361858,"train_tokenizer.py",9289,67,"ckpt = {""model"": train_state}\n orbax_checkpointer = ",python,selection_mouse +54,361865,"train_tokenizer.py",9289,72,"ckpt = {""model"": train_state}\n orbax_checkpointer = orbax",python,selection_mouse +55,361914,"train_tokenizer.py",9289,73,"ckpt = {""model"": train_state}\n orbax_checkpointer = orbax.",python,selection_mouse +56,361941,"train_tokenizer.py",9289,83,"ckpt = {""model"": train_state}\n orbax_checkpointer = orbax.checkpoint",python,selection_mouse +57,362482,"train_tokenizer.py",9369,0,"",python,selection_mouse +58,363001,"train_tokenizer.py",9502,0,"",python,selection_mouse +59,363016,"train_tokenizer.py",9501,0,"",python,selection_command +60,363180,"train_tokenizer.py",9502,0,"",python,selection_mouse +61,363188,"train_tokenizer.py",9501,0,"",python,selection_command +62,363390,"train_tokenizer.py",9501,1,"(",python,selection_mouse +63,363394,"train_tokenizer.py",9502,0,"",python,selection_command +64,363555,"train_tokenizer.py",9501,1,"(",python,selection_mouse +65,363556,"train_tokenizer.py",9497,5,"save(",python,selection_mouse +66,363585,"train_tokenizer.py",9478,24,"orbax_checkpointer.save(",python,selection_mouse +67,363789,"train_tokenizer.py",9410,92,"save_args = orbax_utils.save_args_from_target(ckpt)\n orbax_checkpointer.save(",python,selection_mouse +68,363919,"train_tokenizer.py",9335,167,"orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n save_args = orbax_utils.save_args_from_target(ckpt)\n orbax_checkpointer.save(",python,selection_mouse +69,363954,"train_tokenizer.py",9334,168," orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n save_args = orbax_utils.save_args_from_target(ckpt)\n orbax_checkpointer.save(",python,selection_mouse +70,363986,"train_tokenizer.py",9333,169," orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n save_args = orbax_utils.save_args_from_target(ckpt)\n orbax_checkpointer.save(",python,selection_mouse +71,364042,"train_tokenizer.py",9286,216," ckpt = {""model"": train_state}\n orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n save_args = orbax_utils.save_args_from_target(ckpt)\n orbax_checkpointer.save(",python,selection_mouse +72,364377,"train_tokenizer.py",9286,0,"",python,selection_mouse +73,364378,"train_tokenizer.py",9273,16," ",python,selection_mouse +74,364670,"train_tokenizer.py",9273,60," ckpt = {""model"": train_state}\n ",python,selection_mouse +75,364671,"train_tokenizer.py",9273,62," ckpt = {""model"": train_state}\n ",python,selection_mouse +76,364671,"train_tokenizer.py",9273,80," ckpt = {""model"": train_state}\n orbax_checkpointer",python,selection_mouse +77,364672,"train_tokenizer.py",9273,146," ckpt = {""model"": train_state}\n orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n save_args",python,selection_mouse +78,364672,"train_tokenizer.py",9273,148," ckpt = {""model"": train_state}\n orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n save_args =",python,selection_mouse +79,364673,"train_tokenizer.py",9273,160," ckpt = {""model"": train_state}\n orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n save_args = orbax_utils",python,selection_mouse +80,364726,"train_tokenizer.py",9273,228," ckpt = {""model"": train_state}\n orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n save_args = orbax_utils.save_args_from_target(ckpt)\n orbax_checkpointer.save",python,selection_mouse +81,364751,"train_tokenizer.py",9273,229," ckpt = {""model"": train_state}\n orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n save_args = orbax_utils.save_args_from_target(ckpt)\n orbax_checkpointer.save(",python,selection_mouse +82,365126,"train_tokenizer.py",9502,0,"",python,selection_mouse +83,365134,"train_tokenizer.py",9501,0,"",python,selection_command +84,365285,"train_tokenizer.py",9502,0,"",python,selection_mouse +85,365305,"train_tokenizer.py",9501,0,"",python,selection_command +86,365449,"train_tokenizer.py",9501,1,"(",python,selection_mouse +87,365545,"train_tokenizer.py",9502,0,"",python,selection_command +88,365546,"train_tokenizer.py",9420,82,"= orbax_utils.save_args_from_target(ckpt)\n orbax_checkpointer.save(",python,selection_mouse +89,365547,"train_tokenizer.py",9410,92,"save_args = orbax_utils.save_args_from_target(ckpt)\n orbax_checkpointer.save(",python,selection_mouse +90,365631,"train_tokenizer.py",9335,167,"orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n save_args = orbax_utils.save_args_from_target(ckpt)\n orbax_checkpointer.save(",python,selection_mouse +91,365632,"train_tokenizer.py",9332,170," orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n save_args = orbax_utils.save_args_from_target(ckpt)\n orbax_checkpointer.save(",python,selection_mouse +92,365664,"train_tokenizer.py",9331,171," orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n save_args = orbax_utils.save_args_from_target(ckpt)\n orbax_checkpointer.save(",python,selection_mouse +93,365695,"train_tokenizer.py",9330,172," orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n save_args = orbax_utils.save_args_from_target(ckpt)\n orbax_checkpointer.save(",python,selection_mouse +94,365863,"train_tokenizer.py",9284,218," ckpt = {""model"": train_state}\n orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n save_args = orbax_utils.save_args_from_target(ckpt)\n orbax_checkpointer.save(",python,selection_mouse +95,365929,"train_tokenizer.py",9285,217," ckpt = {""model"": train_state}\n orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n save_args = orbax_utils.save_args_from_target(ckpt)\n orbax_checkpointer.save(",python,selection_mouse +96,365931,"train_tokenizer.py",9228,274,"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(",python,selection_mouse +97,366275,"train_tokenizer.py",9228,0,"",python,selection_mouse +98,372384,"train_tokenizer.py",9190,0,"",python,selection_mouse +99,372969,"train_tokenizer.py",9240,0,"",python,selection_mouse +100,374612,"train_tokenizer.py",9318,0,"",python,selection_mouse +101,374625,"train_tokenizer.py",9317,0,"",python,selection_command +102,375186,"train_tokenizer.py",9249,0,"",python,selection_mouse +103,376066,"train_tokenizer.py",9349,0,"",python,selection_mouse +104,376770,"train_tokenizer.py",9367,0,"",python,selection_mouse +105,377467,"train_tokenizer.py",9340,0,"",python,selection_mouse +106,377630,"train_tokenizer.py",9335,18,"orbax_checkpointer",python,selection_mouse +107,378608,"train_tokenizer.py",9415,0,"",python,selection_mouse +108,378785,"train_tokenizer.py",9410,9,"save_args",python,selection_mouse +109,379646,"train_tokenizer.py",9339,0,"",python,selection_mouse +110,379806,"train_tokenizer.py",9335,18,"orbax_checkpointer",python,selection_mouse +111,380740,"train_tokenizer.py",9523,0,"",python,selection_mouse +112,381379,"train_tokenizer.py",9423,0,"",python,selection_mouse +113,381578,"train_tokenizer.py",9422,11,"orbax_utils",python,selection_mouse +114,382177,"train_tokenizer.py",9532,0,"",python,selection_mouse +115,382352,"train_tokenizer.py",9531,4,"join",python,selection_mouse +116,382942,"train_tokenizer.py",9493,0,"",python,selection_mouse +117,383096,"train_tokenizer.py",9478,18,"orbax_checkpointer",python,selection_mouse +118,384150,"train_tokenizer.py",9436,0,"",python,selection_mouse +119,417320,"train_tokenizer.py",9736,0,"",python,selection_mouse +120,417321,"train_tokenizer.py",9735,0,"",python,selection_command +121,417512,"train_tokenizer.py",9735,1,"k",python,selection_mouse +122,417550,"train_tokenizer.py",9736,0,"",python,selection_command +123,417551,"train_tokenizer.py",9706,30,"m_steps:\n break",python,selection_mouse +124,417613,"train_tokenizer.py",9640,96,"e_args=save_args,\n )\n if step >= args.num_steps:\n break",python,selection_mouse +125,417614,"train_tokenizer.py",9478,258,"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",python,selection_mouse +126,417670,"train_tokenizer.py",9330,406," 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",python,selection_mouse +127,417750,"train_tokenizer.py",9282,454," 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",python,selection_mouse +128,417789,"train_tokenizer.py",9279,457," 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",python,selection_mouse +129,417834,"train_tokenizer.py",9275,461," 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",python,selection_mouse +130,417892,"train_tokenizer.py",9216,520," 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",python,selection_mouse +131,428788,"train_tokenizer.py",9737,0,"",python,selection_mouse +132,434939,"train_tokenizer.py",9652,0,"",python,selection_mouse +133,435109,"train_tokenizer.py",9647,9,"save_args",python,selection_mouse +134,439895,"train_tokenizer.py",9311,0,"",python,selection_mouse +135,446256,"train_tokenizer.py",9310,0,"",python,selection_mouse +136,448728,"train_tokenizer.py",0,0,"",python,tab +137,448729,"train_tokenizer.py",7477,0,"",python,selection_mouse +138,450504,"train_tokenizer.py",7480,0,"",python,selection_mouse +139,450782,"train_tokenizer.py",0,0,"",python,tab +140,452942,"train_tokenizer.py",3466,0,"",python,selection_mouse +141,453142,"train_tokenizer.py",3465,5,"state",python,selection_mouse +142,454655,"train_tokenizer.py",3025,0,"",python,selection_mouse +143,454802,"train_tokenizer.py",3024,5,"state",python,selection_mouse +144,455085,"train_tokenizer.py",3024,21,"state.apply_gradients",python,selection_mouse +145,455500,"train_tokenizer.py",3037,0,"",python,selection_mouse +146,455500,"train_tokenizer.py",3030,15,"apply_gradients",python,selection_mouse +147,456145,"train_tokenizer.py",3025,0,"",python,selection_mouse +148,456304,"train_tokenizer.py",3024,5,"state",python,selection_mouse +149,456907,"train_tokenizer.py",3037,0,"",python,selection_mouse +150,457062,"train_tokenizer.py",3030,15,"apply_gradients",python,selection_mouse +151,457725,"train_tokenizer.py",3028,0,"",python,selection_mouse +152,457890,"train_tokenizer.py",3024,5,"state",python,selection_mouse +153,463644,"train_tokenizer.py",2836,0,"",python,selection_mouse +154,465456,"train_tokenizer.py",7488,0,"",python,selection_mouse +155,484792,"train_tokenizer.py",9616,0,"",python,selection_mouse +156,484794,"train_tokenizer.py",9615,0,"",python,selection_command +157,485406,"train_tokenizer.py",9675,0,"",python,selection_mouse +158,485422,"train_tokenizer.py",9674,0,"",python,selection_command +159,485579,"train_tokenizer.py",9674,1,")",python,selection_mouse +160,485689,"train_tokenizer.py",9675,0,"",python,selection_command +161,485690,"train_tokenizer.py",9657,18,"\n )",python,selection_mouse +162,485690,"train_tokenizer.py",9345,330,"kpointer = 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 )",python,selection_mouse +163,485690,"train_tokenizer.py",9343,332,"eckpointer = 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 )",python,selection_mouse +164,486461,"train_tokenizer.py",9283,0,"",python,selection_mouse +165,486596,"train_tokenizer.py",9273,16," ",python,selection_mouse +166,486770,"train_tokenizer.py",9273,80," ckpt = {""model"": train_state}\n orbax_checkpointer",python,selection_mouse +167,486770,"train_tokenizer.py",9273,146," ckpt = {""model"": train_state}\n orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n save_args",python,selection_mouse +168,486813,"train_tokenizer.py",9273,228," ckpt = {""model"": train_state}\n orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n save_args = orbax_utils.save_args_from_target(ckpt)\n orbax_checkpointer.save",python,selection_mouse +169,486813,"train_tokenizer.py",9273,343," 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,",python,selection_mouse +170,486908,"train_tokenizer.py",9273,384," 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,",python,selection_mouse +171,487089,"train_tokenizer.py",9657,0,"",python,selection_mouse +172,487121,"train_tokenizer.py",9656,0,"",python,selection_command +173,487362,"train_tokenizer.py",9657,0,"",python,selection_mouse +174,487389,"train_tokenizer.py",9656,0,"",python,selection_command +175,487516,"train_tokenizer.py",9656,1,",",python,selection_mouse +176,487518,"train_tokenizer.py",9657,0,"",python,selection_command +177,487659,"train_tokenizer.py",9616,41,"\n save_args=save_args,",python,selection_mouse +178,487659,"train_tokenizer.py",9520,137," os.path.join(os.getcwd(), args.ckpt_dir, f""tokenizer_{ts}_{step}""),\n ckpt,\n save_args=save_args,",python,selection_mouse +179,487660,"train_tokenizer.py",9470,187," orbax_checkpointer.save(\n os.path.join(os.getcwd(), args.ckpt_dir, f""tokenizer_{ts}_{step}""),\n ckpt,\n save_args=save_args,",python,selection_mouse +180,487660,"train_tokenizer.py",9399,258," 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,",python,selection_mouse +181,487661,"train_tokenizer.py",9396,261," 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,",python,selection_mouse +182,487661,"train_tokenizer.py",9395,262," 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,",python,selection_mouse +183,487683,"train_tokenizer.py",9394,263," 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,",python,selection_mouse +184,487763,"train_tokenizer.py",9319,338," 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,",python,selection_mouse +185,487840,"train_tokenizer.py",9273,384," 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,",python,selection_mouse +186,487931,"train_tokenizer.py",9216,441," 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,",python,selection_mouse +187,593439,"train_tokenizer.py",9714,0,"",python,selection_mouse +188,593504,"train_tokenizer.py",9713,0,"",python,selection_command +189,801184,"train_tokenizer.py",9215,0,"",python,selection_mouse +190,801199,"train_tokenizer.py",9214,0,"",python,selection_command +191,802197,"train_tokenizer.py",9452,0,"",python,selection_mouse +192,803383,"train_tokenizer.py",9272,0,"",python,selection_mouse +193,803400,"train_tokenizer.py",9271,0,"",python,selection_command +194,803959,"train_tokenizer.py",9318,0,"",python,selection_mouse +195,803963,"train_tokenizer.py",9317,0,"",python,selection_command +196,895557,"train_tokenizer.py",9639,0,"",python,selection_mouse +197,895748,"train_tokenizer.py",9637,9,"save_args",python,selection_mouse +198,896339,"train_tokenizer.py",9675,0,"",python,selection_mouse +199,896376,"train_tokenizer.py",9674,0,"",python,selection_command +200,898019,"train_tokenizer.py",9483,0,"",python,selection_mouse +201,898177,"train_tokenizer.py",9478,18,"orbax_checkpointer",python,selection_mouse +202,899127,"train_tokenizer.py",9675,0,"",python,selection_mouse +203,899178,"train_tokenizer.py",9674,0,"",python,selection_command +204,1530973,"TERMINAL",0,0,"# save_checkpoint_multiprocess",,terminal_command diff --git a/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-c190e976-4e73-4096-9dfa-66c3a0cce2061752157080469-2025_07_10-16.18.14.262/source.csv b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-c190e976-4e73-4096-9dfa-66c3a0cce2061752157080469-2025_07_10-16.18.14.262/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..a3866264332de5a60d97bf341f366e92f2212ee8 --- /dev/null +++ b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-c190e976-4e73-4096-9dfa-66c3a0cce2061752157080469-2025_07_10-16.18.14.262/source.csv @@ -0,0 +1,4206 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +2,695,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"4:18:14 PM [info] Activating crowd-code\n4:18:14 PM [info] Recording started\n4:18:14 PM [info] Initializing git provider using file system watchers...\n4:18:14 PM [info] Git repository found\n4:18:14 PM [info] Git provider initialized successfully\n",Log,tab +3,697,"extension-output-pdoom-org.crowd-code-#1-crowd-code",245,0,"4:18:14 PM [info] Initial git state: [object Object]\n",Log,content +4,3747,"TERMINAL",0,0,"/bin/python3 /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/printEnvVariablesToFile.py /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash/envVars.txt",,terminal_command +5,3773,"TERMINAL",0,0,"]633;E;2025-07-10 16:18:17 /bin/python3 /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/printEnvVariablesToFile.py /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash/envVars.txt;44d013a9-d4b7-491b-9109-05938bc2908f]633;C]0;tum_cte0515@hkn1993:/hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash]633;D;0",,terminal_output +6,198600,"TERMINAL",0,0,"bash",,terminal_focus +7,386141,"TERMINAL",0,0,"watch",,terminal_focus +8,459809,"TERMINAL",0,0,"bash",,terminal_focus +9,737837,"TERMINAL",0,0,"salloc",,terminal_focus +10,739105,"slurm/jobs/mihir/horeka/yolo-runs/tester.sh",0,0,"\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=""debug""\nslurm_job_id=""debug-mihir""\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/train_lam_action_space_scaling_20/3318547/lam_1751657975_200000/\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=82 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-tf-records-$slurm_job_id \\n --tags dynamics yolo-run tf_records \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n # --lam_checkpoint=$lam_ckpt_dir\n ",shellscript,tab +11,739533,"TERMINAL",0,0,"bash",,terminal_focus +12,749343,"TERMINAL",0,0,"watch",,terminal_focus +13,751007,"TERMINAL",0,0,"bash",,terminal_focus +14,754607,"TERMINAL",0,0,"watch",,terminal_focus +15,757748,"TERMINAL",0,0,"idling",,terminal_command +16,757821,"TERMINAL",0,0,"]633;E;2025-07-10 16:30:51 idling;d0af5434-9a5b-4b8f-9412-c82c37ee36e4]633;C[?1049h(B[?7hEvery 1.0s: sinfo_t_idlehkn1993.localdomain: Thu Jul 10 16:30:51 2025Partition dev_cpuonly: 11 nodes idle\rPartition cpuonly: 36 nodes idle\rPartition dev_accelerated:\t 0 nodes idle\rPartition accelerated:\t 0 nodes idle\rPartition dev_accelerated-h100 :\t 0 nodes idle\rPartition accelerated-h100:\t 0 nodes idle\rPartition large:\t 6 nodes idle",,terminal_output +17,758468,"TERMINAL",0,0,"bash",,terminal_focus 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--partition=dev_accelerated --nodes=2 --ntasks-per-node=4 --gres=gpu:4 --cpus-per-task=5",,terminal_command +102,843027,"TERMINAL",0,0,"]633;E;2025-07-10 16:32:17 salloc --time=01:00:00 --partition=dev_accelerated --nodes=2 --ntasks-per-node=4 --gres=gpu:4 --cpus-per-task=5;cd331af2-3b46-4f78-aca4-9dda848374ca]633;Csalloc: Pending job allocation 3335323\r\nsalloc: job 3335323 queued and waiting for resources\r\n",,terminal_output +103,843929,"TERMINAL",0,0,"8",,terminal_output +104,845060,"TERMINAL",0,0,"9",,terminal_output +105,846003,"TERMINAL",0,0,"20",,terminal_output +106,847112,"TERMINAL",0,0,"1",,terminal_output +107,848078,"TERMINAL",0,0,"2",,terminal_output +108,849156,"TERMINAL",0,0,"3",,terminal_output +109,850179,"TERMINAL",0,0,"4",,terminal_output +110,851205,"TERMINAL",0,0,"5",,terminal_output +111,852333,"TERMINAL",0,0,"6",,terminal_output +112,853268,"TERMINAL",0,0,"7",,terminal_output +113,854378,"TERMINAL",0,0,"8",,terminal_output 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--ntasks-per-node=4 --gres=gpu:4 --cpus-per-task=5",,terminal_command +131,865386,"TERMINAL",0,0,"]633;E;2025-07-10 16:32:39 salloc --time=05:00:00 --partition=accelerated --nodes=2 --ntasks-per-node=4 --gres=gpu:4 --cpus-per-task=5;cd331af2-3b46-4f78-aca4-9dda848374ca]633;Csalloc: Pending job allocation 3335324\r\nsalloc: job 3335324 queued and waiting for resources\r\n",,terminal_output +132,866345,"TERMINAL",0,0,"40",,terminal_output +133,867379,"TERMINAL",0,0,"1",,terminal_output +134,868413,"TERMINAL",0,0,"2",,terminal_output +135,869534,"TERMINAL",0,0,"3",,terminal_output +136,869908,"TERMINAL",0,0,"^Csalloc: Job allocation 3335324 has been revoked.\r\nsalloc: Job aborted due to signal\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;1]633;P;Cwd=/home/hk-project-p0023960/tum_cte0515/Projects/jafar",,terminal_output +137,870512,"TERMINAL",0,0,"4",,terminal_output +138,871536,"TERMINAL",0,0,"5",,terminal_output +139,872568,"TERMINAL",0,0,"6",,terminal_output +140,873637,"TERMINAL",0,0,"7",,terminal_output +141,874686,"TERMINAL",0,0,"8",,terminal_output +142,875296,"TERMINAL",0,0,"salloc --time=01:00:00 --partition=dev_accelerated --nodes=2 --ntasks-per-node=4 --gres=gpu:4 --cpus-per-task=5",,terminal_command +143,875342,"TERMINAL",0,0,"]633;E;2025-07-10 16:32:49 salloc --time=01:00:00 --partition=dev_accelerated --nodes=2 --ntasks-per-node=4 --gres=gpu:4 --cpus-per-task=5;cd331af2-3b46-4f78-aca4-9dda848374ca]633;Csalloc: Pending job allocation 3335325\r\nsalloc: job 3335325 queued and waiting for resources\r\n",,terminal_output +144,875886,"TERMINAL",0,0,"9",,terminal_output +145,876817,"TERMINAL",0,0,"50",,terminal_output +146,877765,"TERMINAL",0,0,"1",,terminal_output +147,878802,"TERMINAL",0,0,"2",,terminal_output +148,879851,"TERMINAL",0,0,"3",,terminal_output +149,880879,"TERMINAL",0,0,"5",,terminal_output +150,881928,"TERMINAL",0,0,"6",,terminal_output +151,883052,"TERMINAL",0,0,"7",,terminal_output 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+172,904734,"TERMINAL",0,0,"8",,terminal_output +173,905783,"TERMINAL",0,0,"9",,terminal_output +174,906912,"TERMINAL",0,0,"20",,terminal_output +175,907937,"TERMINAL",0,0,"1",,terminal_output +176,908882,"TERMINAL",0,0,"3",,terminal_output +177,909984,"TERMINAL",0,0,"4",,terminal_output +178,910957,"TERMINAL",0,0,"5",,terminal_output +179,911998,"TERMINAL",0,0,"6",,terminal_output +180,913056,"TERMINAL",0,0,"7",,terminal_output +181,914082,"TERMINAL",0,0,"8",,terminal_output +182,915207,"TERMINAL",0,0,"9",,terminal_output +183,916228,"TERMINAL",0,0,"30",,terminal_output +184,917196,"TERMINAL",0,0,"1",,terminal_output +185,918254,"train_tokenizer.py",0,0,"from dataclasses import dataclass, field\nimport os\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.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\n\nfrom models.tokenizer import TokenizerVQVAE\nfrom utils.dataloader import get_dataloader\nfrom utils.parameter_utils import count_parameters_by_component\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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n vq_beta: float = 0.25\n batch_size: int = 48\n min_lr: float = 0.0\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() # type: ignore\n ssim = pix.ssim(gt, recon).mean() # type: ignore\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\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\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 wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\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 videos_sharding = NamedSharding(\n mesh, PartitionSpec(""data"", None, None, None, None)\n )\n train_state = jax.device_put(train_state, replicated_sharding)\n\n # --- Initialize checkpoint manager ---\n step = 0\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add('model_state', ocp.args.StandardSave, ocp.handlers.StandardCheckpointHandler)\n handler_registry.add('model_state', ocp.args.StandardRestore, ocp.handlers.StandardCheckpointHandler)\n handler_registry.add('dataloader_state', grain.checkpoint.CheckpointSave, grain.checkpoint.CheckpointHandler) # type: ignore\n handler_registry.add('dataloader_state', grain.checkpoint.CheckpointRestore, grain.checkpoint.CheckpointHandler) # type: ignore\n \n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=10,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n \n checkpoint_manager = ocp.CheckpointManager(\n os.path.abspath(args.ckpt_dir),\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n\n # --- Create DataLoaderIterator from dataloader ---\n array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n \n # --- Restore checkpoint ---\n if args.restore_ckpt:\n abstract_train_state = jax.tree_util.tree_map(ocp.utils.to_shape_dtype_struct, train_state)\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.StandardRestore(abstract_train_state),\n dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator),\n )\n )\n train_state = restored[""model_state""]\n grain_iterator = restored[""dataloader_state""]\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored dataloader and model state from step {step}"")\n\n # --- TRAIN LOOP ---\n dataloader = (jax.make_array_from_process_local_data(videos_sharding, elem) for elem in grain_iterator) # type: ignore\n print(f""Starting training from step {step}..."")\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n rng, _rng, _rng_dropout = jax.random.split(rng, 3)\n\n inputs = dict(videos=videos, rng=_rng, dropout_rng=_rng_dropout)\n train_state, loss, recon, metrics = train_step(train_state, inputs)\n print(f""Step {step}, loss: {loss}"")\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 **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 # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n checkpoint_manager.save(\n step,\n args=ocp.args.Composite(\n model_state=ocp.args.StandardSave(train_state),\n dataloader_state=grain.checkpoint.CheckpointSave(grain_iterator),\n )\n )\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n checkpoint_manager.close()",python,tab +186,918378,"TERMINAL",0,0,"2",,terminal_output +187,919300,"TERMINAL",0,0,"3",,terminal_output +188,920294,"TERMINAL",0,0,"4",,terminal_output +189,921344,"TERMINAL",0,0,"5",,terminal_output +190,922443,"TERMINAL",0,0,"60",,terminal_output +191,922595,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=10:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_dyn_yolorun\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n# tf_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_tfrecord\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/train_lam_action_space_scaling_20/3318547/lam_1751657975_200000/\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-tf-records-$slurm_job_id \\n --tags dynamics yolo-run tf_records \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n --lam_checkpoint=$lam_ckpt_dir\n",shellscript,tab +192,923522,"TERMINAL",0,0,"7",,terminal_output +193,924528,"TERMINAL",0,0,"8",,terminal_output +194,925547,"TERMINAL",0,0,"9",,terminal_output 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objects: 100% (17/17)\rremote: Counting objects: 100% (17/17), done.\r\nremote: Compressing objects: 10% (1/10)\rremote: Compressing objects: 20% (2/10)\rremote: Compressing objects: 30% (3/10)\rremote: Compressing objects: 40% (4/10)\rremote: Compressing objects: 50% (5/10)\rremote: Compressing objects: 60% (6/10)\rremote: Compressing objects: 70% (7/10)\rremote: Compressing objects: 80% (8/10)\rremote: Compressing objects: 90% (9/10)\rremote: Compressing objects: 100% (10/10)\rremote: Compressing objects: 100% (10/10), done.\r\n",,terminal_output +561,1212964,"TERMINAL",0,0,"remote: Total 17 (delta 7), reused 16 (delta 7), pack-reused 0 (from 0)\r\nUnpacking objects: 5% (1/17)\rUnpacking objects: 11% (2/17)\rUnpacking objects: 17% (3/17)\rUnpacking objects: 23% (4/17)\rUnpacking objects: 29% (5/17)\rUnpacking objects: 35% (6/17)\rUnpacking objects: 41% (7/17)\rUnpacking objects: 47% (8/17)\rUnpacking objects: 52% (9/17)\rUnpacking objects: 58% (10/17)\r",,terminal_output 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+749,2109374,"slurm/jobs/mihir/horeka/yolo-runs/tester.sh",884,0,"",shellscript,selection_mouse +750,2110165,"TERMINAL",0,0,"4",,terminal_output +751,2110404,"slurm/jobs/mihir/horeka/yolo-runs/tester.sh",1267,0,"",shellscript,selection_mouse +752,2110559,"slurm/jobs/mihir/horeka/yolo-runs/tester.sh",1250,17,"t=$lam_ckpt_dir\n ",shellscript,selection_mouse +753,2110579,"slurm/jobs/mihir/horeka/yolo-runs/tester.sh",1253,14,"lam_ckpt_dir\n ",shellscript,selection_mouse +754,2110594,"slurm/jobs/mihir/horeka/yolo-runs/tester.sh",1267,0,"",shellscript,selection_mouse +755,2111250,"TERMINAL",0,0,"5",,terminal_output +756,2112273,"TERMINAL",0,0,"6",,terminal_output +757,2113225,"TERMINAL",0,0,"7",,terminal_output +758,2114324,"TERMINAL",0,0,"8",,terminal_output +759,2115272,"TERMINAL",0,0,"9",,terminal_output +760,2116286,"TERMINAL",0,0,"30",,terminal_output +761,2117303,"TERMINAL",0,0,"1",,terminal_output +762,2118324,"TERMINAL",0,0,"2",,terminal_output +763,2119396,"TERMINAL",0,0,"srun",,terminal_focus +764,2119409,"TERMINAL",0,0,"3",,terminal_output +765,2120364,"TERMINAL",0,0,"4",,terminal_output +766,2120809,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn0523:~/Projects/jafar[?2004h]633;A(jafar) ]633;Ajafar[tum_cte0515@hkn0523 jafar]$ ]633;B]633;B",,terminal_output +767,2121506,"TERMINAL",0,0,"[?25lgi[?25h",,terminal_output +768,2121552,"TERMINAL",0,0,"[?25li[?25h",,terminal_output +769,2121652,"TERMINAL",0,0,"[?25lt[?25h[?25l [?25h",,terminal_output +770,2121850,"TERMINAL",0,0,"[?25lb[?25h[?25lr[?25h",,terminal_output +771,2122068,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +772,2122136,"TERMINAL",0,0,"[?25ln[?25h",,terminal_output +773,2122290,"TERMINAL",0,0,"[?25lc[?25h[?25lh[?25h",,terminal_output +774,2122472,"TERMINAL",0,0,"\r\n[?2004l\r[?1h=\r",,terminal_output +775,2122555,"TERMINAL",0,0," add-wandb-name-and-tags\r\n convert-to-jax-array-in-iter\r\n correct-batched-sampling\r\n dev\r\n dont-let-tf-see-gpu\r\n feat/explicit-image-dims\r\n fix-sampling\r\n* grain-dataloader\r\n logging-variants\r\n main\r\n preprocess_video\r\n revised-dataloader\r\n runner\r\n runner-grain\r\n speedup-tfrecord-preprocessing\r\n tmp\r\n\r[?1l>]0;tum_cte0515@hkn0523:~/Projects/jafar[?2004h]633;A(jafar) ]633;Ajafar[tum_cte0515@hkn0523 jafar]$ ]633;B]633;B",,terminal_output +776,2124123,"TERMINAL",0,0,"g",,terminal_output +777,2124192,"TERMINAL",0,0,"[?25li[?25h",,terminal_output +778,2124260,"TERMINAL",0,0,"[?25lt[?25h",,terminal_output +779,2124313,"TERMINAL",0,0,"[?25l [?25h",,terminal_output +780,2124481,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +781,2124823,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +782,2124942,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +783,2125015,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +784,2125081,"TERMINAL",0,0,"[?25lj[?25h",,terminal_output +785,2125234,"TERMINAL",0,0,"[?25lo[?25h",,terminal_output +786,2125359,"TERMINAL",0,0,"[?25lu[?25h",,terminal_output +787,2126415,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +788,2126605,"TERMINAL",0,0,"[?25lo[?25h",,terminal_output +789,2126671,"TERMINAL",0,0,"[?25lu[?25h",,terminal_output +790,2126817,"TERMINAL",0,0,"[?25lt[?25h",,terminal_output +791,2126884,"TERMINAL",0,0,"[?25l [?25h",,terminal_output +792,2128987,"TERMINAL",0,0,"[?25lr[?25h",,terminal_output +793,2129099,"TERMINAL",0,0,"[?25lu[?25h",,terminal_output +794,2129366,"TERMINAL",0,0,"[?25ln[?25h",,terminal_output +795,2129559,"TERMINAL",0,0,"[?25ln[?25h",,terminal_output +796,2129613,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +797,2129709,"TERMINAL",0,0,"[?25lr[?25h",,terminal_output +798,2129773,"TERMINAL",0,0,"[?25l-[?25h",,terminal_output +799,2130089,"TERMINAL",0,0,"[?25lg[?25h",,terminal_output +800,2130143,"TERMINAL",0,0,"[?25lr[?25h",,terminal_output +801,2130333,"TERMINAL",0,0,"[?25la[?25h[?25li[?25h",,terminal_output +802,2130402,"TERMINAL",0,0,"[?25ln[?25h",,terminal_output +803,2130578,"TERMINAL",0,0,"\r\n[?2004l\rgit: 'chechout' is not a git command. See 'git --help'.\r\n\r\nThe most similar command is\r\n\tcheckout\r\n]0;tum_cte0515@hkn0523:~/Projects/jafar[?2004h]633;A(jafar) ]633;Ajafar[tum_cte0515@hkn0523 jafar]$ ]633;B]633;B",,terminal_output +804,2132360,"TERMINAL",0,0,"git chechout runner-grain",,terminal_output +805,2132472,"TERMINAL",0,0,"branch",,terminal_output +806,2132964,"TERMINAL",0,0,"chechout runner-grain",,terminal_output +807,2135341,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +808,2135870,"TERMINAL",0,0,"[1@k",,terminal_output +809,2135969,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +810,2136529,"TERMINAL",0,0,"Switched to branch 'runner-grain'\r\n]0;tum_cte0515@hkn0523:~/Projects/jafar[?2004h]633;A(jafar) ]633;Ajafar[tum_cte0515@hkn0523 jafar]$ ]633;B]633;B",,terminal_output +811,2139969,"slurm/jobs/mihir/horeka/yolo-runs/tester.sh",0,0,"",shellscript,tab +812,2140363,"slurm/jobs/mihir/horeka/yolo-runs/tester.sh",0,0,"Switched from branch 'grain-dataloader' to 'runner-grain'",shellscript,git_branch_checkout +813,2145277,"TERMINAL",0,0,"slurm/jobs/mihir/horeka/yolo-runs/tester.sh",,terminal_output +814,2146536,"TERMINAL",0,0,"[?25lslurm/jobs/mihir/horeka/yolo-runs/tester.sh[?25h",,terminal_output +815,2148990,"TERMINAL",0,0,"slurm/jobs/mihir/horeka/yolo-runs/tester.sh",,terminal_output +816,2149055,"TERMINAL",0,0,"[?25lshslurm/jobs/mihir/horeka/yolo-runs/tester.sh[?25h slurm/jobs/mihir/horeka/yolo-runs/tester.sh",,terminal_output +817,2149361,"TERMINAL",0,0,"\r\n[?2004l\r\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\r\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\r\n\r\njob_name=""debug""\r\nslurm_job_id=""debug-mihir""\r\n\r\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/train_lam_action_space_scaling_20/3318547/lam_1751657975_200000/\r\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\r\n\r\nenv | grep SLURM\r\n\r\nsrun python train_dynamics.py \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=96 \\r\n --min_lr=0 \\r\n --max_lr=1.4e-4 \\r\n --log_image_interval=1000 \\r\n --log \\r\n --num_latent_actions=20 \\r\n --log_checkpoint_interval=1000 \\r\n --name=dynamics-yolorun-tf-records-$slurm_job_id \\r\n --tags dynamics yolo-run tf_records \\r\n --entity instant-uv \\r\n --project jafar \\r\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\r\n --data_dir $array_records_dir\r\n # --lam_checkpoint=$lam_ckpt_dir\r\n ",,terminal_output +818,2149551,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=1\r\nSLURM_JOB_UID=999226\r\nSLURM_STEP_GPUS=0\r\nSLURM_CPU_BIND=quiet,mask_cpu:0x00000003FC00000007F00000003FC00000007F\r\nSLURM_TASK_PID=1458022\r\nSLURM_LOCALID=0\r\nSLURM_CPU_BIND_VERBOSE=quiet\r\nSLURMD_NODENAME=hkn0523\r\nSLURM_JOB_START_TIME=1752092406\r\nSLURM_STEP_NODELIST=hkn0523\r\nSLURM_JOB_END_TIME=1752178806\r\nSLURM_CPUS_ON_NODE=30\r\nSLURM_UMASK=0022\r\nSLURM_JOB_CPUS_PER_NODE=30\r\nSLURM_GPUS_ON_NODE=1\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=accelerated\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_STEPID=0\r\nSLURM_CPU_BIND_LIST=0x00000003FC00000007F00000003FC00000007F\r\nSLURM_JOBID=3333584\r\nSLURM_PTY_PORT=43975\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.201\r\nSLURM_PTY_WIN_ROW=48\r\nSLURM_CPU_BIND_TYPE=mask_cpu:\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_NTASKS=1\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e11.hkn0523\r\nSLURM_DISTRIBUTION=cyclic\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.201\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=228\r\nSLURM_NODELIST=hkn0523\r\nSLURM_SRUN_COMM_PORT=42223\r\nSLURM_STEP_ID=0\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NPROCS=1\r\nSLURM_NNODES=1\r\nSLURM_JOB_ID=3333584\r\nSLURM_NODEID=0\r\nSLURMD_TRES_FREQ=gpu:high,memory=high\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=bash\r\nSLURM_STEP_LAUNCHER_PORT=42223\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn0523\r\n",,terminal_output +819,2149696,"TERMINAL",0,0,"GpuFreq=control_disabled\r\n",,terminal_output +820,2152260,"TERMINAL",0,0,"srun",,terminal_focus +821,2154328,"TERMINAL",0,0,"srun",,terminal_focus +822,2155300,"TERMINAL",0,0,"srun",,terminal_focus +823,2156270,"TERMINAL",0,0,"[?25lsm[?25h[?25lm[?25h[?25li[?25h",,terminal_output +824,2156480,"TERMINAL",0,0,"[?25l[?2004l\r[?25h",,terminal_output +825,2158057,"TERMINAL",0,0,"[?1049h(B[?7hEvery 1.0s: nvidia-smihkn0425.localdomain: Thu Jul 10 16:54:10 2025Thu Jul 10 16:54:10 2025\r+-----------------------------------------------------------------------------------------+\r| NVIDIA-SMI 570.133.20Driver Version: 570.133.20 CUDA Version: 12.8 |\r|-----------------------------------------+------------------------+----------------------+\r| GPU NamePersistence-M | Bus-IdDisp.A | Volatile Uncorr. ECC |\r| Fan Temp PerfPwr:Usage/Cap |Memory-Usage | GPU-Util Compute M. |\r|||MIG M. |\r|=========================================+========================+======================|\r| 0 NVIDIA A100-SXM4-40GBOn | 00000000:31:00.0 Off |0 |\r| N/A 43C P052W / 300W |\t 27MiB / 40960MiB |\t 0%\t Default |\r|||Disabled |\r+-----------------------------------------+------------------------+----------------------+\r| 1 NVIDIA A100-SXM4-40GBOn | 00000000:4B:00.0 Off |0 |\r| N/A 44C P060W / 300W |\t 27MiB / 40960MiB |\t 0%\t Default |\r|||Disabled |\r+-----------------------------------------+------------------------+----------------------+\r| 2 NVIDIA A100-SXM4-40GBOn | 00000000:CA:00.0 Off |0 |\r| N/A 44C P059W / 300W |\t 27MiB / 40960MiB |\t 0%\t Default |\r|||Disabled |\r+-----------------------------------------+------------------------+----------------------+\r| 3 NVIDIA A100-SXM4-40GBOn | 00000000:E3:00.0 Off |0 |\r| N/A 43C P054W / 300W |\t 27MiB / 40960MiB |\t 0%\t Default |\r|||Disabled |\r+-----------------------------------------+------------------------+----------------------+\r+-----------------------------------------------------------------------------------------+\r| Processes:|\r| GPU GI CIPID Type Process nameGPU Memory |\r|ID IDUsage\t |\r|=========================================================================================|\r| 0 N/A N/A2945G /usr/libexec/Xorg17MiB |\r| 1 N/A N/A2945G /usr/libexec/Xorg17MiB |\r| 2 N/A N/A2945G /usr/libexec/Xorg17MiB |\r| 3 N/A N/A2945G /usr/libexec/Xorg17MiB |\r+-----------------------------------------------------------------------------------------+",,terminal_output +826,2159127,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn0425:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0425 jafar]$ ",,terminal_output +827,2160011,"TERMINAL",0,0,"srun",,terminal_focus +828,2172672,"TERMINAL",0,0,"srun",,terminal_focus +829,2173601,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/yolo-runs/tester.sh",,terminal_output +830,2173905,"TERMINAL",0,0,"2025-07-10 16:54:28.040417: E external/xla/xla/service/gpu/gpu_hlo_schedule.cc:654] The byte size of input/output arguments (47390392320) exceeds the base limit (33925523046). This indicates an error in the calculation!\r\n2025-07-10 16:54:28.040460: E external/xla/xla/service/gpu/gpu_hlo_schedule.cc:654] The byte size of input/output arguments (47390392320) exceeds the base limit (33925523046). This indicates an error in the calculation!\r\n2025-07-10 16:54:28.040560: E external/xla/xla/service/gpu/gpu_hlo_schedule.cc:654] The byte size of input/output arguments (47390392320) exceeds the base limit (33925523046). This indicates an error in the calculation!\r\n2025-07-10 16:54:28.040577: E external/xla/xla/service/gpu/gpu_hlo_schedule.cc:654] The byte size of input/output arguments (47390392320) exceeds the base limit (33925523046). This indicates an error in the calculation!\r\n2025-07-10 16:54:28.040670: W external/xla/xla/hlo/transforms/simplifiers/hlo_rematerialization.cc:3022] Can't reduce memory use below 0B (0 bytes) by rematerialization; only reduced to 38.75GiB (41602252800 bytes), down from 38.75GiB (41602252800 bytes) originally\r\n2025-07-10 16:54:28.040680: W external/xla/xla/hlo/transforms/simplifiers/hlo_rematerialization.cc:3022] Can't reduce memory use below 0B (0 bytes) by rematerialization; only reduced to 38.75GiB (41602252800 bytes), down from 38.75GiB (41602252800 bytes) originally\r\n2025-07-10 16:54:28.040805: W external/xla/xla/hlo/transforms/simplifiers/hlo_rematerialization.cc:3022] Can't reduce memory use below 0B (0 bytes) by rematerialization; only reduced to 38.75GiB (41602252800 bytes), down from 38.75GiB (41602252800 bytes) originally\r\n2025-07-10 16:54:28.040817: W external/xla/xla/hlo/transforms/simplifiers/hlo_rematerialization.cc:3022] Can't reduce memory use below 0B (0 bytes) by rematerialization; only reduced to 38.75GiB (41602252800 bytes), down from 38.75GiB (41602252800 bytes) originally\r\n2025-07-10 16:54:28.040832: E external/xla/xla/service/gpu/gpu_hlo_schedule.cc:654] The byte size of input/output arguments (47390392320) exceeds the base limit (33925523046). This indicates an error in the calculation!\r\n2025-07-10 16:54:28.041113: W external/xla/xla/hlo/transforms/simplifiers/hlo_rematerialization.cc:3022] Can't reduce memory use below 0B (0 bytes) by rematerialization; only reduced to 38.75GiB (41606447120 bytes), down from 38.75GiB (41606447120 bytes) originally\r\n",,terminal_output +831,2173979,"TERMINAL",0,0,"[?25l\rsh slurm/jobs/mihir/horeka/yolo-runs/tester.sh\r\n[?2004l\r[?25h\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\r\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\r\n\r\njob_name=""debug""\r\nslurm_job_id=""debug-mihir""\r\n\r\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/train_lam_action_space_scaling_20/3318547/lam_1751657975_200000/\r\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\r\n\r\nenv | grep SLURM\r\n\r\nsrun python train_dynamics.py \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=96 \\r\n --min_lr=0 \\r\n --max_lr=1.4e-4 \\r\n --log_image_interval=1000 \\r\n --log \\r\n --num_latent_actions=20 \\r\n --log_checkpoint_interval=1000 \\r\n --name=dynamics-yolorun-tf-records-$slurm_job_id \\r\n --tags dynamics yolo-run tf_records \\r\n --entity instant-uv \\r\n --project jafar \\r\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\r\n --data_dir $array_records_dir\r\n # --lam_checkpoint=$lam_ckpt_dir\r\n ",,terminal_output +832,2174130,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=4(x2)\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=1196654\r\nSLURM_JOB_GPUS=0,1,2,3\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar\r\nSLURMD_NODENAME=hkn0425\r\nSLURM_JOB_START_TIME=1752159082\r\nSLURM_STEP_NODELIST=hkn0425\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1752195082\r\nSLURM_PMI2_SRUN_PORT=33587\r\nSLURM_CPUS_ON_NODE=24\r\nSLURM_JOB_CPUS_PER_NODE=24(x2)\r\nSLURM_GPUS_ON_NODE=4\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=accelerated\r\nSLURM_TRES_PER_TASK=cpu=5\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=2\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3335335\r\nSLURM_PTY_PORT=45071\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.201\r\nSLURM_PTY_WIN_ROW=48\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=5\r\nSLURM_NTASKS=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e13.hkn0425\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.201\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=228\r\nSLURM_NODELIST=hkn[0425,0428]\r\nSLURM_SRUN_COMM_PORT=37803\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NPROCS=8\r\nSLURM_NNODES=2\r\nSLURM_SUBMIT_HOST=hkn1993.localdomain\r\nSLURM_JOB_ID=3335335\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0425\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_NTASKS_PER_NODE=4\r\nSLURM_STEP_LAUNCHER_PORT=37803\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn[0425,0428]\r\n",,terminal_output +833,2174283,"TERMINAL",0,0,"GpuFreq=control_disabled\r\nGpuFreq=control_disabled\r\n",,terminal_output +834,2175231,"TERMINAL",0,0,"srun",,terminal_focus +835,2178881,"TERMINAL",0,0,"srun",,terminal_focus +836,2184280,"TERMINAL",0,0,"2025-07-10 16:54:38.342206: W external/xla/xla/tsl/framework/bfc_allocator.cc:501] Allocator (GPU_0_bfc) ran out of memory trying to allocate 38.75GiB (rounded to 41602252800)requested by op \r\n2025-07-10 16:54:38.342328: W external/xla/xla/tsl/framework/bfc_allocator.cc:512] ******************************________*****************************_________________________________\r\nRunning on 1 devices.\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 172, in \r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 76, in __call__\r\n tokenizer_outputs = self.tokenizer.vq_encode(batch[""videos""], training=False)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/tokenizer.py"", line 57, in vq_encode\r\n x = self.encoder(x) # (B, T, N, E)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 87, in __call__\r\n x = STBlock(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 41, in __call__\r\n z = nn.MultiHeadAttention(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 674, in __call__\r\n x = self.attention_fn(*attn_args, **attn_kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 266, in dot_product_attention\r\n attn_weights = dot_product_attention_weights(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 132, in dot_product_attention_weights\r\n attn_weights = einsum('...qhd,...khd->...hqk', query, key)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/einsum.py"", line 315, in einsum\r\n return jit_einsum(operand_arrays, contractions, precision,\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/contextlib.py"", line 79, in inner\r\n",,terminal_output +837,2184334,"TERMINAL",0,0," return func(*args, **kwds)\r\njax._src.source_info_util.JaxStackTraceBeforeTransformation: jaxlib._jax.XlaRuntimeError: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 41602252800 bytes.\r\n\r\nThe preceding stack trace is the source of the JAX operation that, once transformed by JAX, triggered the following exception.\r\n\r\n--------------------\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_dynamics.py"", line 172, in \r\n init_params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 76, in __call__\r\n tokenizer_outputs = self.tokenizer.vq_encode(batch[""videos""], training=False)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/tokenizer.py"", line 57, in vq_encode\r\n x = self.encoder(x) # (B, T, N, E)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 87, in __call__\r\n x = STBlock(\r\njaxlib._jax.XlaRuntimeError: RESOURCE_EXHAUSTED: Out of memory while trying to allocate 41602252800 bytes.\r\n",,terminal_output +838,2185337,"TERMINAL",0,0,"srun: error: hkn0523: task 0: Exited with exit code 1\r\n]0;tum_cte0515@hkn0523:~/Projects/jafar[?2004h]633;A(jafar) ]633;Ajafar[tum_cte0515@hkn0523 jafar]$ ]633;B]633;B",,terminal_output +839,2239661,"TERMINAL",0,0,"2025-07-10 16:55:33.736977: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +840,2239891,"TERMINAL",0,0,"2025-07-10 16:55:34.077240: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +841,2240033,"TERMINAL",0,0,"2025-07-10 16:55:34.220355: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +842,2240582,"TERMINAL",0,0,"2025-07-10 16:55:34.707786: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +843,2242526,"TERMINAL",0,0,"2025-07-10 16:55:36.639745: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +844,2243060,"TERMINAL",0,0,"2025-07-10 16:55:37.128454: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-10 16:55:37.247320: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +845,2243553,"TERMINAL",0,0,"2025-07-10 16:55:37.714220: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +846,2244146,"TERMINAL",0,0,"2025-07-10 16:55:38.333436: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +847,2245400,"TERMINAL",0,0,"2025-07-10 16:55:39.469636: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-10 16:55:39.566128: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +848,2246300,"TERMINAL",0,0,"2025-07-10 16:55:40.486438: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +849,2247355,"TERMINAL",0,0,"2025-07-10 16:55:41.542271: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +850,2248470,"TERMINAL",0,0,"2025-07-10 16:55:42.657769: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +851,2248581,"TERMINAL",0,0,"2025-07-10 16:55:42.750127: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +852,2249522,"TERMINAL",0,0,"2025-07-10 16:55:43.708810: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +853,2257250,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +854,2257935,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.19.11\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/wandb/run-20250710_165551-dihg8uvq\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run dynamics-yolorun-tf-records-debug-mihir\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/dihg8uvq\r\n",,terminal_output +855,2353560,"TERMINAL",0,0,"WARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\n",,terminal_output +856,2353734,"TERMINAL",0,0,"WARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\n",,terminal_output +857,2353867,"TERMINAL",0,0,"Running on 8 devices.\r\nCounting all components: ['tokenizer', 'lam', 'dynamics']\r\nParameter counts:\r\n{'tokenizer': 37989616, 'lam': 19349920, 'dynamics': 29735424, 'total': 87074960}\r\n",,terminal_output +858,2353931,"TERMINAL",0,0,"Running on 8 devices.\r\nCounting all components: ['tokenizer', 'lam', 'dynamics']\r\nParameter counts:\r\n{'tokenizer': 37989616, 'lam': 19349920, 'dynamics': 29735424, 'total': 87074960}\r\n",,terminal_output +859,2354040,"TERMINAL",0,0,"Running on 8 devices.\r\nCounting all components: ['tokenizer', 'lam', 'dynamics']\r\nParameter counts:\r\n{'tokenizer': 37989616, 'lam': 19349920, 'dynamics': 29735424, 'total': 87074960}\r\n",,terminal_output +860,2354212,"TERMINAL",0,0,"Running on 8 devices.\r\nCounting all components: ['tokenizer', 'lam', 'dynamics']\r\nParameter counts:\r\n{'tokenizer': 37989616, 'lam': 19349920, 'dynamics': 29735424, 'total': 87074960}\r\n",,terminal_output +861,2359145,"TERMINAL",0,0,"WARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\n",,terminal_output +862,2359570,"TERMINAL",0,0,"Running on 8 devices.\r\nCounting all components: ['tokenizer', 'lam', 'dynamics']\r\nParameter counts:\r\n{'tokenizer': 37989616, 'lam': 19349920, 'dynamics': 29735424, 'total': 87074960}\r\nRunning on 8 devices.\r\nCounting all components: ['tokenizer', 'lam', 'dynamics']\r\nParameter counts:\r\n{'tokenizer': 37989616, 'lam': 19349920, 'dynamics': 29735424, 'total': 87074960}\r\n",,terminal_output +863,2361463,"TERMINAL",0,0,"WARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\n",,terminal_output +864,2361839,"TERMINAL",0,0,"Running on 8 devices.\r\nCounting all components: ['tokenizer', 'lam', 'dynamics']\r\nParameter counts:\r\n{'tokenizer': 37989616, 'lam': 19349920, 'dynamics': 29735424, 'total': 87074960}\r\n",,terminal_output +865,2363968,"TERMINAL",0,0,"WARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\n",,terminal_output +866,2364334,"TERMINAL",0,0,"Running on 8 devices.\r\nCounting all components: ['tokenizer', 'lam', 'dynamics']\r\nParameter counts:\r\n{'tokenizer': 37989616, 'lam': 19349920, 'dynamics': 29735424, 'total': 87074960}\r\n",,terminal_output +867,2377217,"TERMINAL",0,0,"srun",,terminal_focus +868,2378318,"TERMINAL",0,0,"[?25lid[?25h",,terminal_output +869,2378387,"TERMINAL",0,0,"[?25ld[?25h",,terminal_output +870,2378504,"TERMINAL",0,0,"[?25ll[?25h",,terminal_output +871,2378689,"TERMINAL",0,0,"[?25li[?25h[?25ln[?25h",,terminal_output +872,2378754,"TERMINAL",0,0,"[?25lg[?25h",,terminal_output +873,2378945,"TERMINAL",0,0,"\r\n[?2004l\r[?1049h(B[?7hEvery 1.0s: sinfo_t_idlehkn0523.localdomain: Thu Jul 10 16:57:53 2025Partition dev_cpuonly: 11 nodes idle\rPartition cpuonly:\t 2 nodes idle\rPartition dev_accelerated:\t 0 nodes idle\rPartition accelerated:\t 2 nodes idle\rPartition dev_accelerated-h100 :\t 0 nodes idle\rPartition accelerated-h100:\t 0 nodes idle\rPartition large:\t 6 nodes idle",,terminal_output +874,2379949,"TERMINAL",0,0,"4",,terminal_output +875,2380966,"TERMINAL",0,0,"5",,terminal_output +876,2381034,"TERMINAL",0,0,"srun",,terminal_focus +877,2382000,"TERMINAL",0,0,"6",,terminal_output +878,2383009,"TERMINAL",0,0,"7",,terminal_output +879,2384031,"TERMINAL",0,0,"8",,terminal_output +880,2385070,"TERMINAL",0,0,"9",,terminal_output +881,2386100,"TERMINAL",0,0,"8:00",,terminal_output +882,2387089,"TERMINAL",0,0,"1",,terminal_output +883,2388110,"TERMINAL",0,0,"2",,terminal_output +884,2389167,"TERMINAL",0,0,"3",,terminal_output +885,2390190,"TERMINAL",0,0,"4",,terminal_output +886,2392680,"TERMINAL",0,0,"56",,terminal_output +887,2393214,"TERMINAL",0,0,"7",,terminal_output +888,2394242,"TERMINAL",0,0,"8",,terminal_output +889,2395312,"TERMINAL",0,0,"9",,terminal_output +890,2396289,"TERMINAL",0,0,"10",,terminal_output +891,2397357,"TERMINAL",0,0,"1",,terminal_output +892,2398323,"TERMINAL",0,0,"2",,terminal_output +893,2399406,"TERMINAL",0,0,"3",,terminal_output +894,2400443,"TERMINAL",0,0,"4",,terminal_output +895,2401509,"TERMINAL",0,0,"5",,terminal_output +896,2402588,"TERMINAL",0,0,"6",,terminal_output +897,2403505,"TERMINAL",0,0,"7",,terminal_output +898,2404442,"TERMINAL",0,0,"8",,terminal_output +899,2405462,"TERMINAL",0,0,"9",,terminal_output +900,2405758,"TERMINAL",0,0,"2025-07-10 16:58:19.887258: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +901,2406514,"TERMINAL",0,0,"20",,terminal_output +902,2407503,"TERMINAL",0,0,"1",,terminal_output +903,2408523,"TERMINAL",0,0,"2",,terminal_output +904,2409548,"TERMINAL",0,0,"3",,terminal_output +905,2410157,"TERMINAL",0,0,"2025-07-10 16:58:24.280770: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-10 16:58:24.281913: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +906,2410569,"TERMINAL",0,0,"4",,terminal_output +907,2411592,"TERMINAL",0,0,"5",,terminal_output +908,2412716,"TERMINAL",0,0,"6",,terminal_output +909,2413740,"TERMINAL",0,0,"7",,terminal_output +910,2414657,"TERMINAL",0,0,"8",,terminal_output +911,2415562,"TERMINAL",0,0,"2025-07-10 16:58:29.730335: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-10 16:58:29.730370: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +912,2415677,"TERMINAL",0,0,"9",,terminal_output +913,2416714,"TERMINAL",0,0,"30",,terminal_output +914,2417744,"TERMINAL",0,0,"1",,terminal_output +915,2418758,"TERMINAL",0,0,"2",,terminal_output +916,2419773,"TERMINAL",0,0,"3",,terminal_output +917,2420777,"TERMINAL",0,0,"4",,terminal_output +918,2421797,"TERMINAL",0,0,"5",,terminal_output +919,2422857,"TERMINAL",0,0,"6",,terminal_output +920,2423883,"TERMINAL",0,0,"8",,terminal_output +921,2424864,"TERMINAL",0,0,"9",,terminal_output +922,2425926,"TERMINAL",0,0,"40",,terminal_output +923,2426948,"TERMINAL",0,0,"1",,terminal_output +924,2427922,"TERMINAL",0,0,"2",,terminal_output +925,2428942,"TERMINAL",0,0,"3",,terminal_output +926,2429967,"TERMINAL",0,0,"4",,terminal_output +927,2430982,"TERMINAL",0,0,"5",,terminal_output +928,2432075,"TERMINAL",0,0,"6",,terminal_output 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unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n# tf_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_tfrecord\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/train_lam_action_space_scaling_20/3318547/lam_1751657975_200000/\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-tf-records-$slurm_job_id \\n --tags dynamics yolo-run tf_records \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n # --lam_checkpoint=$lam_ckpt_dir\n",shellscript,tab +1209,2716971,"TERMINAL",0,0,"1",,terminal_output +1210,2717273,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1372,0,"",shellscript,selection_mouse +1211,2717914,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1371,0,"",shellscript,selection_mouse +1212,2717989,"TERMINAL",0,0,"2",,terminal_output +1213,2719102,"TERMINAL",0,0,"3",,terminal_output +1214,2719145,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1411,0,"",shellscript,selection_mouse +1215,2719847,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1410,0,"",shellscript,selection_mouse 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+1249,2732697,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1406,0,"",shellscript,selection_mouse +1250,2733147,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1405,1,"",shellscript,content +1251,2733271,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1404,1,"",shellscript,content +1252,2733330,"TERMINAL",0,0,"7",,terminal_output +1253,2733562,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1403,1,"",shellscript,content +1254,2733689,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1403,0,"4",shellscript,content +1255,2733690,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1404,0,"",shellscript,selection_keyboard +1256,2734319,"TERMINAL",0,0,"8",,terminal_output +1257,2734570,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1403,1,"",shellscript,content +1258,2734627,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1403,0,"3",shellscript,content +1259,2734627,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1404,0,"",shellscript,selection_keyboard +1260,2735384,"TERMINAL",0,0,"9",,terminal_output +1261,2736072,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1406,1,"",shellscript,content +1262,2736088,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1406,0,"5",shellscript,content +1263,2736088,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1407,0,"",shellscript,selection_keyboard +1264,2736358,"TERMINAL",0,0,"50",,terminal_output +1265,2737380,"TERMINAL",0,0,"1",,terminal_output +1266,2737453,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1453,0,"",shellscript,selection_mouse +1267,2737708,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1438,15,"0 \\n --log \",shellscript,selection_mouse +1268,2737724,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1437,16,"00 \\n --log \",shellscript,selection_mouse +1269,2737800,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1436,17,"000 \\n --log \",shellscript,selection_mouse +1270,2738092,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1436,0,"",shellscript,selection_mouse +1271,2738390,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1478,0,"",shellscript,selection_mouse +1272,2738431,"TERMINAL",0,0,"2",,terminal_output +1273,2738959,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1502,0,"",shellscript,selection_mouse +1274,2739478,"TERMINAL",0,0,"3",,terminal_output +1275,2739534,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1453,0,"",shellscript,selection_mouse +1276,2740025,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",1549,0,"",shellscript,selection_mouse +1277,2740438,"TERMINAL",0,0,"4",,terminal_output +1278,2741458,"TERMINAL",0,0,"5",,terminal_output +1279,2742174,"slurm/jobs/mihir/horeka/yolo-runs/tester.sh",0,0,"\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=""debug""\nslurm_job_id=""debug-mihir""\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/train_lam_action_space_scaling_20/3318547/lam_1751657975_200000/\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-tf-records-$slurm_job_id \\n --tags dynamics yolo-run tf_records \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n # --lam_checkpoint=$lam_ckpt_dir\n ",shellscript,tab +1280,2742530,"TERMINAL",0,0,"6",,terminal_output +1281,2743513,"TERMINAL",0,0,"7",,terminal_output +1282,2744602,"TERMINAL",0,0,"8",,terminal_output +1283,2745623,"TERMINAL",0,0,"9",,terminal_output +1284,2746562,"TERMINAL",0,0,"4:00",,terminal_output +1285,2747622,"TERMINAL",0,0,"1",,terminal_output +1286,2748612,"TERMINAL",0,0,"2",,terminal_output +1287,2748869,"TERMINAL",0,0,"srun",,terminal_focus +1288,2749621,"TERMINAL",0,0,"3",,terminal_output +1289,2750366,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",0,0,"",shellscript,tab +1290,2750648,"TERMINAL",0,0,"4",,terminal_output +1291,2751765,"TERMINAL",0,0,"52",,terminal_output +1292,2752230,"train_tokenizer.py",0,0,"from dataclasses import dataclass, field\nimport os\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.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\n\nfrom models.tokenizer import TokenizerVQVAE\nfrom utils.dataloader import get_dataloader\nfrom utils.parameter_utils import count_parameters_by_component\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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n vq_beta: float = 0.25\n batch_size: int = 48\n min_lr: float = 0.0\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_checkpoint_keep_period: int = 20000\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() # type: ignore\n ssim = pix.ssim(gt, recon).mean() # type: ignore\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\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\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 wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\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 videos_sharding = NamedSharding(\n mesh, PartitionSpec(""data"", None, None, None, None)\n )\n train_state = jax.device_put(train_state, replicated_sharding)\n\n # --- Initialize checkpoint manager ---\n step = 0\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add('model_state', ocp.args.StandardSave, ocp.handlers.StandardCheckpointHandler)\n handler_registry.add('model_state', ocp.args.StandardRestore, ocp.handlers.StandardCheckpointHandler)\n handler_registry.add('dataloader_state', grain.checkpoint.CheckpointSave, grain.checkpoint.CheckpointHandler) # type: ignore\n handler_registry.add('dataloader_state', grain.checkpoint.CheckpointRestore, grain.checkpoint.CheckpointHandler) # type: ignore\n \n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n \n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n\n # --- Create DataLoaderIterator from dataloader ---\n array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n \n # --- Restore checkpoint ---\n if args.restore_ckpt:\n abstract_train_state = jax.tree_util.tree_map(ocp.utils.to_shape_dtype_struct, train_state)\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.StandardRestore(abstract_train_state),\n dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator),\n )\n )\n train_state = restored[""model_state""]\n grain_iterator = restored[""dataloader_state""]\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored dataloader and model state from step {step}"")\n\n # --- TRAIN LOOP ---\n dataloader = (jax.make_array_from_process_local_data(videos_sharding, elem) for elem in grain_iterator) # type: ignore\n print(f""Starting training from step {step}..."")\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n rng, _rng, _rng_dropout = jax.random.split(rng, 3)\n\n inputs = dict(videos=videos, rng=_rng, dropout_rng=_rng_dropout)\n train_state, loss, recon, metrics = train_step(train_state, inputs)\n print(f""Step {step}, loss: {loss}"")\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 **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 # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n checkpoint_manager.save(\n step,\n args=ocp.args.Composite(\n model_state=ocp.args.StandardSave(train_state),\n dataloader_state=grain.checkpoint.CheckpointSave(grain_iterator),\n )\n )\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n checkpoint_manager.close()",python,tab +1293,2752342,"train_tokenizer.py",284,10832,"\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\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 = """"\n checkpoint: str = """"\n # Optimization\n vq_beta: float = 0.25\n batch_size: int = 48\n min_lr: float = 0.0\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\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\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 wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\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 videos_sharding = NamedSharding(\n mesh, PartitionSpec(""data"", None, None, None, None)\n )\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__\n step += int(args.checkpoint.split(""_"")[-1])\n\n # --- TRAIN LOOP ---\n array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n dataloader = get_dataloader(\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n array_record_files,\n args.seq_len,\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n dataloader = (jax.make_array_from_process_local_data(videos_sharding, elem) for elem in dataloader) # type: ignore\n print(f""Starting training from step {step}..."")\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n rng, _rng, _rng_dropout = jax.random.split(rng, 3)\n\n inputs = dict(videos=videos, rng=_rng, dropout_rng=_rng_dropout)\n train_state, loss, recon, metrics = train_step(train_state, inputs)\n print(f""Step {step}, loss: {loss}"")\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 **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_{step}""),\n ckpt,\n save_args=save_args,\n )\n if step >= args.num_steps:\n break\n",python,content +1294,2752701,"TERMINAL",0,0,"6",,terminal_output +1295,2753159,"slurm/jobs/mihir/horeka/yolo-runs/tester.sh",0,0,"",shellscript,tab +1296,2753745,"TERMINAL",0,0,"srun",,terminal_focus +1297,2753760,"TERMINAL",0,0,"7",,terminal_output +1298,2754736,"TERMINAL",0,0,"8",,terminal_output +1299,2755759,"TERMINAL",0,0,"9",,terminal_output +1300,2756790,"TERMINAL",0,0,"10",,terminal_output +1301,2757347,"slurm/jobs/mihir/horeka/sbatch_dir.sh",0,0,"#!/usr/bin/env bash\n\n# Check if directory argument is provided\nif [ $# -lt 1 ]; then\n echo ""Usage: $0 ""\n exit 1\nfi\n\nDIR=""$1""\n\n# Check if the directory exists\nif [ ! -d ""$DIR"" ]; then\n echo ""Error: Directory '$DIR' does not exist.""\n exit 1\nfi\n\necho ""The following files will be sbatched from directory: $DIR""\nls ""$DIR""\n\nread -p ""Proceed to sbatch all files in $DIR? [y/N] "" confirm\nconfirm=${confirm,,} # tolower\n\nif [[ ""$confirm"" != ""y"" ]]; then\n echo ""Aborted.""\n exit 0\nfi\n\nfor file in ""$DIR""/*; do\n if [ -f ""$file"" ]; then\n echo ""Submitting $file""\n sbatch ""$file""\n fi\ndone\n",shellscript,tab +1302,2757784,"TERMINAL",0,0,"1",,terminal_output +1303,2758576,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",0,0,"",shellscript,tab +1304,2758813,"TERMINAL",0,0,"2",,terminal_output +1305,2759857,"TERMINAL",0,0,"3",,terminal_output +1306,2760850,"TERMINAL",0,0,"5",,terminal_output +1307,2761867,"TERMINAL",0,0,"6",,terminal_output +1308,2762884,"TERMINAL",0,0,"7",,terminal_output +1309,2763708,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn0523:~/Projects/jafar[?2004h]633;A(jafar) ]633;Ajafar[tum_cte0515@hkn0523 jafar]$ ]633;B]633;B",,terminal_output +1310,2764078,"TERMINAL",0,0,"s",,terminal_output +1311,2764194,"TERMINAL",0,0,"[?25lb[?25h",,terminal_output +1312,2764272,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +1313,2764412,"TERMINAL",0,0,"[?25lt[?25h",,terminal_output +1314,2764467,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +1315,2764539,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +1316,2764703,"TERMINAL",0,0,"[?25l [?25h",,terminal_output +1317,2764950,"TERMINAL",0,0,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",,terminal_output +1318,2766824,"TERMINAL",0,0,"\rslurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch\r\n[?2004l\rSubmitted batch job 3335345\r\n]0;tum_cte0515@hkn0523:~/Projects/jafar[?2004h]633;A(jafar) ]633;Ajafar[tum_cte0515@hkn0523 jafar]$ ]633;B]633;B",,terminal_output +1319,2769458,"TERMINAL",0,0,"q",,terminal_output +1320,2769647,"TERMINAL",0,0,"[?25lu[?25h[?25le[?25h",,terminal_output +1321,2769773,"TERMINAL",0,0,"[?25lu[?25h[?25le[?25h",,terminal_output +1322,2769914,"TERMINAL",0,0,"\r\n[?2004l\r[?1049h(B[?7hEvery 1.0s: squeue --mehkn0523.localdomain: Thu Jul 10 17:04:24 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3335345 accelerat train_dy tum_cte0 PD\t0:00\t 2 (None)3335335 accelerat interact tum_cte0 R13:02\t 2 hkn[0425,0428]3333584 accelerat interact tum_cte0 R 18:44:18\t 1 hkn0523",,terminal_output +1323,2770905,"TERMINAL",0,0,"539",,terminal_output +1324,2771913,"TERMINAL",0,0,"6420",,terminal_output +1325,2772929,"TERMINAL",0,0,"751",,terminal_output +1326,2773986,"TERMINAL",0,0,"862",,terminal_output +1327,2775011,"TERMINAL",0,0,"973",,terminal_output +1328,2775440,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn0523:~/Projects/jafar[?2004h]633;A(jafar) ]633;Ajafar[tum_cte0515@hkn0523 jafar]$ ]633;B]633;B",,terminal_output +1329,2778309,"TERMINAL",0,0,"s",,terminal_output +1330,2778543,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +1331,2778658,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +1332,2778784,"TERMINAL",0,0,"[?25ln[?25h",,terminal_output +1333,2778967,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +1334,2780207,"TERMINAL",0,0,"[?2004l\r\r\nexit\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +1335,2782441,"TERMINAL",0,0,"scancel 3333584",,terminal_command +1336,2782506,"TERMINAL",0,0,"]633;E;2025-07-10 17:04:36 scancel 3333584;16c08be6-3885-420f-ac53-f5272aed6e54]633;C]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +1337,2783886,"TERMINAL",0,0,"queue",,terminal_command +1338,2783947,"TERMINAL",0,0,"]633;E;2025-07-10 17:04:38 queue;16c08be6-3885-420f-ac53-f5272aed6e54]633;C[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Thu Jul 10 17:04:38 2025JOBID PARTITION NAME USER ST\tTIME NODES NODELIST(REASON)3333584 accelerat interact tum_cte0 CG 18:44:30\t 1 hkn05233335345 accelerat train_dy tum_cte0 R\t0:01\t 2 hkn[0430-0431]3335335 accelerat interact tum_cte0 R13:16\t 2 hkn[0425,0428]",,terminal_output +1339,2785049,"TERMINAL",0,0,"927",,terminal_output +1340,2786014,"TERMINAL",0,0,"4038",,terminal_output +1341,2787051,"TERMINAL",0,0,"149",,terminal_output +1342,2787676,"TERMINAL",0,0,"srun",,terminal_focus +1343,2788343,"TERMINAL",0,0,"2520",,terminal_output +1344,2789361,"TERMINAL",0,0,"361",,terminal_output +1345,2790366,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",0,0,"",shellscript,tab +1346,2790440,"TERMINAL",0,0,"472",,terminal_output +1347,2791471,"TERMINAL",0,0,"583",,terminal_output +1348,2792534,"TERMINAL",0,0,"694",,terminal_output +1349,2793543,"TERMINAL",0,0,"7105",,terminal_output +1350,2794555,"TERMINAL",0,0,"816",,terminal_output +1351,2795600,"TERMINAL",0,0,"927",,terminal_output +1352,2796624,"TERMINAL",0,0,"5038",,terminal_output +1353,2797666,"TERMINAL",0,0,"149",,terminal_output +1354,2798702,"TERMINAL",0,0,"2530",,terminal_output +1355,2799745,"TERMINAL",0,0,"361",,terminal_output +1356,2800812,"TERMINAL",0,0,"472",,terminal_output +1357,2801839,"TERMINAL",0,0,"594",,terminal_output +1358,2802908,"TERMINAL",0,0,"7205",,terminal_output +1359,2803989,"TERMINAL",0,0,"816",,terminal_output +1360,2805013,"TERMINAL",0,0,"927",,terminal_output +1361,2805993,"TERMINAL",0,0,"5:0038",,terminal_output +1362,2807019,"TERMINAL",0,0,"149",,terminal_output +1363,2808084,"TERMINAL",0,0,"2540",,terminal_output +1364,2809112,"TERMINAL",0,0,"361",,terminal_output +1365,2810134,"TERMINAL",0,0,"45345train_dy 0:27\t 2 hkn[0430-0431]3584interactCG 18:44:30\t 1 hkn05232",,terminal_output +1366,2811297,"TERMINAL",0,0,"53",,terminal_output +1367,2812286,"TERMINAL",0,0,"64",,terminal_output +1368,2813231,"TERMINAL",0,0,"75",,terminal_output +1369,2814336,"TERMINAL",0,0,"86",,terminal_output +1370,2815358,"TERMINAL",0,0,"97",,terminal_output +1371,2816349,"TERMINAL",0,0,"108",,terminal_output +1372,2817405,"TERMINAL",0,0,"19",,terminal_output +1373,2818423,"TERMINAL",0,0,"250",,terminal_output +1374,2819461,"TERMINAL",0,0,"31",,terminal_output +1375,2820579,"TERMINAL",0,0,"\r42",,terminal_output 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+1420,2864441,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",82,8,"01:00:00",shellscript,selection_mouse +1421,2864615,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",90,0,"",shellscript,selection_mouse +1422,2865541,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",147,0,"",shellscript,selection_mouse +1423,2865900,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",147,1,"5",shellscript,selection_mouse +1424,2866047,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",123,26,"#SBATCH --cpus-per-task=5\n",shellscript,selection_mouse +1425,2866723,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",169,0,"",shellscript,selection_mouse +1426,2873897,"TERMINAL",0,0,"cd $ws_dir",,terminal_command +1427,2874479,"TERMINAL",0,0,"ls",,terminal_command +1428,2874492,"TERMINAL",0,0,"]633;E;2025-07-10 17:06:08 ls;16c08be6-3885-420f-ac53-f5272aed6e54]633;Ccheckpoints count_items.sh data data_new huggingface logs scripts\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared]633;D;0",,terminal_output +1429,2877829,"TERMINAL",0,0,"cd logs/logs_mihir/",,terminal_command +1430,2878493,"TERMINAL",0,0,"ls",,terminal_command +1431,2878546,"TERMINAL",0,0,"]633;E;2025-07-10 17:06:12 ls;16c08be6-3885-420f-ac53-f5272aed6e54]633;Ctrain_dyn_yolorun_3333026.log train_lam_action_space_scaling_20_3331285.log train_lam_model_size_scaling_38M_3317115.log train_tokenizer_model_size_scaling_140M_3316019.log\r\ntrain_dyn_yolorun_3333448.log train_lam_action_space_scaling_50_3320180.log train_lam_model_size_scaling_38M_3317231.log train_tokenizer_model_size_scaling_200M_3313563.log\r\ntrain_dyn_yolorun_3335345.log train_lam_action_space_scaling_50_3329789.log train_tokenizer_batch_size_scaling_16_node_3321526.log train_tokenizer_model_size_scaling_200M_3316020.log\r\ntrain_lam_action_space_scaling_10_3320179.log train_lam_action_space_scaling_50_3329804.log train_tokenizer_batch_size_scaling_1_node_3318551.log train_tokenizer_model_size_scaling_227M_3317234.log\r\ntrain_lam_action_space_scaling_10_3321529.log train_lam_action_space_scaling_50_3331286.log train_tokenizer_batch_size_scaling_2_node_3318552.log train_tokenizer_model_size_scaling_227M_3318555.log\r\ntrain_lam_action_space_scaling_10_3329786.log train_lam_action_space_scaling_6_3318549.log train_tokenizer_batch_size_scaling_2_node_3330806.log train_tokenizer_model_size_scaling_227M_3320173.log\r\ntrain_lam_action_space_scaling_10_3329801.log train_lam_action_space_scaling_6_3320178.log train_tokenizer_batch_size_scaling_2_node_3330848.log train_tokenizer_model_size_scaling_227M_3321523.log\r\ntrain_lam_action_space_scaling_10_3331283.log train_lam_action_space_scaling_6_3321528.log train_tokenizer_batch_size_scaling_2_node_3331282.log train_tokenizer_model_size_scaling_37M_3313565.log\r\ntrain_lam_action_space_scaling_12_3318546.log train_lam_action_space_scaling_6_3329790.log train_tokenizer_batch_size_scaling_4_node_3318553.log train_tokenizer_model_size_scaling_37M_3316022.log\r\ntrain_lam_action_space_scaling_12_3320177.log train_lam_action_space_scaling_6_3329805.log train_tokenizer_batch_size_scaling_4_node_3320175.log train_tokenizer_model_size_scaling_37M_3317232.log\r\ntrain_lam_action_space_scaling_12_3321527.log train_lam_action_space_scaling_6_3331287.log train_tokenizer_batch_size_scaling_4_node_3321524.log train_tokenizer_model_size_scaling_37M_3317239.log\r\ntrain_lam_action_space_scaling_12_3329787.log train_lam_action_space_scaling_8_3318550.log train_tokenizer_batch_size_scaling_8_node_3320176.log train_tokenizer_model_size_scaling_37M_3318556.log\r\ntrain_lam_action_space_scaling_12_3329802.log train_lam_action_space_scaling_8_3329791.log train_tokenizer_batch_size_scaling_8_node_3321525.log train_tokenizer_model_size_scaling_74M_3318557.log\r\ntrain_lam_action_space_scaling_12_3331284.log train_lam_action_space_scaling_8_3329806.log train_tokenizer_minecraft_overfit_sample_3309656.log train_tokenizer_model_size_scaling_74M_3320174.log\r\ntrain_lam_action_space_scaling_20_3318547.log train_lam_action_space_scaling_8_3331288.log train_tokenizer_model_size_scaling_127M_3317233.log train_tokenizer_model_size_scaling_74M_3321522.log\r\ntrain_lam_action_space_scaling_20_3329788.log train_lam_minecraft_overfit_sample_3309655.log train_tokenizer_model_size_scaling_127M_3318554.log train_tokenizer_model_size_scaling_80M_3313564.log\r\ntrain_lam_action_space_scaling_20_3329803.log train_lam_model_size_scaling_38M_3317098.log train_tokenizer_model_size_scaling_140M_3313562.log train_tokenizer_model_size_scaling_80M_3316026.log\r\n]0;tum_cte0515@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir]633;D;0",,terminal_output +1432,2883217,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3335345.log",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=01:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_dyn_yolorun\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n# tf_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_tfrecord\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/train_lam_action_space_scaling_20/3318547/lam_1751657975_200000/\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-tf-records-$slurm_job_id \\n --tags dynamics yolo-run tf_records \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n # --lam_checkpoint=$lam_ckpt_dir\nSLURM_STEP_NUM_TASKS=1\nSLURM_JOB_USER=tum_cte0515\nSLURM_TASKS_PER_NODE=4(x2)\nSLURM_JOB_UID=999226\nSLURM_STEP_GPUS=0\nSLURM_CPU_BIND=quiet,mask_cpu:0x00000003FC00000007F00000003FC00000007F\nSLURM_TASK_PID=3141369\nSLURM_JOB_GPUS=0,1,2,3\nSLURM_LOCALID=0\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar\nSLURM_CPU_BIND_VERBOSE=quiet\nSLURMD_NODENAME=hkn0430\nSLURM_JOB_START_TIME=1752159877\nSLURM_STEP_NODELIST=hkn0523\nSLURM_CLUSTER_NAME=hk\nSLURM_JOB_END_TIME=1752163477\nSLURM_CPUS_ON_NODE=24\nSLURM_JOB_CPUS_PER_NODE=24(x2)\nSLURM_GPUS_ON_NODE=4\nSLURM_GTIDS=0\nSLURM_JOB_PARTITION=accelerated\nSLURM_TRES_PER_TASK=cpu=5\nSLURM_OOM_KILL_STEP=0\nSLURM_JOB_NUM_NODES=2\nSLURM_STEPID=0\nSLURM_CPU_BIND_LIST=0x00000003FC00000007F00000003FC00000007F\nSLURM_JOBID=3335345\nSLURM_PTY_PORT=43975\nSLURM_JOB_QOS=normal\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.201\nSLURM_PTY_WIN_ROW=48\nSLURM_CPU_BIND_TYPE=mask_cpu:\nSLURMD_DEBUG=2\nSLURM_PROCID=0\nSLURM_CPUS_PER_TASK=5\nSLURM_NTASKS=8\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e13.hkn0430\nSLURM_DISTRIBUTION=cyclic\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\nSLURM_SRUN_COMM_HOST=10.0.7.201\nSLURM_SCRIPT_CONTEXT=prolog_task\nSLURM_PTY_WIN_COL=228\nSLURM_NODELIST=hkn[0430-0431]\nSLURM_SRUN_COMM_PORT=42223\nSLURM_STEP_ID=0\nSLURM_JOB_ACCOUNT=hk-project-p0023960\nSLURM_PRIO_PROCESS=0\nSLURM_NPROCS=8\nSLURM_NNODES=2\nSLURM_SUBMIT_HOST=hkn0523.localdomain\nSLURM_JOB_ID=3335345\nSLURM_NODEID=0\nSLURMD_TRES_FREQ=gpu:high,memory=high\nSLURM_STEP_NUM_NODES=1\nSLURM_STEP_TASKS_PER_NODE=1\nSLURM_MPI_TYPE=pmi2\nSLURM_CONF=/etc/slurm/slurm.conf\nSLURM_JOB_NAME=train_dyn_yolorun\nSLURM_NTASKS_PER_NODE=4\nSLURM_STEP_LAUNCHER_PORT=42223\nSLURM_JOB_GID=502226\nSLURM_JOB_NODELIST=hkn[0430-0431]\nSLURM_STEP_NUM_TASKS=1\nSLURM_JOB_USER=tum_cte0515\nSLURM_TASKS_PER_NODE=4(x2)\nSLURM_JOB_UID=999226\nSLURM_STEP_GPUS=0\nSLURM_CPU_BIND=quiet,mask_cpu:0x00000003FC00000007F00000003FC00000007F\nSLURM_TASK_PID=3141369\nSLURM_JOB_GPUS=0,1,2,3\nSLURM_LOCALID=0\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar\nSLURM_CPU_BIND_VERBOSE=quiet\nSLURMD_NODENAME=hkn0430\nSLURM_JOB_START_TIME=1752159877\nSLURM_STEP_NODELIST=hkn0523\nSLURM_CLUSTER_NAME=hk\nSLURM_JOB_END_TIME=1752163477\nSLURM_CPUS_ON_NODE=24\nSLURM_JOB_CPUS_PER_NODE=24(x2)\nSLURM_GPUS_ON_NODE=4\nSLURM_GTIDS=0\nSLURM_JOB_PARTITION=accelerated\nSLURM_TRES_PER_TASK=cpu=5\nSLURM_OOM_KILL_STEP=0\nSLURM_JOB_NUM_NODES=2\nSLURM_STEPID=0\nSLURM_CPU_BIND_LIST=0x00000003FC00000007F00000003FC00000007F\nSLURM_JOBID=3335345\nSLURM_PTY_PORT=43975\nSLURM_JOB_QOS=normal\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.201\nSLURM_PTY_WIN_ROW=48\nSLURM_CPU_BIND_TYPE=mask_cpu:\nSLURMD_DEBUG=2\nSLURM_PROCID=0\nSLURM_CPUS_PER_TASK=5\nSLURM_NTASKS=8\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e13.hkn0430\nSLURM_DISTRIBUTION=cyclic\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\nSLURM_SRUN_COMM_HOST=10.0.7.201\nSLURM_SCRIPT_CONTEXT=prolog_task\nSLURM_PTY_WIN_COL=228\nSLURM_NODELIST=hkn[0430-0431]\nSLURM_SRUN_COMM_PORT=42223\nSLURM_STEP_ID=0\nSLURM_JOB_ACCOUNT=hk-project-p0023960\nSLURM_PRIO_PROCESS=0\nSLURM_NPROCS=8\nSLURM_NNODES=2\nSLURM_SUBMIT_HOST=hkn0523.localdomain\nSLURM_JOB_ID=3335345\nSLURM_NODEID=0\nSLURMD_TRES_FREQ=gpu:high,memory=high\nSLURM_STEP_NUM_NODES=1\nSLURM_STEP_TASKS_PER_NODE=1\nSLURM_MPI_TYPE=pmi2\nSLURM_CONF=/etc/slurm/slurm.conf\nSLURM_JOB_NAME=train_dyn_yolorun\nSLURM_NTASKS_PER_NODE=4\nSLURM_STEP_LAUNCHER_PORT=42223\nSLURM_JOB_GID=502226\nSLURM_JOB_NODELIST=hkn[0430-0431]\nsrun: error: CPU binding outside of job step allocation, allocated CPUs are: 0x000000003C00000003F000000003C00000003F.\nsrun: error: CPU binding outside of job step allocation, allocated CPUs are: 0x000000003C00000003F000000003C00000003F.\nsrun: error: Task launch for StepId=3335345.0 failed on node hkn0430: Unable to satisfy cpu bind request\nsrun: error: Task launch for StepId=3335345.0 failed on node hkn0431: Unable to satisfy cpu bind request\nsrun: error: Application launch failed: Unable to satisfy cpu bind request\nsrun: Job step aborted\n\n============================= JOB FEEDBACK =============================\n\nJob ID: 3335345\nCluster: hk\nUser/Group: tum_cte0515/hk-project-p0023960\nAccount: hk-project-p0023960\nState: FAILED (exit code 192)\nPartition: accelerated\nNodes: 2\nCores per node: 24\nNodelist: hkn[0430-0431]\nCPU Utilized: 00:00:00\nCPU Efficiency: 0.00% of 00:21:36 core-walltime\nJob Wall-clock time: 00:00:27\nStarttime: Thu Jul 10 17:04:37 2025\nEndtime: Thu Jul 10 17:05:04 2025\nMemory Utilized: 10.38 MB\nMemory Efficiency: 0.00% of 0.00 MB\nEnergy Consumed: 13356 Joule / 3.71 Watthours\nAverage node power draw: 494.666666666667 Watt\n",log,tab +1433,2883830,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3335345.log",1303,0,"",log,selection_mouse +1434,2884425,"/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/train_dyn_yolorun_3335345.log",6380,0,"",log,selection_command +1435,2907779,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",0,0,"",shellscript,tab +1436,2910928,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",66,0,"",shellscript,selection_mouse +1437,2911068,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",65,1,"4",shellscript,selection_mouse +1438,2911148,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",60,6,"node=4",shellscript,selection_mouse 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.venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=6 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-cotraining-action-space-scaling-6-$slurm_job_id \\n --tags dynamics cotraining action-space-scaling 6 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +1664,3146193,"TERMINAL",0,0,"4078",,terminal_output +1665,3147136,"TERMINAL",0,0,"189",,terminal_output +1666,3148264,"TERMINAL",0,0,"2920",,terminal_output +1667,3149289,"TERMINAL",0,0,"32:001",,terminal_output +1668,3150318,"TERMINAL",0,0,"412",,terminal_output +1669,3151290,"TERMINAL",0,0,"523",,terminal_output +1670,3152334,"TERMINAL",0,0,"634",,terminal_output +1671,3153375,"TERMINAL",0,0,"745",,terminal_output +1672,3154516,"TERMINAL",0,0,"856",,terminal_output +1673,3155534,"TERMINAL",0,0,"967",,terminal_output +1674,3156562,"TERMINAL",0,0,"5078",,terminal_output +1675,3157585,"TERMINAL",0,0,"189",,terminal_output +1676,3158712,"TERMINAL",0,0,"2930",,terminal_output +1677,3159734,"TERMINAL",0,0,"3101",,terminal_output +1678,3160696,"TERMINAL",0,0,"412",,terminal_output +1679,3161738,"TERMINAL",0,0,"523",,terminal_output +1680,3162783,"TERMINAL",0,0,"634",,terminal_output +1681,3163818,"TERMINAL",0,0,"756",,terminal_output 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--cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=dynamics_cotraining_action_space_scaling\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=8 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-cotraining-action-space-scaling-8-$slurm_job_id \\n --tags dynamics cotraining action-space-scaling 8 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +1716,3199313,"TERMINAL",0,0,"3501",,terminal_output +1717,3200350,"TERMINAL",0,0,"412",,terminal_output +1718,3201406,"TERMINAL",0,0,"523",,terminal_output +1719,3202286,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_12_actions.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=dynamics_cotraining_action_space_scaling\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=12 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-cotraining-action-space-scaling-12-$slurm_job_id \\n --tags dynamics cotraining action-space-scaling 12 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +1720,3202465,"TERMINAL",0,0,"634",,terminal_output +1721,3203555,"TERMINAL",0,0,"745",,terminal_output +1722,3204583,"TERMINAL",0,0,"856",,terminal_output +1723,3205577,"TERMINAL",0,0,"967",,terminal_output +1724,3206610,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_20_actions.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=dynamics_cotraining_action_space_scaling\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-cotraining-action-space-scaling-20-$slurm_job_id \\n --tags dynamics cotraining action-space-scaling 20 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +1725,3206675,"TERMINAL",0,0,"4078",,terminal_output +1726,3207703,"TERMINAL",0,0,"189",,terminal_output +1727,3208780,"TERMINAL",0,0,"2920",,terminal_output +1728,3209818,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_50_actions.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=dynamics_cotraining_action_space_scaling\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=50 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-cotraining-action-space-scaling-50-$slurm_job_id \\n --tags dynamics cotraining action-space-scaling 50 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +1729,3209824,"TERMINAL",0,0,"33:001",,terminal_output +1730,3210821,"TERMINAL",0,0,"423",,terminal_output +1731,3211955,"TERMINAL",0,0,"634",,terminal_output +1732,3212905,"TERMINAL",0,0,"745",,terminal_output +1733,3214004,"TERMINAL",0,0,"856",,terminal_output +1734,3215005,"TERMINAL",0,0,"967",,terminal_output +1735,3216157,"TERMINAL",0,0,"5078",,terminal_output +1736,3217177,"TERMINAL",0,0,"189",,terminal_output +1737,3218211,"TERMINAL",0,0,"2930",,terminal_output +1738,3219187,"TERMINAL",0,0,"3101",,terminal_output +1739,3220236,"TERMINAL",0,0,"412",,terminal_output +1740,3221299,"TERMINAL",0,0,"523",,terminal_output +1741,3222328,"TERMINAL",0,0,"634",,terminal_output 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$CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/train_lam_action_space_scaling_20/3318547/lam_1751657975_200000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=12 \\n --min_lr=0 \\n --max_lr=3e-4 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-batch-size-scaling-1-node-$slurm_job_id \\n --tags dynamics debug \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +2035,3523676,"TERMINAL",0,0,"745",,terminal_output +2036,3524838,"TERMINAL",0,0,"856",,terminal_output +2037,3525827,"TERMINAL",0,0,"978",,terminal_output 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.venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1536 \\n --min_lr=3e-4 \\n --max_lr=3e-4 \\n --log_image_interval=250 \\n --log \\n --name=tokenizer-batch-size-scaling-32-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 32-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,tab +2061,3549883,"TERMINAL",0,0,"3412",,terminal_output +2062,3550526,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics/sqrt_lr/train_dynamics_16_nodes.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=16\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_16_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=768 \\n --min_lr=0 \\n --max_lr=4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-16-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 16-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,tab +2063,3550890,"TERMINAL",0,0,"523",,terminal_output +2064,3551933,"TERMINAL",0,0,"634",,terminal_output +2065,3552975,"TERMINAL",0,0,"745",,terminal_output +2066,3554015,"TERMINAL",0,0,"856",,terminal_output +2067,3555059,"TERMINAL",0,0,"967",,terminal_output +2068,3556129,"TERMINAL",0,0,"3078",,terminal_output +2069,3557153,"TERMINAL",0,0,"189",,terminal_output +2070,3558199,"TERMINAL",0,0,"2910",,terminal_output +2071,3559307,"TERMINAL",0,0,"3501",,terminal_output +2072,3560295,"TERMINAL",0,0,"412",,terminal_output +2073,3561348,"TERMINAL",0,0,"523",,terminal_output +2074,3562385,"TERMINAL",0,0,"634",,terminal_output +2075,3563498,"TERMINAL",0,0,"745",,terminal_output +2076,3564354,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_1_nodes.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/train_lam_action_space_scaling_20/3318547/lam_1751657975_200000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=12 \\n --min_lr=0 \\n --max_lr=3e-4 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-batch-size-scaling-1-node-$slurm_job_id \\n --tags dynamics debug \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +2077,3564476,"TERMINAL",0,0,"856",,terminal_output +2078,3565548,"TERMINAL",0,0,"967",,terminal_output 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$CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,tab +2120,3582287,"TERMINAL",0,0,"634",,terminal_output +2121,3583243,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",1007,0,"",shellscript,selection_mouse +2122,3583343,"TERMINAL",0,0,"745",,terminal_output +2123,3584352,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",1007,1,"5",shellscript,content +2124,3584411,"TERMINAL",0,0,"856",,terminal_output +2125,3585429,"TERMINAL",0,0,"967",,terminal_output 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--log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2145,3599731,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",21,1289,"#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2146,3599732,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",0,1310,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2147,3600074,"TERMINAL",0,0,"412",,terminal_output +2148,3601088,"TERMINAL",0,0,"523",,terminal_output +2149,3601318,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",1310,0,"",shellscript,selection_mouse +2150,3601664,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",1309,1,"\n",shellscript,selection_mouse +2151,3601671,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",990,320,"\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2152,3601689,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",715,595,"SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2153,3601714,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",439,871,"sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2154,3601744,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",124,1186,"SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2155,3601759,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",40,1270,"SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2156,3601788,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",22,1288,"SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2157,3602205,"TERMINAL",0,0,"634",,terminal_output +2158,3602276,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",92,1218,"SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2159,3602300,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",150,1160,"SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2160,3602355,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",428,882,"\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2161,3602355,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",498,812,"unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2162,3602356,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",686,624,"SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2163,3602368,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",728,582,"\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2164,3602384,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",851,459,"CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2165,3602441,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",866,444,"\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2166,3602451,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",883,427,"\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2167,3602507,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",866,444,"\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2168,3602508,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",851,459,"CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2169,3602519,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",715,595,"SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2170,3602537,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",460,850,"\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2171,3602556,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",278,1032,"-error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2172,3602573,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",92,1218,"SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2173,3602588,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",40,1270,"SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2174,3602603,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",22,1288,"SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2175,3602670,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",20,1290,"\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2176,3603180,"TERMINAL",0,0,"745",,terminal_output +2177,3603284,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",700,0,"",shellscript,selection_mouse +2178,3603301,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",699,0,"",shellscript,selection_command +2179,3603939,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",635,0,"",shellscript,selection_mouse +2180,3604084,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",631,30,"open_ai_minecraft_arrayrecords",shellscript,selection_mouse 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--log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-tf-records-$slurm_job_id \\n --tags dynamics yolo-run tf_records \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir",shellscript,content +2238,3637445,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_1_nodes.sbatch",597,0,"",shellscript,selection_command +2239,3637815,"TERMINAL",0,0,"189",,terminal_output +2240,3638853,"TERMINAL",0,0,"21031",,terminal_output +2241,3638882,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_1_nodes.sbatch",597,432,"",shellscript,content +2242,3638941,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_1_nodes.sbatch",596,0,"",shellscript,selection_command +2243,3639277,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_1_nodes.sbatch",674,0,"\n",shellscript,content 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+2251,3642582,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/train_lam_action_space_scaling_20/3318547/lam_1751657975_200000/\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n --lam_checkpoint=$lam_ckpt_dir\n",shellscript,tab +2252,3643033,"TERMINAL",0,0,"745",,terminal_output 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+2260,3646284,"TERMINAL",0,0,"9:0078",,terminal_output +2261,3646664,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=4\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=192 \\n --min_lr=0 \\n --max_lr=2.00e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-4-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 4-node sqrt-lr \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,tab +2262,3647210,"TERMINAL",0,0,"189",,terminal_output +2263,3648007,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",623,0,"",shellscript,selection_mouse 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--error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_16_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=768 \\n --min_lr=0 \\n --max_lr=4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-16-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 16-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar 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+2591,3757170,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",39,1261,"#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=192 \\n --min_lr=0 \\n --max_lr=2.00e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-4-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 4-node sqrt-lr \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2592,3757171,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",0,1300,"#!/usr/bin/env bash\n\n#SBATCH --nodes=4\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=192 \\n --min_lr=0 \\n --max_lr=2.00e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-4-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 4-node sqrt-lr \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2593,3757224,"TERMINAL",0,0,"189",,terminal_output 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\\n",shellscript,content +2619,3778480,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",1068,33,"",shellscript,content +2620,3778639,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",1141,0," --max_lr=2e-4 \\n",shellscript,content +2621,3778741,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",1161,23,"",shellscript,content +2622,3778842,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",1193,37,"",shellscript,content +2623,3778859,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",1205,0," --log_checkpoint_interval=1000 \\n --log \\n",shellscript,content +2624,3778986,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",1242,0," --name=dynamics-batch-size-scaling-4-node-$slurm_job_id \\n",shellscript,content +2625,3778986,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",1304,0," --tags dynamics debug \\n",shellscript,content +2626,3779049,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",1332,0," --entity instant-uv \\n",shellscript,content +2627,3779112,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",1358,166,"",shellscript,content +2628,3779170,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",1380,0," --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n",shellscript,content +2629,3779170,"TERMINAL",0,0,"3301",,terminal_output +2630,3780188,"TERMINAL",0,0,"412",,terminal_output +2631,3781216,"TERMINAL",0,0,"523",,terminal_output +2632,3782257,"TERMINAL",0,0,"634",,terminal_output 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+2642,3788303,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",1304,1,"\n",shellscript,selection_mouse +2643,3788322,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",1086,219,"\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2644,3788340,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",875,430,"URM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2645,3788395,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",492,813,"odule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2646,3788399,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",20,1285,"\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2647,3788399,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",0,1305,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2648,3788526,"TERMINAL",0,0,"2930:00",,terminal_output +2649,3789400,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",98,0,"",shellscript,selection_mouse +2650,3789579,"TERMINAL",0,0,"3401",,terminal_output +2651,3790584,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",0,0,"",shellscript,selection_mouse +2652,3790652,"TERMINAL",0,0,"412",,terminal_output +2653,3790725,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",0,20,"#!/usr/bin/env bash\n",shellscript,selection_mouse 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--nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenize",shellscript,selection_mouse +2657,3790813,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",0,1037,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \",shellscript,selection_mouse +2658,3790814,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",0,1192,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-sc",shellscript,selection_mouse +2659,3790826,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",0,1270,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \",shellscript,selection_mouse +2660,3790842,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",0,1304,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir",shellscript,selection_mouse +2661,3790903,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",0,1305,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2662,3791661,"TERMINAL",0,0,"523",,terminal_output +2663,3792435,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",0,0,"",shellscript,selection_command +2664,3792715,"TERMINAL",0,0,"634",,terminal_output +2665,3793770,"TERMINAL",0,0,"745",,terminal_output +2666,3794818,"TERMINAL",0,0,"856",,terminal_output +2667,3795834,"TERMINAL",0,0,"978",,terminal_output +2668,3796881,"TERMINAL",0,0,"3189",,terminal_output +2669,3797942,"TERMINAL",0,0,"2910",,terminal_output +2670,3799061,"TERMINAL",0,0,"3501",,terminal_output +2671,3800037,"TERMINAL",0,0,"412",,terminal_output +2672,3801087,"TERMINAL",0,0,"523",,terminal_output 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+2682,3807945,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",1036,0,"srun python train_dynamics.py \\n",shellscript,content +2683,3808021,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",1068,33,"",shellscript,content +2684,3808198,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",1141,0," --max_lr=2.8e-4 \\n",shellscript,content +2685,3808230,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",1163,20,"",shellscript,content +2686,3808290,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",1195,37,"",shellscript,content +2687,3808299,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",1207,0," --log_checkpoint_interval=1000 \\n --log \\n",shellscript,content 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+2718,3830653,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch",672,637,"\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=768 \\n --min_lr=0 \\n --max_lr=4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-16-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 16-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2719,3830669,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch",68,1241,"#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_16_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=768 \\n --min_lr=0 \\n --max_lr=4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-16-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 16-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2720,3830686,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch",0,1309,"#!/usr/bin/env bash\n\n#SBATCH --nodes=16\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_16_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=768 \\n --min_lr=0 \\n --max_lr=4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-16-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 16-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +2721,3831636,"TERMINAL",0,0,"523",,terminal_output 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+2739,3843981,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch",1195,37,"",shellscript,content +2740,3844004,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch",1207,0," --log_checkpoint_interval=1000 \\n --log \\n",shellscript,content +2741,3844089,"TERMINAL",0,0,"856",,terminal_output +2742,3844152,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch",1244,0," --name=dynamics-batch-size-scaling-16-node-$slurm_job_id \\n",shellscript,content +2743,3844213,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch",1307,0," --tags dynamics debug \\n",shellscript,content +2744,3844272,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch",1335,0," --entity instant-uv \\n",shellscript,content 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.venv/bin/activate\n\ntf_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_tfrecord\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-model-size-scaling-38M-$slurm_job_id \\n --tags tokenizer model-size-scaling 38M \\n --entity instant-uv \\n --project jafar \\n --data_dir $tf_records_dir\n",shellscript,tab +3060,4736968,"slurm/jobs/mihir/horeka/modelsize_scaling/tokenizer/train_tokenizer_37M.sbatch",1249,0,"",shellscript,selection_mouse +3061,4737146,"slurm/jobs/mihir/horeka/modelsize_scaling/tokenizer/train_tokenizer_37M.sbatch",1091,158,"size-scaling-38M-$slurm_job_id 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--output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_model_size_scaling_37M\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_tfrecord\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-model-size-scaling-38M-$slurm_job_id \\n --tags tokenizer model-size-scaling 38M \\n --entity instant-uv \\n --project jafar \\n --data_dir $tf_records_dir\n",shellscript,selection_mouse +3065,4737231,"slurm/jobs/mihir/horeka/modelsize_scaling/tokenizer/train_tokenizer_37M.sbatch",0,1249,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_model_size_scaling_37M\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/open_ai_minecraft_tfrecord\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-model-size-scaling-38M-$slurm_job_id \\n --tags tokenizer model-size-scaling 38M \\n --entity instant-uv \\n --project jafar \\n --data_dir $tf_records_dir\n",shellscript,selection_mouse +3066,4740822,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4",0,0,"",plaintext,tab +3067,4749556,"slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\n# array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked_subset\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,tab +3068,4750301,"slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch",1442,0,"",shellscript,selection_mouse +3069,4750553,"slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch",1435,7,"ds_dir\n",shellscript,selection_mouse +3070,4750564,"slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch",1008,434," SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3071,4750580,"slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch",810,632,"_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3072,4750638,"slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch",548,894,"# array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked_subset\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3073,4750639,"slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch",367,1075,"#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\n# array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked_subset\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3074,4750639,"slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch",0,1442,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\n# array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked_subset\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3075,4756727,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4",0,0,"",plaintext,tab +3076,4758420,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\n# array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked_subset\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",plaintext,content +3077,4768099,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\n# array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked_subset\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,tab 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--log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir",shellscript,selection_mouse +3110,4793293,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",955,350,"ch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir",shellscript,selection_mouse +3111,4793294,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",954,351,"tch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir",shellscript,selection_mouse +3112,4793295,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",920,385,"kpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir",shellscript,selection_mouse +3113,4793299,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",919,386,"ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir",shellscript,selection_mouse +3114,4793317,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",918,387,"-ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir",shellscript,selection_mouse +3115,4793374,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",884,421," python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir",shellscript,selection_mouse +3116,4793374,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",883,422,"n python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir",shellscript,selection_mouse +3117,4793396,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",882,423,"un python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir",shellscript,selection_mouse +3118,4793417,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",879,426,"\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir",shellscript,selection_mouse 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--name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling",shellscript,selection_mouse +3127,4794538,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",880,369,"srun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \",shellscript,selection_mouse +3128,4794539,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",880,391,"srun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \",shellscript,selection_mouse +3129,4794539,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",880,425,"srun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir",shellscript,selection_mouse +3130,4794764,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1305,0,"",shellscript,selection_mouse +3131,4794765,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1304,0,"",shellscript,selection_command 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--log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3138,4799204,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",457,849,"$0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3139,4799204,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",0,1306,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.4e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\n --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3140,4800671,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",0,0,"",shellscript,selection_command 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+3148,4832338,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4 copy.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,tab +3149,4839854,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,tab +3150,4840528,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",1244,0,"",shellscript,selection_mouse +3151,4840687,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",1209,35,"\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3152,4840707,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",969,275,"0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3153,4840797,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",719,525,"INT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3154,4840798,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",417,827,"# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3155,4840798,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",149,1095,"#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3156,4840798,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",67,1177,"#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3157,4840816,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",21,1223,"#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3158,4840816,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",20,1224,"\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3159,4842124,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",1244,0,"",shellscript,selection_mouse +3160,4842624,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",1187,57,"\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3161,4842683,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",972,272,"\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3162,4842683,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",711,533,"\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3163,4842683,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",517,727,"venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3164,4842690,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",426,818," sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3165,4842706,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",416,828,"\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3166,4842762,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",375,869,"--job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3167,4842769,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",277,967,"--error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3168,4842772,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",178,1066,"--output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3169,4842791,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",157,1087,"--gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3170,4842849,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",130,1114," --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3171,4842920,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",124,1120,"SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3172,4842939,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",92,1152,"SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3173,4842995,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",68,1176,"SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3174,4842996,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",40,1204,"SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3175,4842996,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",22,1222,"SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3176,4843056,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",20,1224,"\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3177,4843073,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",2,1242,"/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3178,4843140,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",1,1243,"!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3179,4843154,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",20,1224,"\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3180,4843516,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",0,1244,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,selection_mouse +3181,4844480,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",0,0,"",shellscript,selection_command +3182,4860898,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",367,0,"#SBATCH --job-name=train_tokenizer_lr_sweep_5e-5\n",shellscript,content +3183,4860898,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",416,49,"",shellscript,content +3184,4861841,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",973,0," --max_lr=5e-5 \\n",shellscript,content +3185,4861864,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",993,20,"",shellscript,content +3186,4862099,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",1074,0," --name=tokenizer-lr-sweep-5e-5-$slurm_job_id \\n",shellscript,content +3187,4862130,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",1125,0," --tags tokenizer lr-sweep 5e-5 \\n",shellscript,content +3188,4862185,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",1162,88,"",shellscript,content +3189,4868510,"TERMINAL",0,0,"Step 722, loss: 5.520607948303223\r\nStep 723, loss: 5.279177188873291\r\nStep 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main;16c08be6-3885-420f-ac53-f5272aed6e54]633;C",,terminal_output +3237,5366856,"TERMINAL",0,0,"Switched to branch 'main'\r\n",,terminal_output +3238,5367030,"TERMINAL",0,0,"Your branch is behind 'origin/main' by 28 commits, and can be fast-forwarded.\r\n (use ""git pull"" to update your local branch)\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +3239,5368577,"TERMINAL",0,0,"git pull",,terminal_command +3240,5368628,"TERMINAL",0,0,"]633;E;2025-07-10 17:47:42 git pull;16c08be6-3885-420f-ac53-f5272aed6e54]633;C",,terminal_output +3241,5370271,"TERMINAL",0,0,"remote: Enumerating objects: 10, done.\r\nremote: Counting objects: 10% (1/10)\rremote: Counting objects: 20% (2/10)\rremote: Counting objects: 30% (3/10)\rremote: Counting objects: 40% (4/10)\rremote: Counting objects: 50% (5/10)\rremote: Counting objects: 60% (6/10)\rremote: Counting objects: 70% (7/10)\rremote: Counting objects: 80% (8/10)\rremote: Counting objects: 90% (9/10)\rremote: Counting objects: 100% (10/10)\rremote: Counting objects: 100% (10/10), done.\r\nremote: Compressing objects: 50% (1/2)\rremote: Compressing objects: 100% (2/2)\rremote: Compressing objects: 100% (2/2), done.\r\nremote: Total 10 (delta 8), reused 9 (delta 8), pack-reused 0 (from 0)\r\nUnpacking objects: 10% (1/10)\rUnpacking objects: 20% (2/10)\rUnpacking objects: 30% (3/10)\r",,terminal_output +3242,5370378,"TERMINAL",0,0,"Unpacking objects: 40% (4/10)\rUnpacking objects: 50% (5/10)\rUnpacking objects: 60% (6/10)\rUnpacking objects: 70% (7/10)\rUnpacking objects: 80% (8/10)\r",,terminal_output +3243,5370443,"TERMINAL",0,0,"Unpacking objects: 90% (9/10)\rUnpacking objects: 100% (10/10)\rUnpacking objects: 100% (10/10), 2.38 KiB | 14.00 KiB/s, done.\r\n",,terminal_output +3244,5370552,"TERMINAL",0,0,"From github.com:p-doom/jafar\r\n 55a787f..a7c5361 correct-step-time-logging -> origin/correct-step-time-logging\r\n",,terminal_output +3245,5370623,"",0,0,"Switched from branch 'runner-grain' to 'main'",,git_branch_checkout +3246,5370675,"TERMINAL",0,0,"Updating 01461aa..eb5163c\r\n",,terminal_output +3247,5370728,"TERMINAL",0,0,"Fast-forward\r\n",,terminal_output +3248,5370763,"TERMINAL",0,0," genie.py | 181 +++++++++++++++++++++++++++++++------------------\r\n requirements.txt | 3 +-\r\n sample.py | 36 +++++-----\r\n tests/data/generate_dummy_data.py | 93 ++++++++++++--------------\r\n tests/test_dataloader.py | 33 ++++++---\r\n train_dynamics.py | 97 ++++++++++++++++++++-------\r\n train_lam.py | 102 ++++++++++++++++++----------\r\n train_tokenizer.py | 101 ++++++++++++++++++----------\r\n utils/dataloader.py | 214 +++++++++++++++++++++++++++++++++-------------------------\r\n utils/dataset_utils.py | 255 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n utils/preprocess_dataset.py | 59 ++++++++--------\r\n 11 files changed, 821 insertions(+), 353 deletions(-)\r\n create mode 100644 utils/dataset_utils.py\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0",,terminal_output +3249,5388122,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,tab +3250,5388123,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1244,0,"",shellscript,selection_mouse +3251,5392651,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",972,0,"",shellscript,selection_mouse +3252,5392666,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",971,0,"",shellscript,selection_command +3253,5393128,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",933,0,"",shellscript,selection_mouse +3254,5393129,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",932,0,"",shellscript,selection_command +3255,5393705,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",900,0,"",shellscript,selection_mouse +3256,5393706,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",899,0,"",shellscript,selection_command +3257,5394329,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",933,0,"",shellscript,selection_mouse +3258,5394331,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",932,0,"",shellscript,selection_command +3259,5394920,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",900,0,"",shellscript,selection_mouse +3260,5394925,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",899,0,"",shellscript,selection_command +3261,5397836,"train_tokenizer.py",0,0,"from dataclasses import dataclass, field\nimport os\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.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\n\nfrom models.tokenizer import TokenizerVQVAE\nfrom utils.dataloader import get_dataloader\nfrom utils.parameter_utils import count_parameters_by_component\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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n vq_beta: float = 0.25\n batch_size: int = 48\n min_lr: float = 0.0\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_checkpoint_keep_period: int = 20000\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() # type: ignore\n ssim = pix.ssim(gt, recon).mean() # type: ignore\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\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\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 wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\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 videos_sharding = NamedSharding(\n mesh, PartitionSpec(""data"", None, None, None, None)\n )\n train_state = jax.device_put(train_state, replicated_sharding)\n\n # --- Initialize checkpoint manager ---\n step = 0\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add('model_state', ocp.args.StandardSave, ocp.handlers.StandardCheckpointHandler)\n handler_registry.add('model_state', ocp.args.StandardRestore, ocp.handlers.StandardCheckpointHandler)\n handler_registry.add('dataloader_state', grain.checkpoint.CheckpointSave, grain.checkpoint.CheckpointHandler) # type: ignore\n handler_registry.add('dataloader_state', grain.checkpoint.CheckpointRestore, grain.checkpoint.CheckpointHandler) # type: ignore\n \n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n \n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n\n # --- Create DataLoaderIterator from dataloader ---\n array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n \n # --- Restore checkpoint ---\n if args.restore_ckpt:\n abstract_train_state = jax.tree_util.tree_map(ocp.utils.to_shape_dtype_struct, train_state)\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.StandardRestore(abstract_train_state),\n dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator),\n )\n )\n train_state = restored[""model_state""]\n grain_iterator = restored[""dataloader_state""]\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored dataloader and model state from step {step}"")\n\n # --- TRAIN LOOP ---\n dataloader = (jax.make_array_from_process_local_data(videos_sharding, elem) for elem in grain_iterator) # type: ignore\n print(f""Starting training from step {step}..."")\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n rng, _rng, _rng_dropout = jax.random.split(rng, 3)\n\n inputs = dict(videos=videos, rng=_rng, dropout_rng=_rng_dropout)\n train_state, loss, recon, metrics = train_step(train_state, inputs)\n print(f""Step {step}, loss: {loss}"")\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 **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 # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n checkpoint_manager.save(\n step,\n args=ocp.args.Composite(\n model_state=ocp.args.StandardSave(train_state),\n dataloader_state=grain.checkpoint.CheckpointSave(grain_iterator),\n )\n )\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n 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5.101388454437256\r\nStep 1115, loss: 4.849363803863525\r\nStep 1116, loss: 5.013510704040527\r\nStep 1117, loss: 5.012414932250977\r\nStep 1118, loss: 4.933382987976074\r\nStep 1119, loss: 4.641906261444092\r\nStep 1120, loss: 4.92882776260376\r\nStep 1121, loss: 4.703517913818359\r\nStep 1122, loss: 4.972881317138672\r\nStep 1123, loss: 5.049308776855469\r\nStep 1124, loss: 4.712826728820801\r\nStep 1125, loss: 4.8883585929870605\r\nStep 1126, loss: 5.122026443481445\r\nStep 1127, loss: 5.010403156280518\r\nStep 1128, loss: 4.868868827819824\r\nStep 1129, loss: 5.078428268432617\r\nStep 1130, loss: 4.8772053718566895\r\nStep 1131, loss: 4.8967671394348145\r\nStep 1132, loss: 4.88825798034668\r\nStep 1133, loss: 4.8844780921936035\r\nStep 1134, loss: 4.8840436935424805\r\nStep 1135, loss: 4.7452850341796875\r\nStep 1136, loss: 5.050529479980469\r\nStep 1137, loss: 4.917625904083252\r\nStep 1138, loss: 4.829206943511963\r\nStep 1139, loss: 4.8869781494140625\r\nStep 1140, loss: 4.968005657196045\r\nStep 1141, loss: 4.84523868560791\r\nStep 1142, loss: 4.98886251449585\r\nStep 1143, loss: 4.957108497619629\r\nStep 1144, loss: 5.097373008728027\r\nStep 1145, loss: 4.651041507720947\r\nStep 1146, loss: 4.8319783210754395\r\nStep 1147, loss: 4.675942897796631\r\nStep 1148, loss: 5.058229446411133\r\nStep 1149, loss: 4.955995082855225\r\nStep 1150, loss: 4.861543655395508\r\nStep 1151, loss: 4.811530590057373\r\nStep 1152, loss: 4.847794532775879\r\nStep 1153, loss: 5.00905704498291\r\nStep 1154, loss: 4.871018886566162\r\nStep 1155, loss: 4.829107284545898\r\nStep 1156, loss: 4.926144599914551\r\nStep 1157, loss: 4.929976940155029\r\nStep 1158, loss: 4.581959247589111\r\nStep 1159, loss: 4.8127546310424805\r\nStep 1160, loss: 4.829219341278076\r\nStep 1161, loss: 5.095797061920166\r\nStep 1162, loss: 5.034276008605957\r\nStep 1163, loss: 4.616420269012451\r\nStep 1164, loss: 5.0754852294921875\r\nStep 1165, loss: 4.784201622009277\r\nStep 1166, loss: 4.93991231918335\r\nStep 1167, loss: 4.745754241943359\r\nStep 1168, loss: 4.946484088897705\r\nStep 1169, loss: 4.959706783294678\r\nStep 1170, loss: 5.136929512023926\r\nStep 1171, loss: 4.963235855102539\r\nStep 1172, loss: 4.689530372619629\r\nStep 1173, loss: 4.873706340789795\r\nStep 1174, loss: 5.097964286804199\r\nStep 1175, loss: 4.9454545974731445\r\nStep 1176, loss: 4.84202766418457\r\nStep 1177, loss: 5.002065181732178\r\nStep 1178, loss: 5.085189342498779\r\nStep 1179, loss: 5.059033393859863\r\nStep 1180, loss: 5.012739181518555\r\nStep 1181, loss: 4.655002117156982\r\nStep 1182, loss: 5.119167804718018\r\nStep 1183, loss: 4.922578811645508\r\nStep 1184, loss: 4.863205909729004\r\nStep 1185, loss: 4.81952428817749\r\nStep 1186, loss: 4.806148052215576\r\nStep 1187, loss: 4.85841703414917\r\nStep 1188, loss: 5.1563920974731445\r\nStep 1189, loss: 4.81588077545166\r\nStep 1190, loss: 4.9441118240356445\r\nStep 1191, loss: 4.791698932647705\r\nStep 1192, loss: 5.128923416137695\r\nStep 1193, loss: 4.806921005249023\r\nStep 1194, loss: 5.237899303436279\r\nStep 1195, loss: 4.842819690704346\r\nStep 1195, loss: 4.842819690704346\r\nStep 1195, loss: 4.842819690704346\r\n",,terminal_output +3312,5470472,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_5e-5\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=5e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-5e-5-$slurm_job_id \\n --tags tokenizer lr-sweep 5e-5 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,tab +3313,5471223,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",0,0,"",shellscript,tab +3314,5471224,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",912,0,"",shellscript,selection_mouse +3315,5473965,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",0,0,"",shellscript,tab +3316,5473966,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",945,0,"",shellscript,selection_mouse +3317,5474460,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",914,0,"",shellscript,selection_mouse +3318,5474977,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",884,0,"",shellscript,selection_mouse +3319,5475685,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",900,0,"\n --save_ckpt \",shellscript,content +3320,5475698,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",905,0,"",shellscript,selection_command +3321,5477115,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",0,0,"",shellscript,tab +3322,5479031,"TERMINAL",0,0,"srun",,terminal_focus +3323,5482245,"TERMINAL",0,0,"^Csrun: interrupt (one more within 1 sec to abort)\r\nsrun: StepId=3335335.0 tasks 0-7: running\r\n",,terminal_output +3324,5482434,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3335335.0\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-4:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-4:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-4:\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-5:\r\nsrun: forcing job termination\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-4:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-4:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-4:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-4:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-4:\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-6:\r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\nslurmstepd: error: *** STEP 3335335.0 ON hkn0425 CANCELLED AT 2025-07-10T17:49:36 ***\r\n",,terminal_output +3325,5482598,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3335335.0\r\nsrun: job abort in progress\r\n",,terminal_output +3326,5482769,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3335335.0\r\n",,terminal_output +3327,5483625,"TERMINAL",0,0,"^C",,terminal_output +3328,5483747,"TERMINAL",0,0,"]0;tum_cte0515@hkn0425:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0425 jafar]$ ",,terminal_output +3329,5483868,"TERMINAL",0,0,"^C[?2004l\r[?2004h[?2004l\r\r\n]0;tum_cte0515@hkn0425:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0425 jafar]$ ",,terminal_output +3330,5484035,"TERMINAL",0,0,"^C[?2004l\r[?2004h[?2004l\r\r\n]0;tum_cte0515@hkn0425:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0425 jafar]$ ",,terminal_output +3331,5484832,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/yolo-runs/tester.sh",,terminal_output +3332,5485883,"TERMINAL",0,0,"\r",,terminal_output +3333,5487122,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",0,0,"",shellscript,tab +3334,5490815,"TERMINAL",0,0,"[?25lsh[?25h",,terminal_output +3335,5490911,"TERMINAL",0,0,"[?25lh[?25h[?25l [?25h",,terminal_output +3336,5491271,"TERMINAL",0,0,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",,terminal_output +3337,5494362,"TERMINAL",0,0,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=2\r\n#SBATCH --ntasks-per-node=4\r\n#SBATCH --time=36:00:00\r\n#SBATCH --partition=accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:4\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\r\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\nenv | grep SLURM\r\n\r\nsrun python train_tokenizer.py \\r\n --save_ckpt \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=96 \\r\n --min_lr=0 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=1000 \\r\n --log_checkpoint_interval=1000 \\r\n --log \\r\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\r\n --tags tokenizer lr-sweep 1e-4 \\r\n --entity instant-uv \\r\n --project jafar \\r\n --data_dir $array_records_dir\r\n",,terminal_output +3338,5495062,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=4(x2)\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=1196654\r\nSLURM_JOB_GPUS=0,1,2,3\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar\r\nSLURMD_NODENAME=hkn0425\r\nSLURM_JOB_START_TIME=1752159082\r\nSLURM_STEP_NODELIST=hkn0425\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1752195082\r\nSLURM_PMI2_SRUN_PORT=33587\r\nSLURM_CPUS_ON_NODE=24\r\nSLURM_JOB_CPUS_PER_NODE=24(x2)\r\nSLURM_GPUS_ON_NODE=4\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=accelerated\r\nSLURM_TRES_PER_TASK=cpu=5\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=2\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3335335\r\nSLURM_PTY_PORT=45071\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.201\r\nSLURM_PTY_WIN_ROW=48\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=5\r\nSLURM_NTASKS=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e13.hkn0425\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.201\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=228\r\nSLURM_NODELIST=hkn[0425,0428]\r\nSLURM_SRUN_COMM_PORT=37803\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NPROCS=8\r\nSLURM_NNODES=2\r\nSLURM_SUBMIT_HOST=hkn1993.localdomain\r\nSLURM_JOB_ID=3335335\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0425\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_NTASKS_PER_NODE=4\r\nSLURM_STEP_LAUNCHER_PORT=37803\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn[0425,0428]\r\n",,terminal_output +3339,5495218,"TERMINAL",0,0,"GpuFreq=control_disabled\r\nGpuFreq=control_disabled\r\n",,terminal_output +3340,5547011,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +3341,5547728,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.19.11\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/wandb/run-20250710_175041-le1aybcw\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run tokenizer-lr-sweep-1e-4-3335335\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/le1aybcw\r\n",,terminal_output +3342,5636717,"TERMINAL",0,0,"WARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\n",,terminal_output +3343,5637018,"TERMINAL",0,0,"Running on 8 devices.\r\nCounting all components: ['encoder', 'vq', 'decoder']\r\nParameter counts:\r\n{'encoder': 18978432, 'vq': 32768, 'decoder': 18978416, 'total': 37989616}\r\nStarting training from step 0...\r\nRunning on 8 devices.\r\nCounting all components: ['encoder', 'vq', 'decoder']\r\nParameter counts:\r\n{'encoder': 18978432, 'vq': 32768, 'decoder': 18978416, 'total': 37989616}\r\nStarting training from step 0...\r\nRunning on 8 devices.\r\nCounting all components: ['encoder', 'vq', 'decoder']\r\nParameter counts:\r\n{'encoder': 18978432, 'vq': 32768, 'decoder': 18978416, 'total': 37989616}\r\nStarting training from step 0...\r\nRunning on 8 devices.\r\nCounting all components: ['encoder', 'vq', 'decoder']\r\nParameter counts:\r\n{'encoder': 18978432, 'vq': 32768, 'decoder': 18978416, 'total': 37989616}\r\nStarting training from step 0...\r\n",,terminal_output +3344,5654393,"TERMINAL",0,0,"WARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\n",,terminal_output +3345,5654874,"TERMINAL",0,0,"Running on 8 devices.\r\nCounting all components: ['encoder', 'vq', 'decoder']\r\nParameter counts:\r\n{'encoder': 18978432, 'vq': 32768, 'decoder': 18978416, 'total': 37989616}\r\nStarting training from step 0...\r\nRunning on 8 devices.\r\nCounting all components: ['encoder', 'vq', 'decoder']\r\nParameter counts:\r\n{'encoder': 18978432, 'vq': 32768, 'decoder': 18978416, 'total': 37989616}\r\nStarting training from step 0...\r\n",,terminal_output +3346,5658991,"TERMINAL",0,0,"WARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\n",,terminal_output +3347,5659339,"TERMINAL",0,0,"Running on 8 devices.\r\nCounting all components: ['encoder', 'vq', 'decoder']\r\nParameter counts:\r\n{'encoder': 18978432, 'vq': 32768, 'decoder': 18978416, 'total': 37989616}\r\nStarting training from step 0...\r\n",,terminal_output +3348,5663639,"TERMINAL",0,0,"WARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\n",,terminal_output +3349,5664026,"TERMINAL",0,0,"Running on 8 devices.\r\nCounting all components: ['encoder', 'vq', 'decoder']\r\nParameter counts:\r\n{'encoder': 18978432, 'vq': 32768, 'decoder': 18978416, 'total': 37989616}\r\nStarting training from step 0...\r\n",,terminal_output +3350,5895307,"TERMINAL",0,0,"^Csrun: interrupt (one more within 1 sec to abort)\r\nsrun: StepId=3335335.1 tasks 0-7: running\r\n",,terminal_output +3351,5895520,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3335335.1\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-6:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-6:\r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-6:\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-1:\r\nsrun: forcing job termination\r\nProcess SpawnProcess-6:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-6:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-6:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-6:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-6:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-8:\r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\nslurmstepd: error: *** STEP 3335335.1 ON hkn0425 CANCELLED AT 2025-07-10T17:56:29 ***\r\n",,terminal_output +3352,5895675,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3335335.1\r\nsrun: job abort in progress\r\n",,terminal_output +3353,5895846,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3335335.1\r\n",,terminal_output +3354,5896120,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3335335.1\r\nsrun: job abort in progress\r\n",,terminal_output +3355,5896323,"TERMINAL",0,0,"^C",,terminal_output +3356,5896578,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3335335.1\r\n",,terminal_output +3357,5896820,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3335335.1\r\n]0;tum_cte0515@hkn0425:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0425 jafar]$ ",,terminal_output +3358,5898713,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",0,0,"",shellscript,tab +3359,5898714,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1075,0,"",shellscript,selection_mouse +3360,5899876,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1074,1,"",shellscript,content +3361,5900694,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1074,1,"",shellscript,content +3362,5901085,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1042,0,"",shellscript,selection_command +3363,5901747,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1040,0,"",shellscript,selection_command +3364,5902006,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1039,1,"",shellscript,content +3365,5902143,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1038,1,"",shellscript,content +3366,5903544,"TERMINAL",0,0,"sh slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",,terminal_output +3367,5904347,"TERMINAL",0,0,"\r\n[?2004l\r#!/usr/bin/env bash\r\n\r\n#SBATCH --nodes=2\r\n#SBATCH --ntasks-per-node=4\r\n#SBATCH --time=36:00:00\r\n#SBATCH --partition=accelerated\r\n#SBATCH --cpus-per-task=5\r\n#SBATCH --gres=gpu:4\r\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\r\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\r\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\r\n\r\n# Log the sbatch script\r\ncat $0\r\n\r\nmodule unload mpi/openmpi/5.0\r\nmodule unload devel/cuda/12.4\r\nsource .venv/bin/activate\r\n\r\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\r\n\r\njob_name=$SLURM_JOB_NAME\r\nslurm_job_id=$SLURM_JOB_ID\r\n\r\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\r\nmkdir -p $CHECKPOINT_DIR\r\n\r\nenv | grep SLURM\r\n\r\nsrun python train_tokenizer.py \\r\n --save_ckpt \\r\n --ckpt_dir $CHECKPOINT_DIR \\r\n --batch_size=96 \\r\n --min_lr=0 \\r\n --max_lr=1e-4 \\r\n --log_image_interval=10 \\r\n --log_checkpoint_interval=10 \\r\n --log \\r\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\r\n --tags tokenizer lr-sweep 1e-4 \\r\n --entity instant-uv \\r\n --project jafar \\r\n --data_dir $array_records_dir\r\n",,terminal_output +3368,5904516,"TERMINAL",0,0,"SLURM_STEP_NUM_TASKS=1\r\nSLURM_JOB_USER=tum_cte0515\r\nSLURM_TASKS_PER_NODE=4(x2)\r\nSLURM_JOB_UID=999226\r\nSLURM_TASK_PID=1196654\r\nSLURM_JOB_GPUS=0,1,2,3\r\nSLURM_LOCALID=0\r\nSLURM_SUBMIT_DIR=/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar\r\nSLURMD_NODENAME=hkn0425\r\nSLURM_JOB_START_TIME=1752159082\r\nSLURM_STEP_NODELIST=hkn0425\r\nSLURM_CLUSTER_NAME=hk\r\nSLURM_JOB_END_TIME=1752195082\r\nSLURM_PMI2_SRUN_PORT=33587\r\nSLURM_CPUS_ON_NODE=24\r\nSLURM_JOB_CPUS_PER_NODE=24(x2)\r\nSLURM_GPUS_ON_NODE=4\r\nSLURM_GTIDS=0\r\nSLURM_JOB_PARTITION=accelerated\r\nSLURM_TRES_PER_TASK=cpu=5\r\nSLURM_OOM_KILL_STEP=0\r\nSLURM_JOB_NUM_NODES=2\r\nSLURM_STEPID=4294967290\r\nSLURM_JOBID=3335335\r\nSLURM_PTY_PORT=45071\r\nSLURM_JOB_QOS=normal\r\nSLURM_LAUNCH_NODE_IPADDR=10.0.7.201\r\nSLURM_PTY_WIN_ROW=48\r\nSLURM_PMI2_PROC_MAPPING=(vector,(0,1,1))\r\nSLURMD_DEBUG=2\r\nSLURM_PROCID=0\r\nSLURM_CPUS_PER_TASK=5\r\nSLURM_NTASKS=8\r\nSLURM_TOPOLOGY_ADDR=hkibb.hkibbi1.hkibbi1e13.hkn0425\r\nSLURM_TOPOLOGY_ADDR_PATTERN=switch.switch.switch.node\r\nSLURM_SRUN_COMM_HOST=10.0.7.201\r\nSLURM_SCRIPT_CONTEXT=prolog_task\r\nSLURM_PTY_WIN_COL=228\r\nSLURM_NODELIST=hkn[0425,0428]\r\nSLURM_SRUN_COMM_PORT=37803\r\nSLURM_STEP_ID=4294967290\r\nSLURM_JOB_ACCOUNT=hk-project-p0023960\r\nSLURM_PRIO_PROCESS=0\r\nSLURM_NPROCS=8\r\nSLURM_NNODES=2\r\nSLURM_SUBMIT_HOST=hkn1993.localdomain\r\nSLURM_JOB_ID=3335335\r\nSLURM_NODEID=0\r\nSLURM_STEP_NUM_NODES=1\r\nSLURM_STEP_TASKS_PER_NODE=1\r\nSLURM_MPI_TYPE=pmi2\r\nSLURM_PMI2_STEP_NODES=hkn0425\r\nSLURM_CONF=/etc/slurm/slurm.conf\r\nSLURM_JOB_NAME=interactive\r\nSLURM_NTASKS_PER_NODE=4\r\nSLURM_STEP_LAUNCHER_PORT=37803\r\nSLURM_JOB_GID=502226\r\nSLURM_JOB_NODELIST=hkn[0425,0428]\r\n",,terminal_output +3369,5904651,"TERMINAL",0,0,"GpuFreq=control_disabled\r\nGpuFreq=control_disabled\r\n",,terminal_output +3370,5909460,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",0,0,"",shellscript,tab +3371,5909460,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1072,0,"",shellscript,selection_mouse +3372,5917973,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1138,0,"",shellscript,selection_mouse +3373,5942582,"TERMINAL",0,0,"wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin\r\n",,terminal_output +3374,5943276,"TERMINAL",0,0,"wandb: Tracking run with wandb version 0.19.11\r\nwandb: Run data is saved locally in /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/wandb/run-20250710_175716-a488tl5y\r\nwandb: Run `wandb offline` to turn off syncing.\r\nwandb: Syncing run tokenizer-lr-sweep-1e-4-3335335\r\nwandb: ⭐️ View project at https://wandb.ai/instant-uv/jafar\r\nwandb: 🚀 View run at https://wandb.ai/instant-uv/jafar/runs/a488tl5y\r\n",,terminal_output +3375,6010380,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",0,0,"",shellscript,tab +3376,6010380,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",878,0,"",shellscript,selection_mouse +3377,6010920,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",911,0,"",shellscript,selection_mouse +3378,6011295,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",910,0,"",shellscript,selection_command +3379,6043413,"TERMINAL",0,0,"WARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\nWARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\n",,terminal_output +3380,6043473,"TERMINAL",0,0,"WARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\n",,terminal_output +3381,6043614,"TERMINAL",0,0,"WARNING:absl:Dropping 2 examples of 89394 examples (shard 8).\r\n",,terminal_output +3382,6043751,"TERMINAL",0,0,"Running on 8 devices.\r\nCounting all components: ['encoder', 'vq', 'decoder']\r\nParameter counts:\r\n{'encoder': 18978432, 'vq': 32768, 'decoder': 18978416, 'total': 37989616}\r\nStarting training from step 0...\r\nRunning on 8 devices.\r\nCounting all components: ['encoder', 'vq', 'decoder']\r\nParameter counts:\r\n{'encoder': 18978432, 'vq': 32768, 'decoder': 18978416, 'total': 37989616}\r\nStarting training from step 0...\r\nRunning on 8 devices.\r\nCounting all components: ['encoder', 'vq', 'decoder']\r\nParameter counts:\r\n{'encoder': 18978432, 'vq': 32768, 'decoder': 18978416, 'total': 37989616}\r\nStarting training from step 0...\r\nRunning on 8 devices.\r\nCounting all components: ['encoder', 'vq', 'decoder']\r\nParameter counts:\r\n{'encoder': 18978432, 'vq': 32768, 'decoder': 18978416, 'total': 37989616}\r\nStarting training from step 0...\r\nRunning on 8 devices.\r\nCounting all components: ['encoder', 'vq', 'decoder']\r\nParameter counts:\r\n{'encoder': 18978432, 'vq': 32768, 'decoder': 18978416, 'total': 37989616}\r\nStarting training from step 0...\r\n",,terminal_output +3383,6043859,"TERMINAL",0,0,"Running on 8 devices.\r\nCounting all components: ['encoder', 'vq', 'decoder']\r\nParameter counts:\r\n{'encoder': 18978432, 'vq': 32768, 'decoder': 18978416, 'total': 37989616}\r\nStarting training from step 0...\r\nRunning on 8 devices.\r\nCounting all components: ['encoder', 'vq', 'decoder']\r\nParameter counts:\r\n{'encoder': 18978432, 'vq': 32768, 'decoder': 18978416, 'total': 37989616}\r\nStarting training from step 0...\r\n",,terminal_output +3384,6044165,"TERMINAL",0,0,"Running on 8 devices.\r\nCounting all components: ['encoder', 'vq', 'decoder']\r\nParameter counts:\r\n{'encoder': 18978432, 'vq': 32768, 'decoder': 18978416, 'total': 37989616}\r\nStarting training from step 0...\r\n",,terminal_output +3385,6081246,"TERMINAL",0,0,"bash",,terminal_focus +3386,6082724,"TERMINAL",0,0,"python",,terminal_command +3387,6082775,"TERMINAL",0,0,"]633;E;2025-07-10 17:59:36 python;d0af5434-9a5b-4b8f-9412-c82c37ee36e4]633;C",,terminal_output +3388,6082912,"TERMINAL",0,0,"Python 3.10.18 (main, Jun 4 2025, 17:36:27) [Clang 20.1.4 ] on linux\r\nType ""help"", ""copyright"", ""credits"" or ""license"" for more information.\r\n",,terminal_output +3389,6082979,"TERMINAL",0,0,">>> ",,terminal_output +3390,6084120,"TERMINAL",0,0,"8",,terminal_output +3391,6084401,"TERMINAL",0,0,"[?25l*[?25h",,terminal_output +3392,6084777,"TERMINAL",0,0,"[?25l4[?25h",,terminal_output +3393,6085044,"TERMINAL",0,0,"\r\n32\r\n>>> ",,terminal_output +3394,6085786,"TERMINAL",0,0,"^D\r\n]0;tum_cte0515@hkn1993:~/Projects/jafar]633;D;0]633;P;Cwd=/home/hk-project-p0023960/tum_cte0515/Projects/jafar",,terminal_output +3395,6192480,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1175,0,"",shellscript,selection_mouse +3396,6192482,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1174,0,"",shellscript,selection_command +3397,6193609,"TERMINAL",0,0,"srun",,terminal_focus +3398,6223557,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",0,0,"",shellscript,tab 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+3406,6251242,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_1_nodes.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=48 \\n --min_lr=0 \\n --max_lr=1e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-batch-size-scaling-1-node-$slurm_job_id \\n --tags dynamics debug \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +3407,6252584,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_1_nodes.sbatch",1085,0,"",shellscript,selection_mouse +3408,6253437,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_1_nodes.sbatch",1062,0,"",shellscript,selection_mouse +3409,6254353,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_1_nodes.sbatch",1067,0,"\n",shellscript,content +3410,6254654,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_1_nodes.sbatch",1068,0," --save_ckpt \\n",shellscript,content +3411,6255251,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_1_nodes.sbatch",1085,1,"",shellscript,content +3412,6258676,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n",shellscript,tab +3413,6259493,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1062,0,"",shellscript,selection_mouse +3414,6260226,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1067,0,"\n",shellscript,content +3415,6260505,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1068,0," --save_ckpt \\n",shellscript,content +3416,6260934,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1085,1,"",shellscript,content +3417,6265098,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=4\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=192 \\n --min_lr=0 \\n --max_lr=2.1e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-batch-size-scaling-4-node-$slurm_job_id \\n --tags dynamics debug \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +3418,6266165,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",1084,0,"",shellscript,selection_mouse +3419,6266864,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",1063,0,"",shellscript,selection_mouse +3420,6267569,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",1062,0,"",shellscript,selection_command +3421,6267773,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",1067,0,"\n",shellscript,content +3422,6268500,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",1068,0," --save_ckpt \\n",shellscript,content +3423,6269080,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",1085,1,"",shellscript,content +3424,6273175,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-batch-size-scaling-8-node-$slurm_job_id \\n --tags dynamics debug \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +3425,6275154,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",1063,0,"",shellscript,selection_mouse +3426,6275467,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",1062,0,"",shellscript,selection_command +3427,6275911,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",1067,0,"\n",shellscript,content +3428,6276285,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",1068,0," --save_ckpt \\n",shellscript,content +3429,6276680,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",1085,1,"",shellscript,content +3430,6279238,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=16\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_16_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=768 \\n --min_lr=0 \\n --max_lr=4.2e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-batch-size-scaling-16-node-$slurm_job_id \\n --tags dynamics debug \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +3431,6280377,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch",1058,0,"",shellscript,selection_mouse +3432,6280572,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch",1057,0,"",shellscript,selection_command +3433,6281241,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch",1069,0,"\n",shellscript,content +3434,6281511,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch",1070,0," --save_ckpt \\n",shellscript,content +3435,6281905,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch",1087,1,"",shellscript,content +3436,6296483,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=dynamics_cotraining_action_space_scaling\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=6 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-cotraining-action-space-scaling-6-$slurm_job_id \\n --tags dynamics cotraining action-space-scaling 6 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +3437,6299390,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.sbatch",1113,0,"",shellscript,selection_mouse +3438,6301354,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.sbatch",1084,0,"\n",shellscript,content +3439,6301364,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.sbatch",1085,0," ",shellscript,content +3440,6302081,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.sbatch",1089,0," --save_ckpt \\n",shellscript,content +3441,6302496,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.sbatch",1106,1,"",shellscript,content +3442,6304055,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.sbatch",1094,0,"",shellscript,selection_command +3443,6304268,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.sbatch",1093,0,"",shellscript,selection_command +3444,6304494,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.sbatch",1089,4,"",shellscript,content +3445,6304686,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.sbatch",1085,4,"",shellscript,content +3446,6305237,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.sbatch",1085,0," ",shellscript,content +3447,6305983,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.sbatch",1088,0,"",shellscript,selection_command +3448,6308356,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_8_actions.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=dynamics_cotraining_action_space_scaling\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=8 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-cotraining-action-space-scaling-8-$slurm_job_id \\n --tags dynamics cotraining action-space-scaling 8 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +3449,6309196,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_8_actions.sbatch",1066,0,"",shellscript,selection_mouse +3450,6309907,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_8_actions.sbatch",1084,0,"\n --save_ckpt \",shellscript,content +3451,6309909,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_8_actions.sbatch",1089,0,"",shellscript,selection_command +3452,6312223,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_12_actions.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=dynamics_cotraining_action_space_scaling\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=12 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-cotraining-action-space-scaling-12-$slurm_job_id \\n --tags dynamics cotraining action-space-scaling 12 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +3453,6313287,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_12_actions.sbatch",1095,0,"",shellscript,selection_mouse +3454,6314005,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_12_actions.sbatch",1064,0,"",shellscript,selection_mouse +3455,6314802,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_12_actions.sbatch",1084,0,"\n --save_ckpt \",shellscript,content +3456,6314818,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_12_actions.sbatch",1089,0,"",shellscript,selection_command +3457,6316391,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_20_actions.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=dynamics_cotraining_action_space_scaling\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-cotraining-action-space-scaling-20-$slurm_job_id \\n --tags dynamics cotraining action-space-scaling 20 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +3458,6317284,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_20_actions.sbatch",1069,0,"",shellscript,selection_mouse +3459,6318005,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_20_actions.sbatch",1084,0,"\n --save_ckpt \",shellscript,content +3460,6318006,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_20_actions.sbatch",1089,0,"",shellscript,selection_command +3461,6319872,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_50_actions.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=dynamics_cotraining_action_space_scaling\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=50 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-cotraining-action-space-scaling-50-$slurm_job_id \\n --tags dynamics cotraining action-space-scaling 50 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +3462,6320776,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_50_actions.sbatch",1066,0,"",shellscript,selection_mouse +3463,6321496,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_50_actions.sbatch",1084,0,"\n --save_ckpt \",shellscript,content +3464,6321516,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_50_actions.sbatch",1089,0,"",shellscript,selection_command +3465,6394258,"TERMINAL",0,0,"bash",,terminal_focus +3466,6395085,"TERMINAL",0,0,"srun",,terminal_focus +3467,6405389,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=10:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=logs/logs_training/%x_%j.log\n#SBATCH --error=logs/logs_training/%x_%j.log\n#SBATCH --mail-user=mihir.mahajan2002@gmail.com\n#SBATCH --job-name=train_dynamics_minecraft_overfit_sample_36M\n#SBATCH --mem=50G\n#SBATCH --mail-type=ALL\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-36M-$slurm_job_id \\n --tags dynamics model-size-scaling 36M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir",shellscript,tab +3468,6409868,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1214,0,"",shellscript,selection_mouse +3469,6409869,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1213,0,"",shellscript,selection_command +3470,6410397,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1234,0,"",shellscript,selection_mouse +3471,6410399,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1233,0,"",shellscript,selection_command +3472,6411095,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1607,0,"",shellscript,selection_mouse +3473,6411111,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1606,0,"",shellscript,selection_command +3474,6660791,"TERMINAL",0,0,"Step 0, loss: 0.33292484283447266\r\nStep 1, loss: 0.33973613381385803\r\nStep 2, loss: 0.3459185063838959\r\nStep 3, loss: 0.33234596252441406\r\nStep 4, loss: 0.3411635756492615\r\nStep 5, loss: 0.3499658405780792\r\nStep 6, loss: 0.3499256670475006\r\nStep 7, loss: 0.34394073486328125\r\nStep 8, loss: 0.3490101397037506\r\nStep 9, loss: 0.3501622974872589\r\nSaved checkpoint at step 10\r\nStep 10, loss: 0.3454197645187378\r\nStep 11, loss: 0.3307967782020569\r\nStep 12, loss: 0.3328101336956024\r\nStep 13, loss: 0.34072747826576233\r\nStep 14, loss: 0.3415687382221222\r\nStep 15, loss: 0.3421204090118408\r\nStep 16, loss: 0.34998252987861633\r\nStep 17, loss: 0.3370188772678375\r\nStep 18, loss: 0.3368116021156311\r\nStep 19, loss: 0.3397890627384186\r\nSaved checkpoint at step 20\r\nStep 20, loss: 0.33846819400787354\r\nStep 21, loss: 0.328185111284256\r\nStep 22, loss: 0.33603933453559875\r\nStep 23, loss: 0.3364322781562805\r\nStep 24, loss: 0.3406607210636139\r\nStep 25, loss: 0.3376489579677582\r\nStep 26, loss: 0.3372756540775299\r\nStep 27, loss: 0.3242914080619812\r\nStep 28, loss: 0.32896149158477783\r\nStep 29, loss: 0.32680416107177734\r\nSaved checkpoint at step 30\r\nStep 30, loss: 0.31527817249298096\r\nStep 31, loss: 0.32453596591949463\r\nStep 32, loss: 0.32291722297668457\r\nStep 33, loss: 0.3086308240890503\r\nStep 34, loss: 0.3201759457588196\r\nStep 35, loss: 0.32361075282096863\r\nStep 36, loss: 0.3231964409351349\r\nStep 37, loss: 0.32022643089294434\r\nStep 38, loss: 0.31821075081825256\r\nStep 39, loss: 0.3172142505645752\r\nSaved checkpoint at step 40\r\nStep 40, loss: 0.32197457551956177\r\nStep 41, loss: 0.3097772002220154\r\nStep 42, loss: 0.3095897436141968\r\nStep 43, loss: 0.30802375078201294\r\nStep 44, loss: 0.3085505962371826\r\nStep 45, loss: 0.30728694796562195\r\nStep 46, loss: 0.30426204204559326\r\nStep 47, loss: 0.31653067469596863\r\nStep 48, loss: 0.29350337386131287\r\nStep 49, loss: 0.2900897264480591\r\nSaved checkpoint at step 50\r\nStep 50, loss: 0.3072112500667572\r\nStep 51, loss: 0.29866600036621094\r\nStep 52, loss: 0.2933558225631714\r\nStep 53, loss: 0.28990650177001953\r\nStep 54, loss: 0.2917138338088989\r\nStep 55, loss: 0.29527750611305237\r\nStep 56, loss: 0.2855745553970337\r\nStep 57, loss: 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Ctrl-C to StepId=3335335.2\r\nProcess SpawnProcess-6:\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-6:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-4:\r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-6:\r\nProcess SpawnProcess-4:\r\nProcess SpawnProcess-6:\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-1:\r\nStep 215, loss: 0.07533489167690277\r\nStep 216, loss: 0.07364197075366974\r\nStep 217, loss: 0.07628205418586731\r\nStep 218, loss: 0.08951474726200104\r\nStep 219, loss: 0.07684393227100372\r\nSaved checkpoint at step 220\r\nStep 220, loss: 0.07174445688724518\r\nStep 221, loss: 0.07478570193052292\r\nStep 222, loss: 0.07103043049573898\r\nStep 223, loss: 0.07417479902505875\r\nStep 224, loss: 0.07142274081707001\r\nStep 225, loss: 0.07241654396057129\r\nStep 226, loss: 0.07215618342161179\r\nStep 227, loss: 0.07136914879083633\r\nStep 228, loss: 0.07855178415775299\r\nStep 229, loss: 0.07575874030590057\r\nSaved checkpoint at step 230\r\nStep 230, loss: 0.07756376266479492\r\nStep 231, loss: 0.07309763133525848\r\nStep 232, loss: 0.07378087192773819\r\nStep 233, loss: 0.07816367596387863\r\nStep 234, loss: 0.07020875811576843\r\nStep 235, loss: 0.06862890720367432\r\nStep 236, loss: 0.06681060791015625\r\nStep 237, loss: 0.07396500557661057\r\nStep 238, loss: 0.06766857206821442\r\nStep 239, loss: 0.06940465420484543\r\nSaved checkpoint at step 240\r\nStep 240, loss: 0.06836851686239243\r\nStep 241, loss: 0.06678757816553116\r\nStep 242, loss: 0.0684138759970665\r\nStep 243, loss: 0.07096099853515625\r\nStep 244, loss: 0.07954933494329453\r\nStep 245, loss: 0.0722353458404541\r\nStep 246, loss: 0.06548785418272018\r\nStep 247, loss: 0.07080160826444626\r\nStep 248, loss: 0.07977914065122604\r\nStep 249, loss: 0.07421938329935074\r\nSaved checkpoint at step 250\r\nStep 250, loss: 0.06870173662900925\r\nStep 251, loss: 0.06417525559663773\r\nStep 252, loss: 0.0673845112323761\r\nStep 253, loss: 0.06332477182149887\r\nStep 254, loss: 0.0709465965628624\r\nStep 255, loss: 0.07682745158672333\r\nStep 256, loss: 0.06788769364356995\r\nStep 257, loss: 0.06887709349393845\r\nStep 258, loss: 0.07298767566680908\r\nStep 259, loss: 0.06821200996637344\r\nProcess SpawnProcess-5:\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_tokenizer.py"", line 295, in \r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-3:\r\nsrun: forcing job termination\r\nProcess SpawnProcess-6:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-4:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-4:\r\nProcess SpawnProcess-4:\r\nProcess SpawnProcess-3:\r\nStep 215, loss: 0.07533489167690277\r\nStep 216, loss: 0.07364197075366974\r\nStep 217, loss: 0.07628205418586731\r\nStep 218, loss: 0.08951474726200104\r\nStep 219, loss: 0.07684393227100372\r\nSaved checkpoint at step 220\r\nStep 220, loss: 0.07174445688724518\r\nStep 221, loss: 0.07478570193052292\r\nStep 222, loss: 0.07103043049573898\r\nStep 223, loss: 0.07417479902505875\r\nStep 224, loss: 0.07142274081707001\r\nStep 225, loss: 0.07241654396057129\r\nStep 226, loss: 0.07215618342161179\r\nStep 227, loss: 0.07136914879083633\r\nStep 228, loss: 0.07855178415775299\r\nStep 229, loss: 0.07575874030590057\r\nSaved checkpoint at step 230\r\nStep 230, loss: 0.07756376266479492\r\nStep 231, loss: 0.07309763133525848\r\nStep 232, loss: 0.07378087192773819\r\nStep 233, loss: 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0.06887709349393845\r\nStep 258, loss: 0.07298767566680908\r\nStep 259, loss: 0.06821200996637344\r\nStep 215, loss: 0.07533489167690277\r\nStep 216, loss: 0.07364197075366974\r\nStep 217, loss: 0.07628205418586731\r\nStep 218, loss: 0.08951474726200104\r\nStep 219, loss: 0.07684393227100372\r\nSaved checkpoint at step 220\r\nStep 220, loss: 0.07174445688724518\r\nStep 221, loss: 0.07478570193052292\r\nStep 222, loss: 0.07103043049573898\r\nStep 223, loss: 0.07417479902505875\r\nStep 224, loss: 0.07142274081707001\r\nStep 225, loss: 0.07241654396057129\r\nStep 226, loss: 0.07215618342161179\r\nStep 227, loss: 0.07136914879083633\r\nStep 228, loss: 0.07855178415775299\r\nStep 229, loss: 0.07575874030590057\r\nSaved checkpoint at step 230\r\nStep 230, loss: 0.07756376266479492\r\nStep 231, loss: 0.07309763133525848\r\nStep 232, loss: 0.07378087192773819\r\nStep 233, loss: 0.07816367596387863\r\nStep 234, loss: 0.07020875811576843\r\nStep 235, loss: 0.06862890720367432\r\nStep 236, loss: 0.06681060791015625\r\nStep 237, loss: 0.07396500557661057\r\nStep 238, loss: 0.06766857206821442\r\nStep 239, loss: 0.06940465420484543\r\nSaved checkpoint at step 240\r\nStep 240, loss: 0.06836851686239243\r\nProcess SpawnProcess-3:\r\nTraceback (most recent call last):\r\nStep 241, loss: 0.06678757816553116\r\nStep 242, loss: 0.0684138759970665\r\nStep 243, loss: 0.07096099853515625\r\nStep 244, loss: 0.07954933494329453\r\nStep 245, loss: 0.0722353458404541\r\nStep 246, loss: 0.06548785418272018\r\nStep 247, loss: 0.07080160826444626\r\nStep 248, loss: 0.07977914065122604\r\nStep 249, loss: 0.07421938329935074\r\nSaved checkpoint at step 250\r\nStep 250, loss: 0.06870173662900925\r\nStep 251, loss: 0.06417525559663773\r\nStep 252, loss: 0.0673845112323761\r\nStep 253, loss: 0.06332477182149887\r\nStep 254, loss: 0.0709465965628624\r\nStep 255, loss: 0.07682745158672333\r\nStep 256, loss: 0.06788769364356995\r\nStep 257, loss: 0.06887709349393845\r\nStep 258, loss: 0.07298767566680908\r\nStep 259, loss: 0.06821200996637344\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_tokenizer.py"", line 295, in \r\nProcess SpawnProcess-3:\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_tokenizer.py"", line 295, in \r\nProcess SpawnProcess-5:\r\nProcess SpawnProcess-6:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-7:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-1:\r\nProcess SpawnProcess-5:\r\nStep 215, loss: 0.07533489167690277\r\nStep 216, loss: 0.07364197075366974\r\nStep 217, loss: 0.07628205418586731\r\nStep 218, loss: 0.08951474726200104\r\nStep 219, loss: 0.07684393227100372\r\nSaved checkpoint at step 220\r\nStep 220, loss: 0.07174445688724518\r\nStep 221, loss: 0.07478570193052292\r\nStep 222, loss: 0.07103043049573898\r\nStep 223, loss: 0.07417479902505875\r\nStep 224, loss: 0.07142274081707001\r\nStep 225, loss: 0.07241654396057129\r\nStep 226, loss: 0.07215618342161179\r\nStep 227, loss: 0.07136914879083633\r\nStep 228, loss: 0.07855178415775299\r\nStep 229, loss: 0.07575874030590057\r\nSaved checkpoint at step 230\r\nStep 230, loss: 0.07756376266479492\r\nStep 231, loss: 0.07309763133525848\r\nStep 232, loss: 0.07378087192773819\r\nStep 233, loss: 0.07816367596387863\r\nStep 234, loss: 0.07020875811576843\r\nStep 235, loss: 0.06862890720367432\r\nStep 236, loss: 0.06681060791015625\r\nStep 237, loss: 0.07396500557661057\r\nStep 238, loss: 0.06766857206821442\r\nStep 239, loss: 0.06940465420484543\r\nSaved checkpoint at step 240\r\nStep 240, loss: 0.06836851686239243\r\nProcess SpawnProcess-7:\r\nStep 215, loss: 0.07533489167690277\r\nStep 216, loss: 0.07364197075366974\r\nStep 217, loss: 0.07628205418586731\r\nStep 218, loss: 0.08951474726200104\r\nStep 219, loss: 0.07684393227100372\r\nSaved checkpoint at step 220\r\nStep 220, loss: 0.07174445688724518\r\nStep 221, loss: 0.07478570193052292\r\nStep 222, loss: 0.07103043049573898\r\nStep 223, loss: 0.07417479902505875\r\nStep 224, loss: 0.07142274081707001\r\nStep 225, loss: 0.07241654396057129\r\nStep 226, loss: 0.07215618342161179\r\nStep 227, loss: 0.07136914879083633\r\nStep 228, loss: 0.07855178415775299\r\nStep 229, loss: 0.07575874030590057\r\nSaved checkpoint at step 230\r\nStep 230, loss: 0.07756376266479492\r\nStep 231, loss: 0.07309763133525848\r\nStep 232, loss: 0.07378087192773819\r\nStep 233, loss: 0.07816367596387863\r\nStep 234, loss: 0.07020875811576843\r\nStep 235, loss: 0.06862890720367432\r\nStep 236, loss: 0.06681060791015625\r\nStep 237, loss: 0.07396500557661057\r\nStep 238, loss: 0.06766857206821442\r\nStep 239, loss: 0.06940465420484543\r\nSaved checkpoint at step 240\r\nStep 240, loss: 0.06836851686239243\r\nStep 241, loss: 0.06678757816553116\r\nStep 242, loss: 0.0684138759970665\r\nStep 243, loss: 0.07096099853515625\r\nStep 244, loss: 0.07954933494329453\r\nStep 245, loss: 0.0722353458404541\r\nStep 246, loss: 0.06548785418272018\r\nStep 247, loss: 0.07080160826444626\r\nStep 248, loss: 0.07977914065122604\r\nStep 249, loss: 0.07421938329935074\r\nSaved checkpoint at step 250\r\nStep 250, loss: 0.06870173662900925\r\nStep 251, loss: 0.06417525559663773\r\nStep 252, loss: 0.0673845112323761\r\nStep 253, loss: 0.06332477182149887\r\nStep 254, loss: 0.0709465965628624\r\nStep 255, loss: 0.07682745158672333\r\nStep 256, loss: 0.06788769364356995\r\nStep 257, loss: 0.06887709349393845\r\nStep 258, loss: 0.07298767566680908\r\nStep 259, loss: 0.06821200996637344\r\nProcess SpawnProcess-6:\r\nStep 241, loss: 0.06678757816553116\r\nStep 242, loss: 0.0684138759970665\r\nStep 243, loss: 0.07096099853515625\r\nStep 244, loss: 0.07954933494329453\r\nStep 245, loss: 0.0722353458404541\r\nStep 246, loss: 0.06548785418272018\r\nStep 247, loss: 0.07080160826444626\r\nStep 248, loss: 0.07977914065122604\r\nStep 249, loss: 0.07421938329935074\r\nSaved checkpoint at step 250\r\nStep 250, loss: 0.06870173662900925\r\nStep 251, loss: 0.06417525559663773\r\nStep 252, loss: 0.0673845112323761\r\nStep 253, loss: 0.06332477182149887\r\nStep 254, loss: 0.0709465965628624\r\nStep 255, loss: 0.07682745158672333\r\nStep 256, loss: 0.06788769364356995\r\nStep 257, loss: 0.06887709349393845\r\nStep 258, loss: 0.07298767566680908\r\nStep 259, loss: 0.06821200996637344\r\nProcess SpawnProcess-4:\r\nProcess SpawnProcess-7:\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_tokenizer.py"", line 295, in \r\nProcess SpawnProcess-1:\r\nStep 215, loss: 0.07533489167690277\r\nStep 216, loss: 0.07364197075366974\r\nStep 217, loss: 0.07628205418586731\r\nStep 218, loss: 0.08951474726200104\r\nStep 219, loss: 0.07684393227100372\r\nSaved checkpoint at step 220\r\nStep 220, loss: 0.07174445688724518\r\nStep 221, loss: 0.07478570193052292\r\nStep 222, loss: 0.07103043049573898\r\nStep 223, loss: 0.07417479902505875\r\nStep 224, loss: 0.07142274081707001\r\nStep 225, loss: 0.07241654396057129\r\nStep 226, loss: 0.07215618342161179\r\nStep 227, loss: 0.07136914879083633\r\nStep 228, loss: 0.07855178415775299\r\nStep 229, loss: 0.07575874030590057\r\nSaved checkpoint at step 230\r\nStep 230, loss: 0.07756376266479492\r\nStep 231, loss: 0.07309763133525848\r\nStep 232, loss: 0.07378087192773819\r\nStep 233, loss: 0.07816367596387863\r\nStep 234, loss: 0.07020875811576843\r\nStep 235, loss: 0.06862890720367432\r\nStep 236, loss: 0.06681060791015625\r\nStep 237, loss: 0.07396500557661057\r\nStep 238, loss: 0.06766857206821442\r\nStep 239, loss: 0.06940465420484543\r\nSaved checkpoint at step 240\r\nStep 240, loss: 0.06836851686239243\r\nProcess SpawnProcess-5:\r\nStep 241, loss: 0.06678757816553116\r\nStep 242, loss: 0.0684138759970665\r\nStep 243, loss: 0.07096099853515625\r\nStep 244, loss: 0.07954933494329453\r\nStep 245, loss: 0.0722353458404541\r\nStep 246, loss: 0.06548785418272018\r\nStep 247, loss: 0.07080160826444626\r\nStep 248, loss: 0.07977914065122604\r\nStep 249, loss: 0.07421938329935074\r\nSaved checkpoint at step 250\r\nStep 250, loss: 0.06870173662900925\r\nStep 251, loss: 0.06417525559663773\r\nStep 252, loss: 0.0673845112323761\r\nStep 253, loss: 0.06332477182149887\r\nStep 254, loss: 0.0709465965628624\r\nStep 255, loss: 0.07682745158672333\r\nStep 256, loss: 0.06788769364356995\r\nStep 257, loss: 0.06887709349393845\r\nStep 258, loss: 0.07298767566680908\r\nStep 259, loss: 0.06821200996637344\r\nProcess SpawnProcess-2:\r\nProcess SpawnProcess-6:\r\nProcess SpawnProcess-4:\r\nProcess SpawnProcess-8:\r\nProcess SpawnProcess-3:\r\nProcess SpawnProcess-4:\r\nStep 215, loss: 0.07533489167690277\r\nStep 216, loss: 0.07364197075366974\r\nStep 217, loss: 0.07628205418586731\r\nStep 218, loss: 0.08951474726200104\r\nStep 219, loss: 0.07684393227100372\r\nSaved checkpoint at step 220\r\nStep 220, loss: 0.07174445688724518\r\nStep 221, loss: 0.07478570193052292\r\nStep 222, loss: 0.07103043049573898\r\nStep 223, loss: 0.07417479902505875\r\nStep 224, loss: 0.07142274081707001\r\nStep 225, loss: 0.07241654396057129\r\nStep 226, loss: 0.07215618342161179\r\nStep 227, loss: 0.07136914879083633\r\nStep 228, loss: 0.07855178415775299\r\nStep 229, loss: 0.07575874030590057\r\nSaved checkpoint at step 230\r\nStep 230, loss: 0.07756376266479492\r\nStep 231, loss: 0.07309763133525848\r\nStep 232, loss: 0.07378087192773819\r\nStep 233, loss: 0.07816367596387863\r\nStep 234, loss: 0.07020875811576843\r\nStep 235, loss: 0.06862890720367432\r\nStep 236, loss: 0.06681060791015625\r\nStep 237, loss: 0.07396500557661057\r\nStep 238, loss: 0.06766857206821442\r\nStep 239, loss: 0.06940465420484543\r\nSaved checkpoint at step 240\r\nStep 240, loss: 0.06836851686239243\r\nStep 241, loss: 0.06678757816553116\r\nStep 242, loss: 0.0684138759970665\r\nStep 243, loss: 0.07096099853515625\r\nStep 244, loss: 0.07954933494329453\r\nStep 245, loss: 0.0722353458404541\r\nStep 246, loss: 0.06548785418272018\r\nStep 247, loss: 0.07080160826444626\r\nStep 248, loss: 0.07977914065122604\r\nStep 249, loss: 0.07421938329935074\r\nSaved checkpoint at step 250\r\nStep 250, loss: 0.06870173662900925\r\nStep 251, loss: 0.06417525559663773\r\nStep 252, loss: 0.0673845112323761\r\nStep 253, loss: 0.06332477182149887\r\nStep 254, loss: 0.0709465965628624\r\nStep 255, loss: 0.07682745158672333\r\nStep 256, loss: 0.06788769364356995\r\nStep 257, loss: 0.06887709349393845\r\nStep 258, loss: 0.07298767566680908\r\nStep 259, loss: 0.06821200996637344\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_tokenizer.py"", line 295, in \r\n checkpoint_manager.save(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/checkpoint_manager.py"", line 1412, in save\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/train_tokenizer.py"", line 295, in \r\nProcess SpawnProcess-5:\r\n checkpoint_manager.save(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/checkpoint_manager.py"", line 1412, in save\r\n checkpoint_manager.save(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/checkpoint_manager.py"", line 1412, in save\r\n checkpoint_manager.save(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/checkpoint_manager.py"", line 1412, in save\r\n checkpoint_manager.save(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/checkpoint_manager.py"", line 1412, in save\r\n checkpoint_manager.save(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/orbax/checkpoint/checkpoint_manager.py"", line 1412, in save\r\nsrun: Job step aborted: Waiting up to 32 seconds for job step to finish.\r\nslurmstepd: error: *** STEP 3335335.2 ON hkn0425 CANCELLED AT 2025-07-10T18:11:01 ***\r\n",,terminal_output +3482,6767447,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3335335.2\r\nsrun: job abort in progress\r\n",,terminal_output +3483,6767645,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3335335.2\r\n",,terminal_output +3484,6767858,"TERMINAL",0,0,"^Csrun: sending Ctrl-C to StepId=3335335.2\r\nsrun: job abort in progress\r\n",,terminal_output +3485,6768545,"TERMINAL",0,0,"^C]0;tum_cte0515@hkn0425:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0425 jafar]$ ",,terminal_output +3486,6768731,"TERMINAL",0,0,"^C[?2004l\r[?2004h[?2004l\r\r\n]0;tum_cte0515@hkn0425:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0425 jafar]$ ",,terminal_output +3487,6769836,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",0,0,"",shellscript,tab +3488,6769837,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1387,0,"",shellscript,selection_mouse +3489,6770357,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1302,0,"",shellscript,selection_mouse +3490,6770359,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1301,0,"",shellscript,selection_command +3491,6770882,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1302,0,"",shellscript,selection_mouse +3492,6770883,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1301,0,"",shellscript,selection_command +3493,6771833,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1468,0,"",shellscript,selection_mouse +3494,6771834,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1467,0,"",shellscript,selection_command +3495,6772315,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1439,0,"",shellscript,selection_mouse +3496,6778159,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",0,0,"",shellscript,tab +3497,6779473,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1038,0,"",shellscript,selection_mouse +3498,6780455,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1038,0,"0",shellscript,content +3499,6780457,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1039,0,"",shellscript,selection_keyboard +3500,6780585,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1039,0,"0",shellscript,content +3501,6780586,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1040,0,"",shellscript,selection_keyboard +3502,6781195,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1072,0,"",shellscript,selection_command +3503,6782288,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1075,0,"0",shellscript,content +3504,6782289,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1076,0,"",shellscript,selection_keyboard +3505,6782423,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1076,0,"0",shellscript,content +3506,6782424,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",1077,0,"",shellscript,selection_keyboard +3507,6784309,"TERMINAL",0,0,"\r(jafar) [tum_cte0515@hkn0425 jafar]$ ",,terminal_output +3508,6790252,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",0,0,"",shellscript,tab +3509,6790380,"TERMINAL",0,0,"\r(jafar) [tum_cte0515@hkn0425 jafar]$ ",,terminal_output +3510,6818912,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",844,0,"",shellscript,selection_mouse +3511,6838586,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",823,0,"",shellscript,selection_mouse +3512,6839205,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",872,0,"",shellscript,selection_mouse +3513,6839684,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",817,0,"",shellscript,selection_mouse +3514,6839822,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",812,7,"Running",shellscript,selection_mouse +3515,6839932,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",807,65,"echo Running dynamics model overfit run. Slurm id: $slurm_job_id\n",shellscript,selection_mouse +3516,6840498,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",817,0,"",shellscript,selection_mouse +3517,6840612,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",812,7,"Running",shellscript,selection_mouse +3518,6840839,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",812,8,"Running ",shellscript,selection_mouse +3519,6840858,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",812,16,"Running dynamics",shellscript,selection_mouse +3520,6840917,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",812,17,"Running dynamics ",shellscript,selection_mouse +3521,6840936,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",812,22,"Running dynamics model",shellscript,selection_mouse +3522,6841002,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",812,30,"Running 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--num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3656,6999902,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1128,370,"ch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3657,6999917,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1127,371,"tch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3658,6999972,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1093,405,"kpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3659,7000033,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1092,406,"ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3660,7000089,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1091,407,"-ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3661,7000101,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1090,408,"--ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3662,7000162,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1071,427," --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3663,7000168,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1070,428," --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3664,7000338,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1069,429," --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3665,7000516,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1068,430," --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3666,7003289,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1498,0,"",shellscript,selection_mouse +3667,7003289,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1497,0,"",shellscript,selection_command +3668,7003468,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1497,1,"\",shellscript,selection_mouse +3669,7003469,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1497,2,"\\n",shellscript,selection_mouse +3670,7003469,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1498,0,"",shellscript,selection_command +3671,7003482,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1498,1,"\n",shellscript,selection_mouse +3672,7003546,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1498,0,"",shellscript,selection_mouse +3673,7003546,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1497,0,"",shellscript,selection_command +3674,7003554,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1443,54,"okenizer_ckpt_dir \\n --data_dir $array_records_dir ",shellscript,selection_mouse +3675,7003557,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1443,55,"okenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_command +3676,7003576,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1313,185,"lorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3677,7003591,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1132,366,"ize=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3678,7003620,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",671,827,"job_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3679,7003636,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",461,1037,"module unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3680,7003651,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",453,1045,"cat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3681,7003667,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",428,1070,"\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3682,7003869,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",429,1069,"# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3683,7003895,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",453,1045,"cat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3684,7003953,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",460,1038,"\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3685,7004127,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",453,1045,"cat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3686,7004382,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",460,1038,"\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3687,7004442,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",461,1037,"module unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \",shellscript,selection_mouse +3688,7006194,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",500,0,"",shellscript,selection_mouse +3689,7006899,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1499,0,"",shellscript,selection_mouse +3690,7007054,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1498,1,"\n",shellscript,selection_mouse +3691,7007073,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1391,108,"\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n",shellscript,selection_mouse +3692,7007096,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1106,393,"KPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n",shellscript,selection_mouse +3693,7007110,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",463,1036,"dule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n",shellscript,selection_mouse +3694,7007124,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",269,1230,"#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n",shellscript,selection_mouse +3695,7007139,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",123,1376,"#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n",shellscript,selection_mouse +3696,7007155,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",91,1408,"#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n",shellscript,selection_mouse +3697,7007209,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",67,1432,"#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n",shellscript,selection_mouse +3698,7007210,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",39,1460,"#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n",shellscript,selection_mouse +3699,7007272,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",21,1478,"#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n",shellscript,selection_mouse +3700,7007273,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",20,1479,"\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n",shellscript,selection_mouse +3701,7007274,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",0,1499,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n",shellscript,selection_mouse +3702,7009988,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",0,0,"",shellscript,tab +3703,7011164,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1596,0,"",shellscript,selection_mouse +3704,7011166,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1595,0,"",shellscript,selection_command +3705,7011280,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1595,1,"r",shellscript,selection_mouse +3706,7011281,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1596,0,"",shellscript,selection_command +3707,7011339,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1241,355,"s=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-36M-$slurm_job_id \\n --tags dynamics model-size-scaling 36M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir",shellscript,selection_mouse +3708,7011346,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",807,789,"echo Training dynamics model. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=1.5e-5 \\n --max_lr=0 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-36M-$slurm_job_id \\n --tags dynamics model-size-scaling 36M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir",shellscript,selection_mouse +3709,7011360,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",651,945,"job_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Training dynamics model. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=1.5e-5 \\n --max_lr=0 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-36M-$slurm_job_id \\n --tags dynamics model-size-scaling 36M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir",shellscript,selection_mouse +3710,7011416,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",507,1089,"source .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Training dynamics model. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=1.5e-5 \\n --max_lr=0 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-36M-$slurm_job_id \\n --tags dynamics model-size-scaling 36M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir",shellscript,selection_mouse +3711,7011416,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",477,1119,"module unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Training dynamics model. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=1.5e-5 \\n --max_lr=0 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-36M-$slurm_job_id \\n --tags dynamics model-size-scaling 36M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir",shellscript,selection_mouse +3712,7011417,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",447,1149,"module unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Training dynamics model. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=1.5e-5 \\n --max_lr=0 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-36M-$slurm_job_id \\n --tags dynamics model-size-scaling 36M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir",shellscript,selection_mouse +3713,7011425,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",446,1150,"\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Training dynamics model. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=1.5e-5 \\n --max_lr=0 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-36M-$slurm_job_id \\n --tags dynamics model-size-scaling 36M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir",shellscript,selection_mouse +3714,7011482,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",439,1157,"cat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Training dynamics model. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=1.5e-5 \\n --max_lr=0 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-36M-$slurm_job_id \\n --tags dynamics model-size-scaling 36M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir",shellscript,selection_mouse +3715,7011483,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",415,1181,"# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Training dynamics model. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=1.5e-5 \\n --max_lr=0 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-36M-$slurm_job_id \\n --tags dynamics model-size-scaling 36M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir",shellscript,selection_mouse +3716,7011498,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",414,1182,"\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Training dynamics model. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=1.5e-5 \\n --max_lr=0 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-36M-$slurm_job_id \\n --tags dynamics model-size-scaling 36M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir",shellscript,selection_mouse +3717,7011512,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",372,1224,"#SBATCH --mem=50G\n#SBATCH --mail-type=ALL\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Training dynamics model. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=1.5e-5 \\n --max_lr=0 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-36M-$slurm_job_id \\n --tags dynamics model-size-scaling 36M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir",shellscript,selection_mouse +3718,7011569,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",309,1287,"#SBATCH --job-name=train_dynamics_minecraft_overfit_sample_36M\n#SBATCH --mem=50G\n#SBATCH --mail-type=ALL\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Training dynamics model. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=1.5e-5 \\n --max_lr=0 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-36M-$slurm_job_id \\n --tags dynamics model-size-scaling 36M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir",shellscript,selection_mouse +3719,7011570,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",216,1380,"#SBATCH --error=logs/logs_training/%x_%j.log\n#SBATCH --mail-user=mihir.mahajan2002@gmail.com\n#SBATCH --job-name=train_dynamics_minecraft_overfit_sample_36M\n#SBATCH --mem=50G\n#SBATCH --mail-type=ALL\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Training dynamics model. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=1.5e-5 \\n --max_lr=0 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-36M-$slurm_job_id \\n --tags dynamics model-size-scaling 36M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir",shellscript,selection_mouse +3720,7011570,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",170,1426,"#SBATCH --output=logs/logs_training/%x_%j.log\n#SBATCH --error=logs/logs_training/%x_%j.log\n#SBATCH --mail-user=mihir.mahajan2002@gmail.com\n#SBATCH --job-name=train_dynamics_minecraft_overfit_sample_36M\n#SBATCH --mem=50G\n#SBATCH --mail-type=ALL\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Training dynamics model. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=1.5e-5 \\n --max_lr=0 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-36M-$slurm_job_id \\n --tags dynamics model-size-scaling 36M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir",shellscript,selection_mouse +3721,7011578,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",123,1473,"#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=logs/logs_training/%x_%j.log\n#SBATCH --error=logs/logs_training/%x_%j.log\n#SBATCH --mail-user=mihir.mahajan2002@gmail.com\n#SBATCH --job-name=train_dynamics_minecraft_overfit_sample_36M\n#SBATCH --mem=50G\n#SBATCH --mail-type=ALL\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Training dynamics model. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=1.5e-5 \\n --max_lr=0 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-36M-$slurm_job_id \\n --tags dynamics model-size-scaling 36M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir",shellscript,selection_mouse +3722,7011596,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",91,1505,"#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=logs/logs_training/%x_%j.log\n#SBATCH --error=logs/logs_training/%x_%j.log\n#SBATCH --mail-user=mihir.mahajan2002@gmail.com\n#SBATCH --job-name=train_dynamics_minecraft_overfit_sample_36M\n#SBATCH --mem=50G\n#SBATCH --mail-type=ALL\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Training dynamics model. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=1.5e-5 \\n --max_lr=0 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-36M-$slurm_job_id \\n --tags dynamics model-size-scaling 36M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir",shellscript,selection_mouse +3723,7011656,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",39,1557,"#SBATCH --ntasks-per-node=1\n#SBATCH --time=10:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=logs/logs_training/%x_%j.log\n#SBATCH --error=logs/logs_training/%x_%j.log\n#SBATCH --mail-user=mihir.mahajan2002@gmail.com\n#SBATCH --job-name=train_dynamics_minecraft_overfit_sample_36M\n#SBATCH --mem=50G\n#SBATCH --mail-type=ALL\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Training dynamics model. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=1.5e-5 \\n --max_lr=0 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-36M-$slurm_job_id \\n --tags dynamics model-size-scaling 36M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir",shellscript,selection_mouse +3724,7013446,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",39,1557,"",shellscript,content +3725,7013474,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",21,0,"",shellscript,selection_command +3726,7013629,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",20,0,"",shellscript,selection_command +3727,7013759,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",0,0,"",shellscript,selection_command +3728,7014112,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",0,21,"",shellscript,content +3729,7014291,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",18,0,"",shellscript,selection_command +3730,7014774,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",0,18,"",shellscript,content +3731,7017947,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n",shellscript,content +3732,7019824,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1442,57,"tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n",shellscript,selection_mouse +3733,7019839,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1133,366,"ze=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n",shellscript,selection_mouse +3734,7019858,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",461,1038,"module unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n",shellscript,selection_mouse +3735,7019869,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",453,1046,"cat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n",shellscript,selection_mouse +3736,7019895,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",39,1460,"#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n",shellscript,selection_mouse +3737,7019911,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",0,1499,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=36:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-yolorun-$slurm_job_id \\n --tags dynamics yolo-run \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n",shellscript,selection_mouse +3738,7021049,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",0,0,"",shellscript,selection_command +3739,7042245,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",367,0,"#SBATCH --job-name=train_dynamics_modelsize_scaling_36M_2_node\n",shellscript,content +3740,7042247,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",430,61,"",shellscript,content +3741,7043856,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1293,0," --name=dynamics-modelsize-scaling-36M-$slurm_job_id \\n",shellscript,content +3742,7043878,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1351,0," --tags dynamics modelsize-scaling 36M \\n",shellscript,content +3743,7043936,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1395,75,"",shellscript,content +3744,7046396,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1247,0,"",shellscript,selection_mouse +3745,7047261,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1246,0,"",shellscript,selection_command +3746,7047715,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1226,30,"",shellscript,content +3747,7047730,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1230,0,"",shellscript,selection_command +3748,7050318,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_1_nodes.sbatch",0,0,"",shellscript,tab +3749,7055989,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",0,0,"",shellscript,tab +3750,7064482,"TERMINAL",0,0,"\r(jafar) [tum_cte0515@hkn0425 jafar]$ ",,terminal_output +3751,7066489,"TERMINAL",0,0,"\r(jafar) [tum_cte0515@hkn0425 jafar]$ ",,terminal_output +3752,7067386,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1213,0,"",shellscript,selection_mouse +3753,7067388,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1212,0,"",shellscript,selection_command +3754,7068194,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1497,0,"",shellscript,selection_mouse +3755,7068195,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1496,0,"",shellscript,selection_command +3756,7078922,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=10:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=logs/logs_training/%x_%j.log\n#SBATCH --error=logs/logs_training/%x_%j.log\n#SBATCH --mail-user=mihir.mahajan2002@gmail.com\n#SBATCH --job-name=train_dynamics_minecraft_overfit_sample_110M\n#SBATCH --mem=50G\n#SBATCH --mail-type=ALL\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-110M-$slurm_job_id \\n --tags dynamics model-size-scaling 110M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=768 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=12\n",shellscript,tab +3757,7080504,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",390,0,"",shellscript,selection_mouse +3758,7080505,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",389,0,"",shellscript,selection_command +3759,7081410,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",373,18,"",shellscript,content +3760,7083116,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",0,0,"",shellscript,tab +3761,7083915,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",0,0,"",shellscript,tab +3762,7091723,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1594,0,"",shellscript,selection_mouse +3763,7091728,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1593,0,"",shellscript,selection_command +3764,7092612,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1376,0,"",shellscript,selection_mouse +3765,7093317,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1318,0,"",shellscript,selection_mouse +3766,7094211,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",0,0,"",shellscript,tab +3767,7096917,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",0,0,"",shellscript,tab +3768,7099630,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",0,0,"",shellscript,tab +3769,7105154,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",0,0,"",shellscript,tab +3770,7106275,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1667,0,"",shellscript,selection_mouse +3771,7106471,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1475,192,"\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=768 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=12\n",shellscript,selection_mouse +3772,7106492,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",988,679,"dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-110M-$slurm_job_id \\n --tags dynamics model-size-scaling 110M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=768 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=12\n",shellscript,selection_mouse +3773,7106574,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",778,889,"grep SLURM\n\necho Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-110M-$slurm_job_id \\n --tags dynamics model-size-scaling 110M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=768 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=12\n",shellscript,selection_mouse +3774,7106574,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",398,1269,"# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-110M-$slurm_job_id \\n --tags dynamics model-size-scaling 110M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=768 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=12\n",shellscript,selection_mouse +3775,7106574,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",39,1628,"#SBATCH --ntasks-per-node=1\n#SBATCH --time=10:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=logs/logs_training/%x_%j.log\n#SBATCH --error=logs/logs_training/%x_%j.log\n#SBATCH --mail-user=mihir.mahajan2002@gmail.com\n#SBATCH --job-name=train_dynamics_minecraft_overfit_sample_110M\n#SBATCH --mail-type=ALL\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-110M-$slurm_job_id \\n --tags dynamics model-size-scaling 110M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=768 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=12\n",shellscript,selection_mouse +3776,7106575,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",0,1667,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=10:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=logs/logs_training/%x_%j.log\n#SBATCH --error=logs/logs_training/%x_%j.log\n#SBATCH --mail-user=mihir.mahajan2002@gmail.com\n#SBATCH --job-name=train_dynamics_minecraft_overfit_sample_110M\n#SBATCH --mail-type=ALL\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-110M-$slurm_job_id \\n --tags dynamics model-size-scaling 110M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=768 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=12\n",shellscript,selection_mouse +3777,7108178,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",0,0,"",shellscript,selection_command +3778,7126299,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",21,0,"#SBATCH --nodes=2\n",shellscript,content +3779,7126319,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",39,0,"#SBATCH --ntasks-per-node=4\n",shellscript,content +3780,7126375,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",67,0,"#SBATCH --time=36:00:00\n",shellscript,content +3781,7126406,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",91,0,"#SBATCH --partition=accelerated\n",shellscript,content +3782,7126462,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",123,102,"",shellscript,content +3783,7126463,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",149,0,"#SBATCH --gres=gpu:4\n",shellscript,content +3784,7126759,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",170,0,"#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n",shellscript,content +3785,7126873,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",269,0,"#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n",shellscript,content +3786,7127080,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",367,0,"#SBATCH --job-name=train_dynamics_modelsize_scaling_110M_2_node\n",shellscript,content +3787,7127081,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",431,0,"\n",shellscript,content +3788,7127092,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",432,0,"# Log the sbatch script\n",shellscript,content +3789,7127149,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",432,273,"",shellscript,content +3790,7127522,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",551,0,"array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n",shellscript,content +3791,7127523,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",673,116,"",shellscript,content +3792,7127752,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",727,0,"CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\n",shellscript,content +3793,7127791,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",839,59,"",shellscript,content +3794,7128080,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",865,0,"tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n",shellscript,content +3795,7128081,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1020,17,"",shellscript,content +3796,7128121,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1021,0,"env | grep SLURM\n\n",shellscript,content +3797,7128122,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1039,0,"srun python train_dynamics.py \\n",shellscript,content +3798,7128161,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1071,0," --save_ckpt \\n",shellscript,content +3799,7128263,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1089,334,"",shellscript,content +3800,7128337,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1122,0," --batch_size=96 \\n",shellscript,content +3801,7128356,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1144,0," --min_lr=0 \\n",shellscript,content +3802,7128504,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1161,0," --max_lr=1.5e-5 \\n",shellscript,content +3803,7128535,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1183,0," --log_image_interval=1000 \\n",shellscript,content +3804,7128591,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1215,0," --log \\n",shellscript,content +3805,7128592,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1227,0," --log_checkpoint_interval=1000 \\n",shellscript,content +3806,7128658,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1264,0," --name=dynamics-modelsize-scaling-110M-$slurm_job_id \\n",shellscript,content +3807,7128760,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1323,0," --tags dynamics modelsize-scaling 110M \\n",shellscript,content +3808,7128809,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1368,0," --entity instant-uv \\n",shellscript,content +3809,7128855,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1394,0," --project jafar \\n",shellscript,content +3810,7128967,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1416,319,"",shellscript,content +3811,7129097,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1465,0," --data_dir $array_records_dir \\n",shellscript,content +3812,7129097,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1501,142,"",shellscript,content +3813,7145047,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1465,35," --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=768 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=12",shellscript,content +3814,7145047,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1227,140," --name=dynamics-model-size-scaling-110M-$slurm_job_id \\n --tags dynamics model-size-scaling 110M \\n --log_checkpoint_interval=500 \",shellscript,content +3815,7145048,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1122,92," --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \",shellscript,content +3816,7145048,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1071,18,"",shellscript,content +3817,7145048,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1039,0,"echo Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\n",shellscript,content +3818,7145048,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",865,156,"",shellscript,content +3819,7145048,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",727,111,"CHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id",shellscript,content +3820,7145048,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",551,121,"tf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'",shellscript,content +3821,7145048,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",149,281,"#SBATCH --gres=gpu:1\n#SBATCH --output=logs/logs_training/%x_%j.log\n#SBATCH --error=logs/logs_training/%x_%j.log\n#SBATCH --mail-user=mihir.mahajan2002@gmail.com\n#SBATCH --job-name=train_dynamics_minecraft_overfit_sample_110M\n#SBATCH --mail-type=ALL",shellscript,content +3822,7145048,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",21,69,"#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=10:00:00",shellscript,content +3823,7168303,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",21,0,"#SBATCH --nodes=2\n",shellscript,content +3824,7168330,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",39,0,"#SBATCH --ntasks-per-node=4\n",shellscript,content +3825,7168388,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",67,0,"#SBATCH --time=36:00:00\n",shellscript,content +3826,7168401,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",91,0,"#SBATCH 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\\n --data_dir $array_records_dir \\n --dyna_dim=768 \\n",shellscript,selection_mouse +3899,7444059,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1072,477," --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-modelsize-scaling-110M-$slurm_job_id \\n --tags dynamics modelsize-scaling 110M \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n --dyna_dim=768 \\n --dyna_num_blocks=16 \\n",shellscript,selection_mouse +3900,7444142,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1072,501," --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-modelsize-scaling-110M-$slurm_job_id \\n --tags dynamics modelsize-scaling 110M \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n --dyna_dim=768 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=12\n",shellscript,selection_mouse +3901,7445365,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1573,0,"",shellscript,selection_mouse +3902,7448267,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",915,0,"",shellscript,selection_mouse +3903,7449036,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1458,0,"",shellscript,selection_mouse +3904,7454524,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=10:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=logs/logs_training/%x_%j.log\n#SBATCH --error=logs/logs_training/%x_%j.log\n#SBATCH --mail-user=mihir.mahajan2002@gmail.com\n#SBATCH --job-name=train_dynamics_minecraft_overfit_sample_180M\n#SBATCH --mem=50G\n#SBATCH --mail-type=ALL\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-180M-$slurm_job_id \\n --tags dynamics model-size-scaling 180M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=1024 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=16\n",shellscript,tab +3905,7455920,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1686,0,"",shellscript,selection_mouse +3906,7456109,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1209,477,"5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-180M-$slurm_job_id \\n --tags dynamics model-size-scaling 180M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=1024 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=16\n",shellscript,selection_mouse +3907,7456131,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",808,878,"echo Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-180M-$slurm_job_id \\n --tags dynamics model-size-scaling 180M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=1024 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=16\n",shellscript,selection_mouse +3908,7456163,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",651,1035,"\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-180M-$slurm_job_id \\n --tags dynamics model-size-scaling 180M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=1024 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=16\n",shellscript,selection_mouse +3909,7456179,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",415,1271,"\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-180M-$slurm_job_id \\n --tags dynamics model-size-scaling 180M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=1024 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=16\n",shellscript,selection_mouse +3910,7456192,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",67,1619,"#SBATCH --time=10:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=logs/logs_training/%x_%j.log\n#SBATCH --error=logs/logs_training/%x_%j.log\n#SBATCH --mail-user=mihir.mahajan2002@gmail.com\n#SBATCH --job-name=train_dynamics_minecraft_overfit_sample_180M\n#SBATCH --mem=50G\n#SBATCH --mail-type=ALL\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-180M-$slurm_job_id \\n --tags dynamics model-size-scaling 180M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=1024 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=16\n",shellscript,selection_mouse +3911,7456204,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",0,1686,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=10:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=logs/logs_training/%x_%j.log\n#SBATCH --error=logs/logs_training/%x_%j.log\n#SBATCH --mail-user=mihir.mahajan2002@gmail.com\n#SBATCH --job-name=train_dynamics_minecraft_overfit_sample_180M\n#SBATCH --mem=50G\n#SBATCH --mail-type=ALL\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-180M-$slurm_job_id \\n --tags dynamics model-size-scaling 180M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=1024 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=16\n",shellscript,selection_mouse +3912,7458809,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",0,0,"",shellscript,selection_command +3913,7475044,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",21,0,"#SBATCH --nodes=2\n",shellscript,content +3914,7475116,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",39,0,"#SBATCH --ntasks-per-node=4\n",shellscript,content +3915,7475131,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",67,0,"#SBATCH --time=36:00:00\n",shellscript,content +3916,7475164,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",91,0,"#SBATCH --partition=accelerated\n",shellscript,content +3917,7475220,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",123,102,"",shellscript,content +3918,7475226,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",149,0,"#SBATCH --gres=gpu:4\n",shellscript,content +3919,7475442,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",170,0,"#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n",shellscript,content +3920,7475621,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",269,0,"#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n",shellscript,content +3921,7475744,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",367,0,"#SBATCH --job-name=train_dynamics_modelsize_scaling_180M_2_node\n",shellscript,content +3922,7475745,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",431,0,"\n",shellscript,content +3923,7475762,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",432,0,"# Log the sbatch script\n",shellscript,content +3924,7475823,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",432,291,"",shellscript,content +3925,7476137,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",551,0,"array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n",shellscript,content +3926,7476138,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",673,116,"",shellscript,content +3927,7476453,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",727,0,"CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\n",shellscript,content +3928,7476472,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",839,59,"",shellscript,content +3929,7476901,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",865,0,"tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n",shellscript,content +3930,7476901,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1020,17,"",shellscript,content +3931,7476948,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1021,0,"env | grep SLURM\n\n",shellscript,content +3932,7476981,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1039,0,"srun python train_dynamics.py \\n",shellscript,content +3933,7476982,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1071,0," --save_ckpt \\n",shellscript,content +3934,7477059,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1089,334,"",shellscript,content +3935,7477104,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1122,0," --batch_size=96 \\n",shellscript,content +3936,7477168,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1144,0," --min_lr=0 \\n",shellscript,content +3937,7477226,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1161,0," --max_lr=1.5e-5 \\n",shellscript,content +3938,7477372,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1183,0," --log_image_interval=1000 \\n",shellscript,content +3939,7477387,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1215,0," --log \\n",shellscript,content +3940,7477421,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1227,0," --log_checkpoint_interval=1000 \\n",shellscript,content +3941,7477490,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1264,0," --name=dynamics-modelsize-scaling-180M-$slurm_job_id \\n",shellscript,content +3942,7477548,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1323,0," --tags dynamics modelsize-scaling 180M \\n",shellscript,content +3943,7477616,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1368,0," --entity instant-uv \\n",shellscript,content +3944,7477624,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1394,0," --project jafar \\n",shellscript,content +3945,7477683,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1416,319,"",shellscript,content +3946,7477787,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1465,0," --data_dir $array_records_dir \\n",shellscript,content +3947,7477804,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1501,70,"",shellscript,content +3948,7493303,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=10:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=logs/logs_training/%x_%j.log\n#SBATCH --error=logs/logs_training/%x_%j.log\n#SBATCH --mail-user=mihir.mahajan2002@gmail.com\n#SBATCH --job-name=train_dynamics_minecraft_overfit_sample_270M\n#SBATCH --mem=50G\n#SBATCH --mail-type=ALL\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-270M-$slurm_job_id \\n --tags dynamics model-size-scaling 270M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=1024 \\n --dyna_num_blocks=24 \\n --dyna_num_heads=16\n",shellscript,tab +3949,7494794,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",1686,0,"",shellscript,selection_mouse +3950,7494922,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",1655,31,"s=24 \\n --dyna_num_heads=16\n",shellscript,selection_mouse +3951,7494943,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",813,873,"Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-270M-$slurm_job_id \\n --tags dynamics model-size-scaling 270M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=1024 \\n --dyna_num_blocks=24 \\n --dyna_num_heads=16\n",shellscript,selection_mouse +3952,7495000,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",651,1035,"\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-270M-$slurm_job_id \\n --tags dynamics model-size-scaling 270M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=1024 \\n --dyna_num_blocks=24 \\n --dyna_num_heads=16\n",shellscript,selection_mouse +3953,7495001,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",415,1271,"\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-270M-$slurm_job_id \\n --tags dynamics model-size-scaling 270M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=1024 \\n --dyna_num_blocks=24 \\n --dyna_num_heads=16\n",shellscript,selection_mouse +3954,7495002,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",67,1619,"#SBATCH --time=10:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=logs/logs_training/%x_%j.log\n#SBATCH --error=logs/logs_training/%x_%j.log\n#SBATCH --mail-user=mihir.mahajan2002@gmail.com\n#SBATCH --job-name=train_dynamics_minecraft_overfit_sample_270M\n#SBATCH --mem=50G\n#SBATCH --mail-type=ALL\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-270M-$slurm_job_id \\n --tags dynamics model-size-scaling 270M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=1024 \\n --dyna_num_blocks=24 \\n --dyna_num_heads=16\n",shellscript,selection_mouse +3955,7495011,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",0,1686,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=10:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=logs/logs_training/%x_%j.log\n#SBATCH --error=logs/logs_training/%x_%j.log\n#SBATCH --mail-user=mihir.mahajan2002@gmail.com\n#SBATCH --job-name=train_dynamics_minecraft_overfit_sample_270M\n#SBATCH --mem=50G\n#SBATCH --mail-type=ALL\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-270M-$slurm_job_id \\n --tags dynamics model-size-scaling 270M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=1024 \\n --dyna_num_blocks=24 \\n --dyna_num_heads=16\n",shellscript,selection_mouse +3956,7496593,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",0,0,"",shellscript,selection_command +3957,7506974,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",21,0,"#SBATCH --nodes=2\n",shellscript,content +3958,7507016,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",39,0,"#SBATCH --ntasks-per-node=4\n",shellscript,content +3959,7507032,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",67,0,"#SBATCH --time=36:00:00\n",shellscript,content +3960,7507032,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",91,0,"#SBATCH --partition=accelerated\n",shellscript,content +3961,7507063,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",123,102,"",shellscript,content +3962,7507084,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",149,0,"#SBATCH --gres=gpu:4\n",shellscript,content +3963,7507286,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",170,0,"#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n",shellscript,content +3964,7507829,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",269,0,"#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n",shellscript,content +3965,7507983,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",367,0,"#SBATCH --job-name=train_dynamics_modelsize_scaling_270M_2_node\n",shellscript,content +3966,7507984,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",431,0,"\n",shellscript,content +3967,7508080,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",432,0,"# Log the sbatch script\n",shellscript,content +3968,7508118,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",432,291,"",shellscript,content +3969,7508374,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",551,0,"array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n",shellscript,content +3970,7508375,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",673,116,"",shellscript,content +3971,7508591,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",727,0,"CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id\n",shellscript,content +3972,7508642,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",839,59,"",shellscript,content +3973,7508919,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",865,0,"tokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n",shellscript,content +3974,7508920,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",1020,17,"",shellscript,content +3975,7508990,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",1021,0,"env | grep SLURM\n\n",shellscript,content +3976,7508992,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",1039,0,"srun python train_dynamics.py \\n",shellscript,content +3977,7509017,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",1071,0," --save_ckpt \\n",shellscript,content +3978,7509043,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",1089,334,"",shellscript,content +3979,7509100,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",1122,0," --batch_size=96 \\n",shellscript,content +3980,7509245,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",1144,0," --min_lr=0 \\n",shellscript,content +3981,7509246,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",1161,0," --max_lr=1.5e-5 \\n",shellscript,content +3982,7509309,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",1183,0," --log_image_interval=1000 \\n",shellscript,content +3983,7509310,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",1215,0," --log \\n",shellscript,content +3984,7509380,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",1227,0," --log_checkpoint_interval=1000 \\n",shellscript,content +3985,7509473,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",1264,0," --name=dynamics-modelsize-scaling-270M-$slurm_job_id \\n",shellscript,content +3986,7509505,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",1323,0," --tags dynamics modelsize-scaling 270M \\n",shellscript,content +3987,7509527,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",1368,0," --entity instant-uv \\n",shellscript,content +3988,7509554,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",1394,0," --project jafar \\n",shellscript,content +3989,7509654,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",1416,319,"",shellscript,content +3990,7509677,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",1465,0," --data_dir $array_records_dir \\n",shellscript,content +3991,7509737,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",1501,70,"",shellscript,content +3992,7548377,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=10:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=logs/logs_training/%x_%j.log\n#SBATCH --error=logs/logs_training/%x_%j.log\n#SBATCH --mail-user=mihir.mahajan2002@gmail.com\n#SBATCH --job-name=train_dynamics_minecraft_overfit_sample_500M\n#SBATCH --mem=50G\n#SBATCH --mail-type=ALL\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-500M-$slurm_job_id \\n --tags dynamics model-size-scaling 500M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=1536 \\n --dyna_num_blocks=24 \\n --dyna_num_heads=24\n",shellscript,tab +3993,7549740,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",415,0,"",shellscript,selection_mouse +3994,7549857,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",414,1,"\n",shellscript,selection_mouse +3995,7549901,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",412,3,"LL\n",shellscript,selection_mouse +3996,7549924,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",409,6,"e=ALL\n",shellscript,selection_mouse +3997,7549939,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",405,10,"-type=ALL\n",shellscript,selection_mouse +3998,7549954,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",403,12,"il-type=ALL\n",shellscript,selection_mouse +3999,7549970,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",401,14,"mail-type=ALL\n",shellscript,selection_mouse +4000,7549997,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",398,17," --mail-type=ALL\n",shellscript,selection_mouse +4001,7550010,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",397,18,"H --mail-type=ALL\n",shellscript,selection_mouse +4002,7550025,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",396,19,"CH --mail-type=ALL\n",shellscript,selection_mouse +4003,7550041,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",378,37,"CH --mem=50G\n#SBATCH --mail-type=ALL\n",shellscript,selection_mouse +4004,7550098,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",377,38,"TCH --mem=50G\n#SBATCH --mail-type=ALL\n",shellscript,selection_mouse +4005,7550983,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",409,0,"",shellscript,selection_mouse +4006,7550984,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",406,4,"type",shellscript,selection_mouse +4007,7551127,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",391,24,"#SBATCH --mail-type=ALL\n",shellscript,selection_mouse +4008,7614614,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",437,0,"",shellscript,selection_mouse +4009,7616835,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",0,0,"",shellscript,tab +4010,7619170,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",0,0,"",shellscript,tab +4011,7621454,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",1686,0,"",shellscript,selection_mouse +4012,7621631,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",1658,28,"4 \\n --dyna_num_heads=24\n",shellscript,selection_mouse +4013,7621650,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",1252,434,"ps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-500M-$slurm_job_id \\n --tags dynamics model-size-scaling 500M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=1536 \\n --dyna_num_blocks=24 \\n --dyna_num_heads=24\n",shellscript,selection_mouse +4014,7621665,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",807,879,"\necho Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-500M-$slurm_job_id \\n --tags dynamics model-size-scaling 500M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=1536 \\n --dyna_num_blocks=24 \\n --dyna_num_heads=24\n",shellscript,selection_mouse +4015,7621693,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",705,981,"CHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-500M-$slurm_job_id \\n --tags dynamics model-size-scaling 500M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=1536 \\n --dyna_num_blocks=24 \\n --dyna_num_heads=24\n",shellscript,selection_mouse +4016,7621748,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",447,1239,"\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-500M-$slurm_job_id \\n --tags dynamics model-size-scaling 500M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=1536 \\n --dyna_num_blocks=24 \\n --dyna_num_heads=24\n",shellscript,selection_mouse +4017,7621749,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",149,1537,"#SBATCH --gres=gpu:1\n#SBATCH --output=logs/logs_training/%x_%j.log\n#SBATCH --error=logs/logs_training/%x_%j.log\n#SBATCH --mail-user=mihir.mahajan2002@gmail.com\n#SBATCH --job-name=train_dynamics_minecraft_overfit_sample_500M\n#SBATCH --mem=50G\n#SBATCH --mail-type=ALL\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-500M-$slurm_job_id \\n --tags dynamics model-size-scaling 500M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=1536 \\n --dyna_num_blocks=24 \\n --dyna_num_heads=24\n",shellscript,selection_mouse +4018,7621770,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",0,1686,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=10:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=logs/logs_training/%x_%j.log\n#SBATCH --error=logs/logs_training/%x_%j.log\n#SBATCH --mail-user=mihir.mahajan2002@gmail.com\n#SBATCH --job-name=train_dynamics_minecraft_overfit_sample_500M\n#SBATCH --mem=50G\n#SBATCH --mail-type=ALL\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=$ws_dir/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Running dynamics model overfit run. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-500M-$slurm_job_id \\n --tags dynamics model-size-scaling 500M \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=1536 \\n --dyna_num_blocks=24 \\n --dyna_num_heads=24\n",shellscript,selection_mouse +4019,7622826,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",0,0,"",shellscript,selection_command +4020,7626998,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",21,0,"#SBATCH --nodes=2\n",shellscript,content +4021,7627083,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",39,0,"#SBATCH --ntasks-per-node=4\n",shellscript,content +4022,7627116,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",67,0,"#SBATCH --time=36:00:00\n",shellscript,content +4023,7630153,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",21,70,"",shellscript,content +4024,7637479,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",21,0,"#SBATCH --nodes=2\n",shellscript,content +4025,7637511,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",39,0,"#SBATCH --ntasks-per-node=4\n",shellscript,content +4026,7637570,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",67,0,"#SBATCH --time=36:00:00\n",shellscript,content +4027,7637632,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",91,0,"#SBATCH --partition=accelerated\n",shellscript,content +4028,7637689,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",123,102,"",shellscript,content +4029,7637826,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",149,0,"#SBATCH --gres=gpu:4\n",shellscript,content +4030,7637939,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",170,0,"#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n",shellscript,content +4031,7638199,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",269,0,"#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n",shellscript,content +4032,7638311,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",367,0,"#SBATCH --job-name=train_dynamics_modelsize_scaling_500M_2_node\n",shellscript,content +4033,7638314,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch",0,0,"",shellscript,tab +4034,7640612,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",0,0,"",shellscript,tab +4035,7640670,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",1532,0," --data_dir $array_records_dir \\n",shellscript,content +4036,7640708,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",1568,70,"",shellscript,content +4037,7665633,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",1049,0,"",shellscript,selection_mouse +4038,7666910,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",1020,67,"",shellscript,content +4039,7669452,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",0,0,"",shellscript,tab 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slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch\n\n\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",shellscript,tab +5,4090,"TERMINAL",0,0,"/bin/python3 /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/printEnvVariablesToFile.py /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash/envVars.txt",,terminal_command +6,4106,"TERMINAL",0,0,"]633;E;2025-07-12 12:17:43 /bin/python3 /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/printEnvVariablesToFile.py /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash/envVars.txt;09e26284-9196-4e36-8df8-ec6826b3091e]633;C",,terminal_output +7,4139,"TERMINAL",0,0,"]0;tum_cte0515@hkn1991:/hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash]633;D;0",,terminal_output +8,5692,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=24\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-modelsize-scaling/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-modelsize-scaling/%x_%j.log\n#SBATCH --job-name=train_dynamics_modelsize_scaling_500M_32_node\n#SBATCH --mem=400G\n\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/$job_name\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-modelsize-scaling-500M-$slurm_job_id \\n --tags dynamics modelsize-scaling 500M \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n --dyna_dim=1536 \\n --dyna_num_blocks=24 \\n --dyna_num_heads=24\n",shellscript,tab +9,24942,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer-lr-scaling/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer-lr-scaling/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_5e-5\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/$job_name\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=5e-5 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-5e-5-$slurm_job_id \\n --tags tokenizer lr-sweep 5e-5 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,tab +10,28316,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch",0,0,"",shellscript,tab +11,29924,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer-lr-scaling/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big_run/tokenizer-lr-scaling/%x_%j.log\n#SBATCH --job-name=train_tokenizer_lr_sweep_1e-4\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/$job_name\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\nsrun python train_tokenizer.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log_checkpoint_interval=1000 \\n --log \\n --name=tokenizer-lr-sweep-1e-4-$slurm_job_id \\n --tags tokenizer lr-sweep 1e-4 \\n --entity instant-uv \\n --project jafar \\n --data_dir $array_records_dir\n",shellscript,tab +12,31579,"slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch",0,0,"",shellscript,tab +13,41023,"train_tokenizer.py",0,0,"from dataclasses import dataclass, field\nimport os\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.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\n\nfrom models.tokenizer import TokenizerVQVAE\nfrom utils.dataloader import get_dataloader\nfrom utils.parameter_utils import count_parameters_by_component\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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n vq_beta: float = 0.25\n batch_size: int = 48\n min_lr: float = 0.0\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_checkpoint_keep_period: int = 20000\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() # type: ignore\n ssim = pix.ssim(gt, recon).mean() # type: ignore\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\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\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 wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\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 videos_sharding = NamedSharding(\n mesh, PartitionSpec(""data"", None, None, None, None)\n )\n train_state = jax.device_put(train_state, replicated_sharding)\n\n # --- Initialize checkpoint manager ---\n step = 0\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add('model_state', ocp.args.StandardSave, ocp.handlers.StandardCheckpointHandler)\n handler_registry.add('model_state', ocp.args.StandardRestore, ocp.handlers.StandardCheckpointHandler)\n handler_registry.add('dataloader_state', grain.checkpoint.CheckpointSave, grain.checkpoint.CheckpointHandler) # type: ignore\n handler_registry.add('dataloader_state', grain.checkpoint.CheckpointRestore, grain.checkpoint.CheckpointHandler) # type: ignore\n \n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n \n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n\n # --- Create DataLoaderIterator from dataloader ---\n array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n \n # --- Restore checkpoint ---\n if args.restore_ckpt:\n abstract_train_state = jax.tree_util.tree_map(ocp.utils.to_shape_dtype_struct, train_state)\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.StandardRestore(abstract_train_state),\n dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator),\n )\n )\n train_state = restored[""model_state""]\n grain_iterator = restored[""dataloader_state""]\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored dataloader and model state from step {step}"")\n\n # --- TRAIN LOOP ---\n dataloader = (jax.make_array_from_process_local_data(videos_sharding, elem) for elem in grain_iterator) # type: ignore\n print(f""Starting training from step {step}..."")\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n rng, _rng, _rng_dropout = jax.random.split(rng, 3)\n\n inputs = dict(videos=videos, rng=_rng, dropout_rng=_rng_dropout)\n train_state, loss, recon, metrics = train_step(train_state, inputs)\n print(f""Step {step}, loss: {loss}"")\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 **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 # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n checkpoint_manager.save(\n step,\n args=ocp.args.Composite(\n model_state=ocp.args.StandardSave(train_state),\n dataloader_state=grain.checkpoint.CheckpointSave(grain_iterator),\n )\n )\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n checkpoint_manager.close()",python,tab +14,44707,"train_tokenizer.py",0,0,"",python,tab +15,51914,"train_tokenizer.py",835,0,"",python,selection_mouse +16,54372,"train_tokenizer.py",834,0,"",python,selection_mouse +17,55084,"train_tokenizer.py",7724,0,"",python,selection_mouse +18,55227,"train_tokenizer.py",7718,14,"grain_iterator",python,selection_mouse 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+52,246791,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_1_nodes.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-batchsize-scaling/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-batchsize-scaling/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-batchsize-scaling/$job_name\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=48 \\n --min_lr=0 \\n --max_lr=1e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-batch-size-scaling-1-node-$slurm_job_id \\n --tags dynamics batch-size-scaling 1-node \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir 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--output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-batchsize-scaling/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-batchsize-scaling/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-batchsize-scaling/$job_name\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-batch-size-scaling-2-node-$slurm_job_id \\n --tags dynamics batch-size-scaling 2-node \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n",shellscript,tab +58,254920,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1203,0,"",shellscript,selection_mouse +59,255672,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1207,0,"\n --restore_ckpt \",shellscript,content +60,255688,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch",1212,0,"",shellscript,selection_command 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.venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-batchsize-scaling/$job_name\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=192 \\n --min_lr=0 \\n --max_lr=2.1e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-batch-size-scaling-4-node-$slurm_job_id \\n --tags dynamics batch-size-scaling 4-node \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +62,258236,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",1198,0,"",shellscript,selection_mouse +63,259094,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",1207,0,"\n --restore_ckpt \",shellscript,content +64,259105,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",1212,0,"",shellscript,selection_command +65,260974,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=8\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-batchsize-scaling/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-batchsize-scaling/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-batchsize-scaling/$job_name\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=384 \\n --min_lr=0 \\n --max_lr=3e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-batch-size-scaling-8-node-$slurm_job_id \\n --tags dynamics batch-size-scaling 8-node \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +66,261823,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",1202,0,"",shellscript,selection_mouse +67,262688,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",1207,0,"\n --restore_ckpt \",shellscript,content +68,262694,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch",1212,0,"",shellscript,selection_command +69,264393,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=16\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-batchsize-scaling/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-batchsize-scaling/%x_%j.log\n#SBATCH --job-name=train_tokenizer_batch_size_scaling_16_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-batchsize-scaling/$job_name\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=768 \\n --min_lr=0 \\n --max_lr=4.2e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-batch-size-scaling-16-node-$slurm_job_id \\n --tags dynamics batch-size-scaling 16-node \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +70,265205,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch",1201,0,"",shellscript,selection_mouse +71,265989,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch",1209,0,"\n --restore_ckpt \",shellscript,content +72,266040,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch",1214,0,"",shellscript,selection_command +73,275508,"slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_1_nodes.sbatch",0,0,"",shellscript,tab +74,286629,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-actionspace-scaling/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-actionspace-scaling/%x_%j.log\n#SBATCH --job-name=dynamics_cotraining_action_space_scaling_6\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-actionspace-scaling/$job_name\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=6 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-cotraining-action-space-scaling-6-$slurm_job_id \\n --tags dynamics cotraining action-space-scaling 6 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +75,287973,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.sbatch",1243,0,"",shellscript,selection_mouse +76,289105,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.sbatch",1225,0,"",shellscript,selection_command +77,289728,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.sbatch",1232,0,"\n --restore_ckpt \",shellscript,content +78,289738,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.sbatch",1237,0,"",shellscript,selection_command +79,291407,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_8_actions.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-actionspace-scaling/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-actionspace-scaling/%x_%j.log\n#SBATCH --job-name=dynamics_cotraining_action_space_scaling_8\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-actionspace-scaling/$job_name\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=8 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-cotraining-action-space-scaling-8-$slurm_job_id \\n --tags dynamics cotraining action-space-scaling 8 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +80,292341,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_8_actions.sbatch",1224,0,"",shellscript,selection_mouse +81,293623,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_8_actions.sbatch",1232,0,"\n --restore_ckpt \",shellscript,content +82,293638,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_8_actions.sbatch",1237,0,"",shellscript,selection_command +83,295360,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_12_actions.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-actionspace-scaling/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-actionspace-scaling/%x_%j.log\n#SBATCH --job-name=dynamics_cotraining_action_space_scaling_12\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-actionspace-scaling/$job_name\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=12 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-cotraining-action-space-scaling-12-$slurm_job_id \\n --tags dynamics cotraining action-space-scaling 12 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +84,296689,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_12_actions.sbatch",1229,0,"",shellscript,selection_mouse +85,297658,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_12_actions.sbatch",1233,0,"\n --restore_ckpt \",shellscript,content +86,297659,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_12_actions.sbatch",1238,0,"",shellscript,selection_command +87,299394,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_20_actions.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-actionspace-scaling/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-actionspace-scaling/%x_%j.log\n#SBATCH --job-name=dynamics_cotraining_action_space_scaling_20\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-actionspace-scaling/$job_name\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=20 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-cotraining-action-space-scaling-20-$slurm_job_id \\n --tags dynamics cotraining action-space-scaling 20 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +88,300126,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_20_actions.sbatch",1226,0,"",shellscript,selection_mouse +89,300872,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_20_actions.sbatch",1233,0,"\n --restore_ckpt \",shellscript,content +90,300874,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_20_actions.sbatch",1238,0,"",shellscript,selection_command +91,302406,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_50_actions.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-actionspace-scaling/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-actionspace-scaling/%x_%j.log\n#SBATCH --job-name=dynamics_cotraining_action_space_scaling_50\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-actionspace-scaling/$job_name\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --num_latent_actions=50 \\n --log_checkpoint_interval=1000 \\n --name=dynamics-cotraining-action-space-scaling-50-$slurm_job_id \\n --tags dynamics cotraining action-space-scaling 50 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n",shellscript,tab +92,303120,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_50_actions.sbatch",1226,0,"",shellscript,selection_mouse +93,303905,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_50_actions.sbatch",1233,0,"\n --restore_ckpt \",shellscript,content +94,303907,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_50_actions.sbatch",1238,0,"",shellscript,selection_command +95,311575,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_20_actions.sbatch",0,0,"",shellscript,tab +96,312842,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_12_actions.sbatch",0,0,"",shellscript,tab +97,313374,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_8_actions.sbatch",0,0,"",shellscript,tab +98,313911,"slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.sbatch",0,0,"",shellscript,tab +99,322124,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-modelsize-scaling/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-modelsize-scaling/%x_%j.log\n#SBATCH --job-name=train_dynamics_modelsize_scaling_36M_2_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/$job_name\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-modelsize-scaling-36M-$slurm_job_id \\n --tags dynamics modelsize-scaling 36M \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n",shellscript,tab +100,323606,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1204,0,"",shellscript,selection_mouse +101,324324,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1209,0,"\n --restore_ckpt \",shellscript,content +102,324339,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch",1214,0,"",shellscript,selection_command +103,326959,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=6\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-modelsize-scaling/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-modelsize-scaling/%x_%j.log\n#SBATCH --job-name=train_dynamics_modelsize_scaling_110M_6_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/$job_name\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-modelsize-scaling-110M-$slurm_job_id \\n --tags dynamics modelsize-scaling 110M \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n --dyna_dim=768 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=12\n",shellscript,tab +104,328091,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1204,0,"",shellscript,selection_mouse +105,328840,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1210,0,"\n --restore_ckpt \",shellscript,content +106,328842,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch",1215,0,"",shellscript,selection_command +107,331693,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=12\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-modelsize-scaling/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/big-runs/dynamics-cotraining-modelsize-scaling/%x_%j.log\n#SBATCH --job-name=train_dynamics_modelsize_scaling_180M_12_node\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/$job_name\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_tokenizer_batch_size_scaling_16_node/3321526/tokenizer_22000/\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --min_lr=0 \\n --max_lr=1.5e-5 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-modelsize-scaling-180M-$slurm_job_id \\n --tags dynamics modelsize-scaling 180M \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir \\n --dyna_dim=1024 \\n --dyna_num_blocks=16 \\n --dyna_num_heads=16\n",shellscript,tab +108,332506,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1208,0,"",shellscript,selection_mouse +109,333255,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1212,0,"\n --restore_ckpt \",shellscript,content +110,333257,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",1217,0,"",shellscript,selection_command +111,365080,"weekend-job-starter.sh",0,0,"",shellscript,tab +112,366847,"weekend-job-starter.sh",0,0,"",shellscript,tab +113,383942,"TERMINAL",0,0,"cp weekend-job-starter.sh weekend-job-requeuer.sh",,terminal_command +114,383988,"TERMINAL",0,0,"]633;E;2025-07-12 12:24:03 cp weekend-job-starter.sh weekend-job-requeuer.sh;cd331af2-3b46-4f78-aca4-9dda848374ca]633;C",,terminal_output +115,384105,"TERMINAL",0,0,"]0;tum_cte0515@hkn1991:~/Projects/jafar]633;D;0]633;P;Cwd=/home/hk-project-p0023960/tum_cte0515/Projects/jafar",,terminal_output +116,385774,"weekend-job-requeuer.sh",0,0,"sbatch slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_12_actions.sbatch\nsbatch slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_20_actions.sbatch\nsbatch slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_50_actions.sbatch\nsbatch slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.sbatch\nsbatch slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_8_actions.sbatch\n\n\n\nsbatch slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch\nsbatch slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch\n\n\nsbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch\nsbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_1_nodes.sbatch\nsbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch\nsbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch\nsbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch\n\n\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",shellscript,tab +117,387309,"weekend-job-requeuer.sh",0,0,"",shellscript,tab +118,389212,"weekend-job-requeuer.sh",0,0,"\n",shellscript,content +119,389860,"weekend-job-requeuer.sh",0,0,"\n",shellscript,content +120,390038,"weekend-job-requeuer.sh",1,0,"\n",shellscript,content +121,391215,"weekend-job-requeuer.sh",2,0,"#",shellscript,content +122,391216,"weekend-job-requeuer.sh",3,0,"",shellscript,selection_keyboard +123,391522,"weekend-job-requeuer.sh",3,0," ",shellscript,content +124,391523,"weekend-job-requeuer.sh",4,0,"",shellscript,selection_keyboard +125,393357,"weekend-job-requeuer.sh",4,0,"a",shellscript,content +126,393359,"weekend-job-requeuer.sh",5,0,"",shellscript,selection_keyboard +127,393521,"weekend-job-requeuer.sh",5,0,"c",shellscript,content +128,393522,"weekend-job-requeuer.sh",6,0,"",shellscript,selection_keyboard +129,393745,"weekend-job-requeuer.sh",6,0,"t",shellscript,content +130,393746,"weekend-job-requeuer.sh",7,0,"",shellscript,selection_keyboard +131,393888,"weekend-job-requeuer.sh",7,0,"i",shellscript,content +132,393889,"weekend-job-requeuer.sh",8,0,"",shellscript,selection_keyboard +133,393991,"weekend-job-requeuer.sh",8,0,"o",shellscript,content +134,393992,"weekend-job-requeuer.sh",9,0,"",shellscript,selection_keyboard +135,394142,"weekend-job-requeuer.sh",9,0,"n",shellscript,content +136,394143,"weekend-job-requeuer.sh",10,0,"",shellscript,selection_keyboard +137,394239,"weekend-job-requeuer.sh",10,0,"s",shellscript,content +138,394240,"weekend-job-requeuer.sh",11,0,"",shellscript,selection_keyboard +139,394354,"weekend-job-requeuer.sh",11,0,"p",shellscript,content +140,394355,"weekend-job-requeuer.sh",12,0,"",shellscript,selection_keyboard +141,394455,"weekend-job-requeuer.sh",12,0,"a",shellscript,content +142,394456,"weekend-job-requeuer.sh",13,0,"",shellscript,selection_keyboard +143,394571,"weekend-job-requeuer.sh",13,0,"c",shellscript,content +144,394572,"weekend-job-requeuer.sh",14,0,"",shellscript,selection_keyboard +145,394688,"weekend-job-requeuer.sh",14,0,"e",shellscript,content +146,394689,"weekend-job-requeuer.sh",15,0,"",shellscript,selection_keyboard +147,394805,"weekend-job-requeuer.sh",15,0," ",shellscript,content +148,394806,"weekend-job-requeuer.sh",16,0,"",shellscript,selection_keyboard +149,395038,"weekend-job-requeuer.sh",16,0,"s",shellscript,content +150,395040,"weekend-job-requeuer.sh",17,0,"",shellscript,selection_keyboard +151,395171,"weekend-job-requeuer.sh",17,0,"c",shellscript,content +152,395172,"weekend-job-requeuer.sh",18,0,"",shellscript,selection_keyboard +153,395304,"weekend-job-requeuer.sh",18,0,"a",shellscript,content +154,395305,"weekend-job-requeuer.sh",19,0,"",shellscript,selection_keyboard +155,395404,"weekend-job-requeuer.sh",19,0,"l",shellscript,content +156,395405,"weekend-job-requeuer.sh",20,0,"",shellscript,selection_keyboard +157,395593,"weekend-job-requeuer.sh",20,0,"i",shellscript,content +158,395594,"weekend-job-requeuer.sh",21,0,"",shellscript,selection_keyboard +159,395673,"weekend-job-requeuer.sh",21,0,"n",shellscript,content +160,395674,"weekend-job-requeuer.sh",22,0,"",shellscript,selection_keyboard +161,395859,"weekend-job-requeuer.sh",22,0,"g",shellscript,content +162,395860,"weekend-job-requeuer.sh",23,0,"",shellscript,selection_keyboard +163,396488,"weekend-job-requeuer.sh",22,0,"",shellscript,selection_command +164,397405,"weekend-job-requeuer.sh",44,0,"",shellscript,selection_command +165,397554,"weekend-job-requeuer.sh",152,0,"",shellscript,selection_command +166,397704,"weekend-job-requeuer.sh",260,0,"",shellscript,selection_command +167,397839,"weekend-job-requeuer.sh",368,0,"",shellscript,selection_command +168,398005,"weekend-job-requeuer.sh",475,0,"",shellscript,selection_command +169,398154,"weekend-job-requeuer.sh",562,0,"",shellscript,selection_command +170,398293,"weekend-job-requeuer.sh",563,0,"",shellscript,selection_command +171,398422,"weekend-job-requeuer.sh",564,0,"",shellscript,selection_command +172,398754,"weekend-job-requeuer.sh",564,0,"\n# actionspace scaling",shellscript,content +173,398809,"weekend-job-requeuer.sh",565,0,"",shellscript,selection_command +174,399842,"weekend-job-requeuer.sh",586,0,"",shellscript,selection_command +175,410459,"weekend-job-requeuer.sh",578,0,"",shellscript,selection_command +176,411755,"weekend-job-requeuer.sh",579,7,"",shellscript,content +177,412004,"weekend-job-requeuer.sh",567,12,"",shellscript,content +178,412608,"weekend-job-requeuer.sh",567,0,"l",shellscript,content +179,412609,"weekend-job-requeuer.sh",568,0,"",shellscript,selection_keyboard +180,412691,"weekend-job-requeuer.sh",568,0,"r",shellscript,content +181,412692,"weekend-job-requeuer.sh",569,0,"",shellscript,selection_keyboard +182,412971,"weekend-job-requeuer.sh",569,0," ",shellscript,content +183,412972,"weekend-job-requeuer.sh",570,0,"",shellscript,selection_keyboard +184,413176,"weekend-job-requeuer.sh",570,0,"s",shellscript,content +185,413177,"weekend-job-requeuer.sh",571,0,"",shellscript,selection_keyboard +186,413608,"weekend-job-requeuer.sh",571,0,"w",shellscript,content +187,413609,"weekend-job-requeuer.sh",572,0,"",shellscript,selection_keyboard +188,413922,"weekend-job-requeuer.sh",572,0,"e",shellscript,content +189,413923,"weekend-job-requeuer.sh",573,0,"",shellscript,selection_keyboard +190,414105,"weekend-job-requeuer.sh",573,0,"e",shellscript,content +191,414105,"weekend-job-requeuer.sh",574,0,"",shellscript,selection_keyboard +192,414205,"weekend-job-requeuer.sh",574,0,"p",shellscript,content +193,414205,"weekend-job-requeuer.sh",575,0,"",shellscript,selection_keyboard +194,414605,"weekend-job-requeuer.sh",586,0,"",shellscript,selection_command +195,415126,"weekend-job-requeuer.sh",0,0,"",shellscript,selection_keyboard +196,415688,"weekend-job-requeuer.sh",1,0,"",shellscript,selection_command +197,415724,"weekend-job-requeuer.sh",2,0,"",shellscript,selection_command +198,415938,"weekend-job-requeuer.sh",24,0,"",shellscript,selection_command +199,416423,"weekend-job-requeuer.sh",132,0,"",shellscript,selection_command +200,416466,"weekend-job-requeuer.sh",240,0,"",shellscript,selection_command +201,416482,"weekend-job-requeuer.sh",348,0,"",shellscript,selection_command +202,416527,"weekend-job-requeuer.sh",455,0,"",shellscript,selection_command +203,416588,"weekend-job-requeuer.sh",562,0,"",shellscript,selection_command +204,416589,"weekend-job-requeuer.sh",563,0,"",shellscript,selection_command +205,416641,"weekend-job-requeuer.sh",564,0,"",shellscript,selection_command +206,416642,"weekend-job-requeuer.sh",565,0,"",shellscript,selection_command +207,416688,"weekend-job-requeuer.sh",576,0,"",shellscript,selection_command +208,416689,"weekend-job-requeuer.sh",658,0,"",shellscript,selection_command +209,416727,"weekend-job-requeuer.sh",740,0,"",shellscript,selection_command +210,416760,"weekend-job-requeuer.sh",741,0,"",shellscript,selection_command +211,417674,"weekend-job-requeuer.sh",741,0,"\n# actionspace scaling",shellscript,content +212,417710,"weekend-job-requeuer.sh",742,0,"",shellscript,selection_command +213,418273,"weekend-job-requeuer.sh",763,0,"",shellscript,selection_command +214,418705,"weekend-job-requeuer.sh",762,0,"",shellscript,selection_command +215,419272,"weekend-job-requeuer.sh",755,0,"",shellscript,selection_command +216,419504,"weekend-job-requeuer.sh",744,11,"",shellscript,content +217,420106,"weekend-job-requeuer.sh",744,0,"b",shellscript,content +218,420107,"weekend-job-requeuer.sh",745,0,"",shellscript,selection_keyboard +219,420221,"weekend-job-requeuer.sh",745,0,"a",shellscript,content +220,420222,"weekend-job-requeuer.sh",746,0,"",shellscript,selection_keyboard +221,420321,"weekend-job-requeuer.sh",746,0,"t",shellscript,content +222,420322,"weekend-job-requeuer.sh",747,0,"",shellscript,selection_keyboard +223,420439,"weekend-job-requeuer.sh",747,0,"c",shellscript,content +224,420439,"weekend-job-requeuer.sh",748,0,"",shellscript,selection_keyboard +225,420507,"weekend-job-requeuer.sh",748,0,"h",shellscript,content +226,420508,"weekend-job-requeuer.sh",749,0,"",shellscript,selection_keyboard +227,420708,"weekend-job-requeuer.sh",749,0,"i",shellscript,content +228,420709,"weekend-job-requeuer.sh",750,0,"",shellscript,selection_keyboard +229,420788,"weekend-job-requeuer.sh",750,0,"s",shellscript,content +230,420788,"weekend-job-requeuer.sh",751,0,"",shellscript,selection_keyboard +231,421361,"weekend-job-requeuer.sh",750,1,"",shellscript,content +232,421471,"weekend-job-requeuer.sh",749,1,"",shellscript,content +233,421609,"weekend-job-requeuer.sh",749,0,"s",shellscript,content +234,421610,"weekend-job-requeuer.sh",750,0,"",shellscript,selection_keyboard +235,421704,"weekend-job-requeuer.sh",750,0,"i",shellscript,content +236,421705,"weekend-job-requeuer.sh",751,0,"",shellscript,selection_keyboard +237,421756,"weekend-job-requeuer.sh",751,0,"z",shellscript,content +238,421756,"weekend-job-requeuer.sh",752,0,"",shellscript,selection_keyboard +239,421874,"weekend-job-requeuer.sh",752,0,"e",shellscript,content +240,421875,"weekend-job-requeuer.sh",753,0,"",shellscript,selection_keyboard +241,422238,"weekend-job-requeuer.sh",752,0,"",shellscript,selection_command +242,423210,"weekend-job-requeuer.sh",772,0,"",shellscript,selection_command +243,423374,"weekend-job-requeuer.sh",880,0,"",shellscript,selection_command +244,423504,"weekend-job-requeuer.sh",987,0,"",shellscript,selection_command 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There is NO WARRANTY, to the extent permitted\r\n by law.\r\n\r\nSEE ALSO\r\n sleep(3)\r\n\r\n Full documentation \r\n or available locally via: info '(coreutils) sleep invocation'\r\n\r\n Manual page sleep(1) line 1 (press h for help or q to quit)",,terminal_output +298,482304,"TERMINAL",0,0,"\r ESCESCOOBB\rGNU coreutils 8.32 January 2024 SLEEP(1)\r\n Manual page sleep(1) line 2 (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)\r ESCESCOOBB\r\r Manual page sleep(1) line 2/39 (END) (press h for help or q to quit)",,terminal_output +299,482839,"TERMINAL",0,0,"\r ESCESCOOAA\rMSLEEP(1) User Commands SLEEP(1)\r\n\r Manual page sleep(1) line 1/39 92% (press h for help or q to quit)\r ESCESCOOAA\r\r Manual page sleep(1) line 1/39 92% (press h for help or q to quit)\r ESCESCOOAA\r\r Manual page sleep(1) line 1/39 92% (press h for help or q to quit)\r ESCESCOOAA\r\r Manual page sleep(1) line 1/39 92% (press h for help or q to quit)\r ESCESCOOAA\r\r Manual page sleep(1) line 1/39 92% (press h for help or q to quit)\r ESCESCOOAA\r\r Manual page sleep(1) line 1/39 92% (press h for help or q to quit)\r ESCESCOOAA\r\r Manual page sleep(1) line 1/39 92% (press h for help or q to quit)\r ESCESCOOAA\r\r Manual page sleep(1) line 1/39 92% (press h for help or q to quit)\r ESCESCOOAA\r\r Manual page sleep(1) line 1/39 92% (press h for help or q to quit)\r ESCESCOOAA\r\r Manual page sleep(1) line 1/39 92% (press h for help or q to quit)\r ESCESCOOAA\r\r Manual page sleep(1) line 1/39 92% (press h for help or q to quit)\r ESCESCOOAA\r\r Manual page sleep(1) line 1/39 92% (press h for help or q to quit)\r ESCESCOOAA\r\r Manual page sleep(1) line 1/39 92% (press h for help or q to quit)\r ESCESCOOAA\r\r Manual page sleep(1) line 1/39 92% (press h for help or q to quit)",,terminal_output +300,484806,"TERMINAL",0,0,"SLEEP(1) User Commands SLEEP(1)\r\n\r\nNAME\r\n sleep - delay for a specified amount of time\r\n\r\nSYNOPSIS\r\n sleep NUMBER[SUFFIX]...\r\n sleep OPTION\r\n\r\nDESCRIPTION\r\n Pause for NUMBER seconds. 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+1153,1094390,"weekend-job-requeuer.sh",1984,0,"#",shellscript,content +1154,1094391,"weekend-job-requeuer.sh",1902,0,"#",shellscript,content +1155,1094392,"weekend-job-requeuer.sh",1903,0,"",shellscript,selection_keyboard +1156,1094470,"weekend-job-requeuer.sh",1986,0," ",shellscript,content +1157,1094470,"weekend-job-requeuer.sh",1903,0," ",shellscript,content +1158,1094471,"weekend-job-requeuer.sh",1904,0,"",shellscript,selection_keyboard +1159,1095039,"weekend-job-requeuer.sh",1903,0,"",shellscript,selection_command +1160,1095304,"weekend-job-requeuer.sh",1821,0,"",shellscript,selection_command +1161,1095456,"weekend-job-requeuer.sh",1739,0,"",shellscript,selection_command +1162,1095607,"weekend-job-requeuer.sh",1658,0,"",shellscript,selection_command +1163,1095758,"weekend-job-requeuer.sh",1649,0,"",shellscript,selection_command +1164,1095887,"weekend-job-requeuer.sh",1629,0,"",shellscript,selection_command +1165,1096038,"weekend-job-requeuer.sh",1627,0,"",shellscript,selection_command +1166,1096587,"weekend-job-requeuer.sh",1629,0,"",shellscript,selection_command +1167,1096837,"weekend-job-requeuer.sh",1627,0,"",shellscript,selection_command +1168,1097020,"weekend-job-requeuer.sh",1521,0,"",shellscript,selection_command +1169,1097153,"weekend-job-requeuer.sh",1472,0,"",shellscript,selection_command +1170,1097804,"weekend-job-requeuer.sh",1463,0,"",shellscript,selection_command +1171,1097953,"weekend-job-requeuer.sh",1356,0,"",shellscript,selection_command +1172,1098120,"weekend-job-requeuer.sh",1248,0,"",shellscript,selection_command +1173,1098292,"weekend-job-requeuer.sh",1194,0,"",shellscript,selection_command +1174,1098421,"weekend-job-requeuer.sh",1248,0,"",shellscript,selection_command +1175,1098571,"weekend-job-requeuer.sh",1356,0,"",shellscript,selection_command +1176,1098753,"weekend-job-requeuer.sh",1463,0,"",shellscript,selection_command +1177,1098853,"weekend-job-requeuer.sh",1472,0,"",shellscript,selection_command +1178,1099403,"weekend-job-requeuer.sh",1521,0,"",shellscript,selection_command +1179,1099560,"weekend-job-requeuer.sh",1627,0,"",shellscript,selection_command +1180,1099706,"weekend-job-requeuer.sh",1629,0,"",shellscript,selection_command +1181,1099853,"weekend-job-requeuer.sh",1649,0,"",shellscript,selection_command +1182,1100122,"weekend-job-requeuer.sh",1656,0,"\necho ""requeueing batchsize scaling runs: 8nodes""",shellscript,content +1183,1100123,"weekend-job-requeuer.sh",1657,0,"",shellscript,selection_command +1184,1100460,"weekend-job-requeuer.sh",1658,0,"",shellscript,selection_command +1185,1100971,"weekend-job-requeuer.sh",1659,0,"",shellscript,selection_command +1186,1101003,"weekend-job-requeuer.sh",1660,0,"",shellscript,selection_command +1187,1101020,"weekend-job-requeuer.sh",1661,0,"",shellscript,selection_command +1188,1101053,"weekend-job-requeuer.sh",1662,0,"",shellscript,selection_command +1189,1101103,"weekend-job-requeuer.sh",1663,0,"",shellscript,selection_command +1190,1101142,"weekend-job-requeuer.sh",1664,0,"",shellscript,selection_command +1191,1101143,"weekend-job-requeuer.sh",1665,0,"",shellscript,selection_command +1192,1101193,"weekend-job-requeuer.sh",1666,0,"",shellscript,selection_command +1193,1101203,"weekend-job-requeuer.sh",1667,0,"",shellscript,selection_command +1194,1101239,"weekend-job-requeuer.sh",1668,0,"",shellscript,selection_command +1195,1101298,"weekend-job-requeuer.sh",1669,0,"",shellscript,selection_command +1196,1101303,"weekend-job-requeuer.sh",1670,0,"",shellscript,selection_command +1197,1101344,"weekend-job-requeuer.sh",1671,0,"",shellscript,selection_command +1198,1101389,"weekend-job-requeuer.sh",1672,0,"",shellscript,selection_command +1199,1101503,"weekend-job-requeuer.sh",1673,0,"",shellscript,selection_command +1200,1101639,"weekend-job-requeuer.sh",1674,0,"",shellscript,selection_command +1201,1102003,"weekend-job-requeuer.sh",1674,10,"",shellscript,content +1202,1102576,"weekend-job-requeuer.sh",1674,0,"m",shellscript,content +1203,1102577,"weekend-job-requeuer.sh",1675,0,"",shellscript,selection_keyboard +1204,1102804,"weekend-job-requeuer.sh",1675,0,"o",shellscript,content +1205,1102805,"weekend-job-requeuer.sh",1676,0,"",shellscript,selection_keyboard +1206,1102954,"weekend-job-requeuer.sh",1676,0,"d",shellscript,content +1207,1102954,"weekend-job-requeuer.sh",1677,0,"",shellscript,selection_keyboard +1208,1103157,"weekend-job-requeuer.sh",1677,0,"e",shellscript,content +1209,1103158,"weekend-job-requeuer.sh",1678,0,"",shellscript,selection_keyboard +1210,1103253,"weekend-job-requeuer.sh",1678,0,"l",shellscript,content +1211,1103254,"weekend-job-requeuer.sh",1679,0,"",shellscript,selection_keyboard +1212,1103620,"weekend-job-requeuer.sh",1679,0,"s",shellscript,content +1213,1103621,"weekend-job-requeuer.sh",1680,0,"",shellscript,selection_keyboard +1214,1103670,"weekend-job-requeuer.sh",1680,0,"i",shellscript,content +1215,1103671,"weekend-job-requeuer.sh",1681,0,"",shellscript,selection_keyboard +1216,1103857,"weekend-job-requeuer.sh",1681,0,"z",shellscript,content +1217,1103857,"weekend-job-requeuer.sh",1682,0,"",shellscript,selection_keyboard +1218,1103953,"weekend-job-requeuer.sh",1682,0,"e",shellscript,content +1219,1103954,"weekend-job-requeuer.sh",1683,0,"",shellscript,selection_keyboard +1220,1104054,"weekend-job-requeuer.sh",1683,0," ",shellscript,content +1221,1104055,"weekend-job-requeuer.sh",1684,0,"",shellscript,selection_keyboard +1222,1104570,"weekend-job-requeuer.sh",1683,0,"",shellscript,selection_command +1223,1106372,"weekend-job-requeuer.sh",1696,0,"",shellscript,selection_mouse +1224,1107338,"weekend-job-requeuer.sh",1696,2,"",shellscript,content +1225,1107654,"weekend-job-requeuer.sh",1696,6,"",shellscript,content +1226,1113770,"weekend-job-requeuer.sh",2110,0,"",shellscript,selection_mouse +1227,1113772,"weekend-job-requeuer.sh",2109,0,"",shellscript,selection_command +1228,1113937,"weekend-job-requeuer.sh",2109,1,"h",shellscript,selection_mouse +1229,1113938,"weekend-job-requeuer.sh",2110,0,"",shellscript,selection_command +1230,1114020,"weekend-job-requeuer.sh",1657,453,"echo ""requeueing modelsize scaling runs""\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch\n# sbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch\n# sbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",shellscript,selection_mouse +1231,1114070,"weekend-job-requeuer.sh",1077,1033,"sbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch\nsleep 3h\necho ""requeueing batchsize scaling runs: [4,16]nodes""\nsbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch\nsbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch\nsleep 6h\necho ""requeueing batchsize scaling runs: 8nodes""\nsbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch\n\n# modelsize scaling\nsleep 2h\necho ""requeueing modelsize scaling runs""\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch\n# sbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch\n# sbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",shellscript,selection_mouse +1232,1114071,"weekend-job-requeuer.sh",456,1654,"sbatch slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.sbatch\nsbatch slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_8_actions.sbatch\n\n# lr sweep\nsleep 1h\necho ""requeueing lr sweep jobs""\nsbatch slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch\nsbatch slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch\n\n# batchsize scaling\nsleep 1h\necho ""requeueing batchsize scaling runs: [1,2]nodes""\nsbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_1_nodes.sbatch\nsbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch\nsleep 3h\necho ""requeueing batchsize scaling runs: [4,16]nodes""\nsbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch\nsbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch\nsleep 6h\necho ""requeueing batchsize scaling runs: 8nodes""\nsbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch\n\n# modelsize scaling\nsleep 2h\necho ""requeueing modelsize scaling runs""\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch\n# sbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch\n# sbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",shellscript,selection_mouse +1233,1114103,"weekend-job-requeuer.sh",0,2110,"\necho ""weekend requeuer started...""\nsleep 9h\necho ""9 hours passed""\n# actionspace scaling\necho ""requeueing actionspace scaling jobs""\nsbatch slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_12_actions.sbatch\nsbatch slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_20_actions.sbatch\nsbatch slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_50_actions.sbatch\nsbatch slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_6_actions.sbatch\nsbatch slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_8_actions.sbatch\n\n# lr sweep\nsleep 1h\necho ""requeueing lr sweep jobs""\nsbatch slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_1e-4.sbatch\nsbatch slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_5e-5.sbatch\n\n# batchsize scaling\nsleep 1h\necho ""requeueing batchsize scaling runs: [1,2]nodes""\nsbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_1_nodes.sbatch\nsbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_2_nodes.sbatch\nsleep 3h\necho ""requeueing batchsize scaling runs: [4,16]nodes""\nsbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch\nsbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch\nsleep 6h\necho ""requeueing batchsize scaling runs: 8nodes""\nsbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_8_nodes.sbatch\n\n# modelsize scaling\nsleep 2h\necho ""requeueing modelsize scaling runs""\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch\n# sbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch\n# sbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/5_train_dyn_500M.sbatch",shellscript,selection_mouse +1234,1115271,"weekend-job-requeuer.sh",0,0,"",shellscript,selection_command +1235,1125108,"weekend-job-requeuer.sh",0,0,"\n",shellscript,content +1236,1125420,"weekend-job-requeuer.sh",1,0,"echo ""[INFO] weekend requeuer started at $(date)""",shellscript,content +1237,1125471,"weekend-job-requeuer.sh",51,35,"",shellscript,content +1238,1125687,"weekend-job-requeuer.sh",60,0,"echo ""[INFO] 9 hours passed at $(date)""\n",shellscript,content +1239,1125688,"weekend-job-requeuer.sh",100,0,"\n",shellscript,content +1240,1125720,"weekend-job-requeuer.sh",101,22,"",shellscript,content +1241,1126037,"weekend-job-requeuer.sh",123,0,"echo ""[INFO] Requeueing actionspace scaling jobs at $(date)""\n",shellscript,content +1242,1126170,"weekend-job-requeuer.sh",184,0,"for job in 12 20 50 6 8; do\n",shellscript,content +1243,1126620,"weekend-job-requeuer.sh",212,0," echo ""[INFO] Submitting actionspace scaling job: train_dynamics_cotraining_${job}_actions.sbatch""\n",shellscript,content +1244,1127187,"weekend-job-requeuer.sh",314,0," sbatch slurm/jobs/mihir/horeka/action_space_scaling/co_training/train_dynamics_cotraining_${job}_actions.sbatch\n",shellscript,content +1245,1127225,"weekend-job-requeuer.sh",430,0,"done\n",shellscript,content +1246,1127270,"weekend-job-requeuer.sh",435,0,"\n",shellscript,content +1247,1127271,"weekend-job-requeuer.sh",436,0,"# lr sweep\n",shellscript,content +1248,1127320,"weekend-job-requeuer.sh",436,593,"",shellscript,content +1249,1127653,"weekend-job-requeuer.sh",456,0,"echo ""[INFO] Requeueing lr sweep jobs at $(date)""\n",shellscript,content +1250,1127893,"weekend-job-requeuer.sh",506,0,"for lr in 1e-4 5e-5; do\n",shellscript,content +1251,1128771,"weekend-job-requeuer.sh",530,0," echo ""[INFO] Submitting lr sweep job: train_tokenizer_lr_${lr}.sbatch""\n",shellscript,content +1252,1129138,"weekend-job-requeuer.sh",605,0," sbatch slurm/jobs/mihir/horeka/lr_tuning/tokenizer/train_tokenizer_lr_${lr}.sbatch\n",shellscript,content +1253,1129254,"weekend-job-requeuer.sh",692,0,"done\n",shellscript,content +1254,1129255,"weekend-job-requeuer.sh",697,0,"\n",shellscript,content +1255,1129271,"weekend-job-requeuer.sh",698,197,"",shellscript,content +1256,1129704,"weekend-job-requeuer.sh",727,0,"echo ""[INFO] Requeueing batchsize scaling runs: [1,2]nodes at $(date)""\n",shellscript,content +1257,1129803,"weekend-job-requeuer.sh",798,0,"for nodes in 1 2; do\n",shellscript,content +1258,1130559,"weekend-job-requeuer.sh",819,0," echo ""[INFO] Submitting batchsize scaling job: train_dynamics_${nodes}_nodes.sbatch""\n",shellscript,content +1259,1133620,"weekend-job-requeuer.sh",908,0," sbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_${nodes}_nodes.sbatch\n",shellscript,content +1260,1133654,"weekend-job-requeuer.sh",1026,0,"done\n",shellscript,content +1261,1133655,"weekend-job-requeuer.sh",1031,0,"\n",shellscript,content +1262,1133688,"weekend-job-requeuer.sh",1032,0,"sleep 3h\n",shellscript,content +1263,1134287,"weekend-job-requeuer.sh",1041,0,"echo ""[INFO] Requeueing batchsize scaling runs: [4,16]nodes at $(date)""\n",shellscript,content +1264,1134389,"weekend-job-requeuer.sh",1113,0,"for nodes in 16 4; do\n",shellscript,content +1265,1134856,"weekend-job-requeuer.sh",1135,0," echo ""[INFO] Submitting batchsize scaling job: train_dynamics_${nodes}_nodes.sbatch""\n",shellscript,content +1266,1135737,"weekend-job-requeuer.sh",1224,0," sbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_${nodes}_nodes.sbatch\n",shellscript,content +1267,1135738,"weekend-job-requeuer.sh",1342,0,"done\n",shellscript,content +1268,1135739,"weekend-job-requeuer.sh",1347,0,"\n",shellscript,content +1269,1135753,"weekend-job-requeuer.sh",1348,0,"sleep 6h\n",shellscript,content +1270,1136005,"weekend-job-requeuer.sh",1357,0,"echo ""[INFO] Requeueing batchsize scaling runs: 8nodes at $(date)""\n",shellscript,content +1271,1136403,"weekend-job-requeuer.sh",1424,0,"echo ""[INFO] Submitting batchsize scaling job: train_dynamics_8_nodes.sbatch""\n",shellscript,content +1272,1136936,"weekend-job-requeuer.sh",1502,603,"",shellscript,content +1273,1137553,"weekend-job-requeuer.sh",1639,0,"echo ""[INFO] Requeueing modelsize scaling runs at $(date)""\n",shellscript,content +1274,1137890,"weekend-job-requeuer.sh",1698,0,"for model in 1_train_dyn_36M 2_train_dyn_110M 3_train_dyn_180M; do\n",shellscript,content +1275,1138192,"weekend-job-requeuer.sh",1765,0," echo ""[INFO] Submitting modelsize scaling job: ${model}.sbatch""\n",shellscript,content +1276,1138537,"weekend-job-requeuer.sh",1833,0," sbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/${model}.sbatch\n",shellscript,content +1277,1138538,"weekend-job-requeuer.sh",1911,0,"done\n",shellscript,content +1278,1138886,"weekend-job-requeuer.sh",1916,0,"# echo ""[INFO] Submitting modelsize scaling job: 4_train_dyn_270M.sbatch""\n",shellscript,content +1279,1139187,"weekend-job-requeuer.sh",1990,0,"# sbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/4_train_dyn_270M.sbatch\n",shellscript,content +1280,1139592,"weekend-job-requeuer.sh",2074,0,"# echo ""[INFO] Submitting modelsize scaling job: 5_train_dyn_500M.sbatch""\n",shellscript,content +1281,1139922,"weekend-job-requeuer.sh",2148,370,"",shellscript,content +1282,1148887,"weekend-job-requeuer.sh",0,0,"",shellscript,selection_command +1283,1150189,"weekend-job-requeuer.sh",2074,74,"",shellscript,content +1284,1150189,"weekend-job-requeuer.sh",1639,350,"echo ""requeueing modelsize scaling runs""\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/1_train_dyn_36M.sbatch\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/2_train_dyn_110M.sbatch\nsbatch slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/3_train_dyn_180M.sbatch",shellscript,content +1285,1150189,"weekend-job-requeuer.sh",1357,144,"echo ""requeueing batchsize scaling runs: 8nodes""",shellscript,content +1286,1150189,"weekend-job-requeuer.sh",1041,306,"echo ""requeueing batchsize scaling runs: [4,16]nodes""\nsbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_16_nodes.sbatch\nsbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_4_nodes.sbatch",shellscript,content +1287,1150190,"weekend-job-requeuer.sh",727,304,"echo ""requeueing batchsize scaling runs: [1,2]nodes""\nsbatch slurm/jobs/mihir/horeka/batchsize_scaling/dynamics_cotraining/sqrt_lr/train_dynamics_1_nodes.sbatch\nsbatch 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a/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-e11dd94b-65ba-41d4-a0d1-6151e7930d741752738204303-2025_07_17-09.44.02.899/source.csv b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-e11dd94b-65ba-41d4-a0d1-6151e7930d741752738204303-2025_07_17-09.44.02.899/source.csv new file mode 100644 index 0000000000000000000000000000000000000000..8563620a8663f1ec1f9e35c8ea1ab836effb2f63 --- /dev/null +++ b/927a8af5474e5654810c00ce2e09fd2de87d3e5722f33fa1090d867db114e403/crowd-code-e11dd94b-65ba-41d4-a0d1-6151e7930d741752738204303-2025_07_17-09.44.02.899/source.csv @@ -0,0 +1,3517 @@ +Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type +1,5,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_dyn_yolorun_new_arch\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\n# source .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/train_tokenizer_lr_sweep_1e-4\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-new-arch-run-$slurm_job_id \\n --tags dynamics \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n ",shellscript,tab +2,780,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"9:44:02 AM [info] Activating crowd-code\n9:44:02 AM [info] Recording started\n9:44:02 AM [info] Initializing git provider using file system watchers...\n9:44:03 AM [info] Git repository found\n9:44:03 AM [info] Git provider initialized successfully\n9:44:03 AM [info] Initial git state: [object Object]\n",Log,tab +3,3975,"TERMINAL",0,0,"/bin/python3 /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/printEnvVariablesToFile.py /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash/envVars.txt",,terminal_command +4,4021,"TERMINAL",0,0,"]633;E;2025-07-17 09:44:06 /bin/python3 /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/printEnvVariablesToFile.py /hkfs/home/project/hk-project-p0023960/tum_cte0515/.cursor-server/extensions/ms-python.python-2024.12.3-linux-x64/python_files/deactivate/bash/envVars.txt;93375418-5fef-411f-acff-e36f5d8f8e16]633;C",,terminal_output +5,7025,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",0,0,"",shellscript,tab +6,25708,"genie.py",0,0,"from typing import Dict, Any\n\nimport optax\nimport jax\nimport jax.numpy as jnp\nimport flax.linen as nn\nfrom flax.training.train_state import TrainState\nimport orbax.checkpoint as ocp\n\nfrom models.dynamics import DynamicsMaskGIT, DynamicsAutoregressive\nfrom models.lam import LatentActionModel\nfrom models.tokenizer import TokenizerVQVAE\n\nimport os\nimport grain\n\n\nclass Genie(nn.Module):\n """"""Genie model""""""\n\n # --- Tokenizer ---\n in_dim: int\n tokenizer_dim: int\n latent_patch_dim: int\n num_patch_latents: int\n patch_size: int\n tokenizer_num_blocks: int\n tokenizer_num_heads: int\n # --- LAM ---\n lam_dim: int\n latent_action_dim: int\n num_latent_actions: int\n lam_patch_size: int\n lam_num_blocks: int\n lam_num_heads: int\n lam_co_train: bool\n # --- Dynamics ---\n dyna_dim: int\n dyna_num_blocks: int\n dyna_num_heads: int\n use_maskgit: bool\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n dropout: float = 0.0\n mask_limit: float = 0.0\n\n def setup(self):\n self.tokenizer = TokenizerVQVAE(\n in_dim=self.in_dim,\n model_dim=self.tokenizer_dim,\n latent_dim=self.latent_patch_dim,\n num_latents=self.num_patch_latents,\n patch_size=self.patch_size,\n num_blocks=self.tokenizer_num_blocks,\n num_heads=self.tokenizer_num_heads,\n dropout=0.0,\n codebook_dropout=0.0,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )\n self.lam = LatentActionModel(\n in_dim=self.in_dim,\n model_dim=self.lam_dim,\n latent_dim=self.latent_patch_dim,\n num_latents=self.num_latent_actions,\n patch_size=self.lam_patch_size,\n num_blocks=self.lam_num_blocks,\n num_heads=self.lam_num_heads,\n dropout=0.0,\n codebook_dropout=0.0,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )\n\n if self.use_maskgit:\n self.dynamics = DynamicsMaskGIT(\n model_dim=self.dyna_dim,\n num_latents=self.num_patch_latents,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n mask_limit=self.mask_limit,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n ) \n else:\n self.dynamics = DynamicsAutoregressive(\n model_dim=self.dyna_dim,\n num_latents=self.num_patch_latents,\n num_blocks=self.dyna_num_blocks,\n num_heads=self.dyna_num_heads,\n dropout=self.dropout,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )\n\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\n tokenizer_outputs = self.tokenizer.vq_encode(batch[""videos""], training=False)\n lam_outputs = self.lam.vq_encode(batch[""videos""], training=False)\n latent_actions = jax.lax.cond(\n self.lam_co_train,\n lambda: lam_outputs[""z_q""],\n lambda: jax.lax.stop_gradient(lam_outputs[""z_q""])\n )\n outputs = dict(\n video_tokens=jax.lax.stop_gradient(tokenizer_outputs[""indices""]),\n latent_actions=latent_actions,\n )\n outputs[""mask_rng""] = batch[""mask_rng""]\n dyna_outputs = self.dynamics(outputs, training)\n outputs.update(dyna_outputs)\n mle_indices = jnp.argmax(outputs[""token_logits""], axis=-1)\n outputs[""recon""] = self.tokenizer.decode(\n mle_indices, batch[""videos""].shape[2:4]\n )\n outputs[""lam_indices""] = lam_outputs[""indices""]\n return outputs\n\n\n def sample_causal(\n self,\n batch: Dict[str, Any],\n seq_len: int,\n temperature: float = 1,\n sample_argmax: bool = False,\n ):\n """"""\n Autoregressively samples up to `seq_len` future frames using the causal transformer backend.\n\n - Input frames are tokenized once.\n - Future frames are generated one at a time, each conditioned on all previous frames.\n - All frames are detokenized in a single pass at the end.\n\n Args:\n batch: Dict with at least ""videos"" (B, T, H, W, C)\n seq_len: total number of frames to generate (including context)\n temperature: sampling temperature\n sample_argmax: if True, use argmax instead of sampling\n\n Returns:\n Generated video frames (B, seq_len, H, W, C)\n """"""\n # --- Encode context frames ---\n tokenizer_out = self.tokenizer.vq_encode(batch[""videos""], training=False)\n token_idxs = tokenizer_out[""indices""] # (B, T, N)\n B, T, N = token_idxs.shape\n\n # --- Prepare initial token sequence ---\n # Pad with zeros for future frames\n pad_shape = (B, seq_len - T, N)\n token_idxs_full = jnp.concatenate(\n [token_idxs, jnp.zeros(pad_shape, dtype=token_idxs.dtype)], axis=1\n ) # (B, seq_len, N)\n\n # --- Prepare latent actions ---\n # If you have action tokens, use them; otherwise, use zeros\n if ""latent_actions"" in batch:\n action_tokens = self.lam.vq.get_codes(batch[""latent_actions""])\n else:\n action_tokens = jnp.zeros((B, seq_len, self.num_latent_actions), dtype=jnp.int32)\n\n # --- Autoregressive generation loop ---\n rng = batch.get(""rng"", None)\n for t in range(T, seq_len):\n # Feed all tokens up to t (i.e., frames 0..t-1) to the dynamics model\n dyna_inputs = {\n ""video_tokens"": token_idxs_full[:, :t, :], # (B, t, N)\n ""latent_actions"": action_tokens[:, :t, ...], # (B, t, ...)\n ""mask_rng"": batch.get(""mask_rng"", None),\n }\n dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\n\n # Sample or argmax for each patch\n if sample_argmax:\n next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n else:\n if rng is not None:\n rng, step_rng = jax.random.split(rng)\n else:\n step_rng = jax.random.PRNGKey(0)\n next_token = jax.random.categorical(\n step_rng, next_token_logits / temperature, axis=-1\n ) # (B, N)\n\n # Insert the generated tokens into the sequence\n token_idxs_full = token_idxs_full.at[:, t, :].set(next_token)\n\n # --- Decode all tokens at once at the end ---\n final_frames = self.tokenizer.decode(\n token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n )\n return final_frames\n\n\n @nn.compact\n def sample_maskgit(\n self,\n batch: Dict[str, Any],\n seq_len: int,\n steps: int = 25,\n temperature: float = 1,\n sample_argmax: bool = False,\n ) -> Any:\n """"""\n Autoregressively samples up to `seq_len` future frames, following Figure 8 of the paper.\n\n - Input frames are tokenized once.\n - Future frames are generated autoregressively in token space.\n - All frames are detokenized in a single pass.\n\n Note:\n - For interactive or step-wise sampling, detokenization should occur after each action.\n - To maintain consistent tensor shapes across timesteps, all current and future frames are decoded at every step.\n - Temporal causal structure is preserved by \n a) reapplying the mask before each decoding step.\n b) a temporal causal mask is applied within each ST-transformer block.\n\n Dimension keys:\n B: batch size \n T: number of input (conditioning) frames \n N: patches per frame \n S: sequence length \n A: action space \n D: model latent dimension\n """"""\n # --- Encode videos and actions ---\n tokenizer_out = self.tokenizer.vq_encode(batch[""videos""], training=False)\n token_idxs = tokenizer_out[""indices""] # (B, T, N)\n B, T, N = token_idxs.shape\n pad_shape = (B, seq_len - T, N)\n pad = jnp.zeros(pad_shape, dtype=token_idxs.dtype)\n token_idxs = jnp.concatenate([token_idxs, pad], axis=1) # (B, S, N)\n action_tokens = self.lam.vq.get_codes(batch[""latent_actions""])\n\n MaskGITLoop = nn.scan(\n MaskGITStep,\n variable_broadcast=""params"",\n split_rngs={""params"": False},\n in_axes=0,\n out_axes=0,\n length=steps,\n )\n \n loop_fn = MaskGITLoop(\n dynamics=self.dynamics,\n tokenizer=self.tokenizer,\n temperature=temperature,\n sample_argmax=sample_argmax,\n steps=steps,\n )\n\n def generation_step_fn(carry, step_t):\n rng, current_token_idxs = carry\n rng, step_rng = jax.random.split(rng)\n\n # Mask current and future frames (i.e., t >= step_t)\n mask = jnp.arange(seq_len) >= step_t # (S,)\n mask = jnp.broadcast_to(mask[None, :, None], (B, seq_len, N)) # (B, S, N)\n mask = mask.astype(bool)\n masked_token_idxs = current_token_idxs * ~mask\n\n # --- Initialize and run MaskGIT loop ---\n init_carry_maskgit = (\n step_rng,\n masked_token_idxs,\n mask,\n action_tokens,\n )\n final_carry_maskgit, _ = loop_fn(init_carry_maskgit, jnp.arange(steps))\n updated_token_idxs = final_carry_maskgit[1]\n new_carry = (rng, updated_token_idxs)\n return new_carry, None\n\n # --- Run the autoregressive generation using scan ---\n initial_carry = (batch[""rng""], token_idxs)\n timesteps_to_scan = jnp.arange(T, seq_len)\n final_carry, _ = jax.lax.scan(\n generation_step_fn,\n initial_carry,\n timesteps_to_scan\n )\n final_token_idxs = final_carry[1]\n\n # --- Decode all tokens at once at the end ---\n final_frames = self.tokenizer.decode(\n final_token_idxs,\n video_hw=batch[""videos""].shape[2:4],\n )\n return final_frames\n\n def vq_encode(self, batch, training) -> Dict[str, Any]:\n # --- Preprocess videos ---\n lam_output = self.lam.vq_encode(batch[""videos""], training=training)\n return lam_output[""indices""]\n\n\nclass MaskGITStep(nn.Module):\n dynamics: nn.Module\n tokenizer: nn.Module\n temperature: float\n sample_argmax: bool\n steps: int\n\n @nn.compact\n def __call__(self, carry, x):\n rng, token_idxs, mask, action_tokens = carry\n step = x\n N = token_idxs.shape[2]\n\n # --- Construct + encode video ---\n vid_embed = self.dynamics.patch_embed(token_idxs) # (B, S, N, D)\n mask_token = self.dynamics.mask_token # (1, 1, 1, D,)\n mask_expanded = mask[..., None] # (B, S, N, 1) \n vid_embed = jnp.where(mask_expanded, mask_token, vid_embed)\n\n # --- Predict transition ---\n act_embed = self.dynamics.action_up(action_tokens)\n vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\n unmasked_ratio = jnp.cos(jnp.pi * (step + 1) / (self.steps * 2))\n step_temp = self.temperature * (1.0 - unmasked_ratio)\n final_logits = self.dynamics.dynamics(vid_embed) / step_temp\n\n # --- Sample new tokens for final frame ---\n if self.sample_argmax:\n sampled_token_idxs = jnp.argmax(final_logits, axis=-1)\n else:\n rng, _rng = jax.random.split(rng)\n sampled_token_idxs = jax.random.categorical(_rng, final_logits)\n gather_fn = jax.vmap(jax.vmap(jax.vmap(lambda x, y: x[y])))\n final_token_probs = gather_fn(jax.nn.softmax(final_logits), sampled_token_idxs)\n final_token_probs += ~mask\n # Update masked tokens only\n token_idxs = jnp.where(mask, sampled_token_idxs, token_idxs)\n\n # --- Update mask ---\n num_unmasked_tokens = jnp.round(N * (1.0 - unmasked_ratio)).astype(int)\n idx_mask = jnp.arange(final_token_probs.shape[-1]) > num_unmasked_tokens\n sorted_idxs = jnp.argsort(final_token_probs, axis=-1, descending=True)\n mask_update_fn = jax.vmap(lambda msk, ids: msk.at[ids].set(idx_mask))\n new_mask = mask_update_fn(mask, sorted_idxs)\n\n new_carry = (rng, token_idxs, new_mask, action_tokens)\n return new_carry, None\n\ndef restore_genie_components(\n train_state: TrainState,\n sharding: jax.sharding.NamedSharding,\n grain_iterator: grain.DataLoaderIterator,\n inputs: Dict[str, jax.Array],\n rng: jax.Array,\n args,\n):\n """"""Restore pre-trained Genie components""""""\n rng, _rng = jax.random.split(rng)\n\n # dummy values since we only use tx to initialize the dummy train states\n dummy_tx = optax.adamw(\n learning_rate=optax.constant_schedule(args.max_lr),\n b1=0.9,\n b2=0.9,\n weight_decay=1e-4,\n )\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add('model_state', ocp.args.StandardRestore, ocp.handlers.StandardCheckpointHandler)\n handler_registry.add('dataloader_state', grain.checkpoint.CheckpointRestore, grain.checkpoint.CheckpointHandler)\n \n\n checkpoint_options = ocp.CheckpointManagerOptions(\n step_format_fixed_length=6,\n )\n tokenizer_checkpoint_manager = ocp.CheckpointManager(\n directory=args.tokenizer_checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n dummy_tokenizer = TokenizerVQVAE(\n in_dim=args.image_channels,\n model_dim=args.tokenizer_dim,\n latent_dim=args.latent_patch_dim,\n num_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n num_blocks=args.tokenizer_num_blocks,\n num_heads=args.tokenizer_num_heads,\n dropout=args.dropout,\n codebook_dropout=args.dropout,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n )\n tokenizer_init_params = dummy_tokenizer.init(_rng, inputs)\n dummy_tokenizer_train_state = TrainState.create(\n apply_fn=dummy_tokenizer.apply, params=tokenizer_init_params, tx=dummy_tx\n )\n abstract_sharded_tokenizer_state = _create_abstract_sharded_pytree(\n dummy_tokenizer_train_state, sharding\n )\n restored_tokenizer = tokenizer_checkpoint_manager.restore(\n step=tokenizer_checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.StandardRestore(abstract_sharded_tokenizer_state),\n dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator),\n ),\n )[""model_state""]\n restored_tokenizer_params = restored_tokenizer.params[""params""]\n train_state.params[""params""][""tokenizer""].update(restored_tokenizer_params)\n tokenizer_checkpoint_manager.close()\n\n if args.lam_checkpoint:\n lam_checkpoint_manager = ocp.CheckpointManager(\n directory=args.lam_checkpoint,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n dummy_lam = LatentActionModel(\n in_dim=args.image_channels,\n model_dim=args.lam_dim,\n latent_dim=args.latent_patch_dim,\n num_latents=args.num_latent_actions,\n patch_size=args.lam_patch_size,\n num_blocks=args.lam_num_blocks,\n num_heads=args.lam_num_heads,\n dropout=args.dropout,\n codebook_dropout=args.dropout,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n )\n lam_init_params = dummy_lam.init(_rng, inputs)\n dummy_lam_train_state = TrainState.create(\n apply_fn=dummy_lam.apply, params=lam_init_params, tx=dummy_tx\n )\n abstract_sharded_lam_state = _create_abstract_sharded_pytree(\n dummy_lam_train_state, sharding\n )\n restored_lam = lam_checkpoint_manager.restore(\n step=lam_checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.StandardRestore(abstract_sharded_lam_state),\n dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator),\n ),\n )[""model_state""]\n restored_lam_params = restored_lam.params[""params""]\n # Genie does not initialize all LAM modules, thus we omit those extra modules during restoration\n # (f.srambical) FIXME: Currently, this is a small HBM memory crunch since the LAM's decoder is loaded into HBM and immediately dicarded.\n # A workaround would be to restore to host memory first, and only move the weights to HBM after pruning the decoder\n restored_lam_params = {\n k: v\n for k, v in restored_lam_params.items()\n if k in train_state.params[""params""][""lam""]\n }\n train_state.params[""params""][""lam""].update(restored_lam_params)\n lam_checkpoint_manager.close()\n\n return train_state\n\ndef _create_abstract_sharded_pytree(pytree_template, sharding_spec):\n """"""Replaces arrays in a pytree with ShapeDtypeStructs having the given sharding.""""""\n\n def map_fn(leaf_template):\n if hasattr(leaf_template, ""shape"") and hasattr(leaf_template, ""dtype""):\n return jax.ShapeDtypeStruct(\n leaf_template.shape, leaf_template.dtype, sharding=sharding_spec\n )\n return leaf_template\n\n return jax.tree_util.tree_map(map_fn, pytree_template)",python,tab +7,30708,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py",0,0,"from typing import Dict, Any\n\nimport jax\nimport jax.numpy as jnp\nimport flax.linen as nn\nimport einops\n\nfrom utils.nn import STTransformer\n\n\nclass DynamicsMaskGIT(nn.Module):\n """"""MaskGIT dynamics model""""""\n\n model_dim: int\n num_latents: int\n num_blocks: int\n num_heads: int\n dropout: float\n mask_limit: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n def setup(self):\n self.dynamics = STTransformer(\n self.model_dim,\n self.num_latents,\n self.num_blocks,\n self.num_heads,\n self.dropout,\n self.param_dtype,\n self.dtype,\n spacial_bert=True,\n )\n self.patch_embed = nn.Embed(self.num_latents, self.model_dim)\n self.mask_token = self.param(\n ""mask_token"",\n nn.initializers.lecun_uniform(),\n (1, 1, 1, self.model_dim),\n )\n self.action_up = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )\n\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\n # --- Mask videos ---\n vid_embed = self.patch_embed(batch[""video_tokens""])\n if training:\n rng1, rng2 = jax.random.split(batch[""mask_rng""])\n mask_prob = jax.random.uniform(rng1, minval=self.mask_limit)\n mask = jax.random.bernoulli(rng2, mask_prob, vid_embed.shape[:-1])\n mask = mask.at[:, 0].set(False)\n vid_embed = jnp.where(jnp.expand_dims(mask, -1), self.mask_token, vid_embed)\n else:\n mask = None\n\n # --- Predict transition ---\n act_embed = self.action_up(batch[""latent_actions""])\n vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\n logits = self.dynamics(vid_embed)\n return dict(token_logits=logits, mask=mask)\n\n\nclass DynamicsAutoregressive(nn.Module):\n """"""Autoregressive (causal) dynamics model""""""\n\n model_dim: int\n num_latents: int\n num_blocks: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n def setup(self):\n self.dynamics = STTransformer(\n self.model_dim,\n self.num_latents,\n self.num_blocks,\n self.num_heads,\n self.dropout,\n self.param_dtype,\n self.dtype,\n spacial_bert=False,\n )\n self.patch_embed = nn.Embed(self.num_latents, self.model_dim)\n self.action_up = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )\n\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\n vid_embed = self.patch_embed(batch[""video_tokens""])\n act_embed = self.action_up(batch[""latent_actions""])\n vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\n logits = self.dynamics(vid_embed)\n mask = jnp.ones(vid_embed.shape[:-1])\n return dict(token_logits=logits, mask=mask)",python,tab +8,36439,"train_dynamics.py",0,0,"from dataclasses import dataclass, field\nimport os\n\nimport einops\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.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\n\nfrom genie import Genie, restore_genie_components\nfrom utils.dataloader import get_dataloader\nfrom utils.lr_utils import get_lr_schedule\nfrom utils.parameter_utils import count_parameters_by_component\n\n@dataclass\nclass Args:\n # Experiment\n num_steps: int = 200_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 = """"\n save_ckpt: bool = False\n restore_ckpt: bool = False\n # Optimization\n batch_size: int = 36\n init_lr: float = 0.0\n max_lr: float = 3e-5\n decay_end: float = 0.0\n wsd_decay_steps: int = 10000 # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n warmup_steps: int = 5000\n lr_schedule : str = ""wsd"" # supported options: wsd, cos\n # Tokenizer\n tokenizer_dim: int = 512\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 8\n tokenizer_num_heads: int = 8\n tokenizer_checkpoint: str = """"\n # LAM\n lam_dim: int = 512\n latent_action_dim: int = 32\n num_latent_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 8\n lam_num_heads: int = 8\n lam_checkpoint: str = """"\n # Dynamics\n dyna_dim: int = 512\n dyna_num_blocks: int = 12\n dyna_num_heads: int = 8\n dropout: float = 0.0\n mask_limit: float = 0.5\n use_maskgit: bool = False\n param_dtype: jnp.dtype = jnp.float32\n dtype: jnp.dtype = jnp.bfloat16\n # Logging\n log: bool = False\n entity: str = """"\n project: str = """"\n name: str = ""train_dynamics""\n tags: list[str] = field(default_factory=lambda: [""dynamics""])\n log_interval: int = 5\n log_image_interval: int = 250\n ckpt_dir: str = """"\n log_checkpoint_interval: int = 25000\n log_checkpoint_keep_period: int = 20000\n log_gradients: bool = False\n wandb_id: str = """"\n\n\nargs = tyro.cli(Args)\n\n\ndef dynamics_loss_fn(params, state, inputs):\n """"""Compute masked dynamics loss""""""\n inputs[""videos""] = inputs[""videos""].astype(args.dtype) / 255.0\n outputs = state.apply_fn(\n params,\n inputs,\n training=True,\n rngs={""params"": inputs[""rng""], ""dropout"": inputs[""dropout_rng""]},\n )\n mask = outputs[""mask""]\n outputs[""token_logits""] = outputs[""token_logits""].astype(jnp.float32)\n logits = outputs[""token_logits""]\n targets = outputs[""video_tokens""]\n\n if not args.use_maskgit:\n logits = outputs[""token_logits""][:, :, :-1]\n targets = outputs[""video_tokens""][:, :, 1:]\n mask = outputs[""mask""][:, :, 1:] \n ce_loss = optax.softmax_cross_entropy_with_integer_labels(\n logits, targets\n )\n ce_loss = (mask * ce_loss).sum() / mask.sum()\n acc = logits.argmax(-1) == targets\n acc = (mask * acc).sum() / mask.sum()\n select_probs = jax.nn.softmax(logits)\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() # type: ignore\n ssim = pix.ssim(gt, recon).mean() # type: ignore\n _, index_counts_lam = jnp.unique_counts(\n jnp.ravel(outputs[""lam_indices""]), size=args.num_latent_actions, fill_value=0\n )\n _, index_counts_tokenizer = jnp.unique_counts(\n jnp.ravel(outputs[""video_tokens""]), size=args.num_patch_latents, fill_value=0\n )\n codebook_usage_lam = (index_counts_lam != 0).mean()\n codebook_usage_tokenizer = (index_counts_tokenizer != 0).mean()\n metrics = dict(\n cross_entropy_loss=ce_loss,\n masked_token_accuracy=acc,\n select_logit=logits.max(-1).mean(),\n select_p=select_probs.max(-1).mean(),\n entropy=jax.scipy.special.entr(select_probs).sum(-1).mean(),\n psnr=psnr,\n ssim=ssim,\n codebook_usage_lam=codebook_usage_lam,\n codebook_usage_tokenizer=codebook_usage_tokenizer,\n )\n return ce_loss, (outputs[""recon""], metrics)\n\n\n@jax.jit\ndef train_step(state, inputs):\n """"""Update state and compute metrics""""""\n grad_fn = jax.value_and_grad(dynamics_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[""gradients_std/""] = jax.tree.map(\n lambda x: x.std(), grads[""params""][""dynamics""]\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\n # --- Initialize model ---\n genie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n latent_action_dim=args.latent_action_dim,\n num_latent_actions=args.num_latent_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n lam_co_train=not args.lam_checkpoint,\n # Dynamics\n dyna_dim=args.dyna_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n dropout=args.dropout,\n mask_limit=args.mask_limit,\n use_maskgit=args.use_maskgit,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n )\n rng, _rng = jax.random.split(rng)\n image_shape = (args.image_height, args.image_width, args.image_channels)\n dummy_inputs = dict(\n videos=jnp.zeros(\n (per_device_batch_size_for_init, args.seq_len, *image_shape),\n dtype=args.dtype,\n ),\n action=jnp.zeros(\n (per_device_batch_size_for_init, args.seq_len), dtype=args.dtype\n ),\n mask_rng=_rng,\n )\n rng, _rng = jax.random.split(rng)\n init_params = genie.init(_rng, dummy_inputs)\n\n param_counts = count_parameters_by_component(init_params)\n\n if args.log and jax.process_index() == 0:\n wandb_init_kwargs = {\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 if args.wandb_id:\n wandb_init_kwargs.update(\n {\n ""id"": args.wandb_id,\n ""resume"": ""allow"",\n }\n )\n wandb.init(**wandb_init_kwargs)\n\n wandb.config.update({""model_param_count"": param_counts})\n\n print(""Parameter counts:"")\n print(param_counts)\n\n # --- Initialize optimizer ---\n lr_schedule = get_lr_schedule(args.lr_schedule, \n args.init_lr, \n args.max_lr, \n args.decay_end, \n args.num_steps, \n args.warmup_steps, \n args.wsd_decay_steps)\n tx = optax.adamw(learning_rate=lr_schedule, b1=0.9, b2=0.9, weight_decay=1e-4, mu_dtype=args.dtype)\n train_state = TrainState.create(apply_fn=genie.apply, params=init_params, tx=tx)\n\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 videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n train_state = jax.device_put(train_state, replicated_sharding)\n\n # --- Initialize checkpoint manager ---\n step = 0\n handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n handler_registry.add(\n ""model_state"", ocp.args.StandardSave, ocp.handlers.StandardCheckpointHandler\n )\n handler_registry.add(\n ""model_state"", ocp.args.StandardRestore, ocp.handlers.StandardCheckpointHandler\n )\n handler_registry.add(""dataloader_state"", grain.checkpoint.CheckpointSave, grain.checkpoint.CheckpointHandler) # type: ignore\n handler_registry.add(""dataloader_state"", grain.checkpoint.CheckpointRestore, grain.checkpoint.CheckpointHandler) # type: ignore\n\n checkpoint_options = ocp.CheckpointManagerOptions(\n save_interval_steps=args.log_checkpoint_interval,\n max_to_keep=3,\n keep_period=args.log_checkpoint_keep_period,\n step_format_fixed_length=6,\n cleanup_tmp_directories=True,\n )\n\n checkpoint_manager = ocp.CheckpointManager(\n args.ckpt_dir,\n options=checkpoint_options,\n handler_registry=handler_registry,\n )\n\n # --- Create DataLoaderIterator from dataloader ---\n array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=8,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n\n # --- Restore checkpoint ---\n if args.restore_ckpt:\n # Restore full dynamics model\n abstract_train_state = jax.tree_util.tree_map(\n ocp.utils.to_shape_dtype_struct, train_state\n )\n restored = checkpoint_manager.restore(\n checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.StandardRestore(abstract_train_state),\n dataloader_state=grain.checkpoint.CheckpointRestore(grain_iterator),\n ),\n )\n train_state = restored[""model_state""]\n grain_iterator = restored[""dataloader_state""]\n step = checkpoint_manager.latest_step() or 0\n print(f""Restored dataloader and model state from step {step}"")\n else:\n # Restore from pre-trained tokenizer (and LAM)\n train_state = restore_genie_components(\n train_state, replicated_sharding, grain_iterator, dummy_inputs, rng, args\n )\n\n # --- TRAIN LOOP ---\n dataloader = (jax.make_array_from_process_local_data(videos_sharding, elem) for elem in grain_iterator) # type: ignore\n while step < args.num_steps:\n for videos in dataloader:\n # --- Train step ---\n rng, _rng, _rng_dropout, _rng_mask = jax.random.split(rng, 4)\n\n inputs = dict(\n videos=videos,\n rng=_rng,\n dropout_rng=_rng_dropout,\n mask_rng=_rng_mask,\n )\n train_state, loss, recon, metrics = train_step(train_state, inputs)\n metrics[""lr""] = lr_schedule(step)\n print(f""Step {step}, loss: {loss}"")\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 **metrics,\n }\n )\n if step % args.log_image_interval == 0:\n gt_seq = inputs[""videos""][0].astype(jnp.float32) / 255.0\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 if jax.process_index() == 0:\n log_images = dict(\n image=wandb.Image(np.asarray(gt_seq[args.seq_len - 1])),\n recon=wandb.Image(np.asarray(recon_seq[args.seq_len - 1])),\n true_vs_recon=wandb.Image(\n np.asarray(comparison_seq.astype(np.uint8))\n ),\n )\n wandb.log(log_images)\n # --- Checkpointing ---\n if args.save_ckpt and step % args.log_checkpoint_interval == 0:\n checkpoint_manager.save(\n step,\n args=ocp.args.Composite(\n model_state=ocp.args.StandardSave(train_state),\n dataloader_state=grain.checkpoint.CheckpointSave(\n grain_iterator\n ),\n ),\n )\n print(f""Saved checkpoint at step {step}"")\n if step >= args.num_steps:\n break\n\n checkpoint_manager.close()\n",python,tab +9,37974,"genie.py",0,0,"",python,tab +10,46539,"TERMINAL",0,0,"",,terminal_focus +11,53309,"TERMINAL",0,0,"idling",,terminal_command +12,53374,"TERMINAL",0,0,"]633;E;2025-07-17 09:44:56 idling;fcc07419-26ce-4c93-b0b0-b7e76ab3be27]633;C[?1049h(B[?7hEvery 1.0s: sinfo_t_idlehkn1990.localdomain: Thu Jul 17 09:44:56 2025Partition dev_cpuonly: 11 nodes idle\rPartition cpuonly: 12 nodes idle\rPartition dev_accelerated:\t 3 nodes idle\rPartition accelerated: 53 nodes idle\rPartition dev_accelerated-h100 :\t 1 nodes idle\rPartition accelerated-h100:\t 0 nodes idle\rPartition large:\t 8 nodes idle",,terminal_output +13,54403,"TERMINAL",0,0,"7\t ",,terminal_output +14,54778,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn1990:~/Projects/jafar]633;D;0",,terminal_output +15,58427,"TERMINAL",0,0,"salloc --time=10:00:00 --partition=accelerated --nodes=2 --ntasks-per-node=4 --gres=gpu:4 --cpus-per-task=5",,terminal_command +16,58495,"TERMINAL",0,0,"]633;E;2025-07-17 09:45:01 salloc --time=10:00:00 --partition=accelerated --nodes=2 --ntasks-per-node=4 --gres=gpu:4 --cpus-per-task=5;fcc07419-26ce-4c93-b0b0-b7e76ab3be27]633;Csalloc: Granted job allocation 3352994\r\n",,terminal_output +17,58647,"TERMINAL",0,0,"salloc: Waiting for resource configuration\r\n",,terminal_output +18,60959,"TERMINAL",0,0,"",,terminal_focus +19,75216,"TERMINAL",0,0,"salloc --time=10:00:00 --partition=accelerated --nodes=1 --ntasks-per-node=1 --gres=gpu:1 --cpus-per-task=5",,terminal_command +20,75334,"TERMINAL",0,0,"]633;E;2025-07-17 09:45:18 salloc --time=10:00:00 --partition=accelerated --nodes=1 --ntasks-per-node=1 --gres=gpu:1 --cpus-per-task=5;c200d8ee-3924-40d0-a474-739536917d72]633;Csalloc: Granted job allocation 3352996\r\n",,terminal_output +21,75414,"TERMINAL",0,0,"salloc: Waiting for resource configuration\r\n",,terminal_output +22,85665,"TERMINAL",0,0,"salloc: Nodes hkn[0605,0614] are ready for job\r\n",,terminal_output +23,86509,"TERMINAL",0,0,"]0;tum_cte0515@hkn0605:~/Projects/jafar[?2004h[tum_cte0515@hkn0605 jafar]$ ",,terminal_output +24,95029,"TERMINAL",0,0,"srun",,terminal_focus +25,96448,"TERMINAL",0,0,"[?25lso[?25h",,terminal_output +26,96529,"TERMINAL",0,0,"[?25lo[?25h",,terminal_output +27,96625,"TERMINAL",0,0,"[?25lu[?25h",,terminal_output +28,96679,"TERMINAL",0,0,"[?25lr[?25h",,terminal_output +29,96848,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +30,97032,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +31,97118,"TERMINAL",0,0,"[?25l [?25h",,terminal_output +32,97263,"TERMINAL",0,0,"[?25l.v[?25h",,terminal_output +33,97662,"TERMINAL",0,0,"env/",,terminal_output +34,98234,"TERMINAL",0,0,"",,terminal_output +35,98695,"TERMINAL",0,0,"[?25lv[?25h",,terminal_output +36,98888,"TERMINAL",0,0,"",,terminal_output +37,99775,"TERMINAL",0,0,"[?25lb[?25h",,terminal_output +38,99875,"TERMINAL",0,0,"in/",,terminal_output +39,100138,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +40,100226,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +41,100375,"TERMINAL",0,0,"tivate",,terminal_output +42,100628,"TERMINAL",0,0,"[?25l[?2004l\r]0;tum_cte0515@hkn0605:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0605 jafar]$ [?25h",,terminal_output +43,102441,"TERMINAL",0,0,"salloc: Nodes hkn0804 are ready for job\r\n",,terminal_output +44,103275,"TERMINAL",0,0,"]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h[tum_cte0515@hkn0804 jafar]$ ",,terminal_output +45,128966,"TERMINAL",0,0,"srun",,terminal_focus +46,130579,"TERMINAL",0,0,"[?25lso[?25h",,terminal_output +47,130656,"TERMINAL",0,0,"[?25lo[?25h",,terminal_output +48,130752,"TERMINAL",0,0,"[?25lu[?25h",,terminal_output +49,130806,"TERMINAL",0,0,"[?25lr[?25h",,terminal_output +50,131127,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +51,131248,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +52,131387,"TERMINAL",0,0,"[?25l [?25h",,terminal_output +53,131570,"TERMINAL",0,0,"[?25l.[?25h",,terminal_output +54,131638,"TERMINAL",0,0,"[?25lv[?25h",,terminal_output +55,131851,"TERMINAL",0,0,"env/",,terminal_output +56,132235,"TERMINAL",0,0,"[?25lbin/[?25h",,terminal_output +57,132916,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +58,133013,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +59,133199,"TERMINAL",0,0,"tivate",,terminal_output +60,133577,"TERMINAL",0,0,"[?25l[?2004l\r[?25h]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0804 jafar]$ ",,terminal_output +61,134458,"TERMINAL",0,0,"srun",,terminal_focus +62,136112,"TERMINAL",0,0,"srun",,terminal_focus +63,141857,"TERMINAL",0,0,"\r(reverse-i-search)`': ",,terminal_output +64,142069,"TERMINAL",0,0,"s': source .venv/bin/activate\r",,terminal_output +65,142340,"TERMINAL",0,0,"[?25lsm': smi[?25h",,terminal_output +66,143268,"TERMINAL",0,0,"^C[?2004l\r[?2004h[?2004l\r\r\n]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0804 jafar]$ ",,terminal_output +67,143719,"TERMINAL",0,0,"\r(reverse-i-search)`': ",,terminal_output +68,143982,"TERMINAL",0,0,"s': source .venv/bin/activate\r",,terminal_output +69,144208,"TERMINAL",0,0,"[?25lsa': salloc --time=10:00:00 --partition=accelerated --nodes=2 --ntasks-per-node=4 --gres=gpu:4 --cpus-per-task=5\r[?25h",,terminal_output +70,144346,"TERMINAL",0,0,"[?25lsm': git add sample.py[?25h",,terminal_output +71,144620,"TERMINAL",0,0,"[?25lasp': git add sample.py[?25h[?25lsl': git add sample.py[?25h",,terminal_output +72,144677,"TERMINAL",0,0,"e': git add sample.py",,terminal_output +73,144916,"TERMINAL",0,0,"[?25ls.': git add sample.py[?25h",,terminal_output +74,146383,"TERMINAL",0,0,"python sample.py --checkpoint /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_500M_32_node --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked --dyna_dim=1536 --dyna_num_blocks=24 --dyna_num_heads=24\r",,terminal_output +75,147615,"TERMINAL",0,0,"\r\n\r\n\r",,terminal_output +76,149137,"TERMINAL",0,0,"\rjafar) [tum_cte0515@hkn0804 jafar]$ python sample.py --checkpoint /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/dynamics-cotraining-modelsize-scaling/train_dynamics_modelsize_scaling_500M_32_node --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +77,149828,"TERMINAL",0,0,"",,terminal_output +78,150015,"TERMINAL",0,0,"",,terminal_output +79,150128,"TERMINAL",0,0,"",,terminal_output +80,151337,"TERMINAL",0,0,"",,terminal_output +81,151834,"TERMINAL",0,0,"",,terminal_output +82,151974,"TERMINAL",0,0,"",,terminal_output +83,152207,"TERMINAL",0,0,"",,terminal_output +84,152346,"TERMINAL",0,0,"",,terminal_output +85,152507,"TERMINAL",0,0,"",,terminal_output +86,152699,"TERMINAL",0,0,"",,terminal_output +87,152884,"TERMINAL",0,0,"",,terminal_output +88,153069,"TERMINAL",0,0,"",,terminal_output +89,153248,"TERMINAL",0,0,"",,terminal_output +90,153428,"TERMINAL",0,0,"",,terminal_output +91,153688,"TERMINAL",0,0,"",,terminal_output +92,153832,"TERMINAL",0,0,"",,terminal_output +93,153954,"TERMINAL",0,0,"\r\n\r",,terminal_output +94,154235,"TERMINAL",0,0,"",,terminal_output +95,154422,"TERMINAL",0,0,"",,terminal_output +96,154611,"TERMINAL",0,0,"",,terminal_output +97,154795,"TERMINAL",0,0,"",,terminal_output +98,154981,"TERMINAL",0,0,"",,terminal_output +99,155225,"TERMINAL",0,0,"",,terminal_output +100,155417,"TERMINAL",0,0,"",,terminal_output +101,155825,"TERMINAL",0,0,"",,terminal_output +102,156361,"TERMINAL",0,0,"",,terminal_output +103,157691,"TERMINAL",0,0," --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\r\n\r",,terminal_output +104,173066,"TERMINAL",0,0,"""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared//checkpoints/train_dyn_yolorun_new_arch/3352115"" --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\r",,terminal_output +105,178041,"TERMINAL",0,0,"[?25l""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared//checkpoints/train_dyn_yolorun_new_arch/3352115""\r\n[?2004l\r[?25h",,terminal_output +106,193583,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +107,194204,"TERMINAL",0,0,"ERROR:2025-07-17 09:47:16,952:jax._src.xla_bridge:444: Jax plugin configuration error: Exception when calling jax_plugins.xla_cuda12.initialize()\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax_plugins/xla_cuda12/__init__.py"", line 197, in _version_check\r\n version = get_version()\r\nRuntimeError: jaxlib/cuda/versions_helpers.cc:81: operation cusparseGetProperty(MAJOR_VERSION, &major) failed: The cuSPARSE library was not found.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 442, in discover_pjrt_plugins\r\n plugin_module.initialize()\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax_plugins/xla_cuda12/__init__.py"", line 324, in initialize\r\n _check_cuda_versions(raise_on_first_error=True)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax_plugins/xla_cuda12/__init__.py"", line 262, in _check_cuda_versions\r\n _version_check(""cuSPARSE"", cuda_versions.cusparse_get_version,\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax_plugins/xla_cuda12/__init__.py"", line 201, in _version_check\r\n raise RuntimeError(err_msg) from e\r\nRuntimeError: Unable to load cuSPARSE. Is it installed?\r\nERROR:jax._src.xla_bridge:Jax plugin configuration error: Exception when calling jax_plugins.xla_cuda12.initialize()\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax_plugins/xla_cuda12/__init__.py"", line 197, in _version_check\r\n version = get_version()\r\nRuntimeError: jaxlib/cuda/versions_helpers.cc:81: operation cusparseGetProperty(MAJOR_VERSION, &major) failed: The cuSPARSE library was not found.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/xla_bridge.py"", line 442, in discover_pjrt_plugins\r\n plugin_module.initialize()\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax_plugins/xla_cuda12/__init__.py"", line 324, in initialize\r\n _check_cuda_versions(raise_on_first_error=True)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax_plugins/xla_cuda12/__init__.py"", line 262, in _check_cuda_versions\r\n _version_check(""cuSPARSE"", cuda_versions.cusparse_get_version,\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax_plugins/xla_cuda12/__init__.py"", line 201, in _version_check\r\n raise RuntimeError(err_msg) from e\r\nRuntimeError: Unable to load cuSPARSE. Is it installed?\r\nWARNING:2025-07-17 09:47:16,961:jax._src.xla_bridge:794: An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\r\nWARNING:jax._src.xla_bridge:An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\r\n",,terminal_output +108,199785,"TERMINAL",0,0,"^C",,terminal_output +109,199978,"TERMINAL",0,0,"Traceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 99, in \r\n params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 98, in __call__\r\n tokenizer_outputs = self.tokenizer.vq_encode(batch[""videos""], training=False)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/tokenizer.py"", line 66, in vq_encode\r\n x = self.encoder(x) # (B, T, N, E)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 227, in __call__\r\n x = STBlock(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 154, in __call__\r\n z = nn.MultiHeadAttention(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/attention.py"", line 676, in __call__\r\n out = DenseGeneral(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/linear.py"", line 164, in __call__\r\n kernel = self.param(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/linear.py"", line 154, in kernel_init_wrap\r\n return jnp.reshape(kernel, shape)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/lax_numpy.py"", line 2016, in reshape\r\n return a.reshape(shape, order=order) # type: ignore[call-overload,union-attr]\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 1089, in meth\r\n return getattr(self.aval, name).fun(self, *args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 315, in _reshape\r\n return lax.reshape(self, newshape, None, out_sharding=out_sharding)\r\njax._src.source_info_util.JaxStackTraceBeforeTransformation: KeyboardInterrupt\r\n\r\nThe preceding stack trace is the source of the JAX operation that, once transformed by JAX, triggered the following exception.\r\n\r\n--------------------\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 99, in \r\n params = genie.init(_rng, dummy_inputs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/traceback_util.py"", line 182, in reraise_with_filtered_traceback\r\n return fun(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/module.py"", line 2452, in init\r\n _, v_out = self.init_with_output(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/traceback_util.py"", line 182, in reraise_with_filtered_traceback\r\n return fun(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/module.py"", line 2304, in init_with_output\r\n return init_with_output(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/core/scope.py"", line 1115, in wrapper\r\n return apply(fn, mutable=mutable, flags=init_flags)(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/core/scope.py"", line 1079, in wrapper\r\n y = fn(root, *args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/module.py"", line 3093, in scope_fn\r\n return fn(module.clone(parent=scope, _deep_clone=True), *args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/module.py"", line 699, in wrapped_module_method\r\n return self._call_wrapped_method(fun, args, kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/module.py"", line 1216, in _call_wrapped_method\r\n y = run_fun(self, *args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 98, in __call__\r\n tokenizer_outputs = self.tokenizer.vq_encode(batch[""videos""], training=False)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/module.py"", line 699, in wrapped_module_method\r\n return self._call_wrapped_method(fun, args, kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/module.py"", line 1216, in _call_wrapped_method\r\n y = run_fun(self, *args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/tokenizer.py"", line 66, in vq_encode\r\n x = self.encoder(x) # (B, T, N, E)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/module.py"", line 699, in wrapped_module_method\r\n return self._call_wrapped_method(fun, args, kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/module.py"", line 1216, in _call_wrapped_method\r\n y = run_fun(self, *args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/utils/nn.py"", line 227, in __call__\r\n x = STBlock(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/linen/transforms.py"", line 433, in wrapped_fn\r\n return trafo_fn(module_scopes, *args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/core/lift.py"", line 319, in wrapper\r\n y, out_variable_groups_xs_t = fn(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/flax/core/lift.py"", line 1474, in inner\r\n return rematted(variable_groups, rng_groups, *args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/traceback_util.py"", line 182, in reraise_with_filtered_traceback\r\n return fun(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/ad_checkpoint.py"", line 333, in fun_remat\r\n out_flat = remat_p.bind(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/core.py"", line 536, in bind\r\n return self._true_bind(*args, **params)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/core.py"", line 552, in _true_bind\r\n return self.bind_with_trace(prev_trace, args, params)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/core.py"", line 562, in bind_with_trace\r\n return trace.process_primitive(self, args, params)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/core.py"", line 1066, in process_primitive\r\n return primitive.impl(*args, **params)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/ad_checkpoint.py"", line 514, in remat_impl\r\n return core.eval_jaxpr(jaxpr, (), *args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/core.py"", line 630, in eval_jaxpr\r\n ans = eqn.primitive.bind(*subfuns, *map(read, eqn.invars), **bind_params)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/core.py"", line 536, in bind\r\n return self._true_bind(*args, **params)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/core.py"", line 552, in _true_bind\r\n return self.bind_with_trace(prev_trace, args, params)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/core.py"", line 562, in bind_with_trace\r\n return trace.process_primitive(self, args, params)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/core.py"", line 1066, in process_primitive\r\n return primitive.impl(*args, **params)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/dispatch.py"", line 91, in apply_primitive\r\n outs = fun(*args)\r\nKeyboardInterrupt\r\n",,terminal_output +110,200051,"TERMINAL",0,0,"^CException ignored in: .remove at 0x14f0fc91cc10>\r\nTraceback (most recent call last):\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/weakref.py"", line 370, in remove\r\n def remove(k, selfref=ref(self)):\r\nKeyboardInterrupt: \r\n",,terminal_output +111,200257,"TERMINAL",0,0,"^CException ignored in atexit callback: \r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/api.py"", line 3189, in clean_up\r\n clear_caches()\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/api.py"", line 3210, in clear_caches\r\n util.clear_all_weakref_lru_caches()\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/util.py"", line 337, in clear_all_weakref_lru_caches\r\n cached_call.cache_clear()\r\nKeyboardInterrupt: \r\n",,terminal_output +112,200578,"TERMINAL",0,0,"^C\r\n]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0804 jafar]$ ",,terminal_output +113,200738,"TERMINAL",0,0,"^C[?2004l\r[?2004h[?2004l\r\r\n]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0804 jafar]$ ",,terminal_output +114,200975,"TERMINAL",0,0,"^C[?2004l\r[?2004h[?2004l\r\r\n]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0804 jafar]$ ",,terminal_output +115,303404,"TERMINAL",0,0,"\r(reverse-i-search)`': ",,terminal_output +116,303666,"TERMINAL",0,0,"m': python sample.py --checkpoint ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared//checkpoints/train_dyn_yolorun_new_arch/3352115"" --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +117,303807,"TERMINAL",0,0,"\ro': git checkout -b ""causal-transformer-dynamics-model""\r\n\r",,terminal_output +118,304050,"TERMINAL",0,0,"\rd': git checkout -b ""causal-transformer-dynamics-model""\ru': module unload devel/cuda/12.4\r",,terminal_output +119,304365,"TERMINAL",0,0,"[?25lom\r[8@failed reverse-i-search)`modue': modu[1@l[?25h",,terminal_output +120,305938,"TERMINAL",0,0,"[?25lom[1@r[1@q[?25h",,terminal_output +121,306989,"TERMINAL",0,0,"[?25l [?25h",,terminal_output +122,307170,"TERMINAL",0,0,"[?25l [?25h",,terminal_output +123,307520,"TERMINAL",0,0,"[?25l [?25h",,terminal_output +124,308493,"TERMINAL",0,0,"^C[?2004l\r[?2004h[?2004l\r\r\n]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0804 jafar]$ ",,terminal_output +125,309383,"TERMINAL",0,0,"\r(reverse-i-search)`': ",,terminal_output +126,309537,"TERMINAL",0,0,"m': python sample.py --checkpoint ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared//checkpoints/train_dyn_yolorun_new_arch/3352115"" --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +127,309707,"TERMINAL",0,0,"\ro': git checkout -b ""causal-transformer-dynamics-model""\r\n\r",,terminal_output +128,309813,"TERMINAL",0,0,"\rd': git checkout -b ""causal-transformer-dynamics-model""",,terminal_output +129,310090,"TERMINAL",0,0,"[?25lm\ru': module unload devel/cuda/12.4\r[?25h",,terminal_output +130,310231,"TERMINAL",0,0,"[?25lom[1@l': modul[?25h",,terminal_output +131,310305,"TERMINAL",0,0,"[1@e': module",,terminal_output +132,311464,"TERMINAL",0,0,"mpi/openmpi/5.0",,terminal_output +133,312031,"TERMINAL",0,0,"[?25l\r[9@jafar) [tum_cte0515@hkn0804 jafar]$ module\r\n[?2004l\r[?25h",,terminal_output +134,312192,"TERMINAL",0,0,"]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0804 jafar]$ ",,terminal_output +135,313152,"TERMINAL",0,0,"\r(reverse-i-search)`': ",,terminal_output +136,313398,"TERMINAL",0,0,"m': module unload mpi/openmpi/5.0",,terminal_output +137,313663,"TERMINAL",0,0,"[?25lm\ro': module unload mpi/openmpi/5.0\r[?25h",,terminal_output +138,313810,"TERMINAL",0,0,"[?25lm[1@d': mod[?25h",,terminal_output +139,313910,"TERMINAL",0,0,"[?25lm[1@u': modu[?25h",,terminal_output +140,314047,"TERMINAL",0,0,"[?25lm[1@l': modul[?25h",,terminal_output +141,314184,"TERMINAL",0,0,"[?25lm[1@e': module[?25h",,terminal_output +142,315781,"TERMINAL",0,0,"devel/cuda/12.4",,terminal_output +143,316491,"TERMINAL",0,0,"\r[9@jafar) [tum_cte0515@hkn0804 jafar]$ module\r\n[?2004l\r]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0804 jafar]$ ",,terminal_output +144,316974,"TERMINAL",0,0,"module unload devel/cuda/12.4",,terminal_output +145,317081,"TERMINAL",0,0,"mpi/openmpi/5.0",,terminal_output +146,317627,"TERMINAL",0,0,"python sample.py --checkpoint ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared//checkpoints/train_dyn_yolorun_new_arch/3352115"" --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +147,318194,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +148,319142,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +149,321453,"TERMINAL",0,0,"2025-07-17 09:49:24.246221: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +150,335339,"TERMINAL",0,0,"2025-07-17 09:49:38.114612: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +151,350537,"TERMINAL",0,0,"2025-07-17 09:49:53.330365: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +152,354298,"TERMINAL",0,0,"WARNING:absl:Missing metrics for step 33000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/033000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 32000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/032000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 34000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/034000/metrics/metrics not found.\r\n",,terminal_output +153,395872,"TERMINAL",0,0,"jax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 178, in \r\n vid = _autoreg_sample(rng, video_batch, action_batch)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 141, in _autoreg_sample\r\n generated_vid = sampling_fn(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 133, in _sampling_wrapper\r\n return module.sample_causal(batch, args.seq_len, args.temperature, args.sample_argmax)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 171, in sample_causal\r\n dyna_outputs = self.dynamics(dyna_inputs, training=False)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 1083, in op\r\n return getattr(self.aval, f""_{name}"")(self, *args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 583, in deferring_binary_op\r\n return binary_op(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/ufunc_api.py"", line 182, in __call__\r\n return call(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/ufuncs.py"", line 1237, in add\r\n out = lax.add(x, y)\r\nTypeError: add got incompatible shapes for broadcasting: (1, 2, 920, 512), (1, 3, 1, 512).\r\n",,terminal_output +154,397530,"TERMINAL",0,0,"]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0804 jafar]$ ",,terminal_output +155,546285,"genie.py",0,0,"",python,tab +156,551671,"genie.py",5425,0,"",python,selection_mouse +157,552728,"genie.py",5358,108,"",python,content +158,554660,"genie.py",5358,1,"",python,content +159,554696,"genie.py",5366,0,"",python,selection_command +160,554899,"genie.py",5291,0,"",python,selection_command +161,555103,"genie.py",5253,0,"",python,selection_command +162,555655,"genie.py",5245,38,"",python,content +163,555698,"genie.py",5257,0,"",python,selection_command +164,556602,"genie.py",5253,4,"",python,content +165,557003,"genie.py",5252,0,"",python,selection_command +166,557159,"genie.py",5184,0,"",python,selection_command +167,557536,"genie.py",5177,68,"",python,content +168,557582,"genie.py",5185,0,"",python,selection_command +169,558166,"genie.py",5256,0,"",python,selection_command +170,558441,"genie.py",5185,0,"",python,selection_command +171,558948,"genie.py",5144,0,"",python,selection_command +172,558993,"genie.py",5135,0,"",python,selection_command +173,559130,"genie.py",5114,0,"",python,selection_command +174,559259,"genie.py",5035,0,"",python,selection_command +175,559437,"genie.py",4992,0,"",python,selection_command +176,559938,"genie.py",4952,0,"",python,selection_command +177,560004,"genie.py",4909,0,"",python,selection_command +178,560026,"genie.py",4860,0,"",python,selection_command +179,560063,"genie.py",4851,0,"",python,selection_command +180,560090,"genie.py",4824,0,"",python,selection_command +181,560152,"genie.py",4765,0,"",python,selection_command +182,560181,"genie.py",4683,0,"",python,selection_command +183,560182,"genie.py",4643,0,"",python,selection_command +184,560199,"genie.py",4631,0,"",python,selection_command +185,560240,"genie.py",4574,0,"",python,selection_command +186,560302,"genie.py",4557,0,"",python,selection_command +187,560303,"genie.py",4548,0,"",python,selection_command +188,561368,"genie.py",4557,0,"",python,selection_command +189,561848,"genie.py",4574,0,"",python,selection_command +190,561891,"genie.py",4631,0,"",python,selection_command +191,561955,"genie.py",4643,0,"",python,selection_command +192,561992,"genie.py",4683,0,"",python,selection_command +193,561993,"genie.py",4765,0,"",python,selection_command +194,562028,"genie.py",4824,0,"",python,selection_command +195,562040,"genie.py",4851,0,"",python,selection_command +196,562069,"genie.py",4860,0,"",python,selection_command +197,562086,"genie.py",4909,0,"",python,selection_command +198,562148,"genie.py",4952,0,"",python,selection_command +199,562169,"genie.py",4992,0,"",python,selection_command +200,562232,"genie.py",5035,0,"",python,selection_command +201,562304,"genie.py",5114,0,"",python,selection_command +202,562304,"genie.py",5135,0,"",python,selection_command +203,562356,"genie.py",5144,0,"",python,selection_command +204,562357,"genie.py",5185,0,"",python,selection_command 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+257,953692,"genie.py",5724,0,"a",python,content +258,953694,"genie.py",5725,0,"",python,selection_keyboard +259,953831,"genie.py",5725,0,"k",python,content +260,953834,"genie.py",5726,0,"",python,selection_keyboard +261,954874,"genie.py",5721,5,"breakpoint",python,content +262,955668,"genie.py",5731,0,"()",python,content +263,955669,"genie.py",5732,0,"",python,selection_keyboard +264,955744,"genie.py",5732,1,")",python,content +265,955745,"genie.py",5733,0,"",python,selection_keyboard +266,955944,"genie.py",5732,0,"",python,selection_command +267,957082,"TERMINAL",0,0,"srun",,terminal_focus +268,957776,"TERMINAL",0,0,"srun",,terminal_focus +269,958874,"TERMINAL",0,0,"python sample.py --checkpoint ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared//checkpoints/train_dyn_yolorun_new_arch/3352115"" --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +270,959121,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +271,963465,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +272,965628,"TERMINAL",0,0,"2025-07-17 10:00:08.394391: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +273,966049,"genie.py",0,0,"",python,tab +274,979212,"TERMINAL",0,0,"2025-07-17 10:00:22.001500: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +275,989389,"genie.py",0,0,"",python,tab +276,994271,"TERMINAL",0,0,"2025-07-17 10:00:37.060069: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +277,997899,"TERMINAL",0,0,"WARNING:absl:Missing metrics for step 32000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/032000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 33000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/033000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 34000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/034000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/020000/metrics/metrics not found.\r\n",,terminal_output +278,1039418,"TERMINAL",0,0,"jax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 178, in \r\n vid = _autoreg_sample(rng, video_batch, action_batch)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 141, in _autoreg_sample\r\n generated_vid = sampling_fn(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 133, in _sampling_wrapper\r\n return module.sample_causal(batch, args.seq_len, args.temperature, args.sample_argmax)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 167, in sample_causal\r\n dyna_outputs = self.dynamics(dyna_inputs, training=False)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 1083, in op\r\n return getattr(self.aval, f""_{name}"")(self, *args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 583, in deferring_binary_op\r\n return binary_op(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/ufunc_api.py"", line 182, in __call__\r\n return call(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/ufuncs.py"", line 1237, in add\r\n out = lax.add(x, y)\r\nTypeError: add got incompatible shapes for broadcasting: (1, 2, 920, 512), (1, 3, 1, 512).\r\n",,terminal_output +279,1040786,"TERMINAL",0,0,"]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0804 jafar]$ ",,terminal_output +280,1062397,"TERMINAL",0,0,"srun",,terminal_focus +281,1064299,"genie.py",0,0,"",python,tab +282,1066755,"genie.py",5767,0,"",python,selection_command +283,1067420,"genie.py",5768,0,"",python,selection_command +284,1068037,"genie.py",5769,0,"",python,selection_command +285,1068539,"genie.py",5732,0,"",python,selection_command +286,1069020,"genie.py",5731,0,"",python,selection_command +287,1069517,"genie.py",5730,0,"",python,selection_command +288,1069572,"genie.py",5729,0,"",python,selection_command +289,1069606,"genie.py",5728,0,"",python,selection_command +290,1069638,"genie.py",5727,0,"",python,selection_command +291,1069661,"genie.py",5726,0,"",python,selection_command +292,1069686,"genie.py",5725,0,"",python,selection_command +293,1069711,"genie.py",5724,0,"",python,selection_command +294,1069735,"genie.py",5723,0,"",python,selection_command 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+312,1073985,"genie.py",5715,5,"debug",python,selection_mouse +313,1074132,"genie.py",5699,30," jax.debug.print()\n",python,selection_mouse +314,1075013,"genie.py",5711,0,"",python,selection_command +315,1098182,"genie.py",5699,0,"",python,selection_command +316,1100749,"genie.py",5699,0," jax.debug.print(""token_idxs_full shape: {}"", token_idxs_full.shape)\n",python,content +317,1101766,"genie.py",5779,0," jax.debug.print(""action_tokens shape: {}"", action_tokens.shape)\n",python,content +318,1102918,"genie.py",5855,0," jax.debug.print(""mask_rng: {}"", batch.get(""mask_rng"", None))\n",python,content +319,1102920,"genie.py",5928,30,"",python,content +320,1107666,"genie.py",5979,0,"",python,selection_mouse +321,1108225,"genie.py",5906,0,"",python,selection_mouse +322,1109241,"genie.py",5905,0,"",python,selection_command +323,1110430,"genie.py",4875,0,"",python,selection_mouse +324,1112007,"genie.py",5732,0,"",python,selection_mouse +325,1115189,"genie.py",4851,0,"",python,selection_mouse +326,1117314,"genie.py",4851,0,"\n jax.debug.print(""token_idxs_full shape: {}"", token_idxs_full.shape)",python,content +327,1117384,"genie.py",4864,0,"",python,selection_command +328,1118345,"genie.py",4860,4,"",python,content +329,1118669,"genie.py",4859,0,"",python,selection_command +330,1118845,"genie.py",4860,0,"",python,selection_command +331,1119288,"genie.py",4861,0,"",python,selection_command +332,1119335,"genie.py",4862,0,"",python,selection_command +333,1119361,"genie.py",4863,0,"",python,selection_command +334,1119407,"genie.py",4864,0,"",python,selection_command +335,1119473,"genie.py",4865,0,"",python,selection_command +336,1119474,"genie.py",4866,0,"",python,selection_command +337,1119494,"genie.py",4867,0,"",python,selection_command +338,1119523,"genie.py",4868,0,"",python,selection_command +339,1119668,"genie.py",4869,0,"",python,selection_command +340,1119669,"genie.py",4870,0,"",python,selection_command 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+424,1184157,"genie.py",5008,0,"",python,selection_command +425,1808149,"genie.py",5009,0,"",python,selection_mouse +426,1808165,"genie.py",5008,0,"",python,selection_command +427,1889656,"genie.py",3798,0,"",python,selection_command +428,1890209,"genie.py",4040,0,"",python,selection_mouse +429,1903192,"genie.py",5092,0,"",python,selection_mouse +430,1903221,"genie.py",5091,0,"",python,selection_command +431,1903919,"genie.py",5092,0,"",python,selection_mouse +432,1903922,"genie.py",5091,0,"",python,selection_command +433,1907754,"TERMINAL",0,0,"srun",,terminal_focus +434,1913080,"TERMINAL",0,0,"python sample.py --checkpoint ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared//checkpoints/train_dyn_yolorun_new_arch/3352115"" --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +435,1913359,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +436,1914846,"genie.py",0,0,"",python,tab +437,1915948,"genie.py",5049,0,"",python,selection_mouse +438,1915953,"genie.py",5048,0,"",python,selection_command +439,1916502,"genie.py",5049,0,"",python,selection_mouse +440,1916520,"genie.py",5048,0,"",python,selection_command +441,1916753,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +442,1918822,"TERMINAL",0,0,"2025-07-17 10:16:01.571686: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +443,1925017,"genie.py",5005,0,"",python,selection_command +444,1925369,"genie.py",4956,0,"",python,selection_command +445,1925744,"genie.py",4890,0,"",python,selection_command +446,1925972,"genie.py",4851,0,"",python,selection_command +447,1926151,"genie.py",4890,0,"",python,selection_command +448,1926367,"genie.py",4956,0,"",python,selection_command +449,1926651,"genie.py",4890,0,"",python,selection_command +450,1926836,"genie.py",4956,0,"",python,selection_command +451,1927133,"genie.py",4890,0,"",python,selection_command +452,1927535,"genie.py",4851,0,"",python,selection_command +453,1927866,"genie.py",4890,0,"",python,selection_command +454,1932394,"TERMINAL",0,0,"2025-07-17 10:16:15.167351: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +455,1947300,"TERMINAL",0,0,"2025-07-17 10:16:30.026963: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +456,1951096,"TERMINAL",0,0,"WARNING:absl:Missing metrics for step 33000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/033000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 34000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/034000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/020000/metrics/metrics not found.\r\n",,terminal_output +457,1951151,"TERMINAL",0,0,"WARNING:absl:Missing metrics for step 35000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/035000/metrics/metrics not found.\r\n",,terminal_output +458,1992151,"TERMINAL",0,0,"jax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 178, in \r\n vid = _autoreg_sample(rng, video_batch, action_batch)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 141, in _autoreg_sample\r\n generated_vid = sampling_fn(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 133, in _sampling_wrapper\r\n return module.sample_causal(batch, args.seq_len, args.temperature, args.sample_argmax)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py"", line 170, in sample_causal\r\n dyna_outputs = self.dynamics(dyna_inputs, training=False)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/models/dynamics.py"", line 97, in __call__\r\n vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 1083, in op\r\n return getattr(self.aval, f""_{name}"")(self, *args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py"", line 583, in deferring_binary_op\r\n return binary_op(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/ufunc_api.py"", line 182, in __call__\r\n return call(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/numpy/ufuncs.py"", line 1237, in add\r\n out = lax.add(x, y)\r\nTypeError: add got incompatible shapes for broadcasting: (1, 2, 920, 512), (1, 3, 1, 512).\r\n",,terminal_output +459,1993499,"TERMINAL",0,0,"]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0804 jafar]$ ",,terminal_output +460,2021457,"TERMINAL",0,0,"srun",,terminal_focus +461,2023260,"genie.py",0,0,"",python,tab +462,2028656,"genie.py",4789,0,"",python,selection_mouse +463,2029219,"genie.py",4838,0,"",python,selection_mouse +464,2030763,"genie.py",4827,0,"",python,selection_mouse +465,2031413,"genie.py",4830,0,"",python,selection_mouse +466,2034177,"genie.py",5038,0,"",python,selection_mouse +467,2034726,"genie.py",5047,0,"",python,selection_mouse +468,2036283,"genie.py",5194,0,"",python,selection_mouse +469,2036678,"genie.py",5201,0,"",python,selection_mouse +470,2045012,"genie.py",8712,0,"",python,selection_mouse +471,2050701,"genie.py",5259,0,"",python,selection_mouse +472,2050843,"genie.py",5251,13,"action_tokens",python,selection_mouse +473,2051856,"genie.py",5270,0,"",python,selection_mouse +474,2052041,"genie.py",5267,4,"self",python,selection_mouse +475,2052878,"genie.py",5285,0,"",python,selection_mouse +476,2053030,"genie.py",5279,9,"get_codes",python,selection_mouse +477,2054266,"genie.py",5259,0,"",python,selection_mouse +478,2054387,"genie.py",5251,13,"action_tokens",python,selection_mouse +479,2080227,"genie.py",5785,0,"",python,selection_mouse +480,2080704,"genie.py",5764,0,"",python,selection_mouse +481,2080734,"genie.py",5763,0,"",python,selection_command +482,2090469,"genie.py",5536,0,"",python,selection_mouse +483,2090614,"genie.py",5530,11,"dyna_inputs",python,selection_mouse +484,2091737,"genie.py",6032,0,"",python,selection_mouse +485,2091885,"genie.py",6026,8,"dynamics",python,selection_mouse +486,2092673,"genie.py",6023,0,"",python,selection_mouse +487,2094788,"genie.py",6031,0,"",python,selection_mouse +488,2096767,"genie.py",0,0,"",python,tab +489,2096768,"genie.py",2479,0,"",python,selection_mouse +490,2097414,"models/dynamics.py",0,0,"from typing import Dict, Any\n\nimport jax\nimport jax.numpy as jnp\nimport flax.linen as nn\nimport einops\n\nfrom utils.nn import STTransformer\n\n\nclass DynamicsMaskGIT(nn.Module):\n """"""MaskGIT dynamics model""""""\n\n model_dim: int\n num_latents: int\n num_blocks: int\n num_heads: int\n dropout: float\n mask_limit: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n def setup(self):\n self.dynamics = STTransformer(\n self.model_dim,\n self.num_latents,\n self.num_blocks,\n self.num_heads,\n self.dropout,\n self.param_dtype,\n self.dtype,\n spacial_bert=True,\n )\n self.patch_embed = nn.Embed(self.num_latents, self.model_dim)\n self.mask_token = self.param(\n ""mask_token"",\n nn.initializers.lecun_uniform(),\n (1, 1, 1, self.model_dim),\n )\n self.action_up = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )\n\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\n # --- Mask videos ---\n vid_embed = self.patch_embed(batch[""video_tokens""])\n if training:\n rng1, rng2 = jax.random.split(batch[""mask_rng""])\n mask_prob = jax.random.uniform(rng1, minval=self.mask_limit)\n mask = jax.random.bernoulli(rng2, mask_prob, vid_embed.shape[:-1])\n mask = mask.at[:, 0].set(False)\n vid_embed = jnp.where(jnp.expand_dims(mask, -1), self.mask_token, vid_embed)\n else:\n mask = None\n\n # --- Predict transition ---\n act_embed = self.action_up(batch[""latent_actions""])\n vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\n logits = self.dynamics(vid_embed)\n return dict(token_logits=logits, mask=mask)\n\n\nclass DynamicsAutoregressive(nn.Module):\n """"""Autoregressive (causal) dynamics model""""""\n\n model_dim: int\n num_latents: int\n num_blocks: int\n num_heads: int\n dropout: float\n param_dtype: jnp.dtype\n dtype: jnp.dtype\n\n def setup(self):\n self.dynamics = STTransformer(\n self.model_dim,\n self.num_latents,\n self.num_blocks,\n self.num_heads,\n self.dropout,\n self.param_dtype,\n self.dtype,\n spacial_bert=False,\n )\n self.patch_embed = nn.Embed(self.num_latents, self.model_dim)\n self.action_up = nn.Dense(\n self.model_dim,\n param_dtype=self.param_dtype,\n dtype=self.dtype,\n )\n\n def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\n vid_embed = self.patch_embed(batch[""video_tokens""])\n act_embed = self.action_up(batch[""latent_actions""])\n vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\n logits = self.dynamics(vid_embed)\n mask = jnp.ones(vid_embed.shape[:-1])\n return dict(token_logits=logits, mask=mask)",python,tab +491,2100856,"models/dynamics.py",2750,0,"",python,selection_mouse +492,2101474,"models/dynamics.py",2764,0,"",python,selection_mouse +493,2101631,"models/dynamics.py",2763,11,"patch_embed",python,selection_mouse +494,2102513,"models/dynamics.py",2789,0,"",python,selection_mouse +495,2102693,"models/dynamics.py",2782,12,"video_tokens",python,selection_mouse +496,2102979,"models/dynamics.py",2781,13,"""video_tokens",python,selection_mouse +497,2102979,"models/dynamics.py",2780,14,"[""video_tokens",python,selection_mouse +498,2102980,"models/dynamics.py",2775,19,"batch[""video_tokens",python,selection_mouse +499,2103475,"models/dynamics.py",2775,0,"",python,selection_mouse +500,2103476,"models/dynamics.py",2775,5,"batch",python,selection_mouse +501,2103735,"models/dynamics.py",2775,6,"batch[",python,selection_mouse +502,2103736,"models/dynamics.py",2775,7,"batch[""",python,selection_mouse +503,2103764,"models/dynamics.py",2775,19,"batch[""video_tokens",python,selection_mouse +504,2104387,"models/dynamics.py",2775,20,"batch[""video_tokens""",python,selection_mouse +505,2108211,"models/dynamics.py",2916,0,"",python,selection_mouse +506,2108853,"models/dynamics.py",2855,0,"",python,selection_mouse +507,2108995,"models/dynamics.py",2854,3,"""])",python,selection_mouse +508,2109164,"models/dynamics.py",2854,3,"""])",python,selection_mouse +509,2109209,"models/dynamics.py",2840,17,"latent_actions""])",python,selection_mouse +510,2109355,"models/dynamics.py",2839,18,"""latent_actions""])",python,selection_mouse +511,2109395,"models/dynamics.py",2838,19,"[""latent_actions""])",python,selection_mouse +512,2109443,"models/dynamics.py",2833,24,"batch[""latent_actions""])",python,selection_mouse +513,2110021,"models/dynamics.py",2837,0,"",python,selection_mouse +514,2110021,"models/dynamics.py",2833,5,"batch",python,selection_mouse +515,2110209,"models/dynamics.py",2833,6,"batch[",python,selection_mouse +516,2110237,"models/dynamics.py",2833,21,"batch[""latent_actions",python,selection_mouse +517,2110392,"models/dynamics.py",2833,22,"batch[""latent_actions""",python,selection_mouse +518,2110419,"models/dynamics.py",2833,23,"batch[""latent_actions""]",python,selection_mouse +519,2110420,"models/dynamics.py",2833,24,"batch[""latent_actions""])",python,selection_mouse +520,2110739,"models/dynamics.py",2833,23,"batch[""latent_actions""]",python,selection_mouse +521,2110816,"models/dynamics.py",2833,22,"batch[""latent_actions""",python,selection_mouse +522,2115396,"genie.py",0,0,"",python,tab +523,2116296,"genie.py",6101,0,"",python,selection_mouse +524,2116821,"genie.py",6033,0,"",python,selection_mouse +525,2117440,"genie.py",5964,0,"",python,selection_mouse +526,2119491,"genie.py",6030,0,"",python,selection_mouse +527,2134317,"genie.py",5994,69," dyna_outputs = self.dynamics(dyna_inputs, training=False)",python,selection_command +528,2134540,"genie.py",5994,135," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)",python,selection_command +529,2135076,"genie.py",5994,216," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)",python,selection_command +530,2135129,"genie.py",5994,312," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)",python,selection_command +531,2135130,"genie.py",5994,313," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\n",python,selection_command +532,2135170,"genie.py",5994,359," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\n\n # Sample or argmax for each patch",python,selection_command +533,2135305,"genie.py",5994,389," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\n\n # Sample or argmax for each patch\n if sample_argmax:",python,selection_command +534,2135468,"genie.py",5994,467," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\n\n # Sample or argmax for each patch\n if sample_argmax:\n next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)",python,selection_command +535,2135469,"genie.py",5994,485," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\n\n # Sample or argmax for each patch\n if sample_argmax:\n next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n else:",python,selection_command +536,2135469,"genie.py",5994,521," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\n\n # Sample or argmax for each patch\n if sample_argmax:\n next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n else:\n if rng is not None:",python,selection_command +537,2135469,"genie.py",5994,579," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\n\n # Sample or argmax for each patch\n if sample_argmax:\n next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n else:\n if rng is not None:\n rng, step_rng = jax.random.split(rng)",python,selection_command +538,2135470,"genie.py",5994,601," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\n\n # Sample or argmax for each patch\n if sample_argmax:\n next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n else:\n if rng is not None:\n rng, step_rng = jax.random.split(rng)\n else:",python,selection_command +539,2135630,"genie.py",5994,654," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\n\n # Sample or argmax for each patch\n if sample_argmax:\n next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n else:\n if rng is not None:\n rng, step_rng = jax.random.split(rng)\n else:\n step_rng = jax.random.PRNGKey(0)",python,selection_command +540,2135631,"genie.py",5994,707," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\n\n # Sample or argmax for each patch\n if sample_argmax:\n next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n else:\n if rng is not None:\n rng, step_rng = jax.random.split(rng)\n else:\n step_rng = jax.random.PRNGKey(0)\n next_token = jax.random.categorical(",python,selection_command +541,2135631,"genie.py",5994,778," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\n\n # Sample or argmax for each patch\n if sample_argmax:\n next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n else:\n if rng is not None:\n rng, step_rng = jax.random.split(rng)\n else:\n step_rng = jax.random.PRNGKey(0)\n next_token = jax.random.categorical(\n step_rng, next_token_logits / temperature, axis=-1",python,selection_command +542,2135631,"genie.py",5994,806," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\n\n # Sample or argmax for each patch\n if sample_argmax:\n next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n else:\n if rng is not None:\n rng, step_rng = jax.random.split(rng)\n else:\n step_rng = jax.random.PRNGKey(0)\n next_token = jax.random.categorical(\n step_rng, next_token_logits / temperature, axis=-1\n ) # (B, N)",python,selection_command +543,2135632,"genie.py",5994,807," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\n\n # Sample or argmax for each patch\n if sample_argmax:\n next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n else:\n if rng is not None:\n rng, step_rng = jax.random.split(rng)\n else:\n step_rng = jax.random.PRNGKey(0)\n next_token = jax.random.categorical(\n step_rng, next_token_logits / temperature, axis=-1\n ) # (B, N)\n",python,selection_command +544,2135632,"genie.py",5994,867," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\n\n # Sample or argmax for each patch\n if sample_argmax:\n next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n else:\n if rng is not None:\n rng, step_rng = jax.random.split(rng)\n else:\n step_rng = jax.random.PRNGKey(0)\n next_token = jax.random.categorical(\n step_rng, next_token_logits / temperature, axis=-1\n ) # (B, N)\n\n # Insert the generated tokens into the sequence",python,selection_command +545,2135690,"genie.py",5994,941," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\n\n # Sample or argmax for each patch\n if sample_argmax:\n next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n else:\n if rng is not None:\n rng, step_rng = jax.random.split(rng)\n else:\n step_rng = jax.random.PRNGKey(0)\n next_token = jax.random.categorical(\n step_rng, next_token_logits / temperature, axis=-1\n ) # (B, N)\n\n # Insert the generated tokens into the sequence\n token_idxs_full = token_idxs_full.at[:, t, :].set(next_token)",python,selection_command +546,2135690,"genie.py",5994,942," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\n\n # Sample or argmax for each patch\n if sample_argmax:\n next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n else:\n if rng is not None:\n rng, step_rng = jax.random.split(rng)\n else:\n step_rng = jax.random.PRNGKey(0)\n next_token = jax.random.categorical(\n step_rng, next_token_logits / temperature, axis=-1\n ) # (B, N)\n\n # Insert the generated tokens into the sequence\n token_idxs_full = token_idxs_full.at[:, t, :].set(next_token)\n",python,selection_command +547,2135691,"genie.py",5994,997," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\n\n # Sample or argmax for each patch\n if sample_argmax:\n next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n else:\n if rng is not None:\n rng, step_rng = jax.random.split(rng)\n else:\n step_rng = jax.random.PRNGKey(0)\n next_token = jax.random.categorical(\n step_rng, next_token_logits / temperature, axis=-1\n ) # (B, N)\n\n # Insert the generated tokens into the sequence\n token_idxs_full = token_idxs_full.at[:, t, :].set(next_token)\n\n # --- Decode all tokens at once at the end ---",python,selection_command +548,2135691,"genie.py",5994,1043," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\n\n # Sample or argmax for each patch\n if sample_argmax:\n next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n else:\n if rng is not None:\n rng, step_rng = jax.random.split(rng)\n else:\n step_rng = jax.random.PRNGKey(0)\n next_token = jax.random.categorical(\n step_rng, next_token_logits / temperature, axis=-1\n ) # (B, N)\n\n # Insert the generated tokens into the sequence\n token_idxs_full = token_idxs_full.at[:, t, :].set(next_token)\n\n # --- Decode all tokens at once at the end ---\n final_frames = self.tokenizer.decode(",python,selection_command +549,2135787,"genie.py",5994,1108," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\n\n # Sample or argmax for each patch\n if sample_argmax:\n next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n else:\n if rng is not None:\n rng, step_rng = jax.random.split(rng)\n else:\n step_rng = jax.random.PRNGKey(0)\n next_token = jax.random.categorical(\n step_rng, next_token_logits / temperature, axis=-1\n ) # (B, N)\n\n # Insert the generated tokens into the sequence\n token_idxs_full = token_idxs_full.at[:, t, :].set(next_token)\n\n # --- Decode all tokens at once at the end ---\n final_frames = self.tokenizer.decode(\n token_idxs_full, video_hw=batch[""videos""].shape[2:4]",python,selection_command +550,2135940,"genie.py",5994,1118," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\n\n # Sample or argmax for each patch\n if sample_argmax:\n next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n else:\n if rng is not None:\n rng, step_rng = jax.random.split(rng)\n else:\n step_rng = jax.random.PRNGKey(0)\n next_token = jax.random.categorical(\n step_rng, next_token_logits / temperature, axis=-1\n ) # (B, N)\n\n # Insert the generated tokens into the sequence\n token_idxs_full = token_idxs_full.at[:, t, :].set(next_token)\n\n # --- Decode all tokens at once at the end ---\n final_frames = self.tokenizer.decode(\n token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n )",python,selection_command +551,2136385,"genie.py",5994,1146," dyna_outputs = self.dynamics(dyna_inputs, training=False)\n # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\n # We want the logits for the last time step (frame t-1 predicting t)\n next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\n\n # Sample or argmax for each patch\n if sample_argmax:\n next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n else:\n if rng is not None:\n rng, step_rng = jax.random.split(rng)\n else:\n step_rng = jax.random.PRNGKey(0)\n next_token = jax.random.categorical(\n step_rng, next_token_logits / temperature, axis=-1\n ) # (B, N)\n\n # Insert the generated tokens into the sequence\n token_idxs_full = token_idxs_full.at[:, t, :].set(next_token)\n\n # --- Decode all tokens at once at the end ---\n final_frames = self.tokenizer.decode(\n token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n )\n return final_frames",python,selection_command +552,2136571,"genie.py",6006,0,"",python,selection_command +553,2137422,"genie.py",7121,0,"#",python,content +554,2137422,"genie.py",7111,0,"#",python,content +555,2137422,"genie.py",7050,0,"#",python,content +556,2137422,"genie.py",7000,0,"#",python,content +557,2137422,"genie.py",6945,0,"#",python,content +558,2137422,"genie.py",6874,0,"#",python,content +559,2137423,"genie.py",6814,0,"#",python,content +560,2137423,"genie.py",6789,0,"#",python,content +561,2137423,"genie.py",6722,0,"#",python,content +562,2137423,"genie.py",6665,0,"#",python,content +563,2137423,"genie.py",6616,0,"#",python,content +564,2137423,"genie.py",6590,0,"#",python,content +565,2137423,"genie.py",6536,0,"#",python,content +566,2137423,"genie.py",6496,0,"#",python,content +567,2137423,"genie.py",6474,0,"#",python,content +568,2137423,"genie.py",6400,0,"#",python,content +569,2137423,"genie.py",6366,0,"#",python,content +570,2137423,"genie.py",6320,0,"#",python,content +571,2137423,"genie.py",6223,0,"#",python,content +572,2137424,"genie.py",6142,0,"#",python,content +573,2137424,"genie.py",6076,0,"#",python,content +574,2137424,"genie.py",6006,0,"#",python,content +575,2137425,"genie.py",6007,0,"",python,selection_keyboard +576,2137479,"genie.py",7143,0," ",python,content +577,2137479,"genie.py",7132,0," ",python,content +578,2137479,"genie.py",7070,0," ",python,content +579,2137479,"genie.py",7019,0," ",python,content +580,2137479,"genie.py",6963,0," ",python,content +581,2137479,"genie.py",6891,0," ",python,content +582,2137479,"genie.py",6830,0," ",python,content +583,2137480,"genie.py",6804,0," ",python,content +584,2137480,"genie.py",6736,0," ",python,content +585,2137480,"genie.py",6678,0," ",python,content +586,2137480,"genie.py",6628,0," ",python,content +587,2137480,"genie.py",6601,0," ",python,content +588,2137480,"genie.py",6546,0," ",python,content +589,2137480,"genie.py",6505,0," ",python,content +590,2137480,"genie.py",6482,0," ",python,content +591,2137480,"genie.py",6407,0," ",python,content +592,2137480,"genie.py",6372,0," ",python,content +593,2137480,"genie.py",6325,0," ",python,content +594,2137480,"genie.py",6227,0," ",python,content +595,2137480,"genie.py",6145,0," ",python,content +596,2137481,"genie.py",6078,0," ",python,content +597,2137481,"genie.py",6007,0," ",python,content +598,2137482,"genie.py",6008,0,"",python,selection_keyboard +599,2138298,"genie.py",6007,0,"",python,selection_command +600,2142532,"genie.py",5539,0,"",python,selection_mouse +601,2143330,"genie.py",5788,0,"",python,selection_mouse +602,2144106,"genie.py",6021,0,"",python,selection_mouse +603,2144849,"genie.py",6018,0,"",python,selection_mouse +604,2145577,"genie.py",5941,0,"",python,selection_mouse +605,2158386,"TERMINAL",0,0,"srun",,terminal_focus +606,2159609,"TERMINAL",0,0,"python sample.py --checkpoint ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared//checkpoints/train_dyn_yolorun_new_arch/3352115"" --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +607,2160149,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +608,2162889,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +609,2164940,"TERMINAL",0,0,"2025-07-17 10:20:07.697362: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +610,2178224,"TERMINAL",0,0,"2025-07-17 10:20:20.990720: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +611,2193382,"TERMINAL",0,0,"2025-07-17 10:20:36.168656: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +612,2197054,"TERMINAL",0,0,"WARNING:absl:Missing metrics for step 33000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/033000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 34000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/034000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 35000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/035000/metrics/metrics not found.\r\n",,terminal_output +613,2237728,"TERMINAL",0,0,"action_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\ntoken_idxs shape: (Array(1, dtype=int32), Array(1, dtype=int32), Array(920, dtype=int32))\r\nmask_rng: None\r\nmask_rng: None\r\nmask_rng: None\r\nmask_rng: None\r\nmask_rng: None\r\nmask_rng: None\r\nmask_rng: None\r\nmask_rng: None\r\nmask_rng: None\r\nmask_rng: None\r\nmask_rng: None\r\nmask_rng: None\r\nmask_rng: None\r\nmask_rng: None\r\nmask_rng: None\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 179, in \r\n gt = video_batch[:, : vid.shape[1]].clip(0, 1).reshape(-1, *video_batch.shape[2:])\r\nAttributeError: 'NoneType' object has no attribute 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+711,2463639,"genie.py",5951,0,"",python,selection_keyboard +712,2464271,"genie.py",5950,1,"debug",python,content +713,2464837,"genie.py",5955,0,"-",python,content +714,2464838,"genie.py",5956,0,"",python,selection_keyboard +715,2465405,"genie.py",5955,1,"",python,content +716,2465835,"genie.py",5955,0,".",python,content +717,2465837,"genie.py",5956,0,"",python,selection_keyboard +718,2466116,"genie.py",5956,0,"b",python,content +719,2466118,"genie.py",5957,0,"",python,selection_keyboard +720,2466189,"genie.py",5957,0,"r",python,content +721,2466191,"genie.py",5958,0,"",python,selection_keyboard +722,2466353,"genie.py",5958,0,"e",python,content +723,2466354,"genie.py",5959,0,"",python,selection_keyboard +724,2466755,"genie.py",5956,3,"breakpoint",python,content +725,2467596,"genie.py",5966,0,"()",python,content +726,2467597,"genie.py",5967,0,"",python,selection_keyboard +727,2467691,"genie.py",5967,1,")",python,content +728,2467692,"genie.py",5968,0,"",python,selection_keyboard +729,2468811,"genie.py",5967,0,"",python,selection_command +730,2473508,"TERMINAL",0,0,"python sample.py --checkpoint ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared//checkpoints/train_dyn_yolorun_new_arch/3352115"" --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +731,2473934,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +732,2475701,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +733,2477870,"TERMINAL",0,0,"2025-07-17 10:25:20.649234: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +734,2479323,"genie.py",0,0,"",python,tab +735,2483061,"genie.py",5198,0,"",python,selection_mouse +736,2483231,"genie.py",5198,1,"N",python,selection_mouse +737,2484087,"genie.py",5187,0,"",python,selection_mouse +738,2484241,"genie.py",5186,1,"B",python,selection_mouse +739,2485502,"genie.py",5070,0,"",python,selection_mouse +740,2485637,"genie.py",5058,15,"token_idxs_full",python,selection_mouse +741,2486843,"genie.py",5321,0,"",python,selection_mouse +742,2487995,"genie.py",5320,0,"",python,selection_mouse +743,2488867,"genie.py",5321,0,"",python,selection_mouse +744,2489058,"genie.py",5320,1,"S",python,selection_mouse +745,2489313,"genie.py",5320,2,"S-",python,selection_mouse +746,2489451,"genie.py",5320,3,"S-1",python,selection_mouse +747,2490212,"genie.py",5323,0,"",python,selection_mouse +748,2490881,"genie.py",5375,0,"",python,selection_mouse +749,2490917,"genie.py",5374,0,"",python,selection_command +750,2491030,"genie.py",5375,0,"",python,selection_mouse +751,2491035,"genie.py",5374,0,"",python,selection_command +752,2491200,"genie.py",5374,1,"-",python,selection_mouse +753,2491206,"genie.py",5375,0,"",python,selection_command +754,2491324,"genie.py",5321,54,"-1, )\n # --- Autoregressive generation loop ---",python,selection_mouse +755,2491548,"TERMINAL",0,0,"2025-07-17 10:25:34.339470: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +756,2491857,"genie.py",5321,0,"",python,selection_mouse +757,2492084,"genie.py",5320,1,"S",python,selection_mouse +758,2492831,"genie.py",5321,0,"",python,selection_mouse +759,2493571,"genie.py",5321,1,"-",python,selection_mouse +760,2494110,"genie.py",5322,0,"",python,selection_mouse +761,2506671,"TERMINAL",0,0,"2025-07-17 10:25:49.460547: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +762,2510687,"TERMINAL",0,0,"WARNING:absl:Missing metrics for step 34000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/034000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 33000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/033000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 35000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/035000/metrics/metrics not found.\r\n",,terminal_output +763,2520021,"genie.py",0,0,"",python,tab +764,2522062,"genie.py",5674,0,"",python,selection_mouse +765,2522179,"genie.py",5665,13,"action_tokens",python,selection_mouse +766,2525979,"genie.py",8753,0,"",python,selection_mouse +767,2526114,"genie.py",8745,13,"action_tokens",python,selection_mouse +768,2546746,"genie.py",8860,0,"",python,selection_mouse +769,2549341,"genie.py",11633,0,"",python,selection_mouse +770,2549532,"genie.py",11624,9,"vid_embed",python,selection_mouse +771,2552170,"genie.py",11572,0,"",python,selection_mouse +772,2552446,"TERMINAL",0,0,"2025-07-17 10:26:35.217316: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-17 10:26:35.217761: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +773,2559120,"TERMINAL",0,0,"token_idxs shape: (Array(1, dtype=int32), Array(1, dtype=int32), Array(920, dtype=int32))\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\nEntering jdb:\r\n(jdb) ",,terminal_output +774,2576543,"TERMINAL",0,0,"a",,terminal_output +775,2576739,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +776,2576956,"TERMINAL",0,0,"[?25lt[?25h",,terminal_output +777,2577066,"TERMINAL",0,0,"[?25li[?25h",,terminal_output +778,2577174,"TERMINAL",0,0,"[?25lo[?25h",,terminal_output +779,2577604,"TERMINAL",0,0,"[?25ln[?25h",,terminal_output +780,2577964,"TERMINAL",0,0,"[?25l_[?25h",,terminal_output +781,2578226,"TERMINAL",0,0,"[?25lt[?25h",,terminal_output +782,2578282,"TERMINAL",0,0,"[?25lo[?25h",,terminal_output +783,2578371,"TERMINAL",0,0,"[?25lk[?25h",,terminal_output +784,2578487,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +785,2578622,"TERMINAL",0,0,"[?25ln[?25h",,terminal_output +786,2578735,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +787,2578839,"TERMINAL",0,0,"[?25l.[?25h",,terminal_output +788,2579021,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +789,2579164,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +790,2579309,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +791,2579409,"TERMINAL",0,0,"[?25lp[?25h",,terminal_output +792,2579570,"TERMINAL",0,0,"[?25le[?25h\r\n(1, 15, 1, 32)\r\n(jdb) ",,terminal_output +793,2582046,"TERMINAL",0,0,"t",,terminal_output +794,2582273,"TERMINAL",0,0,"[?25lk[?25h",,terminal_output +795,2582788,"TERMINAL",0,0,"ó",,terminal_output +796,2582841,"TERMINAL",0,0,"k",,terminal_output +797,2583605,"TERMINAL",0,0,"[?25lk\r[?25h",,terminal_output +798,2583901,"TERMINAL",0,0,"[?25ló\r[?25h",,terminal_output +799,2584010,"TERMINAL",0,0,"[?25lk\r[?25h",,terminal_output +800,2584262,"TERMINAL",0,0,"[?25lo[?25h",,terminal_output +801,2584322,"TERMINAL",0,0,"[?25lk[?25h",,terminal_output +802,2584531,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +803,2584946,"TERMINAL",0,0,"[?25ln[?25h",,terminal_output +804,2585319,"TERMINAL",0,0,"[?25l_[?25h",,terminal_output +805,2585640,"TERMINAL",0,0,"[?25li[?25h",,terminal_output +806,2585750,"TERMINAL",0,0,"[?25ld[?25h",,terminal_output +807,2586886,"TERMINAL",0,0,"[?25lx[?25h",,terminal_output +808,2587013,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +809,2587635,"TERMINAL",0,0,"[?25l_[?25h",,terminal_output +810,2587893,"TERMINAL",0,0,"[?25lf[?25h",,terminal_output +811,2588033,"TERMINAL",0,0,"[?25lu[?25h",,terminal_output +812,2588410,"TERMINAL",0,0,"[?25ll[?25h",,terminal_output +813,2588554,"TERMINAL",0,0,"[?25ll[?25h",,terminal_output +814,2588953,"TERMINAL",0,0,"[?25l-[?25h",,terminal_output +815,2589163,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +816,2589279,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +817,2589537,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +818,2589675,"TERMINAL",0,0,"[?25lp[?25h",,terminal_output +819,2589810,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +820,2590772,"TERMINAL",0,0,"[?25le\r[?25h",,terminal_output +821,2590883,"TERMINAL",0,0,"[?25lp\r[?25h",,terminal_output +822,2591086,"TERMINAL",0,0,"[?25lha\r[?25h",,terminal_output +823,2591193,"TERMINAL",0,0,"\r",,terminal_output +824,2591305,"TERMINAL",0,0,"[?25ls\r[?25h",,terminal_output +825,2591884,"TERMINAL",0,0,"[?25l-\r[?25h",,terminal_output +826,2592093,"TERMINAL",0,0,"[?25l.[?25h",,terminal_output +827,2592284,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +828,2592745,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output 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+844,2606877,"genie.py",11600,40,"(action_tokens)\n vid_embed += jnp",python,selection_mouse +845,2606877,"genie.py",11601,39,"action_tokens)\n vid_embed += jnp",python,selection_mouse +846,2606978,"genie.py",11637,34,"jnp.pad(act_embed, ((0, 0), (1, 0)",python,selection_mouse +847,2607003,"genie.py",11637,35,"jnp.pad(act_embed, ((0, 0), (1, 0),",python,selection_mouse +848,2607022,"genie.py",11637,36,"jnp.pad(act_embed, ((0, 0), (1, 0), ",python,selection_mouse +849,2607055,"genie.py",11637,37,"jnp.pad(act_embed, ((0, 0), (1, 0), (",python,selection_mouse +850,2607075,"genie.py",11637,39,"jnp.pad(act_embed, ((0, 0), (1, 0), (0,",python,selection_mouse +851,2607110,"genie.py",11637,40,"jnp.pad(act_embed, ((0, 0), (1, 0), (0, ",python,selection_mouse +852,2607139,"genie.py",11637,42,"jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0)",python,selection_mouse +853,2607230,"genie.py",11637,44,"jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), ",python,selection_mouse 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+990,2694831,"genie.py",5959,1,"",python,content +991,2695015,"genie.py",5958,0,"",python,selection_command +992,2695578,"genie.py",5923,0,"",python,selection_command +993,2696618,"genie.py",5912,35,"",python,content +994,2696692,"genie.py",5924,0,"",python,selection_command +995,2696715,"genie.py",5994,0,"",python,selection_command +996,2697012,"genie.py",6049,0,"\n jax.debug.breakpoint()",python,content +997,2697039,"genie.py",6062,0,"",python,selection_command +998,2697542,"genie.py",5994,0,"",python,selection_command +999,2697858,"genie.py",6062,0,"",python,selection_command +1000,2698241,"genie.py",6050,35,"",python,content +1001,2698344,"genie.py",6062,0,"",python,selection_command +1002,2698416,"genie.py",5994,0,"",python,selection_command +1003,2698642,"genie.py",5924,0,"",python,selection_command +1004,2698811,"genie.py",5981,0,"\n jax.debug.breakpoint()",python,content +1005,2698861,"genie.py",5994,0,"",python,selection_command +1006,2700088,"genie.py",6057,0,"",python,selection_mouse +1007,2700609,"genie.py",6016,0,"",python,selection_mouse +1008,2700665,"genie.py",6015,0,"",python,selection_command +1009,2719622,"TERMINAL",0,0,"--KeyboardInterrupt--\r\nEntering jdb:\r\n(jdb) ",,terminal_output +1010,2720170,"TERMINAL",0,0,"--KeyboardInterrupt--\r\nEntering jdb:\r\n(jdb) ",,terminal_output +1011,2720385,"TERMINAL",0,0,"--KeyboardInterrupt--\r\nEntering jdb:\r\n(jdb) ",,terminal_output +1012,2720514,"TERMINAL",0,0,"--KeyboardInterrupt--\r\nEntering jdb:\r\n(jdb) ",,terminal_output +1013,2720721,"TERMINAL",0,0,"--KeyboardInterrupt--\r\nEntering jdb:\r\n(jdb) ",,terminal_output +1014,2721317,"TERMINAL",0,0,"^DERROR:2025-07-17 10:29:24,036:jax._src.debugging:98: jax.debug.callback failed\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 96, in debug_callback_impl\r\n callback(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 336, in _flat_callback\r\n callback(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/core.py"", line 220, in _breakpoint_callback\r\n debugger(frames, thread_id, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 167, in run_debugger\r\n CliDebugger(frames, thread_id, **kwargs).run()\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 160, in run\r\n self.cmdloop()\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 138, in cmdloop\r\n stop = self.onecmd(line)\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 217, in onecmd\r\n return func(arg)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 146, in do_quit\r\n sys.exit(0)\r\nSystemExit: 0\r\nERROR:jax._src.debugging:jax.debug.callback failed\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 96, in debug_callback_impl\r\n callback(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 336, in _flat_callback\r\n callback(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/core.py"", line 220, in _breakpoint_callback\r\n debugger(frames, thread_id, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 167, in run_debugger\r\n CliDebugger(frames, thread_id, **kwargs).run()\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 160, in run\r\n self.cmdloop()\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 138, in cmdloop\r\n stop = self.onecmd(line)\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 217, in onecmd\r\n return func(arg)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 146, in do_quit\r\n sys.exit(0)\r\nSystemExit: 0\r\nE0717 10:29:24.060070 938302 pjrt_stream_executor_client.cc:2916] Execution of replica 0 failed: INTERNAL: CpuCallback error calling callback: Traceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 178, in \r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 141, in _autoreg_sample\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/traceback_util.py"", line 182, in reraise_with_filtered_traceback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/pjit.py"", line 292, in cache_miss\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/pjit.py"", line 153, in _python_pjit_helper\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/pjit.py"", line 1877, in _pjit_call_impl_python\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/profiler.py"", line 354, in wrapper\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/interpreters/pxla.py"", line 1297, in __call__\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/callback.py"", line 782, in _wrapped_callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 202, in _callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 99, in debug_callback_impl\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 336, in _flat_callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/core.py"", line 220, in _breakpoint_callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 167, in run_debugger\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 162, in run\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 145, in cmdloop\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 217, in onecmd\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 146, in do_quit\r\nSystemExit: 0\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 178, in \r\n vid = _autoreg_sample(rng, video_batch, action_batch)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 141, in _autoreg_sample\r\n generated_vid = sampling_fn(\r\njaxlib._jax.XlaRuntimeError: INTERNAL: CpuCallback error calling callback: Traceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 178, in \r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 141, in _autoreg_sample\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/traceback_util.py"", line 182, in reraise_with_filtered_traceback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/pjit.py"", line 292, in cache_miss\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/pjit.py"", line 153, in _python_pjit_helper\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/pjit.py"", line 1877, in _pjit_call_impl_python\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/profiler.py"", line 354, in wrapper\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/interpreters/pxla.py"", line 1297, in __call__\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/callback.py"", line 782, in _wrapped_callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 202, in _callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 99, in debug_callback_impl\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 336, in _flat_callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/core.py"", line 220, in _breakpoint_callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 167, in run_debugger\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 162, in run\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 145, in cmdloop\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 217, in onecmd\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 146, in do_quit\r\nSystemExit: 0\r\n",,terminal_output +1015,2722888,"TERMINAL",0,0,"]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0804 jafar]$ ",,terminal_output +1016,2723531,"TERMINAL",0,0,"python sample.py --checkpoint ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared//checkpoints/train_dyn_yolorun_new_arch/3352115"" --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +1017,2724173,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +1018,2725845,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +1019,2727889,"TERMINAL",0,0,"2025-07-17 10:29:30.676504: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1020,2742028,"TERMINAL",0,0,"2025-07-17 10:29:44.804006: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1021,2757245,"TERMINAL",0,0,"2025-07-17 10:30:00.028841: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1022,2760985,"TERMINAL",0,0,"WARNING:absl:Missing metrics for step 35000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/035000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 34000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/034000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 36000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/036000/metrics/metrics not found.\r\n",,terminal_output +1023,2820043,"TERMINAL",0,0,"2025-07-17 10:31:02.834126: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-17 10:31:02.834634: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1024,2835900,"TERMINAL",0,0,"token_idxs shape: (Array(1, dtype=int32), Array(1, dtype=int32), Array(920, dtype=int32))\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\nEntering jdb:\r\n(jdb) ",,terminal_output +1025,2889748,"TERMINAL",0,0,"l",,terminal_output +1026,2889965,"TERMINAL",0,0,"\r\n> /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py(170)\r\n ""latent_actions"": action_tokens, # (B, t, ...)\r\n ""mask_rng"": batch.get(""mask_rng"", None),\r\n }\r\n jax.debug.print(""token_idxs_full shape: {}"", token_idxs_full.shape)\r\n jax.debug.print(""action_tokens shape: {}"", action_tokens.shape)\r\n dyna_outputs = self.dynamics(dyna_inputs, training=False)\r\n-> jax.debug.breakpoint()\r\n # # dyna_outputs[""token_logits""]: (B, t, N, vocab_size)\r\n # # We want the logits for the last time step (frame t-1 predicting t)\r\n # next_token_logits = dyna_outputs[""token_logits""][:, -1, :, :] # (B, N, vocab_size)\r\n \r\n # # Sample or argmax for each patch\r\n(jdb) ",,terminal_output +1027,2892796,"TERMINAL",0,0,"d",,terminal_output +1028,2893049,"TERMINAL",0,0,"[?25ly[?25h",,terminal_output +1029,2893173,"TERMINAL",0,0,"[?25ln[?25h",,terminal_output +1030,2893298,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +1031,2893920,"TERMINAL",0,0,"[?25l_[?25h",,terminal_output +1032,2894260,"TERMINAL",0,0,"[?25lo[?25h",,terminal_output +1033,2894489,"TERMINAL",0,0,"[?25lu[?25h",,terminal_output +1034,2894744,"TERMINAL",0,0,"[?25lt[?25h",,terminal_output +1035,2895003,"TERMINAL",0,0,"[?25lp[?25h",,terminal_output +1036,2895144,"TERMINAL",0,0,"[?25lu[?25h",,terminal_output +1037,2895244,"TERMINAL",0,0,"[?25lt[?25h",,terminal_output +1038,2895490,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +1039,2896457,"TERMINAL",0,0,"[?25l.[?25h",,terminal_output +1040,2896721,"TERMINAL",0,0,"[?25lk[?25h",,terminal_output +1041,2896794,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +1042,2897040,"TERMINAL",0,0,"[?25ly[?25h",,terminal_output +1043,2897204,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +1044,2897641,"TERMINAL",0,0,"[?25l([?25h[?25l)[?25h",,terminal_output +1045,2897953,"TERMINAL",0,0,"\r\ndict_keys(['mask', 'token_logits'])\r\n(jdb) ",,terminal_output +1046,2899281,"TERMINAL",0,0,"\rdyna_outputs.keys()",,terminal_output +1047,2900002,"TERMINAL",0,0,"[?25l)\r[?25h",,terminal_output +1048,2900131,"TERMINAL",0,0,"[?25l(\r[?25h",,terminal_output +1049,2900275,"TERMINAL",0,0,"[?25ls\r[?25h",,terminal_output +1050,2900374,"TERMINAL",0,0,"[?25ly\r[?25h",,terminal_output +1051,2900534,"TERMINAL",0,0,"[?25le\r[?25h",,terminal_output +1052,2900684,"TERMINAL",0,0,"[?25lk\r[?25h",,terminal_output +1053,2901024,"TERMINAL",0,0,"[?25l.\r[?25h",,terminal_output +1054,2901564,"TERMINAL",0,0,"[",,terminal_output +1055,2901934,"TERMINAL",0,0,"[?25l""[?25h",,terminal_output +1056,2902288,"TERMINAL",0,0,"[?25lt[?25h[?25lo[?25h",,terminal_output +1057,2902386,"TERMINAL",0,0,"[?25lk[?25h",,terminal_output +1058,2902496,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +1059,2902651,"TERMINAL",0,0,"[?25ln[?25h",,terminal_output +1060,2902888,"TERMINAL",0,0,"[?25l_[?25h",,terminal_output +1061,2903167,"TERMINAL",0,0,"[?25ll[?25h",,terminal_output +1062,2903290,"TERMINAL",0,0,"[?25lo[?25h",,terminal_output +1063,2903456,"TERMINAL",0,0,"[?25lg[?25h",,terminal_output +1064,2903572,"TERMINAL",0,0,"[?25li[?25h",,terminal_output +1065,2903811,"TERMINAL",0,0,"[?25lt[?25h",,terminal_output +1066,2904042,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +1067,2904427,"TERMINAL",0,0,"[?25l""[?25h",,terminal_output +1068,2904800,"TERMINAL",0,0,"[?25l][?25h",,terminal_output +1069,2905161,"TERMINAL",0,0,"[?25l.[?25h",,terminal_output +1070,2905352,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +1071,2905475,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +1072,2905633,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +1073,2905717,"TERMINAL",0,0,"[?25lp[?25h",,terminal_output +1074,2905807,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +1075,2905907,"TERMINAL",0,0,"\r\n(1, 16, 920, 1024)\r\n(jdb) ",,terminal_output +1076,2916265,"TERMINAL",0,0,"[?25lma[?25h",,terminal_output +1077,2916370,"TERMINAL",0,0,"[?25la[?25h[?25ls[?25h",,terminal_output +1078,2916499,"TERMINAL",0,0,"[?25lk[?25h",,terminal_output +1079,2917513,"TERMINAL",0,0,"[?25lk\r[?25h",,terminal_output +1080,2917662,"TERMINAL",0,0,"[?25ls\r[?25h",,terminal_output +1081,2917762,"TERMINAL",0,0,"[?25la\r[?25h",,terminal_output +1082,2917952,"TERMINAL",0,0,"[?25lm\r[?25h",,terminal_output +1083,2918092,"TERMINAL",0,0,"",,terminal_output 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jax.random.",python,selection_mouse +1376,3170537,"genie.py",6342,32,"rng, step_rng = jax.random.split",python,selection_mouse +1377,3170610,"genie.py",6342,33,"rng, step_rng = jax.random.split(",python,selection_mouse +1378,3170656,"genie.py",6342,36,"rng, step_rng = jax.random.split(rng",python,selection_mouse +1379,3170725,"genie.py",6342,37,"rng, step_rng = jax.random.split(rng)",python,selection_mouse +1380,3170992,"genie.py",6379,0,"",python,selection_mouse +1381,3171557,"genie.py",6378,1,")",python,selection_mouse +1382,3171557,"genie.py",6375,4,"rng)",python,selection_mouse +1383,3171558,"genie.py",6369,10,"split(rng)",python,selection_mouse +1384,3171610,"genie.py",6368,11,".split(rng)",python,selection_mouse +1385,3171610,"genie.py",6362,17,"random.split(rng)",python,selection_mouse +1386,3171651,"genie.py",6358,21,"jax.random.split(rng)",python,selection_mouse +1387,3171675,"genie.py",6357,22," jax.random.split(rng)",python,selection_mouse +1388,3171703,"genie.py",6355,24," = jax.random.split(rng)",python,selection_mouse +1389,3171727,"genie.py",6347,32,"step_rng = jax.random.split(rng)",python,selection_mouse +1390,3171845,"genie.py",6346,33," step_rng = jax.random.split(rng)",python,selection_mouse +1391,3171845,"genie.py",6345,34,", step_rng = jax.random.split(rng)",python,selection_mouse +1392,3171917,"genie.py",6342,37,"rng, step_rng = jax.random.split(rng)",python,selection_mouse +1393,3172252,"genie.py",6344,0,"",python,selection_mouse +1394,3172575,"genie.py",6428,0,"",python,selection_mouse +1395,3173516,"genie.py",6427,0,"",python,selection_command +1396,3174297,"genie.py",6125,0,"",python,selection_mouse +1397,3174829,"genie.py",5949,0,"",python,selection_mouse +1398,3175482,"genie.py",6032,0,"",python,selection_mouse +1399,3176613,"genie.py",6004,96," # next_token_logits = dyna_outputs[""token_logits""][:, t, :, :] # (B, N, vocab_size)",python,selection_command +1400,3176806,"genie.py",6004,97," # next_token_logits = dyna_outputs[""token_logits""][:, t, :, :] # (B, N, vocab_size)\n",python,selection_command +1401,3176952,"genie.py",6004,145," # next_token_logits = dyna_outputs[""token_logits""][:, t, :, :] # (B, N, vocab_size)\n\n # # Sample or argmax for each patch",python,selection_command +1402,3177452,"genie.py",6004,177," # next_token_logits = dyna_outputs[""token_logits""][:, t, :, :] # (B, N, vocab_size)\n\n # # Sample or argmax for each patch\n # if sample_argmax:",python,selection_command +1403,3177549,"genie.py",6004,257," # next_token_logits = dyna_outputs[""token_logits""][:, t, :, :] # (B, N, vocab_size)\n\n # # Sample or argmax for each patch\n # if sample_argmax:\n # next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)",python,selection_command +1404,3177549,"genie.py",6004,277," # next_token_logits = dyna_outputs[""token_logits""][:, t, :, :] # (B, N, vocab_size)\n\n # # Sample or argmax for each patch\n # if sample_argmax:\n # next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n # else:",python,selection_command +1405,3177613,"genie.py",6004,315," # next_token_logits = dyna_outputs[""token_logits""][:, t, :, :] # (B, N, vocab_size)\n\n # # Sample or argmax for each patch\n # if sample_argmax:\n # next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n # else:\n # if rng is not None:",python,selection_command +1406,3177702,"genie.py",6004,375," # next_token_logits = dyna_outputs[""token_logits""][:, t, :, :] # (B, N, vocab_size)\n\n # # Sample or argmax for each patch\n # if sample_argmax:\n # next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n # else:\n # if rng is not None:\n # rng, step_rng = jax.random.split(rng)",python,selection_command +1407,3177702,"genie.py",6004,399," # next_token_logits = dyna_outputs[""token_logits""][:, t, :, :] # (B, N, vocab_size)\n\n # # Sample or argmax for each patch\n # if sample_argmax:\n # next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n # else:\n # if rng is not None:\n # rng, step_rng = jax.random.split(rng)\n # else:",python,selection_command +1408,3177770,"genie.py",6004,454," # next_token_logits = dyna_outputs[""token_logits""][:, t, :, :] # (B, N, vocab_size)\n\n # # Sample or argmax for each patch\n # if sample_argmax:\n # next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n # else:\n # if rng is not None:\n # rng, step_rng = jax.random.split(rng)\n # else:\n # step_rng = jax.random.PRNGKey(0)",python,selection_command +1409,3177771,"genie.py",6004,509," # next_token_logits = dyna_outputs[""token_logits""][:, t, :, :] # (B, N, vocab_size)\n\n # # Sample or argmax for each patch\n # if sample_argmax:\n # next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n # else:\n # if rng is not None:\n # rng, step_rng = jax.random.split(rng)\n # else:\n # step_rng = jax.random.PRNGKey(0)\n # next_token = jax.random.categorical(",python,selection_command +1410,3177771,"genie.py",6004,582," # next_token_logits = dyna_outputs[""token_logits""][:, t, :, :] # (B, N, vocab_size)\n\n # # Sample or argmax for each patch\n # if sample_argmax:\n # next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n # else:\n # if rng is not None:\n # rng, step_rng = jax.random.split(rng)\n # else:\n # step_rng = jax.random.PRNGKey(0)\n # next_token = jax.random.categorical(\n # step_rng, next_token_logits / temperature, axis=-1",python,selection_command +1411,3177814,"genie.py",6004,612," # next_token_logits = dyna_outputs[""token_logits""][:, t, :, :] # (B, N, vocab_size)\n\n # # Sample or argmax for each patch\n # if sample_argmax:\n # next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n # else:\n # if rng is not None:\n # rng, step_rng = jax.random.split(rng)\n # else:\n # step_rng = jax.random.PRNGKey(0)\n # next_token = jax.random.categorical(\n # step_rng, next_token_logits / temperature, axis=-1\n # ) # (B, N)",python,selection_command +1412,3177814,"genie.py",6004,613," # next_token_logits = dyna_outputs[""token_logits""][:, t, :, :] # (B, N, vocab_size)\n\n # # Sample or argmax for each patch\n # if sample_argmax:\n # next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n # else:\n # if rng is not None:\n # rng, step_rng = jax.random.split(rng)\n # else:\n # step_rng = jax.random.PRNGKey(0)\n # next_token = jax.random.categorical(\n # step_rng, next_token_logits / temperature, axis=-1\n # ) # (B, N)\n",python,selection_command +1413,3177868,"genie.py",6004,675," # next_token_logits = dyna_outputs[""token_logits""][:, t, :, :] # (B, N, vocab_size)\n\n # # Sample or argmax for each patch\n # if sample_argmax:\n # next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n # else:\n # if rng is not None:\n # rng, step_rng = jax.random.split(rng)\n # else:\n # step_rng = jax.random.PRNGKey(0)\n # next_token = jax.random.categorical(\n # step_rng, next_token_logits / temperature, axis=-1\n # ) # (B, N)\n\n # # Insert the generated tokens into the sequence",python,selection_command +1414,3178038,"genie.py",6004,751," # next_token_logits = dyna_outputs[""token_logits""][:, t, :, :] # (B, N, vocab_size)\n\n # # Sample or argmax for each patch\n # if sample_argmax:\n # next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n # else:\n # if rng is not None:\n # rng, step_rng = jax.random.split(rng)\n # else:\n # step_rng = jax.random.PRNGKey(0)\n # next_token = jax.random.categorical(\n # step_rng, next_token_logits / temperature, axis=-1\n # ) # (B, N)\n\n # # Insert the generated tokens into the sequence\n # token_idxs_full = token_idxs_full.at[:, t, :].set(next_token)",python,selection_command +1415,3178290,"genie.py",6004,752," # next_token_logits = dyna_outputs[""token_logits""][:, t, :, :] # (B, N, vocab_size)\n\n # # Sample or argmax for each patch\n # if sample_argmax:\n # next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n # else:\n # if rng is not None:\n # rng, step_rng = jax.random.split(rng)\n # else:\n # step_rng = jax.random.PRNGKey(0)\n # next_token = jax.random.categorical(\n # step_rng, next_token_logits / temperature, axis=-1\n # ) # (B, N)\n\n # # Insert the generated tokens into the sequence\n # token_idxs_full = token_idxs_full.at[:, t, :].set(next_token)\n",python,selection_command +1416,3178435,"genie.py",6004,809," # next_token_logits = dyna_outputs[""token_logits""][:, t, :, :] # (B, N, vocab_size)\n\n # # Sample or argmax for each patch\n # if sample_argmax:\n # next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n # else:\n # if rng is not None:\n # rng, step_rng = jax.random.split(rng)\n # else:\n # step_rng = jax.random.PRNGKey(0)\n # next_token = jax.random.categorical(\n # step_rng, next_token_logits / temperature, axis=-1\n # ) # (B, N)\n\n # # Insert the generated tokens into the sequence\n # token_idxs_full = token_idxs_full.at[:, t, :].set(next_token)\n\n # # --- Decode all tokens at once at the end ---",python,selection_command +1417,3178678,"genie.py",6004,857," # next_token_logits = dyna_outputs[""token_logits""][:, t, :, :] # (B, N, vocab_size)\n\n # # Sample or argmax for each patch\n # if sample_argmax:\n # next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n # else:\n # if rng is not None:\n # rng, step_rng = jax.random.split(rng)\n # else:\n # step_rng = jax.random.PRNGKey(0)\n # next_token = jax.random.categorical(\n # step_rng, next_token_logits / temperature, axis=-1\n # ) # (B, N)\n\n # # Insert the generated tokens into the sequence\n # token_idxs_full = token_idxs_full.at[:, t, :].set(next_token)\n\n # # --- Decode all tokens at once at the end ---\n # final_frames = self.tokenizer.decode(",python,selection_command +1418,3178757,"genie.py",6004,924," # next_token_logits = dyna_outputs[""token_logits""][:, t, :, :] # (B, N, vocab_size)\n\n # # Sample or argmax for each patch\n # if sample_argmax:\n # next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n # else:\n # if rng is not None:\n # rng, step_rng = jax.random.split(rng)\n # else:\n # step_rng = jax.random.PRNGKey(0)\n # next_token = jax.random.categorical(\n # step_rng, next_token_logits / temperature, axis=-1\n # ) # (B, N)\n\n # # Insert the generated tokens into the sequence\n # token_idxs_full = token_idxs_full.at[:, t, :].set(next_token)\n\n # # --- Decode all tokens at once at the end ---\n # final_frames = self.tokenizer.decode(\n # token_idxs_full, video_hw=batch[""videos""].shape[2:4]",python,selection_command +1419,3178880,"genie.py",6004,936," # next_token_logits = dyna_outputs[""token_logits""][:, t, :, :] # (B, N, vocab_size)\n\n # # Sample or argmax for each patch\n # if sample_argmax:\n # next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n # else:\n # if rng is not None:\n # rng, step_rng = jax.random.split(rng)\n # else:\n # step_rng = jax.random.PRNGKey(0)\n # next_token = jax.random.categorical(\n # step_rng, next_token_logits / temperature, axis=-1\n # ) # (B, N)\n\n # # Insert the generated tokens into the sequence\n # token_idxs_full = token_idxs_full.at[:, t, :].set(next_token)\n\n # # --- Decode all tokens at once at the end ---\n # final_frames = self.tokenizer.decode(\n # token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n # )",python,selection_command +1420,3179047,"genie.py",6004,966," # next_token_logits = dyna_outputs[""token_logits""][:, t, :, :] # (B, N, vocab_size)\n\n # # Sample or argmax for each patch\n # if sample_argmax:\n # next_token = jnp.argmax(next_token_logits, axis=-1) # (B, N)\n # else:\n # if rng is not None:\n # rng, step_rng = jax.random.split(rng)\n # else:\n # step_rng = jax.random.PRNGKey(0)\n # next_token = jax.random.categorical(\n # step_rng, next_token_logits / temperature, axis=-1\n # ) # (B, N)\n\n # # Insert the generated tokens into the sequence\n # token_idxs_full = token_idxs_full.at[:, t, :].set(next_token)\n\n # # --- Decode all tokens at once at the end ---\n # final_frames = self.tokenizer.decode(\n # token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n # )\n # return final_frames",python,selection_command +1421,3179369,"genie.py",6016,0,"",python,selection_command +1422,3179852,"genie.py",6949,1,"",python,content +1423,3179853,"genie.py",6937,1,"",python,content +1424,3179853,"genie.py",6874,1,"",python,content +1425,3179853,"genie.py",6822,1,"",python,content +1426,3179853,"genie.py",6765,1,"",python,content +1427,3179853,"genie.py",6692,1,"",python,content +1428,3179853,"genie.py",6630,1,"",python,content +1429,3179853,"genie.py",6603,1,"",python,content +1430,3179853,"genie.py",6534,1,"",python,content +1431,3179853,"genie.py",6475,1,"",python,content +1432,3179853,"genie.py",6424,1,"",python,content +1433,3179853,"genie.py",6396,1,"",python,content +1434,3179853,"genie.py",6340,1,"",python,content +1435,3179854,"genie.py",6298,1,"",python,content +1436,3179854,"genie.py",6274,1,"",python,content +1437,3179854,"genie.py",6198,1,"",python,content 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+1590,3269542,"genie.py",6779,0,"",python,selection_mouse +1591,3269592,"genie.py",6778,0,"",python,selection_command +1592,3269672,"genie.py",6779,0,"",python,selection_mouse +1593,3269713,"genie.py",6778,0,"",python,selection_command +1594,3269951,"genie.py",6778,1,")",python,selection_mouse +1595,3269952,"genie.py",6713,65," token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n ",python,selection_mouse +1596,3269952,"genie.py",6711,67," token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n ",python,selection_mouse +1597,3269952,"genie.py",6664,114," final_frames = self.tokenizer.decode(\n token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n ",python,selection_mouse +1598,3269953,"genie.py",6663,115," final_frames = self.tokenizer.decode(\n token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n ",python,selection_mouse +1599,3269982,"genie.py",6779,0,"",python,selection_command +1600,3269982,"genie.py",6662,117," final_frames = self.tokenizer.decode(\n token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n )",python,selection_mouse +1601,3270028,"genie.py",6607,172," # --- Decode all tokens at once at the end ---\n final_frames = self.tokenizer.decode(\n token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n )",python,selection_mouse +1602,3270289,"genie.py",6606,173," # --- Decode all tokens at once at the end ---\n final_frames = self.tokenizer.decode(\n token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n )",python,selection_mouse +1603,3270439,"genie.py",6605,174," # --- Decode all tokens at once at the end ---\n final_frames = self.tokenizer.decode(\n token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n )",python,selection_mouse +1604,3270861,"genie.py",6605,0,"",python,selection_mouse +1605,3270862,"genie.py",6604,8," ",python,selection_mouse +1606,3271010,"genie.py",6604,55," # --- Decode all tokens at once at the end ---\n",python,selection_mouse +1607,3271206,"genie.py",6604,101," # --- Decode all tokens at once at the end ---\n final_frames = self.tokenizer.decode(\n",python,selection_mouse +1608,3271261,"genie.py",6604,166," # --- Decode all tokens at once at the end ---\n final_frames = self.tokenizer.decode(\n token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n",python,selection_mouse +1609,3271379,"genie.py",6604,176," # --- Decode all tokens at once at the end ---\n final_frames = self.tokenizer.decode(\n token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n )\n",python,selection_mouse +1610,3271512,"genie.py",6604,204," # --- Decode all tokens at once at the end ---\n final_frames = self.tokenizer.decode(\n token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n )\n return final_frames\n",python,selection_mouse +1611,3272019,"genie.py",6807,0,"",python,selection_mouse +1612,3272047,"genie.py",6806,0,"",python,selection_command +1613,3272159,"genie.py",6795,12,"final_frames",python,selection_mouse +1614,3272203,"genie.py",6796,11,"inal_frames",python,selection_command +1615,3272361,"genie.py",6779,17,"\n return f",python,selection_mouse +1616,3272444,"genie.py",6717,79,"token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n )\n return f",python,selection_mouse +1617,3272526,"genie.py",6667,129,"final_frames = self.tokenizer.decode(\n token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n )\n return f",python,selection_mouse +1618,3272632,"genie.py",6610,186," # --- Decode all tokens at once at the end ---\n final_frames = self.tokenizer.decode(\n token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n )\n return f",python,selection_mouse +1619,3272633,"genie.py",6609,187," # --- Decode all tokens at once at the end ---\n final_frames = self.tokenizer.decode(\n token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n )\n return f",python,selection_mouse +1620,3272673,"genie.py",6608,188," # --- Decode all tokens at once at the end ---\n final_frames = self.tokenizer.decode(\n token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n )\n return f",python,selection_mouse +1621,3272686,"genie.py",6607,189," # --- Decode all tokens at once at the end ---\n final_frames = self.tokenizer.decode(\n token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n )\n return f",python,selection_mouse +1622,3272732,"genie.py",6606,190," # --- Decode all tokens at once at the end ---\n final_frames = self.tokenizer.decode(\n token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n )\n return f",python,selection_mouse +1623,3272762,"genie.py",6605,191," # --- Decode all tokens at once at the end ---\n final_frames = self.tokenizer.decode(\n token_idxs_full, video_hw=batch[""videos""].shape[2:4]\n )\n return f",python,selection_mouse +1624,3273132,"genie.py",6605,0,"",python,selection_mouse +1625,3275273,"genie.py",5835,0,"",python,selection_mouse +1626,3275808,"genie.py",5808,35,"",python,content +1627,3275877,"genie.py",5820,0,"",python,selection_command +1628,3276834,"genie.py",5888,0,"",python,selection_command +1629,3277340,"genie.py",5971,0,"",python,selection_command +1630,3277369,"genie.py",6054,0,"",python,selection_command +1631,3277412,"genie.py",6067,0,"",python,selection_command +1632,3277421,"genie.py",6113,0,"",python,selection_command +1633,3277467,"genie.py",6143,0,"",python,selection_command +1634,3277490,"genie.py",6221,0,"",python,selection_command +1635,3277523,"genie.py",6239,0,"",python,selection_command +1636,3277559,"genie.py",6293,0,"",python,selection_command +1637,3277622,"genie.py",6346,0,"",python,selection_command +1638,3277661,"genie.py",6417,0,"",python,selection_command +1639,3277662,"genie.py",6433,0,"",python,selection_command +1640,3277813,"genie.py",6446,0,"",python,selection_command +1641,3278182,"genie.py",6506,0,"",python,selection_command +1642,3278354,"genie.py",6567,0,"\n jax.debug.breakpoint()",python,content +1643,3278398,"genie.py",6580,0,"",python,selection_command +1644,3281630,"TERMINAL",0,0,"^DERROR:2025-07-17 10:38:44,341:jax._src.debugging:98: jax.debug.callback failed\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 96, in debug_callback_impl\r\n callback(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 336, in _flat_callback\r\n callback(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/core.py"", line 220, in _breakpoint_callback\r\n debugger(frames, thread_id, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 167, in run_debugger\r\n CliDebugger(frames, thread_id, **kwargs).run()\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 160, in run\r\n self.cmdloop()\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 138, in cmdloop\r\n stop = self.onecmd(line)\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 217, in onecmd\r\n return func(arg)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 146, in do_quit\r\n sys.exit(0)\r\nSystemExit: 0\r\nERROR:jax._src.debugging:jax.debug.callback failed\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 96, in debug_callback_impl\r\n callback(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 336, in _flat_callback\r\n callback(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/core.py"", line 220, in _breakpoint_callback\r\n debugger(frames, thread_id, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 167, in run_debugger\r\n CliDebugger(frames, thread_id, **kwargs).run()\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 160, in run\r\n self.cmdloop()\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 138, in cmdloop\r\n stop = self.onecmd(line)\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 217, in onecmd\r\n return func(arg)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 146, in do_quit\r\n sys.exit(0)\r\nSystemExit: 0\r\nE0717 10:38:44.346692 940210 pjrt_stream_executor_client.cc:2916] Execution of replica 0 failed: INTERNAL: CpuCallback error calling callback: Traceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 178, in \r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 141, in _autoreg_sample\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/traceback_util.py"", line 182, in reraise_with_filtered_traceback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/pjit.py"", line 292, in cache_miss\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/pjit.py"", line 153, in _python_pjit_helper\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/pjit.py"", line 1877, in _pjit_call_impl_python\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/profiler.py"", line 354, in wrapper\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/interpreters/pxla.py"", line 1297, in __call__\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/callback.py"", line 782, in _wrapped_callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 202, in _callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 99, in debug_callback_impl\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 336, in _flat_callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/core.py"", line 220, in _breakpoint_callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 167, in run_debugger\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 162, in run\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 145, in cmdloop\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 217, in onecmd\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 146, in do_quit\r\nSystemExit: 0\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 178, in \r\n vid = _autoreg_sample(rng, video_batch, action_batch)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 141, in _autoreg_sample\r\n generated_vid = sampling_fn(\r\njaxlib._jax.XlaRuntimeError: INTERNAL: CpuCallback error calling callback: Traceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 178, in \r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 141, in _autoreg_sample\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/traceback_util.py"", line 182, in reraise_with_filtered_traceback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/pjit.py"", line 292, in cache_miss\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/pjit.py"", line 153, in _python_pjit_helper\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/pjit.py"", line 1877, in _pjit_call_impl_python\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/profiler.py"", line 354, in wrapper\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/interpreters/pxla.py"", line 1297, in __call__\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/callback.py"", line 782, in _wrapped_callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 202, in _callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 99, in debug_callback_impl\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 336, in _flat_callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/core.py"", line 220, in _breakpoint_callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 167, in run_debugger\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 162, in run\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 145, in cmdloop\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 217, in onecmd\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 146, in do_quit\r\nSystemExit: 0\r\n",,terminal_output +1645,3283376,"TERMINAL",0,0,"]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0804 jafar]$ ",,terminal_output +1646,3283989,"TERMINAL",0,0,"python sample.py --checkpoint ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared//checkpoints/train_dyn_yolorun_new_arch/3352115"" --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +1647,3284600,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +1648,3285768,"train_dynamics.py",0,0,"",python,tab +1649,3286300,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +1650,3287578,"genie.py",0,0,"",python,tab +1651,3288441,"TERMINAL",0,0,"2025-07-17 10:38:51.221949: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1652,3288903,"genie.py",6203,0,"",python,selection_mouse +1653,3289511,"genie.py",6054,0,"",python,selection_mouse +1654,3291016,"genie.py",6550,0,"",python,selection_mouse +1655,3291788,"genie.py",5854,0,"",python,selection_mouse +1656,3293323,"genie.py",5624,0,"",python,selection_mouse +1657,3293819,"genie.py",5705,0,"",python,selection_mouse +1658,3294769,"genie.py",5737,0,"\n ",python,content +1659,3295968,"genie.py",5738,12,"",python,content +1660,3297572,"genie.py",5738,0," ",python,content +1661,3297775,"genie.py",5742,0," ",python,content +1662,3298007,"genie.py",5746,0," ",python,content +1663,3298323,"genie.py",5750,0,"p",python,content +1664,3298323,"genie.py",5751,0,"",python,selection_keyboard +1665,3298494,"genie.py",5751,0,"r",python,content +1666,3298495,"genie.py",5752,0,"",python,selection_keyboard +1667,3298598,"genie.py",5752,0,"i",python,content +1668,3298599,"genie.py",5753,0,"",python,selection_keyboard +1669,3298647,"genie.py",5753,0,"n",python,content +1670,3298648,"genie.py",5754,0,"",python,selection_keyboard +1671,3299046,"genie.py",5753,1,"",python,content +1672,3299182,"genie.py",5752,1,"",python,content +1673,3299322,"genie.py",5751,1,"",python,content +1674,3299448,"genie.py",5750,1,"",python,content +1675,3299833,"genie.py",5746,4,"",python,content +1676,3299943,"genie.py",5742,4,"",python,content +1677,3300309,"genie.py",5738,4,"",python,content +1678,3300717,"genie.py",5662,0,"",python,selection_command +1679,3300919,"genie.py",5582,0,"",python,selection_command +1680,3301304,"genie.py",5582,80,"",python,content +1681,3301394,"genie.py",5594,0,"",python,selection_command +1682,3301415,"genie.py",5658,0,"",python,selection_command +1683,3301774,"TERMINAL",0,0,"2025-07-17 10:39:04.546273: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1684,3302009,"genie.py",5594,0,"",python,selection_command +1685,3302563,"genie.py",5611,0,"token_idxs_full shape: {}"", token_idxs_full.shape)\n jax.debug.print(""",python,content +1686,3302586,"genie.py",5738,0,"",python,selection_command +1687,3303173,"genie.py",5674,0,"",python,selection_command +1688,3303340,"genie.py",5594,0,"",python,selection_command +1689,3303782,"genie.py",5674,0,"",python,selection_command +1690,3304049,"genie.py",5737,0,"\n jax.debug.print(""token_idxs_full shape: {}"", token_idxs_full.shape)",python,content +1691,3304080,"genie.py",5750,0,"",python,selection_command +1692,3304789,"genie.py",5818,0,"",python,selection_command +1693,3305283,"genie.py",5818,1,"",python,content +1694,3305292,"genie.py",5830,0,"",python,selection_command +1695,3305359,"genie.py",5750,0,"",python,selection_command +1696,3305611,"genie.py",5751,0,"",python,selection_command +1697,3306133,"genie.py",5752,0,"",python,selection_command +1698,3306139,"genie.py",5753,0,"",python,selection_command +1699,3306173,"genie.py",5754,0,"",python,selection_command +1700,3306213,"genie.py",5755,0,"",python,selection_command +1701,3306241,"genie.py",5756,0,"",python,selection_command +1702,3306283,"genie.py",5757,0,"",python,selection_command +1703,3306291,"genie.py",5758,0,"",python,selection_command +1704,3306354,"genie.py",5759,0,"",python,selection_command +1705,3306358,"genie.py",5760,0,"",python,selection_command +1706,3306431,"genie.py",5761,0,"",python,selection_command +1707,3306435,"genie.py",5762,0,"",python,selection_command +1708,3306442,"genie.py",5763,0,"",python,selection_command +1709,3306509,"genie.py",5764,0,"",python,selection_command +1710,3306520,"genie.py",5765,0,"",python,selection_command +1711,3306529,"genie.py",5766,0,"",python,selection_command +1712,3306573,"genie.py",5767,0,"",python,selection_command +1713,3306609,"genie.py",5768,0,"",python,selection_command +1714,3306625,"genie.py",5769,0,"",python,selection_command +1715,3306664,"genie.py",5770,0,"",python,selection_command +1716,3306676,"genie.py",5771,0,"",python,selection_command +1717,3306742,"genie.py",5772,0,"",python,selection_command +1718,3306751,"genie.py",5773,0,"",python,selection_command +1719,3306795,"genie.py",5774,0,"",python,selection_command +1720,3306813,"genie.py",5775,0,"",python,selection_command +1721,3306844,"genie.py",5776,0,"",python,selection_command +1722,3306893,"genie.py",5777,0,"",python,selection_command +1723,3306916,"genie.py",5778,0,"",python,selection_command +1724,3306941,"genie.py",5779,0,"",python,selection_command +1725,3306986,"genie.py",5780,0,"",python,selection_command +1726,3307471,"genie.py",5779,0,"",python,selection_command +1727,3307687,"genie.py",5778,0,"",python,selection_command +1728,3307838,"genie.py",5779,0,"",python,selection_command +1729,3308009,"genie.py",5780,0,"",python,selection_command +1730,3308155,"genie.py",5781,0,"",python,selection_command +1731,3308286,"genie.py",5782,0,"",python,selection_command +1732,3308434,"genie.py",5783,0,"",python,selection_command +1733,3308779,"genie.py",5783,5,"",python,content +1734,3310014,"genie.py",5783,0,"a",python,content +1735,3310015,"genie.py",5784,0,"",python,selection_keyboard +1736,3310254,"genie.py",5784,0,"t",python,content +1737,3310255,"genie.py",5785,0,"",python,selection_keyboard +1738,3310345,"genie.py",5785,0," ",python,content +1739,3310345,"genie.py",5786,0,"",python,selection_keyboard +1740,3311556,"genie.py",5785,1,"",python,content +1741,3311710,"genie.py",5784,1,"",python,content +1742,3311815,"genie.py",5783,1,"",python,content +1743,3314556,"genie.py",5783,0,"0",python,content +1744,3314557,"genie.py",5784,0,"",python,selection_keyboard +1745,3316575,"genie.py",5807,5,"",python,content +1746,3317005,"genie.py",5806,1,"",python,content +1747,3317133,"TERMINAL",0,0,"2025-07-17 10:39:19.838341: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1748,3317840,"genie.py",5806,0,"[]",python,content +1749,3317841,"genie.py",5807,0,"",python,selection_keyboard +1750,3318932,"genie.py",5807,0,":",python,content +1751,3318933,"genie.py",5808,0,"",python,selection_keyboard +1752,3319166,"genie.py",5808,0,",",python,content +1753,3319166,"genie.py",5809,0,"",python,selection_keyboard +1754,3320152,"genie.py",5808,1,"",python,content +1755,3320306,"genie.py",5807,1,"",python,content +1756,3320650,"TERMINAL",0,0,"WARNING:absl:Missing metrics for step 34000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/034000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 36000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/036000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 35000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/035000/metrics/metrics not found.\r\n",,terminal_output +1757,3320846,"genie.py",5807,0,"0",python,content +1758,3320847,"genie.py",5808,0,"",python,selection_keyboard +1759,3321273,"genie.py",5808,0,",",python,content +1760,3321273,"genie.py",5809,0,"",python,selection_keyboard +1761,3321531,"genie.py",5809,0,":",python,content +1762,3321532,"genie.py",5810,0,"",python,selection_keyboard +1763,3322151,"genie.py",5810,0,",",python,content +1764,3322152,"genie.py",5811,0,"",python,selection_keyboard +1765,3322333,"genie.py",5811,0,"0",python,content +1766,3322334,"genie.py",5812,0,"",python,selection_keyboard +1767,3323505,"genie.py",5913,0,"",python,selection_mouse +1768,3389756,"TERMINAL",0,0,"2025-07-17 10:40:32.537655: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-17 10:40:32.538168: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-17 10:40:32.538843: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1769,3467570,"TERMINAL",0,0,"token_idxs shape: (Array(1, dtype=int32), Array(1, dtype=int32), Array(920, dtype=int32))\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\n",,terminal_output +1770,3467934,"TERMINAL",0,0,"Entering jdb:\r\n(jdb) ",,terminal_output +1771,3483301,"TERMINAL",0,0,"l",,terminal_output +1772,3483507,"TERMINAL",0,0,"\r\n> /hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/genie.py(183)\r\n rng, step_rng = jax.random.split(rng)\r\n next_token = jax.random.categorical(\r\n step_rng, next_token_logits / temperature, axis=-1\r\n ) # (B, N)\r\n \r\n # Insert the generated tokens into the sequence\r\n-> token_idxs_full = token_idxs_full.at[:, t, :].set(next_token)\r\n jax.debug.breakpoint()\r\n \r\n # --- Decode all tokens at once at the end ---\r\n final_frames = self.tokenizer.decode(\r\n token_idxs_full, video_hw=batch[""videos""].shape[2:4]\r\n(jdb) ",,terminal_output +1773,3488520,"TERMINAL",0,0,"token_idxs_full",,terminal_output +1774,3489654,"TERMINAL",0,0,"[?25l[[?25h",,terminal_output +1775,3491811,"TERMINAL",0,0,"[?25l0[?25h",,terminal_output +1776,3492131,"TERMINAL",0,0,"[?25l,[?25h",,terminal_output +1777,3493027,"TERMINAL",0,0,"[?25l:[?25h",,terminal_output +1778,3493276,"TERMINAL",0,0,"[?25l,[?25h",,terminal_output +1779,3493554,"TERMINAL",0,0,"[?25l0[?25h",,terminal_output +1780,3494164,"TERMINAL",0,0,"[?25l][?25h",,terminal_output +1781,3494499,"TERMINAL",0,0,"\r\nArray([148, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0], dtype=int32)\r\n(jdb) ",,terminal_output +1782,3498324,"TERMINAL",0,0,"n",,terminal_output +1783,3498445,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +1784,3498663,"TERMINAL",0,0,"[?25lxt[?25h",,terminal_output +1785,3499105,"TERMINAL",0,0,"[?25l_[?25h",,terminal_output +1786,3499399,"TERMINAL",0,0,"[?25lt[?25h",,terminal_output +1787,3499583,"TERMINAL",0,0,"[?25lo[?25h[?25lk[?25h",,terminal_output +1788,3499724,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +1789,3499777,"TERMINAL",0,0,"[?25ln[?25h",,terminal_output +1790,3500347,"TERMINAL",0,0,"\r\nArray([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\r\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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+1797,3513105,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +1798,3513194,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +1799,3513299,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +1800,3513412,"TERMINAL",0,0,"[?25lp[?25h",,terminal_output +1801,3513485,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +1802,3513693,"TERMINAL",0,0,"\r\n(1, 920)\r\n(jdb) ",,terminal_output +1803,3516274,"TERMINAL",0,0,"\rnext_token.shape",,terminal_output +1804,3516503,"TERMINAL",0,0,"\r",,terminal_output +1805,3517100,"TERMINAL",0,0,"\r_idxs_full[0,:,0]",,terminal_output +1806,3517672,"TERMINAL",0,0,"[?25l]\r[?25h",,terminal_output +1807,3517864,"TERMINAL",0,0,"[?25l0\r[?25h",,terminal_output +1808,3518017,"TERMINAL",0,0,"[?25l:,\r[?25h",,terminal_output +1809,3518155,"TERMINAL",0,0,"[?25l:\r[?25h",,terminal_output +1810,3518254,"TERMINAL",0,0,"\r",,terminal_output +1811,3518394,"TERMINAL",0,0,"[?25l0\r[?25h",,terminal_output +1812,3518503,"TERMINAL",0,0,"[?25l[\r[?25h",,terminal_output +1813,3519380,"TERMINAL",0,0,".",,terminal_output +1814,3519582,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +1815,3519722,"TERMINAL",0,0,"[?25lha[?25h",,terminal_output +1816,3519961,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +1817,3520285,"TERMINAL",0,0,"[?25le\r[?25h",,terminal_output +1818,3520549,"TERMINAL",0,0,"[?25lp[?25h",,terminal_output +1819,3520642,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +1820,3520790,"TERMINAL",0,0,"\r\n(1, 16, 920)\r\n(jdb) ",,terminal_output +1821,3568352,"TERMINAL",0,0,"^DERROR:2025-07-17 10:43:31,081:jax._src.debugging:98: jax.debug.callback failed\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 96, in debug_callback_impl\r\n callback(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 336, in _flat_callback\r\n callback(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/core.py"", line 220, in _breakpoint_callback\r\n debugger(frames, thread_id, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 167, in run_debugger\r\n CliDebugger(frames, thread_id, **kwargs).run()\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 160, in run\r\n self.cmdloop()\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 138, in cmdloop\r\n stop = self.onecmd(line)\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 217, in onecmd\r\n return func(arg)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 146, in do_quit\r\n sys.exit(0)\r\nSystemExit: 0\r\nERROR:jax._src.debugging:jax.debug.callback failed\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 96, in debug_callback_impl\r\n callback(*args)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 336, in _flat_callback\r\n callback(*args, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/core.py"", line 220, in _breakpoint_callback\r\n debugger(frames, thread_id, **kwargs)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 167, in run_debugger\r\n CliDebugger(frames, thread_id, **kwargs).run()\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 160, in run\r\n self.cmdloop()\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 138, in cmdloop\r\n stop = self.onecmd(line)\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 217, in onecmd\r\n return func(arg)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 146, in do_quit\r\n sys.exit(0)\r\nSystemExit: 0\r\nE0717 10:43:31.087060 943237 pjrt_stream_executor_client.cc:2916] Execution of replica 0 failed: INTERNAL: CpuCallback error calling callback: Traceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 178, in \r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 141, in _autoreg_sample\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/traceback_util.py"", line 182, in reraise_with_filtered_traceback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/pjit.py"", line 292, in cache_miss\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/pjit.py"", line 153, in _python_pjit_helper\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/pjit.py"", line 1877, in _pjit_call_impl_python\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/profiler.py"", line 354, in wrapper\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/interpreters/pxla.py"", line 1297, in __call__\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/callback.py"", line 782, in _wrapped_callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 202, in _callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 99, in debug_callback_impl\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 336, in _flat_callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/core.py"", line 220, in _breakpoint_callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 167, in run_debugger\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 162, in run\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 145, in cmdloop\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 217, in onecmd\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 146, in do_quit\r\nSystemExit: 0\r\njax.errors.SimplifiedTraceback: For simplicity, JAX has removed its internal frames from the traceback of the following exception. Set JAX_TRACEBACK_FILTERING=off to include these.\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 178, in \r\n vid = _autoreg_sample(rng, video_batch, action_batch)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 141, in _autoreg_sample\r\n generated_vid = sampling_fn(\r\njaxlib._jax.XlaRuntimeError: INTERNAL: CpuCallback error calling callback: Traceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 178, in \r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 141, in _autoreg_sample\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/traceback_util.py"", line 182, in reraise_with_filtered_traceback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/pjit.py"", line 292, in cache_miss\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/pjit.py"", line 153, in _python_pjit_helper\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/pjit.py"", line 1877, in _pjit_call_impl_python\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/profiler.py"", line 354, in wrapper\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/interpreters/pxla.py"", line 1297, in __call__\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/callback.py"", line 782, in _wrapped_callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 202, in _callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 99, in debug_callback_impl\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugging.py"", line 336, in _flat_callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/core.py"", line 220, in _breakpoint_callback\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 167, in run_debugger\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 162, in run\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 145, in cmdloop\r\n File ""/home/hk-project-p0023960/tum_cte0515/.local/share/uv/python/cpython-3.10.18-linux-x86_64-gnu/lib/python3.10/cmd.py"", line 217, in onecmd\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/jax/_src/debugger/cli_debugger.py"", line 146, in do_quit\r\nSystemExit: 0\r\n",,terminal_output +1822,3569628,"TERMINAL",0,0,"srun",,terminal_focus +1823,3570572,"train_dynamics.py",0,0,"",python,tab +1824,3570763,"TERMINAL",0,0,"]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0804 jafar]$ ",,terminal_output +1825,3572276,"genie.py",0,0,"",python,tab +1826,3573091,"genie.py",6131,0,"",python,selection_mouse +1827,3574672,"genie.py",6132,0,"",python,selection_command +1828,3574830,"genie.py",6178,0,"",python,selection_command +1829,3574974,"genie.py",6208,0,"",python,selection_command +1830,3575122,"genie.py",6286,0,"",python,selection_command +1831,3575267,"genie.py",6304,0,"",python,selection_command +1832,3575433,"genie.py",6358,0,"",python,selection_command +1833,3575568,"genie.py",6411,0,"",python,selection_command +1834,3575715,"genie.py",6482,0,"",python,selection_command +1835,3575886,"genie.py",6510,0,"",python,selection_command +1836,3576019,"genie.py",6511,0,"",python,selection_command +1837,3576178,"genie.py",6571,0,"",python,selection_command +1838,3576549,"genie.py",6645,0,"",python,selection_command +1839,3577488,"genie.py",6645,35,"",python,content +1840,3578333,"genie.py",6571,0,"",python,selection_command +1841,3578589,"genie.py",6645,0,"",python,selection_command +1842,3578883,"genie.py",6571,0,"",python,selection_command +1843,3579010,"genie.py",6645,0,"",python,selection_command +1844,3579190,"genie.py",6646,0,"",python,selection_command +1845,3579354,"genie.py",6645,0,"",python,selection_command +1846,3579429,"genie.py",6646,0,"",python,selection_command +1847,3579624,"genie.py",6645,0,"",python,selection_command +1848,3579799,"genie.py",6571,0,"",python,selection_command +1849,3579960,"genie.py",6511,0,"",python,selection_command +1850,3580095,"genie.py",6571,0,"",python,selection_command +1851,3580310,"genie.py",6645,0,"",python,selection_command +1852,3580449,"genie.py",6571,0,"",python,selection_command +1853,3580653,"genie.py",6511,0,"",python,selection_command +1854,3580827,"genie.py",6571,0,"",python,selection_command +1855,3581025,"genie.py",6511,0,"",python,selection_command +1856,3581193,"genie.py",6571,0,"",python,selection_command +1857,3581345,"genie.py",6511,0,"",python,selection_command +1858,3581467,"genie.py",6571,0,"",python,selection_command +1859,3581524,"genie.py",6511,0,"",python,selection_command +1860,3581600,"genie.py",6571,0,"",python,selection_command +1861,3581736,"genie.py",6511,0,"",python,selection_command +1862,3581791,"genie.py",6571,0,"",python,selection_command +1863,3581933,"genie.py",6511,0,"",python,selection_command +1864,3581996,"genie.py",6571,0,"",python,selection_command +1865,3582147,"genie.py",6511,0,"",python,selection_command +1866,3582224,"genie.py",6571,0,"",python,selection_command +1867,3582376,"genie.py",6511,0,"",python,selection_command +1868,3582437,"genie.py",6571,0,"",python,selection_command +1869,3582598,"genie.py",6511,0,"",python,selection_command +1870,3582702,"genie.py",6571,0,"",python,selection_command +1871,3582847,"genie.py",6511,0,"",python,selection_command +1872,3582917,"genie.py",6571,0,"",python,selection_command +1873,3583043,"genie.py",6511,0,"",python,selection_command +1874,3583104,"genie.py",6571,0,"",python,selection_command +1875,3583232,"genie.py",6511,0,"",python,selection_command +1876,3583333,"genie.py",6571,0,"",python,selection_command +1877,3584822,"TERMINAL",0,0,"srun",,terminal_focus +1878,3586358,"TERMINAL",0,0,"python sample.py --checkpoint ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared//checkpoints/train_dyn_yolorun_new_arch/3352115"" --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +1879,3587085,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +1880,3588947,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +1881,3591082,"TERMINAL",0,0,"2025-07-17 10:43:53.811767: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1882,3605194,"TERMINAL",0,0,"2025-07-17 10:44:07.929642: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1883,3620171,"TERMINAL",0,0,"2025-07-17 10:44:22.962428: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1884,3623921,"TERMINAL",0,0,"WARNING:absl:Missing metrics for step 35000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/035000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 34000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/034000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 36000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/036000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/020000/metrics/metrics not found.\r\n",,terminal_output +1885,3692897,"TERMINAL",0,0,"2025-07-17 10:45:35.686456: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-17 10:45:35.686981: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-17 10:45:35.687655: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1886,3740927,"TERMINAL",0,0,"token_idxs shape: (Array(1, dtype=int32), Array(1, dtype=int32), Array(920, dtype=int32))\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\n",,terminal_output +1887,3741269,"TERMINAL",0,0,"token_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full shape: (Array(1, dtype=int32), Array(16, dtype=int32), Array(920, dtype=int32))\r\naction_tokens shape: (Array(1, dtype=int32), Array(15, dtype=int32), Array(1, dtype=int32), Array(32, dtype=int32))\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\n",,terminal_output +1888,3741638,"TERMINAL",0,0,"Traceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 181, in \r\n ssim = pix.ssim(gt[:, args.start_frame + 1 :], recon[:, args.start_frame + 1 :]).mean()\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/dm_pix/_src/metrics.py"", line 221, in ssim\r\n chex.assert_type([a, b], float)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/chex/_src/asserts_internal.py"", line 279, in _chex_assert_fn\r\n host_assertion_fn(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/chex/_src/asserts_internal.py"", line 197, in _assert_on_host\r\n raise exception_type(error_msg)\r\nAssertionError: [Chex] Assertion assert_type failed: Error in type compatibility check: input 0 has type uint8 but expected .\r\n",,terminal_output +1889,3743101,"TERMINAL",0,0,"]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0804 jafar]$ ",,terminal_output +1890,3797336,"genie.py",0,0,"",python,tab +1891,3798956,"genie.py",5616,0,"",python,selection_mouse +1892,3800990,"genie.py",5582,156,"",python,content +1893,3801058,"genie.py",5594,0,"",python,selection_command +1894,3802168,"genie.py",5693,0,"",python,selection_command +1895,3802665,"genie.py",5763,0,"",python,selection_command +1896,3802688,"genie.py",5831,0,"",python,selection_command +1897,3802853,"genie.py",5763,0,"",python,selection_command +1898,3803089,"genie.py",5693,0,"",python,selection_command +1899,3803276,"genie.py",5616,0,"",python,selection_command +1900,3804359,"genie.py",5581,0,"",python,selection_mouse +1901,3804364,"genie.py",5580,0,"",python,selection_command +1902,3805887,"genie.py",6437,0,"",python,selection_mouse +1903,3806034,"genie.py",6427,15,"token_idxs_full",python,selection_mouse +1904,3827191,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",0,0,"from dataclasses import dataclass\nfrom typing import Optional\nimport time\nimport os\n\nimport dm_pix as pix\nimport einops\nimport jax\nimport jax.numpy as jnp\nimport flax.linen as nn\nimport numpy as np\nfrom flax.training.train_state import TrainState\nimport grain\nimport orbax.checkpoint as ocp\nimport optax\nfrom PIL import Image, ImageDraw\nimport tyro\n\nfrom genie import Genie\nfrom utils.dataloader import get_dataloader\n\n\n@dataclass\nclass Args:\n # Experiment\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/coinrun_episodes""\n checkpoint: str = """"\n checkpoint_step: Optional[int] = None\n # Sampling\n batch_size: int = 1\n maskgit_steps: int = 25\n temperature: float = 1.0\n sample_argmax: bool = True\n start_frame: int = 0\n # Tokenizer checkpoint\n tokenizer_dim: int = 512\n latent_patch_dim: int = 32\n num_patch_latents: int = 1024\n patch_size: int = 4\n tokenizer_num_blocks: int = 8\n tokenizer_num_heads: int = 8\n # LAM checkpoint\n lam_dim: int = 512\n latent_action_dim: int = 32\n num_latent_actions: int = 6\n lam_patch_size: int = 16\n lam_num_blocks: int = 8\n lam_num_heads: int = 8\n lam_co_train: bool = True\n # Dynamics checkpoint\n dyna_dim: int = 512\n dyna_num_blocks: int = 12\n dyna_num_heads: int = 8\n param_dtype: jnp.dtype = jnp.float32\n dtype: jnp.dtype = jnp.bfloat16\n\n\nargs = tyro.cli(Args)\nrng = jax.random.PRNGKey(args.seed)\n\n# --- Load Genie checkpoint ---\ngenie = Genie(\n # Tokenizer\n in_dim=args.image_channels,\n tokenizer_dim=args.tokenizer_dim,\n latent_patch_dim=args.latent_patch_dim,\n num_patch_latents=args.num_patch_latents,\n patch_size=args.patch_size,\n tokenizer_num_blocks=args.tokenizer_num_blocks,\n tokenizer_num_heads=args.tokenizer_num_heads,\n # LAM\n lam_dim=args.lam_dim,\n latent_action_dim=args.latent_action_dim,\n num_latent_actions=args.num_latent_actions,\n lam_patch_size=args.lam_patch_size,\n lam_num_blocks=args.lam_num_blocks,\n lam_num_heads=args.lam_num_heads,\n lam_co_train=args.lam_co_train,\n # Dynamics\n dyna_dim=args.dyna_dim,\n dyna_num_blocks=args.dyna_num_blocks,\n dyna_num_heads=args.dyna_num_heads,\n use_maskgit=False,\n param_dtype=args.param_dtype,\n dtype=args.dtype,\n)\nrng, _rng = jax.random.split(rng)\nimage_shape = (args.image_height, args.image_width, args.image_channels)\ndummy_inputs = dict(\n videos=jnp.zeros((args.batch_size, args.seq_len, *image_shape), dtype=jnp.float32),\n mask_rng=_rng,\n)\nrng, _rng = jax.random.split(rng)\nparams = genie.init(_rng, dummy_inputs)\n\ndummy_train_state = TrainState.create(\n apply_fn=genie.apply,\n params=params,\n tx=optax.adamw(\n optax.warmup_cosine_decay_schedule(\n 0, 0, 1, 2 # dummy values\n )\n ), \n)\nhandler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\nhandler_registry.add('model_state', ocp.args.StandardRestore, ocp.handlers.StandardCheckpointHandler)\ncheckpoint_manager = ocp.CheckpointManager(\n args.checkpoint,\n options=ocp.CheckpointManagerOptions(step_format_fixed_length=6),\n handler_registry=handler_registry\n)\nabstract_train_state = jax.tree_util.tree_map(\n ocp.utils.to_shape_dtype_struct, dummy_train_state\n)\n\nrestored = checkpoint_manager.restore(\n args.checkpoint_step or checkpoint_manager.latest_step(),\n args=ocp.args.Composite(\n model_state=ocp.args.StandardRestore(abstract_train_state),\n ),\n)\nrestored_train_state = restored[""model_state""]\nparams = restored_train_state.params\n\n\ndef _sampling_wrapper(module, batch):\n # return module.sample_maskgit(batch, args.seq_len, args.maskgit_steps, args.temperature, args.sample_argmax)\n return module.sample_causal(batch, args.seq_len, args.temperature, args.sample_argmax)\n\n# --- Define autoregressive sampling loop ---\ndef _autoreg_sample(rng, video_batch, action_batch):\n vid = video_batch[:, : args.start_frame + 1]\n sampling_fn = jax.jit(nn.apply(_sampling_wrapper, genie)) \n rng, _rng = jax.random.split(rng)\n batch = dict(videos=vid, latent_actions=action_batch, rng=_rng)\n generated_vid = sampling_fn(\n params,\n batch\n )\n return generated_vid\n\ndef _get_dataloader_iterator():\n array_record_files = [\n os.path.join(args.data_dir, x)\n for x in os.listdir(args.data_dir)\n if x.endswith("".array_record"")\n ]\n grain_dataloader = get_dataloader(\n array_record_files,\n args.seq_len,\n # NOTE: We deliberately pass the global batch size\n # The dataloader shards the dataset across all processes\n args.batch_size,\n *image_shape,\n num_workers=0,\n prefetch_buffer_size=1,\n seed=args.seed,\n )\n initial_state = grain_dataloader._create_initial_state()\n grain_iterator = grain.DataLoaderIterator(grain_dataloader, initial_state)\n return grain_iterator\n\n# --- Get video + latent actions ---\ngrain_iterator = _get_dataloader_iterator()\nvideo_batch = next(grain_iterator)\n\n# Get latent actions for all videos in the batch\nbatch = dict(videos=video_batch)\naction_batch = genie.apply(params, batch, False, method=Genie.vq_encode)\naction_batch = action_batch.reshape(video_batch.shape[0], args.seq_len - 1, 1)\n\n# --- Sample + evaluate video ---\nvid = _autoreg_sample(rng, video_batch, action_batch)\ngt = video_batch[:, : vid.shape[1]].clip(0, 1).reshape(-1, *video_batch.shape[2:])\nrecon = vid.clip(0, 1).reshape(-1, *vid.shape[2:])\nssim = pix.ssim(gt[:, args.start_frame + 1 :], recon[:, args.start_frame + 1 :]).mean()\nprint(f""SSIM: {ssim}"")\n\n# --- Construct video ---\ntrue_videos = (video_batch * 255).astype(np.uint8)\npred_videos = (vid * 255).astype(np.uint8)\nvideo_comparison = np.zeros((2, *vid.shape), dtype=np.uint8)\nvideo_comparison[0] = true_videos[:, :args.seq_len]\nvideo_comparison[1] = pred_videos\nframes = einops.rearrange(video_comparison, ""n b t h w c -> t (b h) (n w) c"")\n\n# --- Save video --- \nimgs = [Image.fromarray(img) for img in frames]\n# Write actions on each frame, on each row (i.e., for each video in the batch, on the GT row)\nfor t, img in enumerate(imgs[1:]):\n d = ImageDraw.Draw(img)\n for row in range(action_batch.shape[0]):\n action = action_batch[row, t, 0]\n y_offset = row * video_batch.shape[2] + 2\n d.text((2, y_offset), f""{action}"", fill=255)\nimgs[0].save(\n f""generation_{time.time()}.gif"",\n save_all=True,\n append_images=imgs[1:],\n duration=250,\n loop=0,\n)\n",python,tab +1905,3830872,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5525,0,"",python,selection_mouse +1906,3832424,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5577,0,"",python,selection_mouse +1907,3832556,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5576,2,"gt",python,selection_mouse +1908,3836257,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5611,0,"",python,selection_mouse +1909,3836410,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5607,5,"recon",python,selection_mouse +1910,3838294,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5512,0,"",python,selection_mouse +1911,3838443,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5509,5,"recon",python,selection_mouse 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+1936,3928329,"train_dynamics.py",2704,8,".astype(",python,selection_mouse +1937,3928331,"train_dynamics.py",2704,11,".astype(jnp",python,selection_mouse +1938,3928331,"train_dynamics.py",2704,13,".astype(jnp.f",python,selection_mouse +1939,3928331,"train_dynamics.py",2704,15,".astype(jnp.flo",python,selection_mouse +1940,3928359,"train_dynamics.py",2704,17,".astype(jnp.float",python,selection_mouse +1941,3928389,"train_dynamics.py",2704,19,".astype(jnp.float32",python,selection_mouse +1942,3928415,"train_dynamics.py",2704,20,".astype(jnp.float32)",python,selection_mouse +1943,3937888,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",0,0,"",python,tab +1944,3938858,"genie.py",0,0,"",python,tab +1945,3941891,"genie.py",5925,0,"",python,selection_mouse +1946,3943375,"genie.py",5952,0,"",python,selection_mouse +1947,3944645,"genie.py",6489,0,"",python,selection_mouse +1948,3946437,"genie.py",5952,0,"",python,selection_mouse +1949,3947537,"genie.py",5952,0,".astype(jnp.float32)",python,content +1950,3956793,"TERMINAL",0,0,"python sample.py --checkpoint ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared//checkpoints/train_dyn_yolorun_new_arch/3352115"" --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +1951,3957371,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +1952,3958850,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +1953,3960937,"TERMINAL",0,0,"2025-07-17 10:50:03.731602: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1954,3974717,"TERMINAL",0,0,"2025-07-17 10:50:17.512362: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1955,3989592,"TERMINAL",0,0,"2025-07-17 10:50:32.384191: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1956,3993451,"TERMINAL",0,0,"WARNING:absl:Missing metrics for step 34000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/034000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 35000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/035000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 36000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/036000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/020000/metrics/metrics not found.\r\n",,terminal_output +1957,4062236,"TERMINAL",0,0,"2025-07-17 10:51:45.025648: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-17 10:51:45.026186: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-17 10:51:45.026866: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +1958,4111100,"TERMINAL",0,0,"token_idxs shape: (Array(1, dtype=int32), Array(1, dtype=int32), Array(920, dtype=int32))\r\n",,terminal_output +1959,4111440,"TERMINAL",0,0,"token_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\n",,terminal_output +1960,4111916,"TERMINAL",0,0,"Traceback (most recent call last):\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py"", line 181, in \r\n ssim = pix.ssim(gt[:, args.start_frame + 1 :], recon[:, args.start_frame + 1 :]).mean()\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/dm_pix/_src/metrics.py"", line 221, in ssim\r\n chex.assert_type([a, b], float)\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/chex/_src/asserts_internal.py"", line 279, in _chex_assert_fn\r\n host_assertion_fn(\r\n File ""/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/chex/_src/asserts_internal.py"", line 197, in _assert_on_host\r\n raise exception_type(error_msg)\r\nAssertionError: [Chex] Assertion assert_type failed: Error in type compatibility check: input 0 has type uint8 but expected .\r\n",,terminal_output +1961,4113580,"TERMINAL",0,0,"]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0804 jafar]$ ",,terminal_output +1962,4147504,"genie.py",0,0,"",python,tab +1963,4157436,"train_dynamics.py",0,0,"",python,tab +1964,4158700,"genie.py",0,0,"",python,tab +1965,4160102,"genie.py",5953,0,"",python,selection_mouse +1966,4161150,"genie.py",5906,0,"",python,selection_mouse +1967,4161330,"genie.py",5892,17,"next_token_logits",python,selection_mouse +1968,4161850,"genie.py",5924,0,"",python,selection_mouse +1969,4162574,"genie.py",5906,0,"",python,selection_mouse +1970,4162721,"genie.py",5892,17,"next_token_logits",python,selection_mouse +1971,4163195,"genie.py",5995,0,"",python,selection_mouse +1972,4165735,"genie.py",6651,0,"",python,selection_mouse +1973,4165869,"genie.py",6649,5,"batch",python,selection_mouse +1974,4166534,"genie.py",6664,0,"",python,selection_mouse +1975,4166650,"genie.py",6662,3,"""].",python,selection_mouse +1976,4167183,"genie.py",6659,0,"",python,selection_mouse +1977,4167344,"genie.py",6656,6,"videos",python,selection_mouse +1978,4169356,"genie.py",6598,0,"",python,selection_mouse +1979,4169522,"genie.py",6593,9,"tokenizer",python,selection_mouse +1980,4170124,"genie.py",6608,0,"",python,selection_mouse +1981,4170273,"genie.py",6603,6,"decode",python,selection_mouse +1982,4170783,"genie.py",6685,0,"",python,selection_mouse +1983,4171465,"genie.py",6659,0,"",python,selection_mouse +1984,4171627,"genie.py",6656,6,"videos",python,selection_mouse +1985,4177865,"train_dynamics.py",0,0,"",python,tab +1986,4179308,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",0,0,"",python,tab +1987,4181141,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5448,0,"",python,selection_mouse +1988,4181840,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5520,0,"",python,selection_mouse +1989,4187327,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",2366,0,"",python,selection_mouse +1990,4187488,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",2363,5,"dtype",python,selection_mouse +1991,4191391,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",6564,0,"",python,selection_mouse +1992,4193621,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",1397,0,"",python,selection_command +1993,4193994,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",1408,0,"",python,selection_command +1994,4194253,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",1432,0,"",python,selection_command +1995,4194667,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",1443,0,"",python,selection_command +1996,4195005,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",2324,0,"",python,selection_command +1997,4195884,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",2341,0,"",python,selection_command +1998,4197985,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",2352,0,"",python,selection_command +1999,4198354,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",2363,0,"",python,selection_command +2000,4198707,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",2568,0,"",python,selection_command +2001,4206448,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",3304,0,"",python,selection_command +2002,4209071,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5837,0,"",python,selection_command +2003,4232641,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5427,0,"",python,selection_mouse +2004,4233210,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5435,0,"",python,selection_mouse +2005,4233360,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5431,11,"video_batch",python,selection_mouse +2006,4250103,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5439,0,"",python,selection_mouse +2007,4250104,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5431,11,"video_batch",python,selection_mouse +2008,4255935,"train_dynamics.py",0,0,"",python,tab +2009,4257478,"train_dynamics.py",2799,0,"",python,selection_mouse +2010,4257538,"train_dynamics.py",2798,0,"",python,selection_command +2011,4259516,"train_dynamics.py",2409,0,"",python,selection_mouse +2012,4259678,"train_dynamics.py",2404,6,"videos",python,selection_mouse +2013,4260651,"train_dynamics.py",2441,0,"",python,selection_mouse +2014,4260786,"train_dynamics.py",2439,4,"args",python,selection_mouse +2015,4263417,"train_dynamics.py",2446,0,"",python,selection_mouse +2016,4263593,"train_dynamics.py",2444,5,"dtype",python,selection_mouse +2017,4266674,"train_dynamics.py",2431,0,"",python,selection_mouse +2018,4266896,"train_dynamics.py",2431,1,".",python,selection_mouse +2019,4266896,"train_dynamics.py",2431,3,".as",python,selection_mouse +2020,4266897,"train_dynamics.py",2431,57,".astype(args.dtype) / 255.0\n outputs = state.apply_fn(",python,selection_mouse +2021,4267083,"train_dynamics.py",2431,12,".astype(args",python,selection_mouse +2022,4267146,"train_dynamics.py",2431,13,".astype(args.",python,selection_mouse +2023,4267309,"train_dynamics.py",2431,14,".astype(args.d",python,selection_mouse +2024,4267310,"train_dynamics.py",2431,15,".astype(args.dt",python,selection_mouse +2025,4267310,"train_dynamics.py",2431,16,".astype(args.dty",python,selection_mouse +2026,4267310,"train_dynamics.py",2431,17,".astype(args.dtyp",python,selection_mouse +2027,4267345,"train_dynamics.py",2431,18,".astype(args.dtype",python,selection_mouse +2028,4267450,"train_dynamics.py",2431,19,".astype(args.dtype)",python,selection_mouse +2029,4273793,"train_dynamics.py",2458,0,"",python,selection_mouse +2030,4273802,"train_dynamics.py",2457,0,"",python,selection_command +2031,4273993,"train_dynamics.py",2457,1,"0",python,selection_mouse +2032,4274033,"train_dynamics.py",2458,0,"",python,selection_command +2033,4274047,"train_dynamics.py",2455,3,"5.0",python,selection_mouse +2034,4274047,"train_dynamics.py",2452,6," 255.0",python,selection_mouse +2035,4274085,"train_dynamics.py",2448,10,"e) / 255.0",python,selection_mouse +2036,4274085,"train_dynamics.py",2458,30,"\n outputs = state.apply_fn(",python,selection_mouse +2037,4274342,"train_dynamics.py",2438,20,"(args.dtype) / 255.0",python,selection_mouse +2038,4274395,"train_dynamics.py",2439,19,"args.dtype) / 255.0",python,selection_mouse +2039,4274489,"train_dynamics.py",2438,20,"(args.dtype) / 255.0",python,selection_mouse +2040,4274513,"train_dynamics.py",2437,21,"e(args.dtype) / 255.0",python,selection_mouse +2041,4274561,"train_dynamics.py",2436,22,"pe(args.dtype) / 255.0",python,selection_mouse +2042,4274561,"train_dynamics.py",2435,23,"ype(args.dtype) / 255.0",python,selection_mouse +2043,4274624,"train_dynamics.py",2434,24,"type(args.dtype) / 255.0",python,selection_mouse +2044,4274669,"train_dynamics.py",2433,25,"stype(args.dtype) / 255.0",python,selection_mouse +2045,4274718,"train_dynamics.py",2432,26,"astype(args.dtype) / 255.0",python,selection_mouse +2046,4274981,"train_dynamics.py",2431,27,".astype(args.dtype) / 255.0",python,selection_mouse +2047,4281966,"genie.py",0,0,"",python,tab +2048,4286333,"genie.py",4674,0,"",python,selection_mouse +2049,4288298,"genie.py",4673,0,"",python,selection_command +2050,4288753,"genie.py",4674,0,"\n ",python,content +2051,4289207,"genie.py",4683,0,"b",python,content +2052,4289208,"genie.py",4684,0,"",python,selection_keyboard +2053,4289354,"genie.py",4684,0,"a",python,content +2054,4289355,"genie.py",4685,0,"",python,selection_keyboard +2055,4289548,"genie.py",4685,0,"t",python,content +2056,4289549,"genie.py",4686,0,"",python,selection_keyboard +2057,4289661,"genie.py",4686,0,"c",python,content +2058,4289662,"genie.py",4687,0,"",python,selection_keyboard +2059,4289696,"genie.py",4687,0,"h",python,content +2060,4289697,"genie.py",4688,0,"",python,selection_keyboard +2061,4290944,"genie.py",4688,0,"[]",python,content 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+2095,4296669,"genie.py",4698,43," = .astype(args.dtype) / 255.0\n toke",python,selection_mouse +2096,4296694,"genie.py",4698,42," = .astype(args.dtype) / 255.0\n tok",python,selection_mouse +2097,4296729,"genie.py",4698,41," = .astype(args.dtype) / 255.0\n to",python,selection_mouse +2098,4296781,"genie.py",4698,40," = .astype(args.dtype) / 255.0\n t",python,selection_mouse +2099,4296834,"genie.py",4698,39," = .astype(args.dtype) / 255.0\n ",python,selection_mouse +2100,4297093,"genie.py",4683,15,"batch[""videos""]",python,selection_mouse +2101,4299955,"genie.py",4701,0,"",python,selection_mouse +2102,4300490,"genie.py",4701,0,"batch[""videos""]",python,content +2103,4303337,"genie.py",4726,0,"",python,selection_mouse +2104,4303479,"genie.py",4724,4,"args",python,selection_mouse +2105,4303693,"genie.py",4724,5,"args.",python,selection_mouse +2106,4303694,"genie.py",4724,10,"args.dtype",python,selection_mouse +2107,4304107,"genie.py",4732,0,"",python,selection_mouse 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batch[""videos""] = batch[""videos""].astype(args.dtype) / 255.0",python,content +2129,4323055,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5111,0,"",python,selection_command +2130,4324725,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5107,4,"",python,content +2131,4325147,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5103,4,"",python,content +2132,4326012,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5103,5,"",python,content +2133,4326150,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5103,2,"",python,content +2134,4326398,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5103,6,"",python,content +2135,4326724,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5103,2,"",python,content 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+2152,4333773,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5117,2,"video_batch",python,content +2153,4334123,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5128,0,".",python,content +2154,4334124,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5129,0,"",python,selection_keyboard +2155,4334787,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5128,0,"",python,selection_command +2156,4338267,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5750,0,"",python,selection_mouse +2157,4338286,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5749,0,"",python,selection_command +2158,4341970,"TERMINAL",0,0,"python sample.py --checkpoint ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared//checkpoints/train_dyn_yolorun_new_arch/3352115"" --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +2159,4342161,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +2160,4344467,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +2161,4346534,"TERMINAL",0,0,"2025-07-17 10:56:29.324296: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2162,4360415,"TERMINAL",0,0,"2025-07-17 10:56:43.205786: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2163,4375516,"TERMINAL",0,0,"2025-07-17 10:56:58.276862: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2164,4379270,"TERMINAL",0,0,"WARNING:absl:Missing metrics for step 35000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/035000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 36000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/036000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 37000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/037000/metrics/metrics not found.\r\n",,terminal_output +2165,4448498,"TERMINAL",0,0,"2025-07-17 10:58:11.287114: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-17 10:58:11.287629: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-17 10:58:11.288296: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2166,4497287,"TERMINAL",0,0,"token_idxs shape: (Array(1, dtype=int32), Array(1, dtype=int32), Array(920, dtype=int32))\r\n",,terminal_output +2167,4497621,"TERMINAL",0,0,"token_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\n",,terminal_output +2168,4500706,"TERMINAL",0,0,"SSIM: 0.3542162775993347\r\n",,terminal_output +2169,4503177,"TERMINAL",0,0,"]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0804 jafar]$ ",,terminal_output +2170,4612925,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",0,0,"",python,tab +2171,4622392,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5724,0,"",python,selection_mouse +2172,4623927,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5750,0,"",python,selection_mouse +2173,4623947,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5749,0,"",python,selection_command 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+2181,4628028,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5750,0,"",python,selection_mouse +2182,4628047,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5749,0,"",python,selection_command +2183,4628654,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5724,0,"",python,selection_mouse +2184,4673326,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5551,0,"",python,selection_mouse +2185,4673920,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5694,0,"",python,selection_mouse +2186,4674545,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5541,0,"",python,selection_mouse +2187,4674996,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5610,0,"",python,selection_mouse +2188,4675569,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5724,0,"",python,selection_mouse +2189,4676128,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5786,0,"",python,selection_mouse +2190,4676759,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5893,0,"",python,selection_mouse +2191,4677366,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",6092,0,"",python,selection_mouse +2192,4677372,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",6091,0,"",python,selection_command +2193,4677998,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5424,0,"",python,selection_mouse +2194,4677999,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",5423,0,"",python,selection_command +2195,4680326,"train_dynamics.py",0,0,"",python,tab +2196,4688316,"train_dynamics.py",3313,0,"",python,selection_mouse +2197,4694254,"genie.py",0,0,"",python,tab +2198,4696877,"genie.py",4900,0,"",python,selection_mouse +2199,4698333,"genie.py",4839,0,"",python,selection_mouse +2200,4698989,"genie.py",4882,0,"",python,selection_mouse +2201,4699131,"genie.py",4877,10,"token_idxs",python,selection_mouse +2202,4704360,"genie.py",6488,0,"",python,selection_mouse +2203,4707702,"genie.py",5945,0,"",python,selection_mouse +2204,4709987,"genie.py",5945,0,"-",python,content +2205,4709989,"genie.py",5946,0,"",python,selection_keyboard +2206,4710066,"genie.py",5946,0,"1",python,content +2207,4710067,"genie.py",5947,0,"",python,selection_keyboard +2208,4710531,"genie.py",5946,0,"",python,selection_command +2209,4711995,"genie.py",5792,0,"",python,selection_mouse +2210,4712542,"genie.py",5854,0,"",python,selection_mouse +2211,4720165,"TERMINAL",0,0,"python sample.py --checkpoint ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared//checkpoints/train_dyn_yolorun_new_arch/3352115"" --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +2212,4720629,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +2213,4722328,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +2214,4724464,"TERMINAL",0,0,"2025-07-17 11:02:47.254967: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2215,4738194,"TERMINAL",0,0,"2025-07-17 11:03:00.986005: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2216,4753257,"TERMINAL",0,0,"2025-07-17 11:03:16.028164: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2217,4756845,"TERMINAL",0,0,"WARNING:absl:Missing metrics for step 35000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/035000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 37000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/037000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 36000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/036000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/020000/metrics/metrics not found.\r\n",,terminal_output +2218,4825399,"TERMINAL",0,0,"2025-07-17 11:04:28.188739: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-17 11:04:28.189216: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-17 11:04:28.189880: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2219,4873038,"TERMINAL",0,0,"token_idxs shape: (Array(1, dtype=int32), Array(1, dtype=int32), Array(920, dtype=int32))\r\n",,terminal_output +2220,4873376,"TERMINAL",0,0,"token_idxs_full 0: [148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 148 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 148 148 0 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 148 148 148 0 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 148 148 148 148 0 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 148 148 148 148 148 0 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 148 148 148 148 148 575 0 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 148 148 148 148 148 575 575 0 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 148 148 148 148 148 575 575 575 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 148 148 148 148 148 575 575 575 575 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 148 148 148 148 148 575 575 575 575 575 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 148 148 148 148 148 575 575 575 575 575 575 0 0 0 0]\r\ntoken_idxs_full 0: [148 148 148 148 148 148 575 575 575 575 575 575 575 0 0 0]\r\ntoken_idxs_full 0: [148 148 148 148 148 148 575 575 575 575 575 575 575 575 0 0]\r\ntoken_idxs_full 0: [148 148 148 148 148 148 575 575 575 575 575 575 575 575 575 0]\r\n",,terminal_output +2221,4876247,"TERMINAL",0,0,"SSIM: 0.6116350889205933\r\n",,terminal_output +2222,4878157,"TERMINAL",0,0,"]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0804 jafar]$ ",,terminal_output +2223,5058880,"TERMINAL",0,0,"python sample.py --checkpoint ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared//checkpoints/train_dyn_yolorun_new_arch/3352115"" --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked",,terminal_output +2224,5060481,"TERMINAL",0,0," ",,terminal_output +2225,5060663,"TERMINAL",0,0,"[?25l-[?25h",,terminal_output +2226,5060776,"TERMINAL",0,0,"[?25l-[?25h",,terminal_output +2227,5061227,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +2228,5061648,"TERMINAL",0,0,"[?25lt[?25h",,terminal_output +2229,5061702,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +2230,5061880,"TERMINAL",0,0,"[?25lr[?25h",,terminal_output +2231,5061936,"TERMINAL",0,0,"[?25lt[?25h",,terminal_output +2232,5062523,"TERMINAL",0,0,"[?25l_[?25h",,terminal_output +2233,5062711,"TERMINAL",0,0,"[?25lf[?25h",,terminal_output +2234,5062820,"TERMINAL",0,0,"[?25lr[?25h",,terminal_output +2235,5062989,"TERMINAL",0,0,"[?25la[?25h",,terminal_output +2236,5063510,"TERMINAL",0,0,"[?25lm[?25h",,terminal_output +2237,5063621,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +2238,5064253,"TERMINAL",0,0,"[?25l=[?25h",,terminal_output +2239,5064306,"TERMINAL",0,0,"[?25l1[?25h",,terminal_output +2240,5064496,"TERMINAL",0,0,"[?25l0[?25h",,terminal_output +2241,5066267,"TERMINAL",0,0,"[?25l8[?25h",,terminal_output +2242,5067301,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +2243,5069131,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +2244,5071175,"TERMINAL",0,0,"2025-07-17 11:08:33.954623: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2245,5085028,"TERMINAL",0,0,"2025-07-17 11:08:47.821467: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2246,5099868,"TERMINAL",0,0,"2025-07-17 11:09:02.659807: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2247,5103395,"TERMINAL",0,0,"WARNING:absl:Missing metrics for step 37000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/037000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 35000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/035000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/020000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 36000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/036000/metrics/metrics not found.\r\n",,terminal_output +2248,5157741,"TERMINAL",0,0,"2025-07-17 11:10:00.531642: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-17 11:10:00.532209: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-17 11:10:00.532868: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2249,5191694,"TERMINAL",0,0,"token_idxs shape: (Array(1, dtype=int32), Array(9, dtype=int32), Array(920, dtype=int32))\r\n",,terminal_output +2250,5191856,"TERMINAL",0,0,"token_idxs_full 0: [148 842 707 148 148 148 148 148 148 0 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 842 707 148 148 148 148 148 148 148 0 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 842 707 148 148 148 148 148 148 148 148 0 0 0 0 0]\r\ntoken_idxs_full 0: [148 842 707 148 148 148 148 148 148 148 148 148 0 0 0 0]\r\ntoken_idxs_full 0: [148 842 707 148 148 148 148 148 148 148 148 148 575 0 0 0]\r\ntoken_idxs_full 0: [148 842 707 148 148 148 148 148 148 148 148 148 575 575 0 0]\r\ntoken_idxs_full 0: [148 842 707 148 148 148 148 148 148 148 148 148 575 575 575 0]\r\n",,terminal_output +2251,5194253,"TERMINAL",0,0,"SSIM: 0.8266993761062622\r\n",,terminal_output +2252,5196018,"TERMINAL",0,0,"]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0804 jafar]$ ",,terminal_output +2253,5427432,"TERMINAL",0,0,"python sample.py --checkpoint ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared//checkpoints/train_dyn_yolorun_new_arch/3352115"" --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked --start_frame=8",,terminal_output +2254,5428265,"TERMINAL",0,0,"[?25l [?25h",,terminal_output +2255,5429014,"TERMINAL",0,0,"[?25l-[?25h",,terminal_output +2256,5429169,"TERMINAL",0,0,"[?25l-[?25h",,terminal_output +2257,5429452,"TERMINAL",0,0,"[?25lb[?25h",,terminal_output +2258,5429562,"TERMINAL",0,0,"[?25la \r[?25h",,terminal_output +2259,5429748,"TERMINAL",0,0,"[?25lt[?25h",,terminal_output +2260,5429945,"TERMINAL",0,0,"[?25lc[?25h",,terminal_output +2261,5430043,"TERMINAL",0,0,"[?25lh[?25h",,terminal_output +2262,5431049,"TERMINAL",0,0,"[?25l_[?25h",,terminal_output +2263,5432189,"TERMINAL",0,0,"[?25ls[?25h",,terminal_output +2264,5432259,"TERMINAL",0,0,"[?25li[?25h",,terminal_output +2265,5432479,"TERMINAL",0,0,"[?25lz[?25h",,terminal_output +2266,5432616,"TERMINAL",0,0,"[?25le[?25h",,terminal_output +2267,5433056,"TERMINAL",0,0,"[?25l=[?25h",,terminal_output +2268,5433438,"TERMINAL",0,0,"[?25l1[?25h[?25l2[?25h",,terminal_output +2269,5436313,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",0,0,"",python,tab +2270,5437748,"train_dynamics.py",0,0,"",python,tab +2271,5442688,"genie.py",0,0,"",python,tab +2272,5444242,"genie.py",5604,0,"",python,selection_mouse +2273,5445811,"genie.py",5594,0,"",python,selection_command +2274,5446325,"genie.py",5594,0,"#",python,content +2275,5446327,"genie.py",5595,0,"",python,selection_keyboard +2276,5451299,"genie.py",5595,0," ",python,content +2277,5451300,"genie.py",5596,0,"",python,selection_keyboard +2278,5455621,"genie.py",4879,0,"",python,selection_mouse +2279,5457203,"genie.py",4878,0,"",python,selection_command +2280,5457767,"genie.py",4860,0,"",python,selection_command +2281,5458233,"genie.py",4860,0,"#",python,content +2282,5458234,"genie.py",4861,0,"",python,selection_keyboard +2283,5458341,"genie.py",4861,0," ",python,content +2284,5458342,"genie.py",4862,0,"",python,selection_keyboard +2285,5485638,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +2286,5487259,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +2287,5489356,"TERMINAL",0,0,"2025-07-17 11:15:32.139873: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2288,5518644,"TERMINAL",0,0,"2025-07-17 11:16:01.439387: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2289,5523863,"TERMINAL",0,0,"2025-07-17 11:16:06.644648: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2290,5527621,"TERMINAL",0,0,"WARNING:absl:Missing metrics for step 35000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/035000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 37000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/037000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 36000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/036000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/020000/metrics/metrics not found.\r\n",,terminal_output +2291,5596892,"TERMINAL",0,0,"2025-07-17 11:17:19.681381: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-17 11:17:19.681981: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-17 11:17:19.682684: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2292,5648354,"TERMINAL",0,0,"SSIM: 0.7451367378234863\r\n",,terminal_output +2293,5652477,"TERMINAL",0,0,"]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0804 jafar]$ ",,terminal_output +2294,6121108,"TERMINAL",0,0,"srun",,terminal_focus +2295,6122357,"TERMINAL",0,0,"srun",,terminal_focus +2296,6123615,"TERMINAL",0,0,"python sample.py --checkpoint ""/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared//checkpoints/train_dyn_yolorun_new_arch/3352115"" --data_dir /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked --start_frame=8 --batch_size=12",,terminal_output +2297,6127986,"TERMINAL",0,0,"[?25l2[?25h",,terminal_output +2298,6128789,"TERMINAL",0,0,"[?25l[?25h",,terminal_output +2299,6128898,"TERMINAL",0,0,"",,terminal_output +2300,6129114,"TERMINAL",0,0,"",,terminal_output +2301,6130251,"TERMINAL",0,0,"[?25l8 --batch_size=12[?25h",,terminal_output +2302,6130775,"TERMINAL",0,0,"0 --batch_size=12",,terminal_output +2303,6131123,"TERMINAL",0,0,"[?25l  --batch_size=12[?25h",,terminal_output +2304,6131352,"TERMINAL",0,0,"[?25l- --batch_size=12[?25h",,terminal_output +2305,6131463,"TERMINAL",0,0,"[?25l- --batch_size=12[?25h",,terminal_output +2306,6131998,"TERMINAL",0,0,"[?25ls --batch_size=12[?25h",,terminal_output +2307,6132212,"TERMINAL",0,0,"[?25le -batch_size=12\r[?25h",,terminal_output +2308,6132451,"TERMINAL",0,0,"q --batch_size=12\r",,terminal_output +2309,6132869,"TERMINAL",0,0,"[?25l_ --batch_size=12\r[?25h",,terminal_output +2310,6133142,"TERMINAL",0,0,"[?25ll- --batch_size=12\r[?25h",,terminal_output +2311,6133232,"TERMINAL",0,0,"[?25l e --batch_size=12\r[?25h",,terminal_output +2312,6133289,"TERMINAL",0,0,"n --batch_size=12\r",,terminal_output +2313,6134512,"TERMINAL",0,0,"[?25l= --batch_size=12\r[?25h",,terminal_output +2314,6135919,"TERMINAL",0,0,"[?25l2 --batch_size=12\r[?25h",,terminal_output +2315,6139868,"TERMINAL",0,0,"\r\n[?2004l\r",,terminal_output +2316,6141639,"TERMINAL",0,0,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `param-dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/.venv/lib/python3.10/site-packages/tyro/_parsers.py:347: UserWarning: The field `dtype` is annotated with type ``, but the default value `` has type ``. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n warnings.warn(message)\r\n",,terminal_output +2317,6143810,"TERMINAL",0,0,"2025-07-17 11:26:26.605472: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2318,6157952,"TERMINAL",0,0,"2025-07-17 11:26:40.747443: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2319,6173230,"TERMINAL",0,0,"2025-07-17 11:26:56.023415: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2320,6176861,"TERMINAL",0,0,"WARNING:absl:Missing metrics for step 37000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/037000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 38000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/038000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 36000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/036000/metrics/metrics not found.\r\nWARNING:absl:Missing metrics for step 20000\r\nERROR:absl:File /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/train_dyn_yolorun_new_arch/3352115/020000/metrics/metrics not found.\r\n",,terminal_output +2321,6222642,"TERMINAL",0,0,"2025-07-17 11:27:45.419339: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-17 11:27:45.419779: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n2025-07-17 11:27:45.420312: W external/xla/xla/service/gpu/autotuning/dot_search_space.cc:200] All configs were filtered out because none of them sufficiently match the hints. Maybe the hints set does not contain a good representative set of valid configs?Working around this by using the full hints set instead.\r\n",,terminal_output +2322,6246631,"TERMINAL",0,0,"SSIM: 0.7589486837387085\r\n",,terminal_output +2323,6248779,"TERMINAL",0,0,"]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0804 jafar]$ ",,terminal_output +2324,6717374,"TERMINAL",0,0,"bash",,terminal_focus +2325,6718970,"TERMINAL",0,0,"srun",,terminal_focus +2326,6720569,"TERMINAL",0,0,"srun",,terminal_focus +2327,6724524,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",0,0,"",python,tab +2328,6725193,"train_dynamics.py",0,0,"",python,tab +2329,6726339,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",0,0,"",python,tab +2330,6730719,"train_dynamics.py",0,0,"",python,tab +2331,6734980,"TERMINAL",0,0,"[?25li[?25h",,terminal_output +2332,6735112,"TERMINAL",0,0,"[?25ld[?25h",,terminal_output +2333,6735172,"TERMINAL",0,0,"[?25ll[?25h",,terminal_output +2334,6735390,"TERMINAL",0,0,"[?25li[?25h[?25ln[?25h",,terminal_output +2335,6735462,"TERMINAL",0,0,"[?25lg[?25h",,terminal_output +2336,6735666,"TERMINAL",0,0,"\r\n[?2004l\r[?1049h(B[?7hEvery 1.0s: sinfo_t_idlehkn0804.localdomain: Thu Jul 17 11:36:18 2025Partition dev_cpuonly: 10 nodes idle\rPartition cpuonly:\t 3 nodes idle\rPartition dev_accelerated:\t 2 nodes idle\rPartition accelerated: 52 nodes idle\rPartition dev_accelerated-h100 :\t 0 nodes idle\rPartition accelerated-h100:\t 0 nodes idle\rPartition large:\t 8 nodes idle",,terminal_output +2337,6736665,"TERMINAL",0,0,"9\t ",,terminal_output +2338,6737328,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn0804:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0804 jafar]$ ",,terminal_output +2339,6739009,"train_dynamics.py",0,0,"",python,tab +2340,6904802,"train_dynamics.py",3319,0,"",python,selection_mouse +2341,6904809,"train_dynamics.py",3318,0,"",python,selection_command +2342,6943573,"train_dynamics.py",3319,0,"",python,selection_mouse +2343,6943574,"train_dynamics.py",3318,0,"",python,selection_command +2344,6973827,"train_dynamics.py",3241,0,"",python,selection_mouse +2345,6973828,"train_dynamics.py",3240,0,"",python,selection_command +2346,7024179,"train_dynamics.py",0,0,"",python,tab +2347,7025760,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",0,0,"",python,tab +2348,7027184,"genie.py",0,0,"",python,tab +2349,7030532,"models/dynamics.py",0,0,"",python,tab +2350,7031276,"genie.py",0,0,"",python,tab +2351,7032148,"models/dynamics.py",0,0,"",python,tab +2352,7033739,"genie.py",0,0,"",python,tab +2353,7034733,"/hkfs/home/project/hk-project-p0023960/tum_cte0515/Projects/jafar/sample.py",0,0,"",python,tab +2354,7036102,"train_dynamics.py",0,0,"",python,tab +2355,7869514,"TERMINAL",0,0,"srun",,terminal_focus +2356,7882538,"TERMINAL",0,0,"s",,terminal_output +2357,7882636,"TERMINAL",0,0,"[?25lm[?25h",,terminal_output +2358,7882719,"TERMINAL",0,0,"[?25li[?25h",,terminal_output +2359,7883079,"TERMINAL",0,0,"[?25l[?2004l\r[?25h[?1049h(B[?7hEvery 1.0s: nvidia-smihkn0605.localdomain: Thu Jul 17 11:55:25 2025Thu Jul 17 11:55:25 2025\r+-----------------------------------------------------------------------------------------+\r| NVIDIA-SMI 570.133.20Driver Version: 570.133.20 CUDA Version: 12.8 |\r|-----------------------------------------+------------------------+----------------------+\r| GPU NamePersistence-M | Bus-IdDisp.A | Volatile Uncorr. ECC |\r| Fan Temp PerfPwr:Usage/Cap |Memory-Usage | GPU-Util Compute M. |\r|||MIG M. |\r|=========================================+========================+======================|\r| 0 NVIDIA A100-SXM4-40GBOn | 00000000:31:00.0 Off |0 |\r| N/A 45C P056W / 300W |\t 27MiB / 40960MiB |\t 0%\t Default |\r|||Disabled |\r+-----------------------------------------+------------------------+----------------------+\r| 1 NVIDIA A100-SXM4-40GBOn | 00000000:4B:00.0 Off |0 |\r| N/A 44C P062W / 300W |\t 27MiB / 40960MiB |\t 0%\t Default |\r|||Disabled |\r+-----------------------------------------+------------------------+----------------------+\r| 2 NVIDIA A100-SXM4-40GBOn | 00000000:CA:00.0 Off |0 |\r| N/A 44C P052W / 300W |\t 27MiB / 40960MiB |\t 0%\t Default |\r|||Disabled |\r+-----------------------------------------+------------------------+----------------------+\r| 3 NVIDIA A100-SXM4-40GBOn | 00000000:E3:00.0 Off |0 |\r| N/A 44C P053W / 300W |\t 27MiB / 40960MiB |\t 0%\t Default |\r|||Disabled |\r+-----------------------------------------+------------------------+----------------------+\r+-----------------------------------------------------------------------------------------+\r| Processes:|\r| GPU GI CIPID Type Process nameGPU Memory |\r|ID IDUsage\t |\r|=========================================================================================|",,terminal_output +2360,7883940,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_cte0515@hkn0605:~/Projects/jafar[?2004h(jafar) [tum_cte0515@hkn0605 jafar]$ ",,terminal_output +2361,7923970,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun.sbatch",0,0,"",shellscript,tab +2362,7925223,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_yolorun copy.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_dyn_yolorun_new_arch\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\n# source .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/train_tokenizer_lr_sweep_1e-4\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-new-arch-run-$slurm_job_id \\n --tags dynamics \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n ",shellscript,tab +2363,7934465,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_new_arch_speedrun.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=2\n#SBATCH --ntasks-per-node=4\n#SBATCH --time=48:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:4\n#SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log\n#SBATCH --job-name=train_dyn_yolorun_new_arch\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\n# source .venv/bin/activate\n\narray_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords_chunked\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name/$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/big-runs/tokenizer-lr-scaling/train_tokenizer_lr_sweep_1e-4\n\nenv | grep SLURM\n\nsrun python train_dynamics.py \\n --save_ckpt \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=96 \\n --max_lr=1e-4 \\n --log_image_interval=1000 \\n --log \\n --log_checkpoint_interval=1000 \\n --name=dynamics-new-arch-run-$slurm_job_id \\n --tags dynamics \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --data_dir $array_records_dir\n ",shellscript,tab +2364,7947341,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_new_arch_speedrun.sbatch",1084,0,"",shellscript,selection_mouse +2365,7948239,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_new_arch_speedrun.sbatch",1083,0,"",shellscript,selection_command +2366,7949099,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_new_arch_speedrun.sbatch",1083,1,"3",shellscript,content +2367,7950652,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_new_arch_speedrun.sbatch",1130,0,"",shellscript,selection_mouse +2368,7950653,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_new_arch_speedrun.sbatch",1129,0,"",shellscript,selection_command +2369,7951417,"slurm/jobs/mihir/horeka/yolo-runs/train_dynamics_new_arch_speedrun.sbatch",1105,0,"",shellscript,selection_mouse +2370,7964650,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/A_train_dyn_1.5M.sbatch",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=10:00:00\n#SBATCH --partition=accelerated\n#SBATCH --cpus-per-task=5\n#SBATCH --gres=gpu:1\n#SBATCH --output=logs/logs_training/%x_%j.log\n#SBATCH --error=logs/logs_training/%x_%j.log\n#SBATCH --mail-user=mihir.mahajan2002@gmail.com\n#SBATCH --job-name=train_dynamics_minecraft_overfit_sample_1.5M\n#SBATCH --mem=50G\n#SBATCH --mail-type=ALL\n\n# Log the sbatch script\ncat $0\n\nmodule unload mpi/openmpi/5.0\nmodule unload devel/cuda/12.4\nsource .venv/bin/activate\n\ntf_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/knoms_tfrecords_500_shards\nws_dir='/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/'\n\njob_name=$SLURM_JOB_NAME\nslurm_job_id=$SLURM_JOB_ID\n\nCHECKPOINT_DIR=$ws_dir/checkpoints/$job_name_$slurm_job_id\nmkdir -p $CHECKPOINT_DIR\n\nenv | grep SLURM\n\necho Running dynamics model overfit run tiny model A, ~1.5M params. Slurm id: $slurm_job_id\n\ntokenizer_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290391/tokenizer_1750845012_50000/\nlam_ckpt_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/3290392/lam_1750845133_180000/\n\nsrun python train_dynamics.py \\n --ckpt_dir $CHECKPOINT_DIR \\n --batch_size=1 \\n --min_lr=5e-7 \\n --max_lr=5e-6 \\n --warmup_steps=125 \\n --log_image_interval=100 \\n --log \\n --name=dynamics-model-size-scaling-1.5M-$slurm_job_id \\n --tags dynamics model-size-scaling 1.5M tiny A \\n --log_checkpoint_interval=500 \\n --entity instant-uv \\n --project jafar \\n --tokenizer_checkpoint=$tokenizer_ckpt_dir \\n --lam_checkpoint=$lam_ckpt_dir \\n --data_dir $tf_records_dir \\n --dyna_dim=128 \\n --dyna_num_blocks=2 \\n --dyna_num_heads=4",shellscript,tab +2371,7966392,"slurm/jobs/mihir/horeka/modelsize_scaling/dynamics/A_train_dyn_1.5M.sbatch",1769,0,"",shellscript,selection_mouse 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\r\n 3335732 dynamics_cotraining_action_sp+ accelerated 48 TIMEOUT 2-00:00:11 2-00:00:00 \r\n 3335733 dynamics_cotraining_action_sp+ accelerated 48 TIMEOUT 2-00:00:23 2-00:00:00 \r\n 3335734 dynamics_cotraining_action_sp+ accelerated 48 TIMEOUT 2-00:00:23 2-00:00:00 \r\n 3335735 dynamics_cotraining_action_sp+ accelerated 48 TIMEOUT 2-00:00:21 2-00:00:00 \r\n 3335736 dynamics_cotraining_action_sp+ accelerated 48 TIMEOUT 2-00:00:12 2-00:00:00 \r\n 3335737 train_tokenizer_lr_sweep_1e-4 accelerated 48 TIMEOUT 2-00:00:12 2-00:00:00 \r\n 3335738 train_tokenizer_lr_sweep_5e-5 accelerated 48 TIMEOUT 2-00:00:11 2-00:00:00 \r\n 3335744 train_dynamics_modelsize_scal+ accelerated 48 TIMEOUT 2-00:00:05 2-00:00:00 \r\n 3335745 train_dynamics_modelsize_scal+ accelerated 144 TIMEOUT 2-00:00:12 2-00:00:00 \r\n 3335746 train_dynamics_modelsize_scal+ accelerated 288 TIMEOUT 2-00:00:17 2-00:00:00 \r\n 3335747 train_dynamics_modelsize_scal+ accelerated 384 FAILED 00:00:58 2-00:00:00 \r\n 3335748 train_dynamics_modelsize_scal+ accelerated 768 FAILED 00:00:54 2-00:00:00 \r\n 3338176 train_dynamics_modelsize_scal+ accelerated 0 FAILED 00:00:00 2-00:00:00 \r\n 3338177 train_dynamics_modelsize_scal+ accelerated 0 FAILED 00:00:00 2-00:00:00 \r\n 3338178 train_dynamics_modelsize_scal+ accelerated 0 FAILED 00:00:00 2-00:00:00 \r\n 3338179 train_dynamics_modelsize_scal+ accelerated 0 FAILED 00:00:00 2-00:00:00 \r\n 3338180 train_dynamics_modelsize_scal+ accelerated 0 FAILED 00:00:00 2-00:00:00 \r\n 3338240 train_dynamics_modelsize_scal+ accelerated 0 FAILED 00:00:00 2-00:00:00 \r\n 3341079 train_tokenizer_lr_sweep_1e-4 accelerated 48 TIMEOUT 2-00:00:11 2-00:00:00 \r\n 3341080 train_tokenizer_lr_sweep_5e-5 accelerated 48 TIMEOUT 2-00:00:11 2-00:00:00 \r\n 3345116 train_dynamics_modelsize_scal+ accelerated 48 TIMEOUT 2-00:00:17 2-00:00:00 \r\n 3348397 train_dynamics_lr_schedule_co+ accelerated 48 COMPLETED 1-03:21:05 2-00:00:00 \r\n 3348399 train_dynamics_lr_schedule_cos accelerated 48 COMPLETED 1-03:28:39 2-00:00:00 \r\n 3348400 train_dynamics_lr_schedule_wsd accelerated 48 COMPLETED 1-03:19:17 2-00:00:00 \r\n 3352115 train_dyn_yolorun_new_arch accelerated 48 RUNNING 16:22:05 2-00:00:00 \r\n]0;tum_cte0515@hkn1990:~/Projects/jafar]633;D;0",,terminal_output +2465,8118645,"TERMINAL",0,0,"alias",,terminal_command +2466,8118677,"TERMINAL",0,0,"]633;E;2025-07-17 11:59:21 alias;809dfc76-3a3b-47d1-9b49-bee31e96da6c]633;Calias egrep='egrep --color=auto'\r\nalias fgrep='fgrep --color=auto'\r\nalias fqueue='watch -n 1 ""squeue -o \""%.10i %.16P %.30j %.8u %.8T %.10M %.9l %.6D %R\""""'\r\nalias fsacct_week='sacct --format=""JobID%15,JobName%30,Partition%16,AllocCPUS%3,State%12,Elapsed%10,Timelimit%10"" --starttime $(date -d ""last week"" +%Y-%m-%d) | grep -vE ""*.batch|*.extern|*.inter|bash|python|CANCELLED|echo""'\r\nalias grep='grep --color=auto'\r\nalias idle='sinfo_t_idle'\r\nalias idling='watch -n1 sinfo_t_idle'\r\nalias l.='ls -d .* --color=auto'\r\nalias ll='ls -l --color=auto'\r\nalias ls='ls --color=auto'\r\nalias mc='. /usr/libexec/mc/mc-wrapper.sh'\r\nalias queue='watch -n1 squeue --me'\r\nalias runner='cd /home/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/'\r\nalias salloc_cpu='salloc --time=01:00:00 --partition=dev_cpuonly --nodes=1 --cpus-per-task=128'\r\nalias salloc_node='salloc --time=01:00:00 --partition=dev_accelerated --nodes=1 --gres=gpu:1 --cpus-per-task=8'\r\nalias smi='watch -n1 nvidia-smi'\r\nalias sync-runner='sh /home/hk-project-p0023960/tum_cte0515/sync_runner.sh /home/hk-project-p0023960/tum_cte0515/Projects/jafar /home/hk-project-p0023960/tum_cte0515/Projects/jafar_jobs/'\r\nalias xzegrep='xzegrep --color=auto'\r\nalias xzfgrep='xzfgrep --color=auto'\r\nalias xzgrep='xzgrep --color=auto'\r\nalias zegrep='zegrep --color=auto'\r\nalias zfgrep='zfgrep --color=auto'\r\nalias zgrep='zgrep --color=auto'\r\n]0;tum_cte0515@hkn1990:~/Projects/jafar]633;D;0",,terminal_output +2467,8147178,"TERMINAL",0,0,"sacct --format=""JobID%15,JobName%30,Partition%16,AllocCPUS%3,State%12,Elapsed%10,Timelimit%10"" --starttime $(date -d ""last week"" +%Y-%m-%d) ",,terminal_command +2468,8147270,"TERMINAL",0,0,"]633;E;2025-07-17 11:59:49 sacct --format=""JobID%15,JobName%30,Partition%16,AllocCPUS%3,State%12,Elapsed%10,Timelimit%10"" --starttime $(date -d ""last week"" +%Y-%m-%d) ;809dfc76-3a3b-47d1-9b49-bee31e96da6c]633;C JobID JobName Partition All State Elapsed Timelimit \r\n--------------- ------------------------------ ---------------- --- ------------ ---------- ---------- \r\n 3331282 train_tokenizer_batch_size_sc+ accelerated 48 TIMEOUT 1-12:00:22 1-12:00:00 \r\n 3331282.batch batch 24 CANCELLED 1-12:00:23 \r\n 3331282.extern extern 48 COMPLETED 1-12:00:51 \r\n 3331282.0 python 40 CANCELLED 1-12:00:28 \r\n 3331283 train_lam_action_space_scalin+ accelerated 48 TIMEOUT 1-16:00:15 1-16:00:00 \r\n 3331283.batch batch 24 CANCELLED 1-16:00:16 \r\n 3331283.extern extern 48 COMPLETED 1-16:00:44 \r\n 3331283.0 python 40 CANCELLED 1-16:00:23 \r\n 3331284 train_lam_action_space_scalin+ accelerated 48 TIMEOUT 1-16:00:15 1-16:00:00 \r\n 3331284.batch batch 24 CANCELLED 1-16:00:16 \r\n 3331284.extern extern 48 COMPLETED 1-16:00:44 \r\n 3331284.0 python 40 CANCELLED 1-16:00:22 \r\n 3331285 train_lam_action_space_scalin+ accelerated 48 TIMEOUT 1-16:00:15 1-16:00:00 \r\n 3331285.batch batch 24 CANCELLED 1-16:00:16 \r\n 3331285.extern extern 48 COMPLETED 1-16:00:44 \r\n 3331285.0 python 40 CANCELLED 1-16:00:22 \r\n 3331286 train_lam_action_space_scalin+ accelerated 48 TIMEOUT 1-16:00:15 1-16:00:00 \r\n 3331286.batch batch 24 CANCELLED 1-16:00:16 \r\n 3331286.extern extern 48 COMPLETED 1-16:00:44 \r\n 3331286.0 python 40 CANCELLED 1-16:00:22 \r\n 3331287 train_lam_action_space_scalin+ accelerated 48 TIMEOUT 1-16:00:15 1-16:00:00 \r\n 3331287.batch batch 24 CANCELLED 1-16:00:16 \r\n 3331287.extern extern 48 COMPLETED 1-16:00:44 \r\n 3331287.0 python 40 CANCELLED 1-16:00:22 \r\n 3331288 train_lam_action_space_scalin+ accelerated 48 TIMEOUT 1-16:00:15 1-16:00:00 \r\n 3331288.batch batch 24 CANCELLED 1-16:00:16 \r\n 3331288.extern extern 48 COMPLETED 1-16:00:44 \r\n 3331288.0 python 40 CANCELLED 1-16:00:23 \r\n 3333584 interactive accelerated 30 CANCELLED b+ 18:44:30 1-00:00:00 \r\n3333584.intera+ interactive 30 CANCELLED 18:45:00 \r\n 3333584.extern extern 30 COMPLETED 18:45:00 \r\n 3333584.0 bash 30 COMPLETED 00:29:25 \r\n 3333584.1 python 30 FAILED 00:00:36 \r\n 3333586 interactive accelerated 48 CANCELLED b+ 12:08:26 1-00:00:00 \r\n3333586.intera+ interactive 24 CANCELLED 12:08:55 \r\n 3333586.extern extern 48 COMPLETED 12:08:55 \r\n 3333586.0 bash 48 COMPLETED 00:05:02 \r\n 3333586.1 python 48 FAILED 00:00:54 \r\n 3333586.2 python 48 FAILED 00:00:26 \r\n 3333586.3 python 48 FAILED 00:00:24 \r\n 3333586.4 bash 48 FAILED 00:02:14 \r\n 3334542 interactive accelerated 0 CANCELLED b+ 00:00:00 12:00:00 \r\n 3334543 interactive accelerated 48 CANCELLED b+ 05:56:31 12:00:00 \r\n3334543.intera+ interactive 24 CANCELLED 05:57:01 \r\n 3334543.extern extern 48 COMPLETED 05:57:01 \r\n 3334543.0 python 40 FAILED 00:01:32 \r\n 3334543.1 python 40 CANCELLED b+ 00:02:54 \r\n 3334543.2 python 40 CANCELLED b+ 00:05:04 \r\n 3334543.3 python 40 FAILED 00:01:35 \r\n 3334543.4 python 40 FAILED 00:01:53 \r\n 3334543.5 python 40 FAILED 00:01:56 \r\n 3334543.6 python 40 FAILED 00:00:37 \r\n 3334543.7 python 40 COMPLETED 00:05:03 \r\n 3334543.8 python 40 COMPLETED 00:05:08 \r\n 3334543.9 python 40 COMPLETED 00:04:48 \r\n 3334543.10 python 40 COMPLETED 00:05:04 \r\n 3334543.11 python 40 COMPLETED 00:05:07 \r\n 3334543.12 bash 48 FAILED 00:06:28 \r\n 3334543.13 python 48 FAILED 00:00:31 \r\n 3334543.14 python 48 FAILED 00:00:21 \r\n 3334543.15 bash 48 FAILED 00:56:08 \r\n 3335250 interactive accelerated 0 CANCELLED b+ 00:00:00 12:00:00 \r\n 3335323 interactive dev_accelerated 0 CANCELLED b+ 00:00:00 01:00:00 \r\n 3335324 interactive accelerated 0 CANCELLED b+ 00:00:00 05:00:00 \r\n 3335325 interactive dev_accelerated 0 CANCELLED b+ 00:00:00 01:00:00 \r\n 3335335 interactive accelerated 48 CANCELLED b+ 03:32:02 10:00:00 \r\n3335335.intera+ interactive 24 FAILED 03:32:04 \r\n 3335335.extern extern 48 COMPLETED 03:32:05 \r\n 3335335.0 python 40 CANCELLED b+ 00:55:09 \r\n 3335335.1 python 40 CANCELLED b+ 00:06:42 \r\n 3335335.2 python 40 CANCELLED b+ 00:14:24 \r\n 3335345 train_dyn_yolorun accelerated 48 FAILED 00:00:27 01:00:00 \r\n 3335345.batch batch 24 FAILED 00:00:27 \r\n 3335345.extern extern 48 COMPLETED 00:00:27 \r\n 3335345.0 python 40 CANCELLED 00:00:00 \r\n 3335362 train_dyn_yolorun accelerated 48 TIMEOUT 01:00:18 01:00:00 \r\n 3335362.batch batch 24 CANCELLED 01:00:19 \r\n 3335362.extern extern 48 COMPLETED 01:00:47 \r\n 3335362.0 python 40 CANCELLED 01:00:26 \r\n 3335606 train_tokenizer_lr_sweep_1e-4 accelerated 48 CANCELLED b+ 00:14:11 2-00:00:00 \r\n 3335606.batch batch 24 CANCELLED 00:14:12 \r\n 3335606.extern extern 48 COMPLETED 00:14:41 \r\n 3335606.0 python 40 CANCELLED 00:14:19 \r\n 3335607 train_tokenizer_lr_sweep_5e-5 accelerated 48 CANCELLED b+ 00:04:48 2-00:00:00 \r\n 3335607.batch batch 24 CANCELLED 00:04:49 \r\n 3335607.extern extern 48 COMPLETED 00:05:18 \r\n 3335607.0 python 40 CANCELLED 00:04:57 \r\n 3335610 train_tokenizer_batch_size_sc+ accelerated 24 FAILED 00:03:25 2-00:00:00 \r\n 3335610.batch batch 24 FAILED 00:03:25 \r\n 3335610.extern extern 24 COMPLETED 00:03:25 \r\n 3335610.0 python 20 FAILED 00:02:58 \r\n 3335611 train_tokenizer_batch_size_sc+ accelerated 48 FAILED 00:02:57 2-00:00:00 \r\n 3335611.batch batch 24 FAILED 00:02:57 \r\n 3335611.extern extern 48 COMPLETED 00:02:57 \r\n 3335611.0 python 40 FAILED 00:02:30 \r\n 3335612 train_tokenizer_batch_size_sc+ accelerated 96 CANCELLED b+ 00:02:48 2-00:00:00 \r\n 3335612.batch batch 24 CANCELLED 00:02:49 \r\n 3335612.extern extern 96 COMPLETED 00:03:09 \r\n 3335612.0 python 80 FAILED 00:02:34 \r\n 3335613 train_tokenizer_batch_size_sc+ accelerated 0 CANCELLED b+ 00:00:00 2-00:00:00 \r\n 3335614 train_tokenizer_batch_size_sc+ accelerated 0 CANCELLED b+ 00:00:00 2-00:00:00 \r\n 3335660 interactive accelerated 0 CANCELLED b+ 00:00:00 10:00:00 \r\n 3335661 interactive accelerated 48 COMPLETED 00:53:32 10:00:00 \r\n3335661.intera+ interactive 24 COMPLETED 00:53:32 \r\n 3335661.extern extern 48 COMPLETED 00:53:32 \r\n 3335661.0 python 40 FAILED 00:02:51 \r\n 3335661.1 python 40 FAILED 00:02:28 \r\n 3335661.2 python 40 CANCELLED b+ 00:00:02 \r\n 3335661.3 python 40 CANCELLED b+ 00:06:00 \r\n 3335732 dynamics_cotraining_action_sp+ accelerated 48 TIMEOUT 2-00:00:11 2-00:00:00 \r\n 3335732.batch batch 24 CANCELLED 2-00:00:12 \r\n 3335732.extern extern 48 COMPLETED 2-00:00:40 \r\n 3335732.0 python 40 CANCELLED 2-00:00:19 \r\n 3335733 dynamics_cotraining_action_sp+ accelerated 48 TIMEOUT 2-00:00:23 2-00:00:00 \r\n 3335733.batch batch 24 CANCELLED 2-00:00:24 \r\n 3335733.extern extern 48 COMPLETED 2-00:00:52 \r\n 3335733.0 python 40 CANCELLED 2-00:00:27 \r\n 3335734 dynamics_cotraining_action_sp+ accelerated 48 TIMEOUT 2-00:00:23 2-00:00:00 \r\n 3335734.batch batch 24 CANCELLED 2-00:00:24 \r\n 3335734.extern extern 48 COMPLETED 2-00:00:52 \r\n 3335734.0 python 40 CANCELLED 2-00:00:31 \r\n 3335735 dynamics_cotraining_action_sp+ accelerated 48 TIMEOUT 2-00:00:21 2-00:00:00 \r\n 3335735.batch batch 24 CANCELLED 2-00:00:22 \r\n 3335735.extern extern 48 COMPLETED 2-00:00:50 \r\n 3335735.0 python 40 CANCELLED 2-00:00:29 \r\n 3335736 dynamics_cotraining_action_sp+ accelerated 48 TIMEOUT 2-00:00:12 2-00:00:00 \r\n 3335736.batch batch 24 CANCELLED 2-00:00:13 \r\n 3335736.extern extern 48 COMPLETED 2-00:00:41 \r\n 3335736.0 python 40 CANCELLED 2-00:00:18 \r\n 3335737 train_tokenizer_lr_sweep_1e-4 accelerated 48 TIMEOUT 2-00:00:12 2-00:00:00 \r\n 3335737.batch batch 24 CANCELLED 2-00:00:13 \r\n 3335737.extern extern 48 COMPLETED 2-00:00:41 \r\n 3335737.0 python 40 CANCELLED 2-00:00:16 \r\n 3335738 train_tokenizer_lr_sweep_5e-5 accelerated 48 TIMEOUT 2-00:00:11 2-00:00:00 \r\n 3335738.batch batch 24 CANCELLED 2-00:00:11 \r\n 3335738.extern extern 48 COMPLETED 2-00:00:40 \r\n 3335738.0 python 40 CANCELLED 2-00:00:19 \r\n 3335739 train_tokenizer_batch_size_sc+ accelerated 384 TIMEOUT 2-00:00:14 2-00:00:00 \r\n 3335739.batch batch 24 CANCELLED 2-00:00:15 \r\n 3335739.extern extern 384 COMPLETED 2-00:00:43 \r\n 3335739.0 python 320 CANCELLED 2-00:00:20 \r\n 3335740 train_tokenizer_batch_size_sc+ accelerated 24 TIMEOUT 2-00:00:10 2-00:00:00 \r\n 3335740.batch batch 24 CANCELLED 2-00:00:10 \r\n 3335740.extern extern 24 COMPLETED 2-00:00:39 \r\n 3335740.0 python 20 CANCELLED 2-00:00:17 \r\n 3335741 train_tokenizer_batch_size_sc+ accelerated 48 TIMEOUT 2-00:00:05 2-00:00:00 \r\n 3335741.batch batch 24 CANCELLED 2-00:00:06 \r\n 3335741.extern extern 48 COMPLETED 2-00:00:34 \r\n 3335741.0 python 40 CANCELLED 2-00:00:09 \r\n 3335742 train_tokenizer_batch_size_sc+ accelerated 96 TIMEOUT 2-00:00:02 2-00:00:00 \r\n 3335742.batch batch 24 CANCELLED 2-00:00:03 \r\n 3335742.extern extern 96 COMPLETED 2-00:00:31 \r\n 3335742.0 python 80 CANCELLED 2-00:00:09 \r\n 3335743 train_tokenizer_batch_size_sc+ accelerated 192 TIMEOUT 2-00:00:28 2-00:00:00 \r\n 3335743.batch batch 24 CANCELLED 2-00:00:29 \r\n 3335743.extern extern 192 COMPLETED 2-00:00:57 \r\n 3335743.0 python 160 CANCELLED 2-00:00:35 \r\n 3335744 train_dynamics_modelsize_scal+ accelerated 48 TIMEOUT 2-00:00:05 2-00:00:00 \r\n 3335744.batch batch 24 CANCELLED 2-00:00:06 \r\n 3335744.extern extern 48 COMPLETED 2-00:00:34 \r\n 3335744.0 python 40 CANCELLED 2-00:00:12 \r\n 3335745 train_dynamics_modelsize_scal+ accelerated 144 TIMEOUT 2-00:00:12 2-00:00:00 \r\n 3335745.batch batch 24 CANCELLED 2-00:00:13 \r\n 3335745.extern extern 144 COMPLETED 2-00:00:41 \r\n 3335745.0 python 120 CANCELLED 2-00:00:19 \r\n 3335746 train_dynamics_modelsize_scal+ accelerated 288 TIMEOUT 2-00:00:17 2-00:00:00 \r\n 3335746.batch batch 24 CANCELLED 2-00:00:18 \r\n 3335746.extern extern 288 COMPLETED 2-00:00:46 \r\n 3335746.0 python 240 CANCELLED 2-00:00:32 \r\n 3335747 train_dynamics_modelsize_scal+ accelerated 384 FAILED 00:00:58 2-00:00:00 \r\n 3335747.batch batch 24 FAILED 00:00:58 \r\n 3335747.extern extern 384 COMPLETED 00:00:58 \r\n 3335747.0 python 320 FAILED 00:00:26 \r\n 3335748 train_dynamics_modelsize_scal+ accelerated 768 FAILED 00:00:54 2-00:00:00 \r\n 3335748.batch batch 24 FAILED 00:00:54 \r\n 3335748.extern extern 768 COMPLETED 00:00:54 \r\n 3335748.0 python 640 FAILED 00:00:24 \r\n 3337618 train_tokenizer_lr_sweep_1e-4+ accelerated 192 CANCELLED b+ 12:08:41 2-00:00:00 \r\n 3337618.batch batch 24 CANCELLED 12:08:42 \r\n 3337618.extern extern 192 COMPLETED 12:09:11 \r\n 3337618.0 python 160 CANCELLED 12:08:47 \r\n 3337619 train_tokenizer_lr_sweep_5e-5+ accelerated 192 CANCELLED b+ 12:08:44 2-00:00:00 \r\n 3337619.batch batch 24 CANCELLED 12:08:45 \r\n 3337619.extern extern 192 COMPLETED 12:09:14 \r\n 3337619.0 python 160 CANCELLED 12:08:52 \r\n 3338125 train_dynamics_modelsize_scal+ accelerated 0 CANCELLED b+ 00:00:00 2-00:00:00 \r\n 3338176 train_dynamics_modelsize_scal+ accelerated 0 FAILED 00:00:00 2-00:00:00 \r\n 3338177 train_dynamics_modelsize_scal+ accelerated 0 FAILED 00:00:00 2-00:00:00 \r\n 3338178 train_dynamics_modelsize_scal+ accelerated 0 FAILED 00:00:00 2-00:00:00 \r\n 3338179 train_dynamics_modelsize_scal+ accelerated 0 FAILED 00:00:00 2-00:00:00 \r\n 3338180 train_dynamics_modelsize_scal+ accelerated 0 FAILED 00:00:00 2-00:00:00 \r\n 3338181 train_dynamics_modelsize_scal+ accelerated 0 CANCELLED b+ 00:00:00 2-00:00:00 \r\n 3338239 train_dynamics_modelsize_scal+ accelerated 0 CANCELLED b+ 00:00:00 2-00:00:00 \r\n 3338240 train_dynamics_modelsize_scal+ accelerated 0 FAILED 00:00:00 2-00:00:00 \r\n 3338241 train_dynamics_modelsize_scal+ accelerated 576 CANCELLED b+ 1-22:44:52 2-00:00:00 \r\n 3338241.batch batch 24 CANCELLED 1-22:44:53 \r\n 3338241.extern extern 576 COMPLETED 1-22:45:22 \r\n 3338241.0 python 480 CANCELLED 1-22:44:58 \r\n 3338537 train_dynamics_modelsize_scal+ accelerated 288 CANCELLED b+ 1-22:10:24 2-00:00:00 \r\n 3338537.batch batch 24 CANCELLED 1-22:10:25 \r\n 3338537.extern extern 288 COMPLETED 1-22:10:53 \r\n 3338537.0 python 240 CANCELLED 1-22:10:30 \r\n 3340985 dynamics_cotraining_action_sp+ accelerated 48 CANCELLED b+ 1-05:42:39 2-00:00:00 \r\n 3340985.batch batch 24 CANCELLED 1-05:42:40 \r\n 3340985.extern extern 48 COMPLETED 1-05:43:09 \r\n 3340985.0 python 40 CANCELLED 1-05:42:46 \r\n 3340986 dynamics_cotraining_action_sp+ accelerated 48 CANCELLED b+ 1-05:36:50 2-00:00:00 \r\n 3340986.batch batch 24 CANCELLED 1-05:36:51 \r\n 3340986.extern extern 48 COMPLETED 1-05:37:20 \r\n 3340986.0 python 40 CANCELLED 1-05:36:57 \r\n 3340987 dynamics_cotraining_action_sp+ accelerated 48 CANCELLED b+ 1-03:44:55 2-00:00:00 \r\n 3340987.batch batch 24 CANCELLED 1-03:44:56 \r\n 3340987.extern extern 48 COMPLETED 1-03:45:25 \r\n 3340987.0 python 40 CANCELLED 1-03:45:02 \r\n 3340988 dynamics_cotraining_action_sp+ accelerated 48 CANCELLED b+ 1-03:45:04 2-00:00:00 \r\n 3340988.batch batch 24 CANCELLED 1-03:45:05 \r\n 3340988.extern extern 48 COMPLETED 1-03:45:34 \r\n 3340988.0 python 40 CANCELLED 1-03:45:11 \r\n 3340989 dynamics_cotraining_action_sp+ accelerated 48 CANCELLED b+ 1-03:45:19 2-00:00:00 \r\n 3340989.batch batch 24 CANCELLED 1-03:45:20 \r\n 3340989.extern extern 48 COMPLETED 1-03:45:49 \r\n 3340989.0 python 40 CANCELLED 1-03:45:26 \r\n 3341079 train_tokenizer_lr_sweep_1e-4 accelerated 48 TIMEOUT 2-00:00:11 2-00:00:00 \r\n 3341079.batch batch 24 CANCELLED 2-00:00:12 \r\n 3341079.extern extern 48 COMPLETED 2-00:00:40 \r\n 3341079.0 python 40 CANCELLED 2-00:00:18 \r\n 3341080 train_tokenizer_lr_sweep_5e-5 accelerated 48 TIMEOUT 2-00:00:11 2-00:00:00 \r\n 3341080.batch batch 24 CANCELLED 2-00:00:12 \r\n 3341080.extern extern 48 COMPLETED 2-00:00:40 \r\n 3341080.0 python 40 CANCELLED 2-00:00:18 \r\n 3341118 train_tokenizer_batch_size_sc+ accelerated 24 CANCELLED b+ 1-03:43:20 2-00:00:00 \r\n 3341118.batch batch 24 CANCELLED 1-03:43:21 \r\n 3341118.extern extern 24 COMPLETED 1-03:43:49 \r\n 3341118.0 python 20 CANCELLED 1-03:43:27 \r\n 3341119 train_tokenizer_batch_size_sc+ accelerated 48 CANCELLED b+ 1-01:19:46 2-00:00:00 \r\n 3341119.batch batch 24 CANCELLED 1-01:19:47 \r\n 3341119.extern extern 48 COMPLETED 1-01:20:15 \r\n 3341119.0 python 40 CANCELLED 1-01:19:53 \r\n 3341273 train_tokenizer_batch_size_sc+ accelerated 384 CANCELLED b+ 15:55:30 2-00:00:00 \r\n 3341273.batch batch 24 CANCELLED 15:55:31 \r\n 3341273.extern extern 384 COMPLETED 15:56:00 \r\n 3341273.0 python 320 CANCELLED 15:55:36 \r\n 3341274 train_tokenizer_batch_size_sc+ accelerated 96 CANCELLED b+ 22:21:26 2-00:00:00 \r\n 3341274.batch batch 24 CANCELLED 22:21:27 \r\n 3341274.extern extern 96 COMPLETED 22:21:56 \r\n 3341274.0 python 80 CANCELLED 22:21:33 \r\n 3341406 train_tokenizer_batch_size_sc+ accelerated 192 CANCELLED b+ 15:13:15 2-00:00:00 \r\n 3341406.batch batch 24 CANCELLED 15:13:16 \r\n 3341406.extern extern 192 COMPLETED 15:13:45 \r\n 3341406.0 python 160 CANCELLED 15:13:21 \r\n 3341476 train_dynamics_modelsize_scal+ accelerated 48 CANCELLED b+ 14:33:58 2-00:00:00 \r\n 3341476.batch batch 24 CANCELLED 14:33:59 \r\n 3341476.extern extern 48 COMPLETED 14:34:28 \r\n 3341476.0 python 40 CANCELLED 14:34:05 \r\n 3341477 train_dynamics_modelsize_scal+ accelerated 144 CANCELLED b+ 13:37:43 2-00:00:00 \r\n 3341477.batch batch 24 CANCELLED 13:37:44 \r\n 3341477.extern extern 144 COMPLETED 13:38:13 \r\n 3341477.0 python 120 CANCELLED 13:37:50 \r\n 3341478 train_dynamics_modelsize_scal+ accelerated 288 CANCELLED b+ 05:57:14 2-00:00:00 \r\n 3341478.batch batch 24 CANCELLED 05:57:15 \r\n 3341478.extern extern 288 COMPLETED 05:57:44 \r\n 3341478.0 python 240 CANCELLED 05:57:21 \r\n 3343465 interactive accelerated 0 CANCELLED b+ 00:00:00 10:00:00 \r\n 3343466 interactive accelerated 0 CANCELLED b+ 00:00:00 10:00:00 \r\n 3343467 interactive dev_accelerated 12 COMPLETED 00:36:30 01:00:00 \r\n3343467.intera+ interactive 12 COMPLETED 00:36:30 \r\n 3343467.extern extern 12 COMPLETED 00:36:30 \r\n 3344012 interactive dev_accelerated 6 TIMEOUT 01:00:08 01:00:00 \r\n3344012.intera+ interactive 6 CANCELLED 01:00:37 \r\n 3344012.extern extern 6 COMPLETED 01:00:37 \r\n 3344246 interactive accelerated 48 COMPLETED 08:34:29 10:00:00 \r\n3344246.intera+ interactive 24 CANCELLED 08:34:59 \r\n 3344246.extern extern 48 COMPLETED 08:34:59 \r\n 3344246.0 python 40 FAILED 00:03:15 \r\n 3344246.1 python 40 FAILED 00:02:25 \r\n 3344246.2 python 40 CANCELLED b+ 00:19:08 \r\n 3344246.3 python 40 FAILED 00:02:54 \r\n 3344246.4 python 40 FAILED 00:02:27 \r\n 3344246.5 python 40 FAILED 00:03:30 \r\n 3344246.6 python 40 CANCELLED 04:40:24 \r\n 3344502 interactive accelerated 0 CANCELLED b+ 00:00:00 10:00:00 \r\n 3344503 interactive accelerated 0 CANCELLED b+ 00:00:00 01:00:00 \r\n 3344505 interactive dev_accelerated 6 FAILED 00:10:16 01:00:00 \r\n3344505.intera+ interactive 6 FAILED 00:10:16 \r\n 3344505.extern extern 6 COMPLETED 00:10:16 \r\n 3344574 interactive dev_accelerated 20 TIMEOUT 01:00:04 01:00:00 \r\n3344574.intera+ interactive 20 FAILED 01:00:08 \r\n 3344574.extern extern 20 COMPLETED 01:00:08 \r\n 3344887 interactive dev_accelerated 20 TIMEOUT 01:00:28 01:00:00 \r\n3344887.intera+ interactive 20 CANCELLED 01:00:57 \r\n 3344887.extern extern 20 COMPLETED 01:00:57 \r\n 3345079 train_dynamics_modelsize_scal+ accelerated 0 CANCELLED b+ 00:00:00 2-00:00:00 \r\n 3345116 train_dynamics_modelsize_scal+ accelerated 48 TIMEOUT 2-00:00:17 2-00:00:00 \r\n 3345116.batch batch 24 CANCELLED 2-00:00:18 \r\n 3345116.extern extern 48 COMPLETED 2-00:00:46 \r\n 3345116.0 python 40 CANCELLED 2-00:00:25 \r\n 3345348 interactive dev_accelerated 0 CANCELLED b+ 00:00:00 01:00:00 \r\n 3345349 interactive dev_accelerated 0 CANCELLED b+ 00:00:00 01:00:00 \r\n 3345355 interactive accelerated 20 TIMEOUT 01:00:24 01:00:00 \r\n3345355.intera+ interactive 20 CANCELLED 01:00:53 \r\n 3345355.extern extern 20 COMPLETED 01:00:53 \r\n 3347272 interactive accelerated 48 FAILED 00:22:03 10:00:00 \r\n3347272.intera+ interactive 24 FAILED 00:22:03 \r\n 3347272.extern extern 48 COMPLETED 00:22:03 \r\n 3347272.0 python 40 CANCELLED b+ 00:13:45 \r\n 3347273 interactive accelerated 6 COMPLETED 02:16:38 10:00:00 \r\n3347273.intera+ interactive 6 CANCELLED 02:17:08 \r\n 3347273.extern extern 6 COMPLETED 02:17:08 \r\n 3347289 interactive accelerated 48 TIMEOUT 10:00:21 10:00:00 \r\n3347289.intera+ interactive 24 CANCELLED 10:00:50 \r\n 3347289.extern extern 48 COMPLETED 10:00:50 \r\n 3347289.0 python 40 FAILED 00:00:15 \r\n 3347289.1 python 40 COMPLETED 00:09:27 \r\n 3347289.2 python 40 CANCELLED b+ 00:00:27 \r\n 3347289.3 python 40 FAILED 00:00:03 \r\n 3347289.4 python 40 FAILED 00:00:02 \r\n 3347289.5 python 40 COMPLETED 00:09:57 \r\n 3347289.6 python 40 FAILED 00:00:14 \r\n 3347289.7 python 40 CANCELLED b+ 00:06:03 \r\n 3347289.8 python 40 CANCELLED b+ 00:06:22 \r\n 3347289.9 python 40 CANCELLED b+ 00:06:56 \r\n 3347289.10 python 40 FAILED 00:01:10 \r\n 3347289.11 python 40 CANCELLED b+ 00:01:20 \r\n 3347289.12 python 40 CANCELLED b+ 00:11:14 \r\n 3347289.13 python 40 CANCELLED b+ 00:07:04 \r\n 3347289.14 python 40 CANCELLED b+ 00:09:02 \r\n 3347289.15 python 40 CANCELLED b+ 00:25:32 \r\n 3347518 interactive accelerated 6 COMPLETED 09:04:25 10:00:00 \r\n3347518.intera+ interactive 6 CANCELLED b+ 09:04:25 \r\n 3347518.extern extern 6 COMPLETED 09:04:25 \r\n 3348397 train_dynamics_lr_schedule_co+ accelerated 48 COMPLETED 1-03:21:05 2-00:00:00 \r\n 3348397.batch batch 24 COMPLETED 1-03:21:05 \r\n 3348397.extern extern 48 COMPLETED 1-03:21:05 \r\n 3348397.0 python 40 COMPLETED 1-03:20:36 \r\n 3348399 train_dynamics_lr_schedule_cos accelerated 48 COMPLETED 1-03:28:39 2-00:00:00 \r\n 3348399.batch batch 24 COMPLETED 1-03:28:39 \r\n 3348399.extern extern 48 COMPLETED 1-03:28:39 \r\n 3348399.0 python 40 COMPLETED 1-03:28:10 \r\n 3348400 train_dynamics_lr_schedule_wsd accelerated 48 COMPLETED 1-03:19:17 2-00:00:00 \r\n 3348400.batch batch 24 COMPLETED 1-03:19:17 \r\n 3348400.extern extern 48 COMPLETED 1-03:19:17 \r\n 3348400.0 python 40 COMPLETED 1-03:18:48 \r\n 3348592 train_dyn_yolorun accelerated 48 CANCELLED b+ 19:29:04 1-00:00:00 \r\n 3348592.batch batch 24 CANCELLED 19:29:05 \r\n 3348592.extern extern 48 COMPLETED 19:29:34 \r\n 3348592.0 python 40 CANCELLED 19:29:13 \r\n 3349982 interactive accelerated 48 CANCELLED b+ 00:10:32 10:00:00 \r\n3349982.intera+ interactive 24 CANCELLED 00:11:02 \r\n 3349982.extern extern 48 COMPLETED 00:11:02 \r\n 3350109 interactive accelerated 0 CANCELLED b+ 00:00:00 10:00:00 \r\n 3350110 interactive dev_accelerated 0 CANCELLED b+ 00:00:00 01:00:00 \r\n 3350111 interactive accelerated 48 CANCELLED b+ 00:09:37 10:00:00 \r\n3350111.intera+ interactive 24 CANCELLED 00:10:06 \r\n 3350111.extern extern 48 COMPLETED 00:10:06 \r\n 3350245 interactive accelerated 48 CANCELLED b+ 00:17:59 10:00:00 \r\n3350245.intera+ interactive 24 CANCELLED 00:18:28 \r\n 3350245.extern extern 48 COMPLETED 00:18:28 \r\n 3350302 interactive accelerated 48 CANCELLED b+ 00:40:22 10:00:00 \r\n3350302.intera+ interactive 24 CANCELLED 00:40:52 \r\n 3350302.extern extern 48 COMPLETED 00:40:52 \r\n 3350302.0 python 40 FAILED 00:00:27 \r\n 3350302.1 python 40 FAILED 00:05:27 \r\n 3350302.2 python 40 FAILED 00:03:41 \r\n 3350302.3 python 40 CANCELLED b+ 00:03:28 \r\n 3350302.4 python 40 CANCELLED b+ 00:04:30 \r\n 3350302.5 python 40 FAILED 00:01:21 \r\n 3350302.6 python 40 FAILED 00:00:27 \r\n 3350302.7 python 40 CANCELLED 00:04:03 \r\n 3350418 interactive accelerated 48 COMPLETED 08:32:29 10:00:00 \r\n3350418.intera+ interactive 24 CANCELLED 08:32:59 \r\n 3350418.extern extern 48 COMPLETED 08:32:59 \r\n 3350418.0 python 40 CANCELLED b+ 00:01:30 \r\n 3350418.1 python 40 FAILED 00:02:41 \r\n 3350418.2 python 40 CANCELLED b+ 01:19:51 \r\n 3350418.3 python 40 FAILED 00:00:16 \r\n 3350418.4 python 40 FAILED 00:00:03 \r\n 3350418.5 python 40 FAILED 00:00:07 \r\n 3350418.6 python 40 FAILED 00:00:03 \r\n 3350418.7 python 40 FAILED 00:02:57 \r\n 3350418.8 python 40 CANCELLED b+ 00:08:48 \r\n 3350418.9 python 40 CANCELLED b+ 00:10:49 \r\n 3350418.10 python 40 CANCELLED b+ 00:05:51 \r\n 3350418.11 python 40 CANCELLED b+ 00:06:24 \r\n 3351743 train_dyn_yolorun_new_arch accelerated 48 CANCELLED b+ 01:58:23 2-00:00:00 \r\n 3351743.batch batch 24 CANCELLED 01:58:24 \r\n 3351743.extern extern 48 COMPLETED 01:58:53 \r\n 3351743.0 python 40 CANCELLED 01:58:29 \r\n 3352103 train_dyn_yolorun_new_arch accelerated 48 CANCELLED b+ 00:09:58 2-00:00:00 \r\n 3352103.batch batch 24 CANCELLED 00:09:59 \r\n 3352103.extern extern 48 COMPLETED 00:10:28 \r\n 3352103.0 python 40 CANCELLED 00:10:03 \r\n 3352115 train_dyn_yolorun_new_arch accelerated 48 RUNNING 16:22:45 2-00:00:00 \r\n 3352115.batch batch 24 RUNNING 16:22:45 \r\n 3352115.extern extern 48 RUNNING 16:22:45 \r\n 3352115.0 python 40 RUNNING 16:22:17 \r\n 3352588 train_dyn_yolorun_new_arch accelerated 0 CANCELLED b+ 00:00:00 2-00:00:00 \r\n 3352994 interactive accelerated 48 RUNNING 02:14:49 10:00:00 \r\n3352994.intera+ interactive 24 RUNNING 02:14:49 \r\n 3352994.extern extern 48 RUNNING 02:14:49 \r\n 3352996 interactive accelerated 6 RUNNING 02:14:32 10:00:00 \r\n3352996.intera+ interactive 6 RUNNING 02:14:32 \r\n 3352996.extern extern 6 RUNNING 02:14:32 \r\n",,terminal_output +2469,8216919,"TERMINAL",0,0,"sacct --format=""JobID%15,JobName%30,Partition%16,AllocCPUS%3,State%12,Elapsed%10,Timelimit%10"" --starttime $(date -d ""last week"" +%Y-%m-%d) | grep -vE ""*.batch|*.extern|*.inter|bash|python|CANCELLED|echo""",,terminal_command +2470,8216994,"TERMINAL",0,0,"]633;E;2025-07-17 12:00:59 sacct --format=""JobID%15,JobName%30,Partition%16,AllocCPUS%3,State%12,Elapsed%10,Timelimit%10"" --starttime $(date -d ""last week"" +%Y-%m-%d) | grep -vE ""*.batch|*.extern|*.inter|bash|python|CANCELLED|echo"";809dfc76-3a3b-47d1-9b49-bee31e96da6c]633;C JobID JobName Partition All State Elapsed Timelimit \r\n--------------- ------------------------------ ---------------- --- ------------ ---------- ---------- \r\n 3331283 train_lam_action_space_scalin+ accelerated 48 TIMEOUT 1-16:00:15 1-16:00:00 \r\n 3331284 train_lam_action_space_scalin+ accelerated 48 TIMEOUT 1-16:00:15 1-16:00:00 \r\n 3331285 train_lam_action_space_scalin+ accelerated 48 TIMEOUT 1-16:00:15 1-16:00:00 \r\n 3331286 train_lam_action_space_scalin+ accelerated 48 TIMEOUT 1-16:00:15 1-16:00:00 \r\n 3331287 train_lam_action_space_scalin+ accelerated 48 TIMEOUT 1-16:00:15 1-16:00:00 \r\n 3331288 train_lam_action_space_scalin+ accelerated 48 TIMEOUT 1-16:00:15 1-16:00:00 \r\n 3335345 train_dyn_yolorun accelerated 48 FAILED 00:00:27 01:00:00 \r\n 3335362 train_dyn_yolorun accelerated 48 TIMEOUT 01:00:18 01:00:00 \r\n 3335732 dynamics_cotraining_action_sp+ accelerated 48 TIMEOUT 2-00:00:11 2-00:00:00 \r\n 3335733 dynamics_cotraining_action_sp+ accelerated 48 TIMEOUT 2-00:00:23 2-00:00:00 \r\n 3335734 dynamics_cotraining_action_sp+ accelerated 48 TIMEOUT 2-00:00:23 2-00:00:00 \r\n 3335735 dynamics_cotraining_action_sp+ accelerated 48 TIMEOUT 2-00:00:21 2-00:00:00 \r\n 3335736 dynamics_cotraining_action_sp+ accelerated 48 TIMEOUT 2-00:00:12 2-00:00:00 \r\n 3335737 train_tokenizer_lr_sweep_1e-4 accelerated 48 TIMEOUT 2-00:00:12 2-00:00:00 \r\n 3335738 train_tokenizer_lr_sweep_5e-5 accelerated 48 TIMEOUT 2-00:00:11 2-00:00:00 \r\n 3335744 train_dynamics_modelsize_scal+ accelerated 48 TIMEOUT 2-00:00:05 2-00:00:00 \r\n 3335745 train_dynamics_modelsize_scal+ accelerated 144 TIMEOUT 2-00:00:12 2-00:00:00 \r\n 3335746 train_dynamics_modelsize_scal+ accelerated 288 TIMEOUT 2-00:00:17 2-00:00:00 \r\n 3335747 train_dynamics_modelsize_scal+ accelerated 384 FAILED 00:00:58 2-00:00:00 \r\n 3335748 train_dynamics_modelsize_scal+ accelerated 768 FAILED 00:00:54 2-00:00:00 \r\n 3338176 train_dynamics_modelsize_scal+ accelerated 0 FAILED 00:00:00 2-00:00:00 \r\n 3338177 train_dynamics_modelsize_scal+ accelerated 0 FAILED 00:00:00 2-00:00:00 \r\n 3338178 train_dynamics_modelsize_scal+ accelerated 0 FAILED 00:00:00 2-00:00:00 \r\n 3338179 train_dynamics_modelsize_scal+ accelerated 0 FAILED 00:00:00 2-00:00:00 \r\n 3338180 train_dynamics_modelsize_scal+ accelerated 0 FAILED 00:00:00 2-00:00:00 \r\n 3338240 train_dynamics_modelsize_scal+ accelerated 0 FAILED 00:00:00 2-00:00:00 \r\n 3341079 train_tokenizer_lr_sweep_1e-4 accelerated 48 TIMEOUT 2-00:00:11 2-00:00:00 \r\n 3341080 train_tokenizer_lr_sweep_5e-5 accelerated 48 TIMEOUT 2-00:00:11 2-00:00:00 \r\n 3345116 train_dynamics_modelsize_scal+ accelerated 48 TIMEOUT 2-00:00:17 2-00:00:00 \r\n 3348397 train_dynamics_lr_schedule_co+ accelerated 48 COMPLETED 1-03:21:05 2-00:00:00 \r\n 3348399 train_dynamics_lr_schedule_cos accelerated 48 COMPLETED 1-03:28:39 2-00:00:00 \r\n 3348400 train_dynamics_lr_schedule_wsd accelerated 48 COMPLETED 1-03:19:17 2-00:00:00 \r\n 3352115 train_dyn_yolorun_new_arch accelerated 48 RUNNING 16:23:54 2-00:00:00 \r\n]0;tum_cte0515@hkn1990:~/Projects/jafar]633;D;0",,terminal_output +2471,8227788,"TERMINAL",0,0,"sacct --help",,terminal_command +2472,8227832,"TERMINAL",0,0,"]633;E;2025-07-17 12:01:10 sacct --help;809dfc76-3a3b-47d1-9b49-bee31e96da6c]633;Csacct [