Add files using upload-large-folder tool

4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-0aba6691-eb18-4884-b411-e702ed3c10ea1762435058937-2025_11_06-14.17.43.16/source.csv

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+10,58512,"TERMINAL",0,0,"]633;CVSCode test\r\n%                                                                                                                                                                           \r \r",,terminal_output
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4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-13abb9ec-f400-4b07-a3bb-8f8000a533f01766266331804-2025_12_20-22.32.20.901/source.csv

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+1,2,"train.py",0,0,"import ray\nfrom sglang.srt.constants import GPU_MEMORY_TYPE_KV_CACHE, GPU_MEMORY_TYPE_WEIGHTS\n\ntry:\n    from sglang.srt.constants import GPU_MEMORY_TYPE_CUDA_GRAPH\nexcept ImportError:\n    GPU_MEMORY_TYPE_CUDA_GRAPH = None\n\nfrom miles.ray.placement_group import create_placement_groups, create_rollout_manager, create_training_models\nfrom miles.utils.arguments import parse_args\nfrom miles.utils.logging_utils import configure_logger\nfrom miles.utils.misc import should_run_periodic_action\nfrom miles.utils.tracking_utils import init_tracking\n\n\ndef train(args):\n    configure_logger()\n    # allocate the GPUs\n    pgs = create_placement_groups(args)\n    init_tracking(args)\n\n    # create the rollout manager, with sglang engines inside.\n    # need to initialize rollout manager first to calculate num_rollout\n    rollout_manager, num_rollout_per_epoch = create_rollout_manager(args, pgs[""rollout""])\n\n    # create the actor and critic models\n    actor_model, critic_model = create_training_models(args, pgs, rollout_manager)\n\n    if args.offload_rollout:\n        ray.get(rollout_manager.onload.remote(tags=[GPU_MEMORY_TYPE_WEIGHTS]))\n\n    # always update weight first so that sglang has the loaded weights from training.\n    actor_model.update_weights()\n\n    if args.check_weight_update_equal:\n        ray.get(rollout_manager.check_weights.remote(action=""compare""))\n\n    if args.offload_rollout:\n        if GPU_MEMORY_TYPE_CUDA_GRAPH is not None:\n            ray.get(rollout_manager.onload.remote(tags=[GPU_MEMORY_TYPE_CUDA_GRAPH]))\n        ray.get(rollout_manager.onload.remote(tags=[GPU_MEMORY_TYPE_KV_CACHE]))\n\n    # special case for eval-only\n    if args.num_rollout == 0 and args.eval_interval is not None:\n        ray.get(rollout_manager.eval.remote(rollout_id=0))\n\n    def offload_train():\n        if args.offload_train:\n            if args.use_critic:\n                critic_model.offload()\n                if rollout_id >= args.num_critic_only_steps:\n                    actor_model.offload()\n            else:\n                actor_model.offload()\n        else:\n            actor_model.clear_memory()\n\n    def onload_rollout():\n        if args.offload_rollout:\n            ray.get(rollout_manager.onload.remote(tags=[GPU_MEMORY_TYPE_WEIGHTS]))\n\n    # train loop.\n    # note that for async training, one can change the position of the sync operation(ray.get).\n    for rollout_id in range(args.start_rollout_id, args.num_rollout):\n        if args.eval_interval is not None and rollout_id == 0:\n            ray.get(rollout_manager.eval.remote(rollout_id))\n\n        rollout_data_ref = ray.get(rollout_manager.generate.remote(rollout_id))\n\n        if args.offload_rollout:\n            ray.get(rollout_manager.offload.remote())\n\n        if args.use_critic:\n            critic_train_handle = critic_model.async_train(rollout_id, rollout_data_ref)\n            if rollout_id >= args.num_critic_only_steps:\n                ray.get(actor_model.async_train(rollout_id, rollout_data_ref))\n            ray.get(critic_train_handle)\n        else:\n            ray.get(actor_model.async_train(rollout_id, rollout_data_ref))\n\n        if should_run_periodic_action(rollout_id, args.save_interval, num_rollout_per_epoch):\n            if (not args.use_critic) or (rollout_id >= args.num_critic_only_steps):\n                actor_model.save_model(rollout_id)\n            if args.use_critic:\n                critic_model.save_model(rollout_id)\n            if args.rollout_global_dataset:\n                ray.get(rollout_manager.save.remote(rollout_id))\n\n        offload_train()\n        onload_rollout()\n        actor_model.update_weights()\n\n        if args.offload_rollout:\n            if GPU_MEMORY_TYPE_CUDA_GRAPH is not None:\n                ray.get(rollout_manager.onload.remote(tags=[GPU_MEMORY_TYPE_CUDA_GRAPH]))\n            ray.get(rollout_manager.onload.remote(tags=[GPU_MEMORY_TYPE_KV_CACHE]))\n\n        if should_run_periodic_action(rollout_id, args.eval_interval, num_rollout_per_epoch):\n            ray.get(rollout_manager.eval.remote(rollout_id))\n\n    ray.get(rollout_manager.dispose.remote())\n\n\nif __name__ == ""__main__"":\n    args = parse_args()\n    train(args)\n",python,tab
+2,154,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"10:32:20 PM [info] Activating crowd-code\n10:32:20 PM [info] Recording started\n10:32:20 PM [info] Initializing git provider using file system watchers...\n",Log,tab
+3,181,"extension-output-pdoom-org.crowd-code-#1-crowd-code",153,0,"10:32:21 PM [info] Git repository found\n10:32:21 PM [info] Git provider initialized successfully\n10:32:21 PM [info] Initial git state: [object Object]\n",Log,content
+4,800734,"train.py",0,0,"",python,tab
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+7,2043278,"scripts/run-qwen3-0.6B-torch-sft-crowd-code.sh",0,0,"#!/bin/bash\n\n# for rerun the task\npkill -9 sglang\nsleep 3\nray stop --force\npkill -9 ray\npkill -9 python\nsleep 3\npkill -9 ray\npkill -9 python\n\n# set -ex\n\n# will prevent ray from buffering stdout/stderr\nexport PYTHONUNBUFFERED=1\nexport CUDA_VISIBLE_DEVICES=0,1\n\nNVLINK_COUNT=$(nvidia-smi | grep -o ""NVLink"" | wc -l)\nif [ ""$NVLINK_COUNT"" -gt 0 ]; then\n    HAS_NVLINK=1\nelse\n    HAS_NVLINK=0\nfi\necho ""HAS_NVLINK: $HAS_NVLINK (detected $NVLINK_COUNT NVLink references)""\n\n\n# --- 1. DYNAMIC HOST IP DETECTION (CRITICAL FOR SLURM) ---\n# Don't hardcode IP. Get the actual IP of the current node.\nexport HEAD_NODE_IP=$(hostname -I | awk '{print $1}')\necho ""Detected Head Node IP: ${HEAD_NODE_IP}""\n\n# --- 2. PROXY CONFIGURATION ---\n# Ensure local traffic doesn't go through a corporate proxy\nexport no_proxy=""${HEAD_NODE_IP},localhost,127.0.0.1,0.0.0.0""\nexport NO_PROXY=""${HEAD_NODE_IP},localhost,127.0.0.1,0.0.0.0""\n\n# --- 3. DEBUGGING & STABILITY ENV VARS ---\n# Force NCCL/Distributed into a robust mode to prevent initialization hangs\n# export NCCL_P2P_DISABLE=1\n# export NCCL_IB_DISABLE=1\nexport NCCL_DEBUG=INFO\nexport TORCH_DISTRIBUTED_DEBUG=INFO\n\nSCRIPT_DIR=""$(cd -- ""$(dirname -- ""${BASH_SOURCE[0]}"")"" &>/dev/null && pwd)""\n\nRUN_ID=${RUN_ID:-""run_$(date +%Y%m%d_%H%M%S)""}\nLOAD_SAVE_PATH=""/fast/project/HFMI_SynergyUnit/tab_model/huggingface/shared_data/${RUN_ID}/checkpoints""\n\nCKPT_ARGS=(\n   --hf-checkpoint /fast/project/HFMI_SynergyUnit/tab_model/huggingface/Qwen3-0.6B\n   --load /fast/project/HFMI_SynergyUnit/tab_model/huggingface/Qwen3-0.6B\n   --ref-load /fast/project/HFMI_SynergyUnit/tab_model/huggingface/Qwen3-0.6B\n)\n\nSFT_ARGS=(\n   --rollout-function-path miles.rollout.sft_rollout.generate_rollout\n   --prompt-data /fast/project/HFMI_SynergyUnit/tab_model/huggingface/nemo_hf_part_jsonl_4k_tokens.parquet\n   --input-key messages\n   --apply-chat-template\n   --rollout-shuffle\n   --num-epoch 3\n   --rollout-batch-size 16\n   --global-batch-size 16\n\n   --loss-type sft_loss\n   --calculate-per-token-loss\n   --disable-compute-advantages-and-returns\n   --num-rollout 2000\n   --debug-train-only\n)\n\nOPTIMIZER_ARGS=(\n   --optimizer adam\n   --lr 1e-5\n   --lr-decay-style WSD\n   --lr-wsd-decay-style linear\n   --lr-warmup-iters 100\n   --lr-decay-iters 2000\n   --lr-wsd-decay-iters 500\n   --weight-decay 0.1\n   --adam-beta1 0.9\n   --adam-beta2 0.98\n)\n\nWANDB_ARGS=(\n   --use-wandb\n   --wandb-project crowd-pilot-miles\n   --wandb-team instant-uv\n   --wandb-group qwen3-0.6b-sft-torch\n)\n\nTRAIN_BACKEND_ARGS=(\n   --train-backend fsdp\n   --update-weight-buffer-size 536870912\n   --gradient-checkpointing\n   --attn-implementation flash_attention_3\n   --train-env-vars '{""PYTORCH_CUDA_ALLOC_CONF"":""expandable_segments:True""}'\n   --actor-num-gpus-per-node 2\n)\n\nPERF_ARGS=(\n   --use-dynamic-batch-size\n   --max-tokens-per-gpu 9216\n)\n\nMISC_ARGS=(\n   --actor-num-nodes 1\n   --actor-num-gpus-per-node 2\n   --colocate\n   --rollout-max-context-len 8192\n   --rollout-max-prompt-len 8000\n   --rollout-max-response-len 8192\n   --use-fault-tolerance\n   --dump-details /fast/project/HFMI_SynergyUnit/tab_model/huggingface/shared_data/qwen3-600M-fsdp-1116-noref/dump_details\n)\n\n# launch the master node of ray in container - 2 GPUs for training\nexport MASTER_ADDR=${MASTER_ADDR:-""127.0.0.1""}\npython3 -m ray.scripts.scripts start --head \\n    --node-ip-address=${HEAD_NODE_IP} \\n    --num-gpus 2 \\n    --num-cpus 4 \\n    --memory=214748364800 \\n    --disable-usage-stats \\n    --dashboard-host=0.0.0.0 \\n    --dashboard-port=8265 \\n    --port=6379\n\necho ""Ray started. Waiting for Dashboard to be ready...""\n\n# --- 4. WAIT FOR DASHBOARD (FIX FOR 504 ERROR) ---\n# Loop until the dashboard port accepts connections\nfor i in {1..30}; do\n    if curl -s ""http://${HEAD_NODE_IP}:8265"" > /dev/null; then\n        echo ""Dashboard is up!""\n        break\n    fi\n    echo ""Waiting for Ray Dashboard...""\n    sleep 2\ndone\n# Add a small safety buffer\nsleep 5\n\n# Build runtime env\nRUNTIME_ENV_JSON=""{\n  \""env_vars\"": {\n    \""PYTHONPATH\"": \""/fast/project/HFMI_SynergyUnit/mihir/Megatron-LM/\"",\n    \""CUDA_DEVICE_MAX_CONNECTIONS\"": \""1\"",\n    \""NCCL_NVLS_ENABLE\"": \""${HAS_NVLINK}\"",\n    \""PYTORCH_CUDA_ALLOC_CONF\"": \""expandable_segments:True\""\n  }\n}""\n\npython3 -m ray.scripts.scripts job submit --address=""http://${HEAD_NODE_IP}:8265"" \\n   --runtime-env-json=""${RUNTIME_ENV_JSON}"" \\n   -- python3 train.py \\n   ${CKPT_ARGS[@]} \\n   ${SFT_ARGS[@]} \\n   ${OPTIMIZER_ARGS[@]} \\n   ${WANDB_ARGS[@]} \\n   ${SGLANG_ARGS[@]} \\n   ${TRAIN_BACKEND_ARGS[@]} \\n   ${PERF_ARGS[@]} \\n   ${MISC_ARGS[@]}\n\n\n\n",shellscript,tab
+8,2047087,"train.py",0,0,"",python,tab
+9,2047968,"scripts/run-qwen3-0.6B-torch-sft-crowd-code.sh",0,0,"",shellscript,tab
+10,2048525,"train.py",0,0,"",python,tab
+11,2475294,"train_sft.py",0,0,"#!/usr/bin/env python3\n""""""\nRay-free SFT Training Script for Miles.\n\nThis script provides a simplified training path for Supervised Fine-Tuning (SFT)\nthat bypasses Ray entirely and uses torchrun for distributed training.\n\nUsage:\n    torchrun --nproc_per_node=2 train_sft.py \\n        --hf-checkpoint /path/to/model \\n        --prompt-data /path/to/data.parquet \\n        --input-key messages \\n        --apply-chat-template \\n        ...\n\nThis is equivalent to the Ray-based SFT with --debug-train-only, but without\nthe Ray overhead.\n""""""\n\nimport copy\nimport logging\nimport os\nfrom argparse import Namespace\nfrom datetime import timedelta\nfrom itertools import accumulate\nfrom pathlib import Path\n\nimport torch\nimport torch.distributed as dist\nfrom torch.distributed.device_mesh import init_device_mesh\nfrom tqdm import tqdm\nfrom transformers import AutoConfig\n\nfrom miles.backends.fsdp_utils import checkpoint\nfrom miles.backends.fsdp_utils.actor import (\n    apply_fsdp2,\n    get_logprob_and_entropy_with_cp,\n    sum_of_sample_mean,\n)\nfrom miles.backends.fsdp_utils.data_packing import pack_sequences, unpack_sequences\nfrom miles.backends.fsdp_utils.lr_scheduler import get_lr_scheduler\nfrom miles.rollout.data_source import RolloutDataSource\nfrom miles.utils import tracking_utils\nfrom miles.utils.arguments import parse_args\nfrom miles.utils.data import Dataset, get_minimum_num_micro_batch_size\nfrom miles.utils.distributed_utils import get_gloo_group, init_gloo_group\nfrom miles.utils.logging_utils import configure_logger\nfrom miles.utils.mask_utils import MultiTurnLossMaskGenerator\nfrom miles.utils.metric_utils import compute_rollout_step\nfrom miles.utils.misc import load_function, should_run_periodic_action\nfrom miles.utils.processing_utils import load_processor, load_tokenizer\nfrom miles.utils.profile_utils import TrainProfiler\nfrom miles.utils.timer import timer\nfrom miles.utils.tracking_utils import init_tracking\nfrom miles.utils.types import Sample\n\nlogger = logging.getLogger(__name__)\n\n\nclass SFTTrainer:\n    """"""\n    A simplified trainer for SFT that runs without Ray.\n\n    This class combines the functionality of:\n    - FSDPTrainRayActor (model initialization, FSDP wrapping, training)\n    - RolloutManager (data loading via generate_rollout)\n    - The main training loop from train.py\n    """"""\n\n    def __init__(self, args: Namespace):\n        self.args = args\n        self.device = torch.device(""cuda"")\n\n        # Setup distributed\n        self._init_distributed()\n\n        # Setup device mesh for FSDP\n        self._setup_device_mesh()\n\n        torch.manual_seed(args.seed)\n\n        # Initialize tracking on rank 0\n        if dist.get_rank() == 0:\n            init_tracking(args, primary=True)\n\n        # Load tokenizer and config\n        self._load_tokenizer_and_config()\n\n        # Initialize data source\n        self._init_data_source()\n\n        # Initialize model\n        self._init_model()\n\n        # Initialize optimizer and scheduler\n        self._init_optimizer()\n\n        # Load checkpoint if available\n        self._load_checkpoint()\n\n        # Initialize profiler\n        self.prof = TrainProfiler(args)\n        self.prof.on_init_end()\n\n        logger.info(f""[Rank {dist.get_rank()}] SFTTrainer initialized successfully"")\n\n    def _init_distributed(self):\n        """"""Initialize distributed training.""""""\n        # torchrun sets these environment variables\n        local_rank = int(os.environ.get(""LOCAL_RANK"", 0))\n        torch.cuda.set_device(f""cuda:{local_rank}"")\n\n        backend = self.args.distributed_backend\n        dist.init_process_group(\n            backend=backend,\n            timeout=timedelta(minutes=self.args.distributed_timeout_minutes),\n        )\n        init_gloo_group()\n\n        self.args.rank = dist.get_rank()\n        self.args.world_size = dist.get_world_size()\n\n        logger.info(\n            f""[Rank {self.args.rank}] Distributed initialized: ""\n            f""world_size={self.args.world_size}, local_rank={local_rank}""\n        )\n\n    def _setup_device_mesh(self):\n        """"""Setup device mesh for FSDP (pure DP mode for SFT).""""""\n        world_size = dist.get_world_size()\n        rank = dist.get_rank()\n\n        # For SFT, we use pure DP (cp_size=1)\n        self.cp_size = getattr(self.args, ""context_parallel_size"", 1)\n        self.dp_size = world_size // self.cp_size\n\n        self.mesh = init_device_mesh(\n            ""cuda"",\n            mesh_shape=(self.dp_size, self.cp_size),\n            mesh_dim_names=(""dp"", ""cp""),\n        )\n\n        self.dp_group = self.mesh.get_group(""dp"")\n        self.cp_group = self.mesh.get_group(""cp"")\n        self.dp_mesh = self.mesh[""dp""]\n\n        self.dp_rank = rank // self.cp_size\n        self.cp_rank = rank % self.cp_size\n\n        logger.info(\n            f""[Rank {rank}] Device mesh: dp_size={self.dp_size}, cp_size={self.cp_size}, ""\n            f""dp_rank={self.dp_rank}, cp_rank={self.cp_rank}""\n        )\n\n    def _load_tokenizer_and_config(self):\n        """"""Load tokenizer and model config sequentially to avoid race conditions.""""""\n        for i in range(dist.get_world_size()):\n            if i == dist.get_rank():\n                self.hf_config = AutoConfig.from_pretrained(\n                    self.args.hf_checkpoint, trust_remote_code=True\n                )\n                self.tokenizer = load_tokenizer(\n                    self.args.hf_checkpoint, trust_remote_code=True\n                )\n                self.processor = None\n                if self.args.multimodal_keys:\n                    self.processor = load_processor(\n                        self.args.hf_checkpoint, trust_remote_code=True\n                    )\n            dist.barrier(group=get_gloo_group())\n\n        # Initialize loss mask generator for SFT\n        self.mask_generator = MultiTurnLossMaskGenerator(\n            self.tokenizer,\n            tokenizer_type=getattr(self.args, ""loss_mask_type"", None),\n        )\n\n    def _init_data_source(self):\n        """"""Initialize the data source for SFT training.""""""\n        self.data_source = RolloutDataSource(self.args)\n\n        # Calculate num_rollout from dataset size\n        if self.args.num_rollout is None:\n            num_rollout_per_epoch = len(self.data_source.dataset) // self.args.rollout_batch_size\n            self.args.num_rollout = num_rollout_per_epoch * self.args.num_epoch\n            self.num_rollout_per_epoch = num_rollout_per_epoch\n        else:\n            self.num_rollout_per_epoch = None\n\n        if getattr(self.args, ""start_rollout_id"", None) is None:\n            self.args.start_rollout_id = 0\n\n        logger.info(\n            f""[Rank {dist.get_rank()}] Data source initialized: ""\n            f""dataset_size={len(self.data_source.dataset)}, ""\n            f""num_rollout={self.args.num_rollout}""\n        )\n\n    def _get_init_weight_context_manager(self):\n        """"""Get context manager for model initialization.""""""\n        from accelerate import init_empty_weights\n\n        use_meta_tensor = not self.hf_config.tie_word_embeddings\n\n        def cpu_init_weights():\n            return torch.device(""cpu"")\n\n        if use_meta_tensor:\n            return init_empty_weights if dist.get_rank() != 0 else cpu_init_weights\n        else:\n            return cpu_init_weights\n\n    def _fsdp2_load_full_state_dict(self, model, full_state, device_mesh, cpu_offload):\n        """"""Load full state dict into FSDP2 model with broadcast from rank 0.""""""\n        from torch.distributed.checkpoint.state_dict import (\n            StateDictOptions,\n            set_model_state_dict,\n        )\n\n        if dist.get_rank() == 0:\n            model = model.to(device=torch.cuda.current_device(), non_blocking=True)\n        else:\n            model = model.to_empty(device=torch.cuda.current_device())\n\n        is_cpu_offload = cpu_offload is not None\n        options = StateDictOptions(\n            full_state_dict=True, cpu_offload=is_cpu_offload, broadcast_from_rank0=True\n        )\n\n        set_model_state_dict(model, full_state, options=options)\n\n        for _name, buf in model.named_buffers():\n            dist.broadcast(buf, src=0)\n\n        if is_cpu_offload:\n            model.to(""cpu"", non_blocking=True)\n            for buf in model.buffers():\n                buf.data = buf.data.to(torch.cuda.current_device())\n\n        return model\n\n    def _get_model_cls(self):\n        """"""Get the appropriate model class based on config.""""""\n        if hasattr(self.hf_config, ""vision_config""):\n            from transformers import AutoModelForImageTextToText\n\n            return AutoModelForImageTextToText\n        else:\n            from transformers import AutoModelForCausalLM\n\n            return AutoModelForCausalLM\n\n    def _init_model(self):\n        """"""Initialize and wrap model with FSDP.""""""\n        self.fsdp_cpu_offload = getattr(self.args, ""fsdp_cpu_offload"", False)\n\n        init_context = self._get_init_weight_context_manager()\n\n        with init_context():\n            model = self._get_model_cls().from_pretrained(\n                self.args.hf_checkpoint,\n                trust_remote_code=True,\n                attn_implementation=self.args.attn_implementation,\n            )\n\n        model.train()\n        full_state = model.state_dict()\n\n        model = apply_fsdp2(\n            model, mesh=self.dp_mesh, cpu_offload=self.fsdp_cpu_offload, args=self.args\n        )\n\n        model = self._fsdp2_load_full_state_dict(\n            model,\n            full_state,\n            self.dp_mesh,\n            cpu_offload=True if self.fsdp_cpu_offload else None,\n        )\n\n        self.model = model\n\n        if self.args.gradient_checkpointing:\n            self.model.gradient_checkpointing_enable()\n\n        logger.info(f""[Rank {dist.get_rank()}] Model initialized with FSDP"")\n\n    def _init_optimizer(self):\n        """"""Initialize optimizer and learning rate scheduler.""""""\n        if self.args.optimizer == ""adam"":\n            self.optimizer = torch.optim.AdamW(\n                self.model.parameters(),\n                lr=self.args.lr,\n                betas=(self.args.adam_beta1, self.args.adam_beta2),\n                eps=self.args.adam_eps,\n                weight_decay=self.args.weight_decay,\n            )\n        else:\n            raise ValueError(f""Unsupported optimizer: {self.args.optimizer}"")\n\n        self.lr_scheduler = get_lr_scheduler(self.args, self.optimizer)\n        self.global_step = 0\n        self.micro_step = 0\n\n    def _load_checkpoint(self):\n        """"""Load checkpoint if available.""""""\n        checkpoint_payload = checkpoint.load(self)\n        checkpoint.finalize_load(self, checkpoint_payload)\n\n    def generate_sft_rollout(self, rollout_id: int) -> list[Sample]:\n        """"""Generate SFT rollout data (tokenize and create loss masks).""""""\n        samples = self.data_source.get_samples(self.args.rollout_batch_size)\n\n        result = []\n        for i, (sample,) in enumerate(samples):\n            messages = sample.prompt\n            token_ids, loss_mask = self.mask_generator.get_loss_mask(messages)\n            response_length = self.mask_generator.get_response_lengths([loss_mask])[0]\n\n            sample.tokens = token_ids\n            sample.response_length = response_length\n            sample.reward = 0\n            sample.loss_mask = loss_mask[-response_length:]\n            result.append(sample)\n\n            if i == 0 and rollout_id == 0 and dist.get_rank() == 0:\n                logger.info(\n                    f""SFT rollout sample: tokens_len={len(token_ids)}, ""\n                    f""response_length={response_length}""\n                )\n\n        return result\n\n    def _convert_samples_to_train_data(self, samples: list[Sample]) -> dict:\n        """"""Convert samples to training data format.""""""\n        train_data = {\n            ""tokens"": [sample.tokens for sample in samples],\n            ""response_lengths"": [sample.response_length for sample in samples],\n            ""rewards"": [0.0 for _ in samples],\n            ""raw_reward"": [0.0 for _ in samples],\n            ""truncated"": [0 for _ in samples],\n            ""sample_indices"": [sample.index for sample in samples],\n        }\n\n        loss_masks = []\n        for sample in samples:\n            if sample.loss_mask is None:\n                sample.loss_mask = [1] * sample.response_length\n            loss_masks.append(sample.loss_mask)\n        train_data[""loss_masks""] = loss_masks\n\n        return train_data\n\n    def _split_train_data_by_dp(self, data: dict) -> dict:\n        """"""Split training data for current DP rank.""""""\n        total_lengths = [len(t) for t in data[""tokens""]]\n        data[""total_lengths""] = total_lengths\n\n        # Simple round-robin partitioning\n        partition = list(range(self.dp_rank, len(total_lengths), self.dp_size))\n\n        rollout_data = {""partition"": partition, ""total_lengths"": total_lengths}\n\n        for key in [\n            ""tokens"",\n            ""response_lengths"",\n            ""rewards"",\n            ""raw_reward"",\n            ""truncated"",\n            ""loss_masks"",\n            ""sample_indices"",\n        ]:\n            if key in data:\n                rollout_data[key] = [data[key][j] for j in partition]\n\n        return rollout_data\n\n    def _packed_data(self, rollout_data: dict) -> tuple[list[dict], list[int]]:\n        """"""Pack variable-length sequences for efficient processing.""""""\n        tokens = rollout_data[""tokens""]\n\n        packed_batches = []\n        mbs_size_list = []\n        local_batch_size = self.args.global_batch_size // self.dp_size\n\n        if self.args.use_dynamic_batch_size:\n            max_tokens = self.args.max_tokens_per_gpu\n            if self.cp_size > 1:\n                max_tokens = max_tokens * self.cp_size\n\n            for i in range(0, len(tokens), local_batch_size):\n                mbs_size_list.append(\n                    get_minimum_num_micro_batch_size(\n                        [len(t) for t in rollout_data[""tokens""][i : i + local_batch_size]],\n                        max_tokens,\n                    )\n                )\n            num_microbatches = torch.tensor(\n                mbs_size_list, dtype=torch.int, device=torch.cuda.current_device()\n            )\n            dist.all_reduce(num_microbatches, op=dist.ReduceOp.MAX, group=self.dp_group)\n            num_microbatches = num_microbatches.tolist()\n        else:\n            num_microbatches = [\n                self.args.global_batch_size // (self.args.micro_batch_size * self.dp_size)\n            ] * (len(tokens) // local_batch_size)\n\n        start = 0\n        for mbs_size in num_microbatches:\n            end = start + local_batch_size\n            # Create dummy advantages/returns for SFT (not used but required by pack_sequences)\n            dummy_advantages = [\n                torch.zeros(rollout_data[""response_lengths""][i])\n                for i in range(start, end)\n            ]\n            packed_batches.extend(\n                pack_sequences(\n                    rollout_data[""tokens""][start:end],\n                    rollout_data[""loss_masks""][start:end],\n                    rollout_data[""rewards""][start:end],\n                    rollout_data[""raw_reward""][start:end],\n                    rollout_data[""response_lengths""][start:end],\n                    dummy_advantages,  # advantages\n                    dummy_advantages,  # returns\n                    num_packs=mbs_size,\n                )\n            )\n            start = end\n\n        grad_accum = list(accumulate(num_microbatches))\n        return packed_batches, grad_accum\n\n    def _get_model_inputs_args(self, packed_sequence: dict) -> dict:\n        """"""Prepare model input arguments from packed sequence.""""""\n        input_ids = packed_sequence[""tokens""].unsqueeze(0)\n        position_ids = packed_sequence[""position_ids""].unsqueeze(0)\n\n        return {\n            ""input_ids"": input_ids,\n            ""position_ids"": position_ids,\n            ""attention_mask"": None,\n        }\n\n    def _compute_log_prob(self, packed_batches: list[dict]) -> None:\n        """"""Compute log probabilities for SFT loss.""""""\n        with timer(""log_probs""), torch.no_grad():\n            for batch in tqdm(\n                packed_batches,\n                desc=""log_probs"",\n                disable=dist.get_rank() != 0,\n            ):\n                model_args = self._get_model_inputs_args(batch)\n                logits = self.model(**model_args).logits.squeeze(0).float()\n                log_probs_result, entropy_result = get_logprob_and_entropy_with_cp(\n                    logits=logits,\n                    target_tokens=batch[""tokens""],\n                    cp_rank=self.cp_rank,\n                    cp_size=self.cp_size,\n                    cp_group=self.cp_group,\n                    model_input_ids=model_args[""input_ids""],\n                    allow_compile=True,\n                    temperature=self.args.rollout_temperature,\n                )\n                batch[""log_probs""] = log_probs_result\n                batch[""entropy""] = entropy_result\n\n    def _compute_sft_loss(self, unpacked_batches: list[dict], logits: torch.Tensor):\n        """"""Compute SFT loss (negative log likelihood).""""""\n        loss_masks = [\n            batch[""loss_masks""].to(device=logits.device) for batch in unpacked_batches\n        ]\n        response_lengths = [batch[""response_lengths""] for batch in unpacked_batches]\n        log_probs = torch.cat(\n            [batch[""cur_log_probs""] for batch in unpacked_batches], dim=0\n        )\n        loss = -sum_of_sample_mean(log_probs, response_lengths, loss_masks)\n\n        if log_probs.numel() == 0:\n            loss += 0 * logits.sum()\n\n        return loss, {""loss"": loss.detach()}\n\n    def _train_step(\n        self,\n        packed_batch: dict,\n        reported_accum: dict,\n        mbs_id: int,\n        grad_accum: list[int],\n    ):\n        """"""Execute one training step.""""""\n        model_args = self._get_model_inputs_args(packed_batch)\n        logits = self.model(**model_args).logits.squeeze(0).float()\n\n        log_probs, entropy_result = get_logprob_and_entropy_with_cp(\n            logits=logits,\n            target_tokens=packed_batch[""tokens""],\n            cp_rank=self.cp_rank,\n            cp_size=self.cp_size,\n            cp_group=self.cp_group,\n            model_input_ids=model_args[""input_ids""],\n            allow_compile=True,\n            temperature=self.args.rollout_temperature,\n        )\n        packed_batch[""cur_log_probs""] = log_probs\n        packed_batch[""entropy""] = entropy_result\n\n        unpacked_batches = unpack_sequences(packed_batch)\n        loss, reported = self._compute_sft_loss(unpacked_batches, logits)\n\n        # Scale loss for gradient accumulation\n        loss = loss * self.dp_size / self.args.global_batch_size\n        loss.backward()\n\n        for k, v in reported.items():\n            reported_accum.setdefault(k, []).append(v)\n\n        if (mbs_id + 1) in grad_accum:\n            grad_norm = torch.nn.utils.clip_grad_norm_(\n                self.model.parameters(), self.args.clip_grad\n            )\n            grad_norm = float(grad_norm)\n\n            self.optimizer.step()\n            self.lr_scheduler.step()\n            self.optimizer.zero_grad(set_to_none=True)\n\n            # Aggregate and log metrics\n            aggregated = {k: torch.stack(v).sum().item() for k, v in reported_accum.items()}\n            reduced_aggregated = [None] * self.dp_size\n            dist.all_gather_object(reduced_aggregated, aggregated, group=self.dp_group)\n            aggregated = {}\n            for k in reported_accum.keys():\n                aggregated[k] = sum([r[k] for r in reduced_aggregated]) / self.args.global_batch_size\n            reported_accum.clear()\n\n            if dist.get_rank() == 0:\n                log_dict = {\n                    f""train/{k}"": (val.item() if torch.is_tensor(val) else val)\n                    for k, val in aggregated.items()\n                }\n                log_dict[""train/grad_norm""] = grad_norm\n\n                lr_values = self.lr_scheduler.get_last_lr()\n                for gid, _group in enumerate(self.optimizer.param_groups):\n                    log_dict[f""train/lr_{gid}""] = lr_values[gid]\n\n                logger.info(f""step {self.global_step}: {log_dict}"")\n                log_dict[""train/step""] = self.global_step\n                tracking_utils.log(self.args, log_dict, step_key=""train/step"")\n\n            self.global_step += 1\n\n    def train_one_rollout(self, rollout_id: int):\n        """"""Execute one rollout's worth of training.""""""\n        # Generate SFT data\n        samples = self.generate_sft_rollout(rollout_id)\n\n        # Convert to training format\n        train_data = self._convert_samples_to_train_data(samples)\n\n        # Split by DP rank\n        rollout_data = self._split_train_data_by_dp(train_data)\n\n        # Create advantages/returns (dummy for SFT)\n        rollout_data[""advantages""] = rollout_data[""returns""] = [\n            torch.tensor([0.0] * rollout_data[""response_lengths""][i])\n            for i in range(len(rollout_data[""rewards""]))\n        ]\n\n        # Pack sequences\n        packed_batches, grad_accum = self._packed_data(rollout_data)\n\n        if len(grad_accum) == 0:\n            logger.warning(f""[Rank {dist.get_rank()}] No batches to train on rollout {rollout_id}"")\n            return\n\n        # Compute log probs for logging\n        self._compute_log_prob(packed_batches)\n\n        # Training loop\n        with timer(""actor_train""):\n            reported_accum = {}\n            self.optimizer.zero_grad(set_to_none=True)\n\n            for mbs_id, packed_batch in enumerate(\n                tqdm(packed_batches, desc=""actor_train"", disable=dist.get_rank() != 0)\n            ):\n                self._train_step(packed_batch, reported_accum, mbs_id, grad_accum)\n\n        self.prof.step(rollout_id=rollout_id)\n\n    def save_model(self, iteration: int):\n        """"""Save model checkpoint.""""""\n        if self.args.save is None:\n            return\n        checkpoint.save(self, iteration)\n\n    def train(self):\n        """"""Main training loop.""""""\n        logger.info(\n            f""[Rank {dist.get_rank()}] Starting training: ""\n            f""rollout_id {self.args.start_rollout_id} -> {self.args.num_rollout}""\n        )\n\n        for rollout_id in range(self.args.start_rollout_id, self.args.num_rollout):\n            self.train_one_rollout(rollout_id)\n\n            # Save checkpoint periodically\n            if should_run_periodic_action(\n                rollout_id, self.args.save_interval, self.num_rollout_per_epoch\n            ):\n                self.save_model(rollout_id)\n\n        logger.info(f""[Rank {dist.get_rank()}] Training completed!"")\n\n\ndef set_sft_defaults(args: Namespace) -> Namespace:\n    """"""Set default values appropriate for SFT training.""""""\n    # These flags are normally set by debug_train_only in Ray mode\n    if not hasattr(args, ""debug_train_only""):\n        args.debug_train_only = True\n\n    # SFT-specific defaults\n    if not hasattr(args, ""loss_type"") or args.loss_type is None:\n        args.loss_type = ""sft_loss""\n\n    if not hasattr(args, ""advantage_estimator"") or args.advantage_estimator is None:\n        args.advantage_estimator = ""grpo""\n\n    if not hasattr(args, ""n_samples_per_prompt"") or args.n_samples_per_prompt is None:\n        args.n_samples_per_prompt = 1\n\n    if not hasattr(args, ""rollout_global_dataset""):\n        args.rollout_global_dataset = True\n\n    if not hasattr(args, ""context_parallel_size"") or args.context_parallel_size is None:\n        args.context_parallel_size = 1\n\n    if not hasattr(args, ""attn_implementation"") or args.attn_implementation is None:\n        args.attn_implementation = ""flash_attention_2""\n\n    if not hasattr(args, ""rollout_temperature"") or args.rollout_temperature is None:\n        args.rollout_temperature = 1.0\n\n    # Ensure we don't try to use rollout engines\n    args.offload_train = False\n    args.offload_rollout = False\n    args.colocate = False\n\n    return args\n\n\ndef main():\n    configure_logger()\n\n    # Parse arguments\n    args = parse_args()\n\n    # Apply SFT-specific defaults\n    args = set_sft_defaults(args)\n\n    # Create trainer and run\n    trainer = SFTTrainer(args)\n    trainer.train()\n\n\nif __name__ == ""__main__"":\n    main()\n\n",python,tab
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4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-14a0bec6-a4a9-4b59-996b-ff826d7cb6461762440546881-2025_11_06-15.49.10.583/source.csv

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+3,151,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"3:49:10 PM [info] Activating crowd-code\n3:49:10 PM [info] Recording started\n3:49:10 PM [info] Initializing git provider using file system watchers...\n3:49:10 PM [info] No workspace folder found\n",Log,tab
+4,2029,"extension-output-pdoom-org.crowd-code-#1-crowd-code",194,0,"3:49:12 PM [info] Retrying git provider initialization...\n3:49:12 PM [info] No workspace folder found\n",Log,content
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+9,105613,"TERMINAL",0,0,"]633;CVSCode test\r\n%                                                                                                                                                                                                          \r \r",,terminal_output
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4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-222fe98e-29ac-4b20-9a65-fe2e31f8eb701751128122769-2025_06_28-09.28.47.536/source.csv

@@ -0,0 +1,29 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+1,2,"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[str] = field(default_factory=lambda: [""tokenizer""])\n    log_interval: int = 5\n    log_image_interval: int = 250\n    ckpt_dir: str = """"\n    log_checkpoint_interval: int = 10000\n    log_gradients: bool = False\n\n\nargs = tyro.cli(Args)\n\n\ndef tokenizer_loss_fn(params, state, inputs):\n    # --- Compute loss ---\n    outputs = state.apply_fn(\n        params,\n        inputs,\n        training=True,\n        rngs={""params"": inputs[""rng""], ""dropout"": inputs[""dropout_rng""]},\n    )\n    mse = jnp.square(inputs[""videos""] - outputs[""recon""]).mean()\n    q_loss = jnp.square(jax.lax.stop_gradient(outputs[""emb""]) - outputs[""z""]).mean()\n    commitment_loss = jnp.square(\n        outputs[""emb""] - jax.lax.stop_gradient(outputs[""z""])\n    ).mean()\n    loss = mse + q_loss + args.vq_beta * commitment_loss\n\n    # --- Compute validation metrics ---\n    gt = inputs[""videos""].clip(0, 1).reshape(-1, *inputs[""videos""].shape[2:])\n    recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n    psnr = pix.psnr(gt, recon).mean()\n    ssim = pix.ssim(gt, recon).mean()\n    _, index_counts = jnp.unique_counts(\n        jnp.ravel(outputs[""indices""]), size=args.num_latents, fill_value=0\n    )\n    codebook_usage = (index_counts != 0).mean()\n    metrics = dict(\n        loss=loss,\n        mse=mse,\n        q_loss=q_loss,\n        commitment_loss=commitment_loss,\n        psnr=psnr,\n        ssim=ssim,\n        codebook_usage=codebook_usage,\n    )\n    return loss, (outputs[""recon""], metrics)\n\n\n@jax.jit\ndef train_step(state, inputs):\n    grad_fn = jax.value_and_grad(tokenizer_loss_fn, has_aux=True, allow_int=True)\n    (loss, (recon, metrics)), grads = grad_fn(state.params, state, inputs)\n    state = state.apply_gradients(grads=grads)\n    if args.log_gradients:\n        metrics[""encoder_gradients_std/""] = jax.tree.map(\n            lambda x: x.std(), grads[""params""][""encoder""]\n        )\n        metrics[""vq_gradients_std/""] = jax.tree.map(\n            lambda x: x.std(), grads[""params""][""vq""]\n        )\n        metrics[""decoder_gradients_std/""] = jax.tree.map(\n            lambda x: x.std(), grads[""params""][""decoder""]\n        )\n    return state, loss, recon, metrics\n\n\nif __name__ == ""__main__"":\n    jax.distributed.initialize()\n    num_devices = jax.device_count()\n    if num_devices == 0:\n        raise ValueError(""No JAX devices found."")\n    print(f""Running on {num_devices} devices."")\n\n    if args.batch_size % num_devices != 0:\n        raise ValueError(\n            f""Global batch size {args.batch_size} must be divisible by ""\n            f""number of devices {num_devices}.""\n        )\n\n    per_device_batch_size_for_init = args.batch_size // num_devices\n\n    rng = jax.random.PRNGKey(args.seed)\n    if args.log and jax.process_index() == 0:\n        wandb.init(\n            entity=args.entity,\n            project=args.project,\n            name=args.name,\n            tags=args.tags,\n            group=""debug"",\n            config=args\n        )\n\n    # --- Initialize model ---\n    tokenizer = TokenizerVQVAE(\n        in_dim=args.image_channels,\n        model_dim=args.model_dim,\n        latent_dim=args.latent_dim,\n        num_latents=args.num_latents,\n        patch_size=args.patch_size,\n        num_blocks=args.num_blocks,\n        num_heads=args.num_heads,\n        dropout=args.dropout,\n        codebook_dropout=args.codebook_dropout,\n    )\n    rng, _rng = jax.random.split(rng)\n    image_shape = (args.image_height, args.image_width, args.image_channels)\n    inputs = dict(\n        videos=jnp.zeros(\n            (per_device_batch_size_for_init, args.seq_len, *image_shape),\n            dtype=jnp.float32,\n        ),\n    )\n    init_params = tokenizer.init(_rng, inputs)\n\n    # --- Initialize optimizer ---\n    lr_schedule = optax.warmup_cosine_decay_schedule(\n        args.min_lr, args.max_lr, args.warmup_steps, args.num_steps\n    )\n    tx = optax.adamw(learning_rate=lr_schedule, b1=0.9, b2=0.9, weight_decay=1e-4)\n    train_state = TrainState.create(apply_fn=tokenizer.apply, params=init_params, tx=tx)\n\n    # FIXME: switch to create_hybrid_device_mesh for runs spanning multiple nodes\n    device_mesh_arr = create_device_mesh((num_devices,))\n    mesh = Mesh(devices=device_mesh_arr, axis_names=(""data"",))\n\n    replicated_sharding = NamedSharding(mesh, PartitionSpec())\n    train_state = jax.device_put(train_state, replicated_sharding)\n\n    # --- Load checkpoint ---\n    step = 0\n    if args.checkpoint:\n        restore_target = {""model"": train_state}\n        restore_args = orbax_utils.restore_args_from_target(restore_target)\n        train_state.params[""params""].update(\n            PyTreeCheckpointer()\n            .restore(args.checkpoint, item=restore_target, restore_args=restore_args)[\n                ""model""\n            ]\n            .params[""params""]\n        )\n        # Assume checkpoint is of the form tokenizer_<timestamp>_<step>\n        step += int(args.checkpoint.split(""_"")[-1])\n\n    # --- TRAIN LOOP ---\n    tfrecord_files = [\n        os.path.join(args.data_dir, x)\n        for x in os.listdir(args.data_dir)\n        if x.endswith("".tfrecord"")\n    ]\n    dataloader = get_dataloader(\n        # NOTE: We deliberately pass the global batch size\n        # The dataloader shards the dataset across all processes\n        tfrecord_files,\n        args.seq_len,\n        args.batch_size,\n        *image_shape,\n    )\n    print(f""Starting training from step {step}..."")\n    while step < args.num_steps:\n        for videos in dataloader:\n            # --- 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            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
+2,62,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"",Log,tab
+3,97,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"9:28:47 AM [info] Activating crowd-code\n9:28:47 AM [info] Recording started\n9:28:47 AM [info] Initializing git provider using file system watchers...\n9:28:47 AM [info] Git repository found\n9:28:47 AM [info] Git provider initialized successfully\n",Log,content
+4,195,"extension-output-pdoom-org.crowd-code-#1-crowd-code",245,0,"9:28:47 AM [info] Initial git state: [object Object]\n",Log,content
+5,109366,"train_tokenizer.py",0,0,"",python,tab
+6,120114,"train_tokenizer.py",0,0,"Switched from branch 'dataloader-reproducibility-test' to 'main'",python,git_branch_checkout
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+9,584544,"utils/dataloader.py",0,0,"import functools\nimport jax\n\nimport tensorflow as tf\n\n# reserve GPU memory for JAX only if tensorflow is built with GPU support\ntf.config.experimental.set_visible_devices([], ""GPU"")\n\n\n# --- TensorFlow function for processing: slicing, normalization ---\ndef _tf_process_episode(episode_tensor, seq_len, image_h, image_w, image_c):\n    """"""\n    Processes a raw episode tensor in TensorFlow.\n    Takes a full episode, extracts a random sequence, and normalizes it.\n    Args:\n        episode_tensor: A TensorFlow tensor representing a full video episode.\n                        Expected shape: (dynamic_length, image_h, image_w, image_c)\n                        Expected dtype: e.g., tf.uint8 (raw pixel values)\n        seq_len: The desired length of the sub-sequence to extract.\n        image_h: The height of each frame.\n        image_w: The width of each frame.\n        image_c: The number of channels in each frame.\n    Returns:\n        A TensorFlow tensor representing the processed video sequence.\n        Shape: (seq_len, image_h, image_w, image_c)\n        Dtype: tf.float32 (normalized pixel values)\n    """"""\n    current_episode_len = tf.shape(episode_tensor)[0]\n\n    max_start_idx = current_episode_len - seq_len\n\n    start_idx = tf.random.uniform(\n        shape=(), minval=0, maxval=max_start_idx + 1, dtype=tf.int32\n    )\n\n    seq = episode_tensor[start_idx : start_idx + seq_len]\n\n    seq = tf.cast(seq, tf.float32) / 255.0\n\n    # Ensure the final shape is statically known for batching.\n    # tf.reshape is robust, but tf.ensure_shape or set_shape can also be used if confident.\n    processed_sequence = tf.reshape(seq, [seq_len, image_h, image_w, image_c])\n\n    return processed_sequence\n\n\ndef _parse_tfrecord_fn(example_proto, image_h, image_w, image_c):\n    feature_description = {\n        ""height"": tf.io.FixedLenFeature([], tf.int64),\n        ""width"": tf.io.FixedLenFeature([], tf.int64),\n        ""channels"": tf.io.FixedLenFeature([], tf.int64),\n        ""sequence_length"": tf.io.FixedLenFeature([], tf.int64),\n        ""raw_video"": tf.io.FixedLenFeature([], tf.string),\n    }\n    example = tf.io.parse_single_example(example_proto, feature_description)\n\n    video_shape = (example[""sequence_length""], image_h, image_w, image_c)\n\n    episode_tensor = tf.io.decode_raw(example[""raw_video""], out_type=tf.uint8)\n    episode_tensor = tf.reshape(episode_tensor, video_shape)\n\n    episode_tensor = tf.ensure_shape(episode_tensor, [None, image_h, image_w, image_c])\n    return episode_tensor\n\n\ndef get_dataloader(\n    tfrecord_paths: list[str],  # List of TFRecord file paths\n    seq_len: int,\n    global_batch_size: int,\n    image_h: int,\n    image_w: int,\n    image_c: int,\n    shuffle_buffer_size: int = 1000,\n    num_parallel_calls: int = tf.data.AUTOTUNE,\n    seed: int = 42,\n):\n    """"""\n    Creates a tf.data.Dataset pipeline from TFRecord files.\n    """"""\n    if not tfrecord_paths:\n        raise ValueError(""tfrecord_paths list cannot be empty."")\n\n    process_id = jax.process_index()\n    num_processes = jax.process_count()\n\n    assert (\n        global_batch_size % num_processes == 0\n    ), ""Global batch size {global_batch_size} \\n        must be divisible by the number of JAX processes {num_processes} for proper sharding.""\n    per_process_batch_size = global_batch_size // num_processes\n\n    dataset = tf.data.TFRecordDataset(\n        tfrecord_paths, num_parallel_reads=tf.data.AUTOTUNE\n    )\n\n    dataset = dataset.shard(num_shards=num_processes, index=process_id)\n\n    # (f.srambical) NOTE: For TFRecords, it's often good to have a large shuffle buffer.\n    if shuffle_buffer_size > 0:\n        dataset = dataset.shuffle(\n            buffer_size=shuffle_buffer_size, seed=seed, reshuffle_each_iteration=True\n        )\n    parse_fn = functools.partial(\n        _parse_tfrecord_fn, image_h=image_h, image_w=image_w, image_c=image_c\n    )\n    dataset = dataset.map(parse_fn, num_parallel_calls=num_parallel_calls)\n\n    tf_process_fn = functools.partial(\n        _tf_process_episode,\n        seq_len=seq_len,\n        image_h=image_h,\n        image_w=image_w,\n        image_c=image_c,\n    )\n    dataset = dataset.map(tf_process_fn, num_parallel_calls=num_parallel_calls)\n\n    dataset = dataset.repeat(None)\n    dataset = dataset.batch(per_process_batch_size, drop_remainder=True)\n    dataset = dataset.prefetch(tf.data.AUTOTUNE)\n\n    return dataset.as_numpy_iterator()\n",python,tab
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4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-266a35fd-8e9a-45ca-bab6-73f8e208e8e31764440451347-2025_11_29-19.20.59.166/source.csv

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+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+1,11,"Untitled-1",0,0,"",plaintext,tab
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+4,2180,"TERMINAL",0,0,"Test",,terminal_focus
+5,2186,"Untitled-1",0,0,"/* crowd-pilot: insert start */\nline A\nline B\n/* crowd-pilot: insert end */\n",plaintext,content
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4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-3126c08d-d6fe-466c-828c-ddc956b35e091762367702624-2025_11_05-19.35.09.972/source.csv

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+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+1,2,"package.json",0,0,"{\n  ""name"": ""crowd-pilot"",\n  ""displayName"": ""crowd-pilot"",\n  ""description"": ""Teaching language models to code like humans."",\n  ""version"": ""0.0.1"",\n  ""engines"": {\n    ""vscode"": ""^1.99.3""\n  },\n  ""categories"": [\n    ""Other""\n  ],\n  ""activationEvents"": [],\n  ""main"": ""./out/extension.js"",\n  ""contributes"": {\n    ""commands"": [\n      {\n        ""command"": ""crowd-pilot.helloWorld"",\n        ""title"": ""Hello World""\n      }\n    ]\n  },\n  ""scripts"": {\n    ""vscode:prepublish"": ""npm run compile"",\n    ""compile"": ""tsc -p ./"",\n    ""watch"": ""tsc -watch -p ./"",\n    ""pretest"": ""npm run compile && npm run lint"",\n    ""lint"": ""eslint src"",\n    ""test"": ""vscode-test""\n  },\n  ""devDependencies"": {\n    ""@types/vscode"": ""^1.105.0"",\n    ""@types/mocha"": ""^10.0.10"",\n    ""@types/node"": ""22.x"",\n    ""@typescript-eslint/eslint-plugin"": ""^8.45.0"",\n    ""@typescript-eslint/parser"": ""^8.45.0"",\n    ""eslint"": ""^9.36.0"",\n    ""typescript"": ""^5.9.3"",\n    ""@vscode/test-cli"": ""^0.0.11"",\n    ""@vscode/test-electron"": ""^2.5.2""\n  }\n}\n",json,tab
+2,329,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"7:35:09 PM [info] Activating crowd-code\n7:35:09 PM [info] Recording started\n7:35:09 PM [info] Initializing git provider using file system watchers...\n7:35:10 PM [error] Not a git repository: EntryNotFound (FileSystemError): Error: ENOENT: no such file or directory, stat '/home/franz.srambical/crowd-pilot/crowd-pilot-extension/.git'\n",Log,tab
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+7,186317,"TERMINAL",0,0,"]633;CCHANGELOG.md  eslint.config.mjs  node_modules  package.json  package-lock.json  README.md  src  tsconfig.json  vsc-extension-quickstart.md\r\n]0;franz.srambical@hai-login1:~/crowd-pilot/crowd-pilot-extension",,terminal_output
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4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-46c4a757-02fe-4d2b-80c9-10277f3bc6421757061314730-2025_09_05-10.35.23.869/source.csv

@@ -0,0 +1,4 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+1,2,"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
+2,89,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"10:35:23 AM [info] Activating crowd-code\n10:35:23 AM [info] Recording started\n10:35:23 AM [info] Initializing git provider using file system watchers...\n10:35:23 AM [info] Git repository found\n10:35:23 AM [info] Git provider initialized successfully\n",Log,tab
+3,125,"extension-output-pdoom-org.crowd-code-#1-crowd-code",250,0,"10:35:23 AM [info] Initial git state: [object Object]\n",Log,content

4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-48fbb415-6db9-4d35-b548-561e828791bf1751383187013-2025_07_01-17.19.57.60/source.csv

@@ -0,0 +1,4 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+1,2,"utils/dataloader.py",0,0,"import functools\nimport jax\n\nimport tensorflow as tf\n\n# reserve GPU memory for JAX only if tensorflow is built with GPU support\ntf.config.experimental.set_visible_devices([], ""GPU"")\n\n\n# --- TensorFlow function for processing: slicing, normalization ---\ndef _tf_process_episode(episode_tensor, seq_len, image_h, image_w, image_c):\n    """"""\n    Processes a raw episode tensor in TensorFlow.\n    Takes a full episode, extracts a random sequence, and normalizes it.\n    Args:\n        episode_tensor: A TensorFlow tensor representing a full video episode.\n                        Expected shape: (dynamic_length, image_h, image_w, image_c)\n                        Expected dtype: e.g., tf.uint8 (raw pixel values)\n        seq_len: The desired length of the sub-sequence to extract.\n        image_h: The height of each frame.\n        image_w: The width of each frame.\n        image_c: The number of channels in each frame.\n    Returns:\n        A TensorFlow tensor representing the processed video sequence.\n        Shape: (seq_len, image_h, image_w, image_c)\n        Dtype: tf.float32 (normalized pixel values)\n    """"""\n    current_episode_len = tf.shape(episode_tensor)[0]\n\n    max_start_idx = current_episode_len - seq_len\n\n    start_idx = tf.random.uniform(\n        shape=(), minval=0, maxval=max_start_idx + 1, dtype=tf.int32\n    )\n\n    seq = episode_tensor[start_idx : start_idx + seq_len]\n\n    seq = tf.cast(seq, tf.float32) / 255.0\n\n    # Ensure the final shape is statically known for batching.\n    # tf.reshape is robust, but tf.ensure_shape or set_shape can also be used if confident.\n    processed_sequence = tf.reshape(seq, [seq_len, image_h, image_w, image_c])\n\n    return processed_sequence\n\n\ndef _parse_tfrecord_fn(example_proto, image_h, image_w, image_c):\n    feature_description = {\n        ""height"": tf.io.FixedLenFeature([], tf.int64),\n        ""width"": tf.io.FixedLenFeature([], tf.int64),\n        ""channels"": tf.io.FixedLenFeature([], tf.int64),\n        ""sequence_length"": tf.io.FixedLenFeature([], tf.int64),\n        ""raw_video"": tf.io.FixedLenFeature([], tf.string),\n    }\n    example = tf.io.parse_single_example(example_proto, feature_description)\n\n    video_shape = (example[""sequence_length""], image_h, image_w, image_c)\n\n    episode_tensor = tf.io.decode_raw(example[""raw_video""], out_type=tf.uint8)\n    episode_tensor = tf.reshape(episode_tensor, video_shape)\n\n    episode_tensor = tf.ensure_shape(episode_tensor, [None, image_h, image_w, image_c])\n    return episode_tensor\n\n\ndef get_dataloader(\n    tfrecord_paths: list[str],  # List of TFRecord file paths\n    seq_len: int,\n    global_batch_size: int,\n    image_h: int,\n    image_w: int,\n    image_c: int,\n    shuffle_buffer_size: int = 1000,\n    num_parallel_calls: int = tf.data.AUTOTUNE,\n    seed: int = 42,\n):\n    """"""\n    Creates a tf.data.Dataset pipeline from TFRecord files.\n    """"""\n    if not tfrecord_paths:\n        raise ValueError(""tfrecord_paths list cannot be empty."")\n\n    process_id = jax.process_index()\n    num_processes = jax.process_count()\n\n    assert (\n        global_batch_size % num_processes == 0\n    ), ""Global batch size {global_batch_size} \\n        must be divisible by the number of JAX processes {num_processes} for proper sharding.""\n    per_process_batch_size = global_batch_size // num_processes\n\n    dataset = tf.data.TFRecordDataset(\n        tfrecord_paths, num_parallel_reads=tf.data.AUTOTUNE\n    )\n\n    dataset = dataset.shard(num_shards=num_processes, index=process_id)\n\n    # (f.srambical) NOTE: For TFRecords, it's often good to have a large shuffle buffer.\n    if shuffle_buffer_size > 0:\n        dataset = dataset.shuffle(\n            buffer_size=shuffle_buffer_size, seed=seed, reshuffle_each_iteration=True\n        )\n    parse_fn = functools.partial(\n        _parse_tfrecord_fn, image_h=image_h, image_w=image_w, image_c=image_c\n    )\n    dataset = dataset.map(parse_fn, num_parallel_calls=num_parallel_calls)\n\n    tf_process_fn = functools.partial(\n        _tf_process_episode,\n        seq_len=seq_len,\n        image_h=image_h,\n        image_w=image_w,\n        image_c=image_c,\n    )\n    dataset = dataset.map(tf_process_fn, num_parallel_calls=num_parallel_calls)\n\n    dataset = dataset.repeat(None)\n    dataset = dataset.batch(per_process_batch_size, drop_remainder=True)\n    dataset = dataset.prefetch(tf.data.AUTOTUNE)\n\n    return dataset.as_numpy_iterator()\n",python,tab
+2,121,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"",Log,tab
+3,886,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"5:19:57 PM [info] Activating crowd-code\n5:19:57 PM [info] Recording started\n5:19:57 PM [info] Initializing git provider using file system watchers...\n5:19:57 PM [info] Git repository found\n5:19:57 PM [info] Git provider initialized successfully\n5:19:57 PM [info] Initial git state: [object Object]\n",Log,content

4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-5f817451-b135-49e8-83cd-ad3d90c69e831764422803521-2025_11_29-14.26.46.563/source.csv

@@ -0,0 +1,106 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+2,119,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"2:26:46 PM [info] Activating crowd-code\n2:26:46 PM [info] Recording started\n2:26:46 PM [info] Initializing git provider using file system watchers...\n2:26:46 PM [info] No workspace folder found\n",Log,tab
+3,1785,"Untitled-1",0,0,"",plaintext,tab
+4,8014,"TERMINAL",0,0,"Test",,terminal_focus
+5,8018,"Untitled-1",0,0,"// crowd-pilot mock insert\n",plaintext,content
+6,14335,"Untitled-1",27,0," ",plaintext,content
+7,14384,"Untitled-1",28,0,"",plaintext,selection_keyboard
+8,17027,"Untitled-1",27,1,"",plaintext,content
+9,17440,"Untitled-1",0,0,"",plaintext,selection_command
+10,17927,"Untitled-1",27,0,"const crowdPilotMockInsert = async (req, res) => {",plaintext,content
+11,17930,"Untitled-1",77,0,"",plaintext,selection_command
+12,18887,"Untitled-1",76,0,"",plaintext,selection_command
+13,19642,"Untitled-1",26,51,"",plaintext,content
+14,19651,"Untitled-1",0,0,"",plaintext,selection_command
+15,20927,"Untitled-1",0,26,"",plaintext,content
+16,23781,"Untitled-1",0,0," // replaced by crowd-pilot",plaintext,content
+17,28652,"Untitled-1",0,27,"",plaintext,content
+18,32471,"Untitled-1",0,0,"// crowd-pilot mock insert\n",plaintext,content
+19,35166,"Untitled-1",27,0," // replaced by crowd-pilot",plaintext,content
+20,40747,"Untitled-1",54,0," mock insert",plaintext,content
+21,41943,"Untitled-1",65,0,"",plaintext,selection_command
+22,42514,"Untitled-1",26,40,"",plaintext,content
+23,42524,"Untitled-1",0,0,"",plaintext,selection_command
+24,42869,"Untitled-1",0,26,"",plaintext,content
+25,259053,"Untitled-1",0,0,"",plaintext,tab
+26,320097,"Untitled-1",0,0,"// crowd-pilot mock insert\n",plaintext,content
+27,322120,"Untitled-1",0,0,"",plaintext,selection_command
+28,325219,"Untitled-1",27,0,"",plaintext,selection_command
+29,325368,"Untitled-1",0,0,"",plaintext,selection_command
+30,326434,"Untitled-1",27,0,"",plaintext,selection_command
+31,327065,"Untitled-1",0,0,"",plaintext,selection_command
+32,327494,"Untitled-1",27,0,"",plaintext,selection_command
+33,327669,"Untitled-1",0,0,"",plaintext,selection_command
+34,328074,"Untitled-1",27,0," // replaced by crowd-pilot",plaintext,content
+35,334354,"Untitled-1",27,0,"",plaintext,selection_command
+36,334754,"Untitled-1",0,0,"",plaintext,selection_command
+37,334956,"Untitled-1",27,0,"",plaintext,selection_command
+38,335352,"Untitled-1",0,0,"",plaintext,selection_command
+39,338682,"Untitled-1",0,26,"",plaintext,content
+40,339703,"Untitled-1",1,0,"",plaintext,selection_command
+41,341925,"Untitled-1",1,0,"d",plaintext,content
+42,341927,"Untitled-1",2,0,"",plaintext,selection_keyboard
+43,342108,"Untitled-1",2,0,"d",plaintext,content
+44,342111,"Untitled-1",3,0,"",plaintext,selection_keyboard
+45,342457,"Untitled-1",2,0,"",plaintext,selection_command
+46,342898,"Untitled-1",0,30,"",plaintext,content
+47,344478,"Untitled-1",0,0,"// crowd-pilot mock insert\n",plaintext,content
+48,345380,"Untitled-1",27,0," // replaced by crowd-pilot",plaintext,content
+49,345858,"Untitled-1",27,27,"",plaintext,content
+50,346851,"Untitled-1",27,0,"// crowd-pilot mock insert\n",plaintext,content
+51,352488,"Untitled-1",27,0,"",plaintext,selection_command
+52,353793,"Untitled-1",54,0,"",plaintext,selection_command
+53,353922,"Untitled-1",27,0,"",plaintext,selection_command
+54,354141,"Untitled-1",0,0,"",plaintext,selection_command
+55,354542,"Untitled-1",27,0,"",plaintext,selection_command
+56,354840,"Untitled-1",0,0,"",plaintext,selection_command
+57,355330,"Untitled-1",54,0," // replaced by crowd-pilot",plaintext,content
+58,359823,"Untitled-1",0,26,"",plaintext,content
+59,360305,"Untitled-1",0,0,"// crowd-pilot mock insert\n",plaintext,content
+60,361249,"Untitled-1",28,0,"",plaintext,selection_command
+61,361421,"Untitled-1",27,0,"",plaintext,selection_command
+62,361591,"Untitled-1",0,0,"",plaintext,selection_command
+63,361946,"Untitled-1",27,0," // replaced by crowd-pilot",plaintext,content
+64,362415,"Untitled-1",0,26,"",plaintext,content
+65,538480,"Untitled-1",0,1,"",plaintext,content
+66,538482,"Untitled-1",1,0,"",plaintext,selection_command
+67,538810,"Untitled-1",0,28,"",plaintext,content
+68,539108,"Untitled-1",0,27,"",plaintext,content
+69,539113,"Untitled-1",1,0,"",plaintext,selection_command
+70,539444,"Untitled-1",0,27,"",plaintext,content
+71,553294,"Untitled-1",0,0,"// crowd-pilot mock insert\n",plaintext,content
+72,555984,"Untitled-1",0,0,"",plaintext,selection_command
+73,556512,"Untitled-1",27,0," // replaced by crowd-pilot",plaintext,content
+74,557430,"Untitled-1",0,26,"",plaintext,content
+75,558025,"Untitled-1",0,0,"// crowd-pilot mock insert\n",plaintext,content
+76,558583,"Untitled-1",27,0," // replaced by crowd-pilot",plaintext,content
+77,559571,"Untitled-1",27,27,"",plaintext,content
+78,559844,"Untitled-1",27,0,"// crowd-pilot mock insert\n",plaintext,content
+79,560020,"Untitled-1",54,0," // replaced by crowd-pilot",plaintext,content
+80,560244,"Untitled-1",54,27,"",plaintext,content
+81,560435,"Untitled-1",54,0,"// crowd-pilot mock insert\n",plaintext,content
+82,560652,"Untitled-1",81,0," // replaced by crowd-pilot",plaintext,content
+83,560845,"Untitled-1",81,27,"",plaintext,content
+84,561028,"Untitled-1",81,0,"// crowd-pilot mock insert\n",plaintext,content
+85,561222,"Untitled-1",108,0," // replaced by crowd-pilot",plaintext,content
+86,561999,"Untitled-1",135,0,"j",plaintext,content
+87,562001,"Untitled-1",136,0,"",plaintext,selection_keyboard
+88,562154,"Untitled-1",136,0,"j",plaintext,content
+89,562156,"Untitled-1",137,0,"",plaintext,selection_keyboard
+90,562566,"Untitled-1",136,0,"",plaintext,selection_command
+91,562748,"Untitled-1",164,0,"",plaintext,selection_command
+92,563786,"Untitled-1",138,27," // replaced by crowd-pilot",plaintext,selection_command
+93,564097,"Untitled-1",108,57," // replaced by crowd-pilotjj\n // replaced by crowd-pilot",plaintext,selection_command
+94,564260,"Untitled-1",81,84,"// crowd-pilot mock insert\n // replaced by crowd-pilotjj\n // replaced by crowd-pilot",plaintext,selection_command
+95,564438,"Untitled-1",54,111,"// crowd-pilot mock insert\n// crowd-pilot mock insert\n // replaced by crowd-pilotjj\n // replaced by crowd-pilot",plaintext,selection_command
+96,564600,"Untitled-1",27,138,"// crowd-pilot mock insert\n// crowd-pilot mock insert\n// crowd-pilot mock insert\n // replaced by crowd-pilotjj\n // replaced by crowd-pilot",plaintext,selection_command
+97,564789,"Untitled-1",0,165,"// crowd-pilot mock insert\n// crowd-pilot mock insert\n// crowd-pilot mock insert\n// crowd-pilot mock insert\n // replaced by crowd-pilotjj\n // replaced by crowd-pilot",plaintext,selection_command
+98,565053,"Untitled-1",0,165,"",plaintext,content
+99,577385,"Untitled-1",0,0,"// crowd-pilot mock insert\n",plaintext,content
+100,578345,"Untitled-1",0,0,"",plaintext,selection_command
+101,579764,"Untitled-1",27,0,"",plaintext,selection_command
+102,580043,"Untitled-1",0,0,"",plaintext,selection_command
+103,587897,"Untitled-1",27,0," // replaced by crowd-pilot",plaintext,content
+104,591971,"Untitled-1",0,26,"",plaintext,content
+105,592879,"Untitled-1",1,0,"",plaintext,selection_command
+106,594302,"Untitled-1",0,0,"// crowd-pilot mock insert\n",plaintext,content

4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-63ef9b25-3351-4174-890b-f49574ab1a3c1758993863516-2025_09_27-19.24.25.912/source.csv

@@ -0,0 +1,512 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+1,1,"index.html",0,0,"<html>\n<head>\n  <style>\n    body {\n      margin: 20px;\n      margin-left: 12%;\n      margin-right:12%;\n      a {\n        color: green;\n        text-decoration-style: dotted;\n      }\n    }\n  </style>\n</head>\n<p>\n  Franz Srambical\n  <br>\n  ============\n  <br>\nHi! I'm Franz. I started and scaled <a href=""https://pdoom.org"">p(doom)</a>, a Discord-based research community, from zero >200 members. We work on addressing core blockers towards general intelligence that cannot be solved by scaling up compute. We work on everything from kernel-level optimizations to large-scale distributed systems for pre-training and reinforcement learning. A lot of our current work involves finding, investigating and exploiting novel data troves <a href=""https://pdoom.org/crowd_code.html"">[1]</a> <a href=""https://pdoom.org/jasmine.html"">[2]</a>, and building open infrastructure/codebases.\nI am ex-distributed systems software engineer at <a href=""https://celonis.com"">Celonis</a>, dropped out of uni (Informatics at <a href=""https://tum.de"">TUM</a>), minored in Computational Neuroscience (courses at <a href=""https://www.gsn.uni-muenchen.de"">LMU Graduate School of Systemic Neurosciences</a>), <a href=""https://www.youtube.com/watch?v=N5nVSXV9Hbk&t=21971s"">ex-linux kernel developer</a>, ex-<a href=""public/vwa.pdf"">'alignment researcher'</a> in my high school years, ex-RA at <a href=""https://aidos.group"">Bastian Rieck's lab</a>, ex-'BCI researcher' & founding member at neuroTUM.\n<br>\n<br>\nI want to create AGI (i.e. design architectures that push the pareto-frontier of intelligent systems).\n<br>\n<br>\nIf any of our research work interests you, check out the <a href=""https://pdoom.org/blog.html"">p(doom) blog</a>. I also have <a href=""https://www.linkedin.com/in/franz-srambical-418630178/"" >linkedin</a>, <a href=""https://twitter.com/lemergenz"" >twitter</a> and <a href=""https://scholar.google.com/citations?user=W26dT4EAAAAJ&hl=en&oi=ao"" >google scholar</a>.\n<br>\n<br>\nList of preprints accumulated throughout my education that are not worthy of publication (& thus not on arxiv), but valuable to some nonetheless:\n<ul>\n  <li>\n    <a href=""https://pdoom.org/jax_assert.html"">\n      A blog post on performance-degradation free value assertions in JAX using a very recent and still private JAX API.\n    </a>\n  </li>\n  <li>\n    <a href=""https://pdoom.org/gae_rlax.html"">\n      A blog post on how a lot of PPO implementations are technically wrong (including Deepmind's reference implementation in RLax).\n    </a>\n  </li>\n  <li>\n    <a href=""https://pdoom.org/ppo.html"">\n        A blog post on how practically everyone in LLM post-training is currently implicitly using REINFORCE with baseline, clipping and a likelihood ratio, and not PPO (in the classical sense).\n    </a>\n  </li>\n  <li>\n    <a href=""https://pdoom.org/thesis.html"">\n        A blog post on a simple mental model that permits straightforward contextualization of the current research frontier and extrapolation of what the most important future research directions are going to be.\n    </a>\n  </li>\n  <li>\n    <a href=""https://pdoom.org/causal_mask.html"">\n      A blog post on why the Transformer's causal mask is its ultimate feature, not a bug\n    </a>\n  </li>\n  <li>\n    <a href=""public/vwa.pdf"">\n      A 30-page paper on AGI alignment written in 2018/19 during my high school years (unfortunately in German)\n    </a>\n  </li>\n  <li>\n    <a href=""public/cfr.pdf"">\n      A review paper on superhuman poker bots\n    </a>\n  </li>\n  <li>\n    <a href=""public/mup-lr-warmup.pdf"">\n      A 2-page writeup on the necessity of learning rate warmup under muParametrization\n    </a> (with <a href=""https://github.com/emergenz/mup-lr-warmup"">code</a>)\n  </li>\n  <li>\n    <a href=""public/causal_mask_poster.pdf"">\n      A poster on why the Transformer's causal mask is its ultimate feature, not a bug\n    </a>\n  </li>\n  <li>\n    <a href=""public/wenn_besitzen_unfair_ist.pdf"">\n      A poster on the 99-year leasehold system in Singapore as a means to mitigate generational wealth (unfortunately in German)\n    </a>\n  </li>\n  <li>\n    <a href=""public/panoptic-3d-reconstruction.pdf"">\n      A crappy 'it's just x but with y'-type computer vision paper on 2% better (and 3% worse) panoptic 3D reconstruction (which included one-shot finetuning a 1B parameter diffusion model hours before the deadline)\n    </a>\n  </li>\n  <li>\n    <a href=""https://docs.google.com/document/d/14xx883ywhbJeaPz13S2lu9NY5RBwUzbNnh6K-o-Y06I/edit?usp=sharing"">\n      A 2-page doc outlining my thoughts on whether machines can think \n    </a>\n  </li>\n</ul>\n\nIncomplete list of talks I have given:\n<ul>\n  <li>\n    <a href=""https://docs.google.com/presentation/d/1fq_JiOP9zZS0w_fi9sZxMb-9TmKnleugl9ik3sJK_sc/edit?usp=sharing"">\n      Talk on the necessity of learning rate warmup under muParametrization (and a real-time case study on the absurd speed of modern AI research)\n    </a>\n  </li>\n  <li>\n    <a href=""https://docs.google.com/presentation/d/1V-lfRj54czkQiZw9ziEnFPdByIuM6lkuSF5gKmHvbQQ/edit?usp=sharing"">\n      Talk on AlphaFold 3, motivating its architectural design through the lens of the Transformer architecture and its modern variants\n    </a>    \n  </li>\n  <li>\n    <a href=""https://docs.google.com/presentation/d/1jxDhbyrCtgme_ebzchh8qIIlr8vriu4H7FClAphuDpU/edit?usp=sharing"">\n      Talk at MunichNLP on p(doom), adaptive compute at inference time and predicting text-based protein function descriptions directly from sequence, bypassing structure\n    </a>    \n  </li>\n  <li>\n    <a href=""https://docs.google.com/presentation/d/1JVqy-0HdfE7POWw5LWiD02fYMa7VKgi2fFkYaxibJKI/edit?usp=sharing"">\n      Talk on the 'translation gap' between core and applied machine learning research, scaling protein function prediction as neural machine translation, ESM-3, AlphaFold 3, the causal mask, muTransfer and ARC-AGI\n    </a>    \n  </li>\n  <li>\n    <a href=""https://docs.google.com/presentation/d/1KXu-bkNRr_0VLh1KdZazt3u3IFlFPtfkAu4AeYFqnAw/edit?usp=sharing"">\n      Talk on beating modern dynamic graph classification baselines using a 1-layer LSTM\n    </a>    \n  </li>\n  <li>\n    <a href=""public/counting_neurons_and_ultrasonic_communication.pdf"">\n      On neurons that count and ultrasonic communication in frogs (I have catastrophically forgotten the contents of this talk)\n    </a>\n  </li>\n</ul>\n</p>\n<br>\n<br>\nPS: no free lunch is a myth\n</html>",html,tab
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+140,75386,"index.html",211,0,"  Franz Srambical\n  <br>\n  ============\n  <br>\nYou can reach me at \<my_first_name\>@<my_last_name\>.com\n  <br>\nHi! I'm Franz. I started and scaled <a href=""https://pdoom.org"">p(doom)</a>, a Discord-based research community, from zero >200 members. We work on addressing core blockers towards general intelligence that cannot be solved by scaling up compute. We work on everything from kernel-level optimizations to large-scale distributed systems for pre-training and reinforcement learning. A lot of our current work involves finding, investigating and exploiting novel data troves <a href=""https://pdoom.org/crowd_code.html"">[1]</a> <a href=""https://pdoom.org/jasmine.html"">[2]</a>, and building open infrastructure/codebases.\nI am ex-distributed systems software engineer at <a href=""https://celonis.com"">Celonis</a>, dropped out of uni (Informatics at <a href=""https://tum.de"">TUM</a>), minored in Computational Neuroscience (courses at <a href=""https://www.gsn.uni-muenchen.de"">LMU Graduate School of Systemic Neurosciences</a>), <a href=""https://www.youtube.com/watch?v=N5nVSXV9Hbk&t=21971s"">ex-linux kernel developer</a>, ex-<a href=""public/vwa.pdf"">'alignment researcher'</a> in my high school years, ex-RA at <a href=""https://aidos.group"">Bastian Rieck's lab</a>, ex-'BCI researcher' & founding member at neuroTUM.\n<br>\n<br>\nI want to create AGI (i.e. design architectures that push the pareto-frontier of intelligent systems).\n<br>\n<br>\nIf any of our research work interests you, check out the <a href=""https://pdoom.org/blog.html"">p(doom) blog</a>. I also have <a href=""https://www.linkedin.com/in/franz-srambical-418630178/"" >linkedin</a>, <a href=""https://twitter.com/lemergenz"" >twitter</a> and <a href=""https://scholar.google.com/citations?user=W26dT4EAAAAJ&hl=en&oi=ao"" >google scholar</a>.\n<br>\n<br>\nList of preprints accumulated throughout my education that are not worthy of publication (& thus not on arxiv), but valuable to some nonetheless:\n<ul>\n  <li>\n    <a href=""https://pdoom.org/jax_assert.html"">\n      A blog post on performance-degradation free value assertions in JAX using a very recent and still private JAX API.\n    </a>\n  </li>\n  <li>\n    <a href=""https://pdoom.org/gae_rlax.html"">\n      A blog post on how a lot of PPO implementations are technically wrong (including Deepmind's reference implementation in RLax).\n    </a>\n  </li>\n  <li>\n    <a href=""https://pdoom.org/ppo.html"">\n        A blog post on how practically everyone in LLM post-training is currently implicitly using REINFORCE with baseline, clipping and a likelihood ratio, and not PPO (in the classical sense).\n    </a>\n  </li>\n  <li>\n    <a href=""https://pdoom.org/thesis.html"">\n        A blog post on a simple mental model that permits straightforward contextualization of the current research frontier and extrapolation of what the most important future research directions are going to be.\n    </a>\n  </li>\n  <li>\n    <a href=""https://pdoom.org/causal_mask.html"">\n      A blog post on why the Transformer's causal mask is its ultimate feature, not a bug\n    </a>\n  </li>\n  <li>\n    <a href=""public/vwa.pdf"">\n      A 30-page paper on AGI alignment written in 2018/19 during my high school years (unfortunately in German)\n    </a>\n  </li>\n  <li>\n    <a href=""public/cfr.pdf"">\n      A review paper on superhuman poker bots\n    </a>\n  </li>\n  <li>\n    <a href=""public/mup-lr-warmup.pdf"">\n      A 2-page writeup on the necessity of learning rate warmup under muParametrization\n    </a> (with <a href=""https://github.com/emergenz/mup-lr-warmup"">code</a>)\n  </li>\n  <li>\n    <a href=""public/causal_mask_poster.pdf"">\n      A poster on why the Transformer's causal mask is its ultimate feature, not a bug\n    </a>\n  </li>\n  <li>\n    <a href=""public/wenn_besitzen_unfair_ist.pdf"">\n      A poster on the 99-year leasehold system in Singapore as a means to mitigate generational wealth (unfortunately in German)\n    </a>\n  </li>\n  <li>\n    <a href=""public/panoptic-3d-reconstruction.pdf"">\n      A crappy 'it's just x but with y'-type computer vision paper on 2% better (and 3% worse) panoptic 3D reconstruction (which included one-shot finetuning a 1B parameter diffusion model hours before the deadline)\n    </a>\n  </li>\n  <li>\n    <a href=""https://docs.google.com/document/d/14xx883ywhbJeaPz13S2lu9NY5RBwUzbNnh6K-o-Y06I/edit?usp=sharing"">\n      A 2-page doc outlining my thoughts on whether machines can think \n    </a>\n  </li>\n</ul>\n\nIncomplete list of talks I have given:\n<ul>\n  <li>\n    <a href=""https://docs.google.com/presentation/d/1fq_JiOP9zZS0w_fi9sZxMb-9TmKnleugl9ik3sJK_sc/edit?usp=sharing"">\n      Talk on the necessity of learning rate warmup under muParametrization (and a real-time case study on the absurd speed of modern AI research)\n    </a>\n  </li>\n  <li>\n    <a href=""https://docs.google.com/presentation/d/1V-lfRj54czkQiZw9ziEnFPdByIuM6lkuSF5gKmHvbQQ/edit?usp=sharing"">\n      Talk on AlphaFold 3, motivating its architectural design through the lens of the Transformer architecture and its modern variants\n    </a>    \n  </li>\n  <li>\n    <a href=""https://docs.google.com/presentation/d/1jxDhbyrCtgme_ebzchh8qIIlr8vriu4H7FClAphuDpU/edit?usp=sharing"">\n      Talk at MunichNLP on p(doom), adaptive compute at inference time and predicting text-based protein function descriptions directly from sequence, bypassing structure\n    </a>    \n  </li>\n  <li>\n    <a href=""https://docs.google.com/presentation/d/1JVqy-0HdfE7POWw5LWiD02fYMa7VKgi2fFkYaxibJKI/edit?usp=sharing"">\n      Talk on the 'translation gap' between core and applied machine learning research, scaling protein function prediction as neural machine translation, ESM-3, AlphaFold 3, the causal mask, muTransfer and ARC-AGI\n    </a>    \n  </li>\n  <li>\n    <a href=""https://docs.google.com/presentation/d/1KXu-bkNRr_0VLh1KdZazt3u3IFlFPtfkAu4AeYFqnAw/edit?usp=sharing"">\n      Talk on beating modern dynamic graph classification baselines using a 1-layer LSTM\n    </a>    \n  </li>\n  <li>\n    <a href=""public/counting_neurons_and_ultrasonic_communication.pdf"">\n      On neurons that count and ultrasonic communication in frogs (I have catastrophically forgotten the contents of this talk)\n    </a>\n  </li>\n</ul>\n",html,content
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4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-67ee49a3-cbd3-48c6-b0d1-b2c0dde2809c1764447402473-2025_11_29-21.16.46.562/source.csv

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4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-76df12ce-63e6-4f0f-97c2-bd8d132564c31762368713747-2025_11_05-19.51.58.593/source.csv

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4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-847fd734-88e4-48ed-bb91-f08cf4e2ebd91762367961128-2025_11_05-19.39.26.989/source.csv

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+407,63453,"TERMINAL",0,0,"? What's the description of your extension? Teaching language models to code like humans",,terminal_output
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+409,64002,"TERMINAL",0,0,"? What's the description of your extension? Teaching language models to code like humans.\r\n[?25h? Initialize a git repository? (Y/n) ",,terminal_output
+410,65236,"TERMINAL",0,0,"? Initialize a git repository? (Y/n) n",,terminal_output
+411,66247,"TERMINAL",0,0,"? Initialize a git repository? No\r\n[?25h? Which bundler to use? (Use arrow keys)\r\n❯ unbundled \r\n  webpack \r\n  esbuild [?25l",,terminal_output
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+533,78940,"TERMINAL",0,0,"npm warn deprecated inflight@1.0.6: This module is not supported, and leaks memory. Do not use it. Check out lru-cache if you want a good and tested way to coalesce async requests by a key value, which is much more comprehensive and powerful.\r\n⠋",,terminal_output
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+559,81286,"TERMINAL",0,0,"⠧\r\nadded 260 packages, and audited 261 packages in 13s\r\n⠧\r\n⠧74 packages are looking for funding\r\n⠧  run `npm fund` for details\r\n⠧\r\nfound 0 vulnerabilities\r\n⠧\r\nYour extension crowd-pilot has been created!\r\n\r\nTo start editing with Visual Studio Code, use the following commands:\r\n\r\n     code crowd-pilot\r\n\r\nOpen vsc-extension-quickstart.md inside the new extension for further instructions\r\non how to modify, test and publish your extension.\r\n\r\nFor more information, also visit http://code.visualstudio.com and follow us @code.\r\n\r\r\n\r\n? Do you want to open the new folder with Visual Studio Code? (Use arrow keys)\r\n❯ Open with `code` \r\n  Skip [?25l",,terminal_output

4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-84bc9952-c4b0-4456-bdc2-984faf53684f1751163593750-2025_06_28-19.19.55.196/source.csv

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+2,18,"utils/dataloader.py",0,0,"import functools\nimport jax\n\nimport tensorflow as tf\n\n# reserve GPU memory for JAX only if tensorflow is built with GPU support\ntf.config.experimental.set_visible_devices([], ""GPU"")\n\n\n# --- TensorFlow function for processing: slicing, normalization ---\ndef _tf_process_episode(episode_tensor, seq_len, image_h, image_w, image_c):\n    """"""\n    Processes a raw episode tensor in TensorFlow.\n    Takes a full episode, extracts a random sequence, and normalizes it.\n    Args:\n        episode_tensor: A TensorFlow tensor representing a full video episode.\n                        Expected shape: (dynamic_length, image_h, image_w, image_c)\n                        Expected dtype: e.g., tf.uint8 (raw pixel values)\n        seq_len: The desired length of the sub-sequence to extract.\n        image_h: The height of each frame.\n        image_w: The width of each frame.\n        image_c: The number of channels in each frame.\n    Returns:\n        A TensorFlow tensor representing the processed video sequence.\n        Shape: (seq_len, image_h, image_w, image_c)\n        Dtype: tf.float32 (normalized pixel values)\n    """"""\n    current_episode_len = tf.shape(episode_tensor)[0]\n\n    max_start_idx = current_episode_len - seq_len\n\n    start_idx = tf.random.uniform(\n        shape=(), minval=0, maxval=max_start_idx + 1, dtype=tf.int32\n    )\n\n    seq = episode_tensor[start_idx : start_idx + seq_len]\n\n    seq = tf.cast(seq, tf.float32) / 255.0\n\n    # Ensure the final shape is statically known for batching.\n    # tf.reshape is robust, but tf.ensure_shape or set_shape can also be used if confident.\n    processed_sequence = tf.reshape(seq, [seq_len, image_h, image_w, image_c])\n\n    return processed_sequence\n\n\ndef _parse_tfrecord_fn(example_proto, image_h, image_w, image_c):\n    feature_description = {\n        ""height"": tf.io.FixedLenFeature([], tf.int64),\n        ""width"": tf.io.FixedLenFeature([], tf.int64),\n        ""channels"": tf.io.FixedLenFeature([], tf.int64),\n        ""sequence_length"": tf.io.FixedLenFeature([], tf.int64),\n        ""raw_video"": tf.io.FixedLenFeature([], tf.string),\n    }\n    example = tf.io.parse_single_example(example_proto, feature_description)\n\n    video_shape = (example[""sequence_length""], image_h, image_w, image_c)\n\n    episode_tensor = tf.io.decode_raw(example[""raw_video""], out_type=tf.uint8)\n    episode_tensor = tf.reshape(episode_tensor, video_shape)\n\n    episode_tensor = tf.ensure_shape(episode_tensor, [None, image_h, image_w, image_c])\n    return episode_tensor\n\n\ndef get_dataloader(\n    tfrecord_paths: list[str],  # List of TFRecord file paths\n    seq_len: int,\n    global_batch_size: int,\n    image_h: int,\n    image_w: int,\n    image_c: int,\n    shuffle_buffer_size: int = 1000,\n    num_parallel_calls: int = tf.data.AUTOTUNE,\n    seed: int = 42,\n):\n    """"""\n    Creates a tf.data.Dataset pipeline from TFRecord files.\n    """"""\n    if not tfrecord_paths:\n        raise ValueError(""tfrecord_paths list cannot be empty."")\n\n    process_id = jax.process_index()\n    num_processes = jax.process_count()\n\n    assert (\n        global_batch_size % num_processes == 0\n    ), ""Global batch size {global_batch_size} \\n        must be divisible by the number of JAX processes {num_processes} for proper sharding.""\n    per_process_batch_size = global_batch_size // num_processes\n\n    dataset = tf.data.TFRecordDataset(\n        tfrecord_paths, num_parallel_reads=tf.data.AUTOTUNE\n    )\n\n    dataset = dataset.shard(num_shards=num_processes, index=process_id)\n\n    # (f.srambical) NOTE: For TFRecords, it's often good to have a large shuffle buffer.\n    if shuffle_buffer_size > 0:\n        dataset = dataset.shuffle(\n            buffer_size=shuffle_buffer_size, seed=seed, reshuffle_each_iteration=True\n        )\n    parse_fn = functools.partial(\n        _parse_tfrecord_fn, image_h=image_h, image_w=image_w, image_c=image_c\n    )\n    dataset = dataset.map(parse_fn, num_parallel_calls=num_parallel_calls)\n\n    tf_process_fn = functools.partial(\n        _tf_process_episode,\n        seq_len=seq_len,\n        image_h=image_h,\n        image_w=image_w,\n        image_c=image_c,\n    )\n    dataset = dataset.map(tf_process_fn, num_parallel_calls=num_parallel_calls)\n\n    dataset = dataset.repeat(None)\n    dataset = dataset.batch(per_process_batch_size, drop_remainder=True)\n    dataset = dataset.prefetch(tf.data.AUTOTUNE)\n\n    return dataset.as_numpy_iterator()\n",python,tab
+3,40,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"",Log,tab
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+6,3873,"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[str] = field(default_factory=lambda: [""tokenizer""])\n    log_interval: int = 5\n    log_image_interval: int = 250\n    ckpt_dir: str = """"\n    log_checkpoint_interval: int = 10000\n    log_gradients: bool = False\n\n\nargs = tyro.cli(Args)\n\n\ndef tokenizer_loss_fn(params, state, inputs):\n    # --- Compute loss ---\n    outputs = state.apply_fn(\n        params,\n        inputs,\n        training=True,\n        rngs={""params"": inputs[""rng""], ""dropout"": inputs[""dropout_rng""]},\n    )\n    mse = jnp.square(inputs[""videos""] - outputs[""recon""]).mean()\n    q_loss = jnp.square(jax.lax.stop_gradient(outputs[""emb""]) - outputs[""z""]).mean()\n    commitment_loss = jnp.square(\n        outputs[""emb""] - jax.lax.stop_gradient(outputs[""z""])\n    ).mean()\n    loss = mse + q_loss + args.vq_beta * commitment_loss\n\n    # --- Compute validation metrics ---\n    gt = inputs[""videos""].clip(0, 1).reshape(-1, *inputs[""videos""].shape[2:])\n    recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n    psnr = pix.psnr(gt, recon).mean()\n    ssim = pix.ssim(gt, recon).mean()\n    _, index_counts = jnp.unique_counts(\n        jnp.ravel(outputs[""indices""]), size=args.num_latents, fill_value=0\n    )\n    codebook_usage = (index_counts != 0).mean()\n    metrics = dict(\n        loss=loss,\n        mse=mse,\n        q_loss=q_loss,\n        commitment_loss=commitment_loss,\n        psnr=psnr,\n        ssim=ssim,\n        codebook_usage=codebook_usage,\n    )\n    return loss, (outputs[""recon""], metrics)\n\n\n@jax.jit\ndef train_step(state, inputs):\n    grad_fn = jax.value_and_grad(tokenizer_loss_fn, has_aux=True, allow_int=True)\n    (loss, (recon, metrics)), grads = grad_fn(state.params, state, inputs)\n    state = state.apply_gradients(grads=grads)\n    if args.log_gradients:\n        metrics[""encoder_gradients_std/""] = jax.tree.map(\n            lambda x: x.std(), grads[""params""][""encoder""]\n        )\n        metrics[""vq_gradients_std/""] = jax.tree.map(\n            lambda x: x.std(), grads[""params""][""vq""]\n        )\n        metrics[""decoder_gradients_std/""] = jax.tree.map(\n            lambda x: x.std(), grads[""params""][""decoder""]\n        )\n    return state, loss, recon, metrics\n\n\nif __name__ == ""__main__"":\n    jax.distributed.initialize()\n    num_devices = jax.device_count()\n    if num_devices == 0:\n        raise ValueError(""No JAX devices found."")\n    print(f""Running on {num_devices} devices."")\n\n    if args.batch_size % num_devices != 0:\n        raise ValueError(\n            f""Global batch size {args.batch_size} must be divisible by ""\n            f""number of devices {num_devices}.""\n        )\n\n    per_device_batch_size_for_init = args.batch_size // num_devices\n\n    rng = jax.random.PRNGKey(args.seed)\n    if args.log and jax.process_index() == 0:\n        wandb.init(\n            entity=args.entity,\n            project=args.project,\n            name=args.name,\n            tags=args.tags,\n            group=""debug"",\n            config=args\n        )\n\n    # --- Initialize model ---\n    tokenizer = TokenizerVQVAE(\n        in_dim=args.image_channels,\n        model_dim=args.model_dim,\n        latent_dim=args.latent_dim,\n        num_latents=args.num_latents,\n        patch_size=args.patch_size,\n        num_blocks=args.num_blocks,\n        num_heads=args.num_heads,\n        dropout=args.dropout,\n        codebook_dropout=args.codebook_dropout,\n    )\n    rng, _rng = jax.random.split(rng)\n    image_shape = (args.image_height, args.image_width, args.image_channels)\n    inputs = dict(\n        videos=jnp.zeros(\n            (per_device_batch_size_for_init, args.seq_len, *image_shape),\n            dtype=jnp.float32,\n        ),\n    )\n    init_params = tokenizer.init(_rng, inputs)\n\n    # --- Initialize optimizer ---\n    lr_schedule = optax.warmup_cosine_decay_schedule(\n        args.min_lr, args.max_lr, args.warmup_steps, args.num_steps\n    )\n    tx = optax.adamw(learning_rate=lr_schedule, b1=0.9, b2=0.9, weight_decay=1e-4)\n    train_state = TrainState.create(apply_fn=tokenizer.apply, params=init_params, tx=tx)\n\n    # FIXME: switch to create_hybrid_device_mesh for runs spanning multiple nodes\n    device_mesh_arr = create_device_mesh((num_devices,))\n    mesh = Mesh(devices=device_mesh_arr, axis_names=(""data"",))\n\n    replicated_sharding = NamedSharding(mesh, PartitionSpec())\n    train_state = jax.device_put(train_state, replicated_sharding)\n\n    # --- Load checkpoint ---\n    step = 0\n    if args.checkpoint:\n        restore_target = {""model"": train_state}\n        restore_args = orbax_utils.restore_args_from_target(restore_target)\n        train_state.params[""params""].update(\n            PyTreeCheckpointer()\n            .restore(args.checkpoint, item=restore_target, restore_args=restore_args)[\n                ""model""\n            ]\n            .params[""params""]\n        )\n        # Assume checkpoint is of the form tokenizer_<timestamp>_<step>\n        step += int(args.checkpoint.split(""_"")[-1])\n\n    # --- TRAIN LOOP ---\n    tfrecord_files = [\n        os.path.join(args.data_dir, x)\n        for x in os.listdir(args.data_dir)\n        if x.endswith("".tfrecord"")\n    ]\n    dataloader = get_dataloader(\n        # NOTE: We deliberately pass the global batch size\n        # The dataloader shards the dataset across all processes\n        tfrecord_files,\n        args.seq_len,\n        args.batch_size,\n        *image_shape,\n    )\n    print(f""Starting training from step {step}..."")\n    while step < args.num_steps:\n        for videos in dataloader:\n            # --- 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            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
+7,5631,"models/tokenizer.py",0,0,"from typing import Dict, Any, Tuple\n\nimport flax.linen as nn\n\nfrom utils.preprocess import patchify, unpatchify\nfrom utils.nn import STTransformer, VectorQuantizer\n\n\nclass TokenizerVQVAE(nn.Module):\n    """"""ST-ViVit VQ-VAE""""""\n\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\n    def setup(self):\n        self.encoder = STTransformer(\n            self.model_dim,\n            self.latent_dim,\n            self.num_blocks,\n            self.num_heads,\n            self.dropout,\n        )\n        self.vq = VectorQuantizer(\n            self.latent_dim,\n            self.num_latents,\n            self.codebook_dropout,\n        )\n        self.out_dim = self.in_dim * self.patch_size**2\n        self.decoder = STTransformer(\n            self.model_dim,\n            self.out_dim,\n            self.num_blocks,\n            self.num_heads,\n            self.dropout,\n        )\n\n    def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\n        H, W = batch[""videos""].shape[2:4]\n        outputs = self.vq_encode(batch[""videos""], training)\n        recon = self.decoder(outputs[""z_q""])  # (B, T, H_down * W_down, C)\n        recon = nn.sigmoid(recon)\n        outputs[""recon""] = unpatchify(recon, self.patch_size, H, W)\n        return outputs\n\n    def vq_encode(self, videos: Any, training: bool = True) -> Dict[str, Any]:\n        # --- Preprocess + encode ---\n        B, T = videos.shape[:2]\n        x = patchify(videos, self.patch_size)\n        N = x.shape[2]\n        x = self.encoder(x)  # (B, T, N, E)\n\n        # --- Vector quantize ---\n        x = x.reshape(B * T * N, self.latent_dim)\n        z_q, z, emb, indices = self.vq(x, training)\n        z_q = z_q.reshape(B, T, N, self.latent_dim)\n        indices = indices.reshape(B, T, N)\n        return dict(z_q=z_q, z=z, emb=emb, indices=indices)\n\n    def decode(self, indices: Any, video_hw: Tuple[int, int]):\n        z = self.vq.codebook[indices]\n        recon = self.decoder(z)\n        recon = nn.sigmoid(recon)\n        return unpatchify(recon, self.patch_size, *video_hw)\n",python,tab
+8,6632,"models/tokenizer.py",1009,0,"",python,selection_command

4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-978ed4eb-d9c9-4380-b981-e501087459181750623968304-2025_06_22-13.26.11.394/source.csv

@@ -0,0 +1,7 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+1,13,"utils/dataloader.py",0,0,"from cgi import test\nimport functools\nimport jax\n\nimport tensorflow as tf\n\n\n# --- TensorFlow function for processing: slicing, normalization ---\ndef _tf_process_episode(episode_tensor, seq_len, image_h, image_w, image_c):\n    """"""\n    Processes a raw episode tensor in TensorFlow.\n    Takes a full episode, extracts a random sequence, and normalizes it.\n    Args:\n        episode_tensor: A TensorFlow tensor representing a full video episode.\n                        Expected shape: (dynamic_length, image_h, image_w, image_c)\n                        Expected dtype: e.g., tf.uint8 (raw pixel values)\n        seq_len: The desired length of the sub-sequence to extract.\n        image_h: The height of each frame.\n        image_w: The width of each frame.\n        image_c: The number of channels in each frame.\n    Returns:\n        A TensorFlow tensor representing the processed video sequence.\n        Shape: (seq_len, image_h, image_w, image_c)\n        Dtype: tf.float32 (normalized pixel values)\n    """"""\n    current_episode_len = tf.shape(episode_tensor)[0]\n\n    max_start_idx = current_episode_len - seq_len\n\n    start_idx = tf.random.uniform(\n        shape=(), minval=0, maxval=max_start_idx + 1, dtype=tf.int32\n    )\n\n    seq = episode_tensor[start_idx : start_idx + seq_len]\n\n    seq = tf.cast(seq, tf.float32) / 255.0\n\n    # Ensure the final shape is statically known for batching.\n    # tf.reshape is robust, but tf.ensure_shape or set_shape can also be used if confident.\n    processed_sequence = tf.reshape(seq, [seq_len, image_h, image_w, image_c])\n\n    return processed_sequence\n\n\ndef _parse_tfrecord_fn(example_proto, image_h, image_w, image_c):\n    feature_description = {\n        ""height"": tf.io.FixedLenFeature([], tf.int64),\n        ""width"": tf.io.FixedLenFeature([], tf.int64),\n        ""channels"": tf.io.FixedLenFeature([], tf.int64),\n        ""sequence_length"": tf.io.FixedLenFeature([], tf.int64),\n        ""raw_video"": tf.io.FixedLenFeature([], tf.string),\n    }\n    example = tf.io.parse_single_example(example_proto, feature_description)\n\n    video_shape = (example[""sequence_length""], image_h, image_w, image_c)\n\n    episode_tensor = tf.io.decode_raw(example[""raw_video""], out_type=tf.uint8)\n    episode_tensor = tf.reshape(episode_tensor, video_shape)\n\n    episode_tensor = tf.ensure_shape(episode_tensor, [None, image_h, image_w, image_c])\n    return episode_tensor\n\n\ndef get_dataloader(\n    tfrecord_paths: list[str],  # List of TFRecord file paths\n    seq_len: int,\n    global_batch_size: int,\n    image_h: int,\n    image_w: int,\n    image_c: int,\n    shuffle_buffer_size: int = 1000,\n    num_parallel_calls: int = tf.data.AUTOTUNE,\n    cache_processed_data: bool = True,\n    seed: int = 42,\n):\n    """"""\n    Creates a tf.data.Dataset pipeline from TFRecord files.\n    """"""\n    if not tfrecord_paths:\n        raise ValueError(""tfrecord_paths list cannot be empty."")\n\n    process_id = jax.process_index()\n    num_processes = jax.process_count()\n\n    assert global_batch_size % num_processes == 0, ""Global batch size {global_batch_size} \\n        must be divisible by the number of JAX processes {num_processes} for proper sharding.""\n    per_process_batch_size = global_batch_size // num_processes\n\n    dataset = tf.data.TFRecordDataset(\n        tfrecord_paths, num_parallel_reads=tf.data.AUTOTUNE\n    )\n    \n    dataset = dataset.shard(num_shards=num_processes, index=process_id)\n\n    # (f.srambical) NOTE: For TFRecords, it's often good to have a large shuffle buffer.\n    if shuffle_buffer_size > 0:\n        dataset = dataset.shuffle(\n            buffer_size=shuffle_buffer_size, seed=seed, reshuffle_each_iteration=True\n        )\n    parse_fn = functools.partial(\n        _parse_tfrecord_fn, image_h=image_h, image_w=image_w, image_c=image_c\n    )\n    dataset = dataset.map(parse_fn, num_parallel_calls=num_parallel_calls)\n\n    dataset = dataset.cache() if cache_processed_data else dataset\n\n    tf_process_fn = functools.partial(\n        _tf_process_episode,\n        seq_len=seq_len,\n        image_h=image_h,\n        image_w=image_w,\n        image_c=image_c,\n    )\n    dataset = dataset.map(tf_process_fn, num_parallel_calls=num_parallel_calls)\n\n    dataset = dataset.repeat(None)\n    dataset = dataset.batch(per_process_batch_size, drop_remainder=True)\n    dataset = dataset.prefetch(tf.data.AUTOTUNE)\n\n    return dataset.as_numpy_iterator()\n",python,tab
+2,27,"tasks",0,0,"",Log,tab
+3,36,"utils/dataloader.py",0,0,"",python,tab
+4,56,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"",Log,tab
+5,869,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"1:26:11 PM [info] Activating crowd-code\n1:26:11 PM [info] Recording started\n",Log,content
+6,39651,"utils/dataloader.py",0,0,"",python,tab

4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-9deb94c8-8483-468e-9e97-468dc0f8f81a1766266039649-2025_12_20-22.27.31.492/source.csv

@@ -0,0 +1,6 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+1,3,"crates/cli/src/main.rs",0,0,"//! CLI tool for serializing crowd-pilot IDE interaction data.\n//!\n//! This tool processes CSV session files and outputs JSONL format suitable for\n//! NeMo SFT training. It uses the HuggingFace tokenizers Rust library for\n//! accurate token counting.\n\nuse std::path::PathBuf;\n\nuse clap::Parser;\nuse tokenizers::Tokenizer as HfTokenizer;\n\nuse crowd_pilot_serializer_core::{\n    pipeline::{PipelineConfig, PipelineResult},\n    process_all_sessions, write_jsonl_output, Tokenizer,\n};\n\n/// Serialize crowd-pilot CSV sessions to NeMo JSONL format.\n#[derive(Parser, Debug)]\n#[command(name = ""crowd-pilot-serialize"")]\n#[command(author, version, about, long_about = None)]\nstruct Args {\n    /// Root directory containing CSV session files\n    #[arg(long)]\n    csv_root: PathBuf,\n\n    /// Output directory for JSONL files\n    #[arg(long)]\n    output_dir: PathBuf,\n\n    /// HuggingFace tokenizer model name or path\n    #[arg(long)]\n    tokenizer: String,\n\n    /// Maximum tokens per conversation chunk\n    #[arg(long, default_value = ""8192"")]\n    max_tokens_per_conversation: usize,\n\n    /// Maximum tokens per message\n    #[arg(long, default_value = ""2048"")]\n    max_tokens_per_message: usize,\n\n    /// Minimum messages required to keep a conversation\n    #[arg(long, default_value = ""5"")]\n    min_conversation_messages: usize,\n\n    /// Viewport radius (lines above/below cursor)\n    #[arg(long, default_value = ""10"")]\n    viewport_radius: usize,\n\n    /// Coalesce radius for grouping nearby edits\n    #[arg(long, default_value = ""5"")]\n    coalesce_radius: usize,\n\n    /// Fraction of sessions for validation (0.0-1.0)\n    #[arg(long, default_value = ""0.1"")]\n    val_ratio: f64,\n\n    /// Custom system prompt (optional)\n    #[arg(long)]\n    system_prompt: Option<String>,\n}\n\nconst DEFAULT_SYSTEM_PROMPT: &str = r#""You are a helpful assistant that can interact multiple times with a computer shell to solve programming tasks.\nYour response must contain exactly ONE bash code block with ONE command (or commands connected with && or ||).\n\nFormat your response as shown in <format_example>.\n\n<format_example>\n```bash\nyour_command_here\n```\n</format_example>\n\nFailure to follow these rules will cause your response to be rejected.""#;\n\n/// Wrapper around HuggingFace tokenizers for token counting and truncation.\n///\n/// This uses the Rust-native tokenizers library, which is `Send + Sync`\n/// and enables true parallel tokenization without the Python GIL.\nstruct RustTokenizer {\n    inner: HfTokenizer,\n}\n\nimpl RustTokenizer {\n    /// Load a HuggingFace tokenizer from a model name or path.\n    fn load(model_name: &str) -> Result<Self, Box<dyn std::error::Error>> {\n        let inner = HfTokenizer::from_pretrained(model_name, None)\n            .map_err(|e| e as Box<dyn std::error::Error>)?;\n        Ok(Self { inner })\n    }\n}\n\nimpl Tokenizer for RustTokenizer {\n    fn count_tokens(&self, text: &str) -> usize {\n        self.inner\n            .encode(text, false)\n            .expect(""Failed to encode text with tokenizer"")\n            .get_ids()\n            .len()\n    }\n\n    fn truncate_to_max_tokens(&self, text: &str, max_tokens: usize) -> String {\n        let encoding = self.inner\n            .encode(text, false)\n            .expect(""Failed to encode text with tokenizer"");\n        \n        let ids = encoding.get_ids();\n        if ids.len() <= max_tokens {\n            return text.to_string();\n        }\n        \n        let truncated_ids: Vec<u32> = ids[..max_tokens].to_vec();\n        self.inner\n            .decode(&truncated_ids, true)\n            .expect(""Failed to decode truncated tokens"")\n    }\n}\n\nfn main() -> Result<(), Box<dyn std::error::Error>> {\n    let args = Args::parse();\n\n    println!(""Loading tokenizer from {}..."", args.tokenizer);\n    let tokenizer = RustTokenizer::load(&args.tokenizer)?;\n\n    let config = PipelineConfig {\n        max_tokens_per_conversation: args.max_tokens_per_conversation,\n        max_tokens_per_message: args.max_tokens_per_message,\n        min_conversation_messages: args.min_conversation_messages,\n        viewport_radius: args.viewport_radius,\n        coalesce_radius: args.coalesce_radius,\n        val_ratio: args.val_ratio,\n    };\n\n    println!(""Processing CSV files from {:?}..."", args.csv_root);\n    let session_results = process_all_sessions(\n        &args.csv_root,\n        &tokenizer,\n        &config,\n    )?;\n\n    let total_sessions = session_results.len();\n    println!(""Processed {} sessions"", total_sessions);\n\n    let system_prompt = args.system_prompt.as_deref().unwrap_or(DEFAULT_SYSTEM_PROMPT);\n\n    println!(""Writing output to {:?}..."", args.output_dir);\n    let result: PipelineResult = write_jsonl_output(\n        session_results,\n        &args.output_dir,\n        args.val_ratio,\n        system_prompt,\n    )?;\n\n    let metadata_path = args.output_dir.join(""metadata.json"");\n    let metadata = serde_json::json!({\n        ""config"": {\n            ""csv_root"": args.csv_root.to_string_lossy(),\n            ""output_dir"": args.output_dir.to_string_lossy(),\n            ""tokenizer"": args.tokenizer,\n            ""max_tokens_per_conversation"": args.max_tokens_per_conversation,\n            ""max_tokens_per_message"": args.max_tokens_per_message,\n            ""min_conversation_messages"": args.min_conversation_messages,\n            ""viewport_radius"": args.viewport_radius,\n            ""coalesce_radius"": args.coalesce_radius,\n            ""val_ratio"": args.val_ratio,\n        },\n        ""counts"": {\n            ""total_sessions"": result.total_sessions,\n            ""total_conversations"": result.total_conversations,\n            ""train_conversations"": result.train_conversations,\n            ""val_conversations"": result.val_conversations,\n        },\n        ""stats"": {\n            ""total_messages"": result.total_messages,\n            ""total_tokens"": result.total_tokens,\n            ""avg_messages_per_conversation"": if result.total_conversations > 0 {\n                result.total_messages as f64 / result.total_conversations as f64\n            } else {\n                0.0\n            },\n            ""avg_tokens_per_conversation"": if result.total_conversations > 0 {\n                result.total_tokens as f64 / result.total_conversations as f64\n            } else {\n                0.0\n            },\n        },\n        ""files"": {\n            ""train_path"": args.output_dir.join(""training.jsonl"").to_string_lossy(),\n            ""val_path"": args.output_dir.join(""validation.jsonl"").to_string_lossy(),\n        },\n    });\n    std::fs::write(&metadata_path, serde_json::to_string_pretty(&metadata)?)?;\n\n    println!(""\n[summary]"");\n    println!(""  Total sessions processed: {}"", result.total_sessions);\n    println!(""  Train conversations: {}"", result.train_conversations);\n    println!(""  Val conversations: {}"", result.val_conversations);\n    println!(""  Total messages: {}"", result.total_messages);\n    println!(""  Total tokens: {}"", result.total_tokens);\n    println!(""  Output: {:?}/{{training,validation}}.jsonl"", args.output_dir);\n    println!(""  Metadata: {:?}"", metadata_path);\n\n    Ok(())\n}\n",rust,tab
+2,220,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"10:27:31 PM [info] Activating crowd-code\n10:27:31 PM [info] Recording started\n10:27:31 PM [info] Initializing git provider using file system watchers...\n",Log,tab
+3,246,"extension-output-pdoom-org.crowd-code-#1-crowd-code",153,0,"10:27:31 PM [info] Git repository found\n10:27:31 PM [info] Git provider initialized successfully\n10:27:31 PM [info] Initial git state: [object Object]\n",Log,content
+4,1927,"crates/cli/src/main.rs",0,0,"",rust,tab
+5,3595,"TERMINAL",0,0,"",,terminal_focus

4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-b8ef8518-bf52-489e-b2a9-5e402fd02c471760857710761-2025_10_19-09.08.39.428/source.csv

@@ -0,0 +1,7 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+1,1,"crowd_code_player/replay_file.py",0,0,"import pandas as pd\nimport curses\nimport time\nimport argparse\n\ndef offset_to_yx(content, offset):\n    """"""Converts a 1D string offset to 2D (y, x) coordinates.""""""\n    # Ensure offset is within the bounds of the content length\n    offset = min(len(content), int(offset))\n    \n    # Find the line number by counting newlines before the offset\n    y = content.count('\n', 0, offset)\n    \n    # Find the column number by finding the last newline before the offset\n    last_newline_pos = content.rfind('\n', 0, offset)\n    if last_newline_pos == -1:\n        x = offset\n    else:\n        x = offset - last_newline_pos - 1\n        \n    return y, x\n\ndef apply_change(content, offset, length, new_text):\n    """"""Applies a text change to the content string.""""""\n    content = str(content)\n    new_text = str(new_text) if pd.notna(new_text) else """"\n    offset, length = int(offset), int(length)\n    \n    # Convert literal \n and \r characters to actual newlines and carriage returns\n    new_text = new_text.replace('\\n', '\n').replace('\\r', '\r')\n    \n    if offset > len(content):\n        content += ' ' * (offset - len(content)) # Pad if offset is out of bounds\n        \n    return content[:offset] + new_text + content[offset + length:]\n\ndef replay_trace(stdscr, filepath, speed_factor, long_pause_threshold=120000):\n    """"""Main function to replay the coding trace in the terminal.""""""\n    # --- Curses Setup ---\n    curses.curs_set(0) # We'll draw our own cursor\n    stdscr.nodelay(1)\n    curses.start_color()\n    curses.use_default_colors()\n    curses.init_pair(1, curses.COLOR_WHITE, -1) # For status bar\n    curses.init_pair(2, curses.COLOR_BLACK, curses.COLOR_WHITE) # For our cursor\n\n    # --- Data Loading ---\n    try:\n        df = pd.read_csv(filepath).sort_values('Time').reset_index(drop=True)\n    except FileNotFoundError:\n        print(f""Error: The file '{filepath}' was not found."")\n        return\n\n    # --- State Management ---\n    file_states = {}\n    scroll_states = {} # Tracks the top-line for each file's viewport\n    active_file = None\n    paused = False\n    \n    # --- Main Replay Loop ---\n    for i in range(len(df)):\n        # --- Handle User Input for Playback Control ---\n        key = stdscr.getch()\n        if key == ord('q'): break\n        if key == ord(' '): paused = not paused\n        if key == curses.KEY_UP: speed_factor = min(100, speed_factor * 1.5)\n        if key == curses.KEY_DOWN: speed_factor = max(0.1, speed_factor / 1.5)\n        \n        # Handle Paused State\n        if paused:\n            height, width = stdscr.getmaxyx()\n            stdscr.addstr(height - 1, 0, ""PAUSED"".ljust(width - 1), curses.A_REVERSE)\n            stdscr.refresh()\n            while paused:\n                time.sleep(0.1)\n                key = stdscr.getch()\n                if key == ord(' '): paused = False\n                elif key == ord('q'): return\n\n        # --- Process Event ---\n        event = df.iloc[i]\n        active_file = event['File']\n        \n        # Initialize state for new files\n        if active_file not in file_states:\n            file_states[active_file] = """"\n            scroll_states[active_file] = 0\n\n        \n        # Apply content change based on event type\n        if active_file == ""TERMINAL"":\n            # For terminal, just append text and add a newline\n            terminal_text = str(event['Text']) if pd.notna(event['Text']) else """"\n            # Convert literal \n and \r characters to actual newlines and carriage returns\n            terminal_text = terminal_text.replace('\\n', '\n').replace('\\r', '\r')\n            file_states[active_file] += terminal_text + '\n'\n        else:\n            file_states[active_file] = apply_change(\n                file_states[active_file], event['RangeOffset'], \n                event['RangeLength'], event['Text']\n            )\n            \n        # --- Calculate Cursor and Scrolling ---\n        content = file_states[active_file]\n        cursor_y, cursor_x = offset_to_yx(content, event['RangeOffset'])\n        scroll_y = scroll_states[active_file]\n        height, width = stdscr.getmaxyx()\n        visible_height = height - 2 # Account for status bars\n\n        # Adjust scroll to keep cursor in view\n        if active_file == ""TERMINAL"":\n            # For terminal, always scroll to bottom to show latest content\n            lines = content.split('\n')\n            total_lines = len(lines)\n            if total_lines > visible_height:\n                scroll_y = max(0, total_lines - visible_height)\n        else:\n            # For regular files, keep cursor in view\n            if cursor_y < scroll_y:\n                scroll_y = cursor_y\n            elif cursor_y >= scroll_y + visible_height:\n                scroll_y = cursor_y - visible_height + 1\n        \n        scroll_states[active_file] = scroll_y\n\n        # --- Render to Screen ---\n        stdscr.clear()\n        \n        # Display file content with scrolling\n        lines = content.split('\n')\n        for j in range(visible_height):\n            line_idx = scroll_y + j\n            if line_idx < len(lines):\n                stdscr.addstr(j, 0, lines[line_idx][:width - 1])\n        \n        # Draw our custom cursor\n        display_y = cursor_y - scroll_y\n        if 0 <= display_y < visible_height and 0 <= cursor_x < width:\n            # Ensure we don't try to draw on a non-existent character\n            line_len = len(lines[cursor_y]) if cursor_y < len(lines) else 0\n            char_to_draw_under = lines[cursor_y][cursor_x] if cursor_x < line_len else "" ""\n            stdscr.attron(curses.color_pair(2))\n            stdscr.addstr(display_y, cursor_x, char_to_draw_under)\n            stdscr.attroff(curses.color_pair(2))\n\n        # Status Bar\n        status_bar_text = f""File: {active_file} | Time: {event['Time']/1000:.1f}s | Event: {event['Type']} | Speed: {speed_factor:.1f}x""\n        stdscr.attron(curses.color_pair(1) | curses.A_REVERSE)\n        stdscr.addstr(height - 2, 0, status_bar_text.ljust(width - 1))\n        stdscr.attroff(curses.color_pair(1) | curses.A_REVERSE)\n\n        # Help Text\n        help_text = ""PAUSE/PLAY [space] | FASTER [↑] | SLOWER [↓] | QUIT [q]""\n        stdscr.addstr(height - 1, 0, help_text)\n\n        stdscr.refresh()\n\n        # --- Wait for Next Event ---\n        if i + 1 < len(df):\n            time_delta_ms = df.iloc[i+1]['Time'] - event['Time']\n            sleep_duration_s = max(0, time_delta_ms / 1000.0)\n            \n            # Check for long pauses\n            if time_delta_ms > long_pause_threshold:\n                # Display long pause message\n                height, width = stdscr.getmaxyx()\n                pause_message = ""Long pause detected. User might be googling, thinking or might have gone for a coffee...""\n                stdscr.addstr(height - 3, 0, pause_message.ljust(width - 1), curses.A_REVERSE)\n                stdscr.refresh()\n                time.sleep(1)  # Show message for 1 seconds\n                stdscr.clear()\n            else:\n                time.sleep(sleep_duration_s / speed_factor)\n\nif __name__ == ""__main__"":\n    parser = argparse.ArgumentParser(description=""Replay coding traces from a CSV file in the terminal."")\n    parser.add_argument(""filepath"", help=""The path to the source CSV file."")\n    parser.add_argument(""--speed"", type=float, default=20.0, help=""Initial playback speed multiplier."")\n    parser.add_argument(""--long_pause_threshold"", type=int, default=120000, help=""Threshold for long pause in milliseconds."")\n    args = parser.parse_args()\n\n    curses.wrapper(replay_trace, args.filepath, args.speed, args.long_pause_threshold)",python,tab
+2,70,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"9:08:39 AM [info] Activating crowd-code\n9:08:39 AM [info] Recording started\n9:08:39 AM [info] Initializing git provider using file system watchers...\n9:08:39 AM [info] Git repository found\n9:08:39 AM [info] Git provider initialized successfully\n",Log,tab
+3,207,"extension-output-pdoom-org.crowd-code-#1-crowd-code",245,0,"9:08:39 AM [info] Initial git state: [object Object]\n",Log,content
+4,13185962,"crowd_code_player/replay_file.py",0,0,"",python,tab
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+6,13194346,"crowd_code_player/replay_file.py",0,0,"",python,tab

4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-c3986074-5718-42fa-9de0-e87adef2d7e21764449343379-2025_11_29-21.49.07.420/source.csv

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+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
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+22,21490,"TERMINAL",0,0,"]633;CHello World\r\n%                                                                                                                                                                  \r \r",,terminal_output
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+48,67391,"TERMINAL",0,0,"]633;CHello World\r\n%                                                                                                                                                                  \r \r",,terminal_output
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4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-c8c1182e-9d15-45c9-9466-b9340fc3403b1754557906412-2025_08_07-11.11.49.271/source.csv

@@ -0,0 +1,27 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+1,2,"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_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_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_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        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        return model.sample(\n            batch,\n            args.seq_len,\n            args.maskgit_steps,\n            args.temperature,\n            args.sample_argmax,\n        )\n\n    # --- Define autoregressive sampling loop ---\n    @nnx.jit\n    def _autoreg_sample(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    )\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(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
+2,54,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"11:11:49 AM [info] Activating crowd-code\n11:11:49 AM [info] Recording started\n11:11:49 AM [info] Initializing git provider using file system watchers...\n11:11:49 AM [info] Git repository found\n11:11:49 AM [info] Git provider initialized successfully\n",Log,tab
+3,94,"extension-output-pdoom-org.crowd-code-#1-crowd-code",250,0,"11:11:49 AM [info] Initial git state: [object Object]\n",Log,content
+4,910,"sample.py",0,0,"",python,tab
+5,2878,"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
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+24,17735,"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    # 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        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
+25,18531,"train_lam.py",0,0,"",python,selection_command
+26,19228,"train_lam.py",1966,0,"",python,selection_command

4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-ccf7a99f-954c-4a69-b08a-cef496899ce51762421470970-2025_11_06-10.31.14.52/source.csv

@@ -0,0 +1,22 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+2,130,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"10:31:14 AM [info] Activating crowd-code\n10:31:14 AM [info] Recording started\n10:31:14 AM [info] Initializing git provider using file system watchers...\n10:31:14 AM [info] No workspace folder found\n",Log,tab
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+7,3805,"TERMINAL",0,0,"]633;CVSCode test\r\n%                                                                                                                                                                           \r \r",,terminal_output
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4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-ce6b4bab-9c2d-4454-8220-eaa00cd55d321764450093947-2025_11_29-22.01.38.36/source.csv

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+49,36661,"TERMINAL",0,0,"]633;CHello World\r\n%                                                                               \r \r",,terminal_output
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4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-dbb18611-0352-4268-ab16-fb8c7e2b82741763481981825-2025_11_18-17.06.23.992/source.csv

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+4,4028,"nemo_run/run/api.py",0,0,"# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n# SPDX-License-Identifier: Apache-2.0\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\nfrom typing import List, Optional, Union\n\nfrom fiddle import Buildable\n\nfrom nemo_run.config import Script, get_type_namespace\nfrom nemo_run.core.execution.base import Executor\nfrom nemo_run.run.experiment import Experiment\nfrom nemo_run.run.plugin import ExperimentPlugin as Plugin\nfrom nemo_run.run.task import direct_run_fn\n\n\ndef run(\n    fn_or_script: Union[Buildable, Script],\n    executor: Optional[Executor] = None,\n    plugins: Optional[Union[Plugin, List[Plugin]]] = None,\n    name: str = """",\n    dryrun: bool = False,\n    direct: bool = False,\n    detach: bool = False,\n    tail_logs: bool = True,\n    log_level: str = ""INFO"",\n):\n    """"""\n    Runs a single configured function on the specified executor.\n    If no executor is specified, it runs the run.Partial function directly\n    i.e. equivalent to calling the python function directly.\n\n    Examples\n    --------\n    .. code-block:: python\n\n        import nemo_run as run\n\n        # Run it directly in the same process\n        run.run(configured_fn)\n\n        # Do a dryrun\n        run.run(configured_fn, dryrun=True)\n\n        # Specify a custom executor\n        local_executor = LocalExecutor()\n        run.run(configured_fn, executor=local_executor)\n\n        slurm_executor = run.SlurmExecutor(...)\n        run.run(configured_fn, executor=slurm_executor)\n\n    """"""\n    if not isinstance(fn_or_script, (Buildable, Script)):\n        raise TypeError(f""Need a configured Buildable or run.Script. Got {fn_or_script}."")\n\n    if direct or executor is None:\n        direct_run_fn(fn_or_script, dryrun=dryrun)\n        return\n\n    if plugins:\n        plugins = [plugins] if not isinstance(plugins, list) else plugins\n\n    if getattr(fn_or_script, ""is_lazy"", False):\n        fn_or_script = fn_or_script.resolve()\n\n    default_name = (\n        fn_or_script.get_name()\n        if isinstance(fn_or_script, Script)\n        else get_type_namespace(fn_or_script.__fn_or_cls__)\n    )\n    name = name or default_name\n    with Experiment(title=name, executor=executor, log_level=log_level) as exp:\n        exp.add(fn_or_script, tail_logs=tail_logs, plugins=plugins, name=name)\n        if dryrun:\n            exp.dryrun()\n            return\n\n        exp.run(detach=detach)\n",python,tab
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+20,28484,"nemo_run/run/experiment.py",0,0,"# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n# SPDX-License-Identifier: Apache-2.0\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\nimport contextvars\nimport copy\nimport importlib.util\nimport inspect\nimport json\nimport os\nimport pprint\nimport shutil\nimport sys\nimport time\nimport traceback\nfrom concurrent.futures import Future, ThreadPoolExecutor, as_completed\nfrom pathlib import Path\nfrom typing import Optional, Type, Union\n\nimport fiddle as fdl\nimport networkx as nx\nimport rich\nfrom fiddle._src import daglish, diffing\nfrom rich.console import Group\nfrom rich.live import Live\nfrom rich.panel import Panel\nfrom rich.progress import BarColumn, Progress, SpinnerColumn, TaskID, TimeElapsedColumn\nfrom rich.progress import Task as RichTask\nfrom rich.syntax import Syntax\nfrom torchx.specs.api import AppState\n\nimport nemo_run as run\nfrom nemo_run.config import (\n    Config,\n    ConfigurableMixin,\n    Partial,\n    Script,\n    get_nemorun_home,\n    get_type_namespace,\n)\nfrom nemo_run.core.execution.base import Executor\nfrom nemo_run.core.execution.dgxcloud import DGXCloudExecutor\nfrom nemo_run.core.execution.docker import DockerExecutor\nfrom nemo_run.core.execution.lepton import LeptonExecutor\nfrom nemo_run.core.execution.local import LocalExecutor\nfrom nemo_run.core.execution.skypilot import SkypilotExecutor\nfrom nemo_run.core.execution.skypilot_jobs import SkypilotJobsExecutor\nfrom nemo_run.core.execution.slurm import SlurmExecutor\nfrom nemo_run.core.frontend.console.api import CONSOLE, configure_logging, deconfigure_logging\nfrom nemo_run.core.serialization.zlib_json import ZlibJSONSerializer\nfrom nemo_run.core.tunnel.client import SSHTunnel, Tunnel\nfrom nemo_run.core.tunnel.rsync import rsync\nfrom nemo_run.run.job import Job, JobGroup\nfrom nemo_run.run.plugin import ExperimentPlugin\nfrom nemo_run.run.torchx_backend.runner import get_runner\nfrom nemo_run.run.utils import TeeStdoutStderr\n\n_current_experiment: contextvars.ContextVar[""Experiment""] = contextvars.ContextVar(\n    ""nemo_current_experiment""\n)\n\n\nclass DummyConsole:\n    """"""A dummy console that mimics rich.console.Console but does nothing.""""""\n\n    def __getattr__(self, name):\n        """"""Return a no-op function for any attribute access.""""""\n\n        def no_op(*args, **kwargs):\n            pass\n\n        return no_op\n\n\nclass Experiment(ConfigurableMixin):\n    """"""\n    A context manager to launch and manage multiple runs, all using pure Python.\n\n    run.Experiment provides researchers with\n    a simple and flexible way to create and manage their ML experiments.\n\n    Building on the core blocks of nemo_run,\n    the Experiment can be used as an umbrella under which a user can\n    launch different configured functions on multiple remote clusters.\n\n    The Experiment takes care of storing the run metadata,\n    launching it on the specified cluster, and syncing the logs and artifacts.\n\n    Additionally, the Experiment also provides management tools to easily inspect and reproduce past experiments.\n    Some of the use-cases that it enables are listed below:\n\n    1. Check the status and logs of a past experiment\n    2. Reconstruct a past experiment and relaunch it after some changes\n    3. Compare different runs of the same experiment.\n\n    This API allows users to programmatically define their experiments.\n    To get a glance of the flexibility provided, here are some use cases\n    which can be supported by the Experiment in just a few lines of code.\n\n    1. Launch a benchmarking run on different GPUs at the same time in parallel\n    2. Launch a sequential data processing pipeline on a CPU heavy cluster\n    3. Launch hyperparameter grid search runs on a single cluster in parallel\n    4. Launch hyperparameter search runs distributed across all available clusters\n\n    The design is heavily inspired from `XManager <https://github.com/google-deepmind/xmanager/blob/main/docs/xm_launch_api_principles.md>`_.\n\n    Under the hood, the Experiment metadata is stored in the local filesystem\n    inside a user specified directory controlled by get_nemorun_home() env var.\n    We will explore making the metadata more persistent in the future.\n\n    .. note::\n        `Experiment.add` and `Experiment.run` methods inside Experiment can currently only be used within its context manager.\n\n    Examples\n    --------\n    .. code-block:: python\n\n        # An experiment that runs a pre-configured training example\n        # on multiple GPU specific clusters (A100 and H100 shown here) in parallel using torchrun\n        # Assumes that example_to_run is pre-configured using run.Partial\n        with run.Experiment(""example-multiple-gpus"", executor=""h100_cluster"") as exp:\n            # Set up the run on H100\n            # Setting up a single task is identical to setting up a single run outside the experiment\n            h100_cluster: run.SlurmExecutor = exp.executor.clone()\n            h100_cluster.nodes = 2\n\n            # torchrun manages the processes on a single node\n            h100_cluster.ntasks_per_node = 1\n            h100_cluster.gpus_per_task = 8\n\n            h100_cluster.packager.subpath = ""subpath/to/your/code/repo""\n            h100_cluster.launcher = ""torchrun""\n\n            exp.add(\n                ""example_h100"",\n                fn=example_to_run,\n                tail_logs=True,\n                executor=h100_cluster,\n            )\n\n            # Set up the run on A100\n            a100_cluster: run.Config[SlurmExecutor] = h100_cluster.clone()\n            a100_cluster.tunnel = run.Config(\n                SSHTunnel,\n                host=os.environ[""A100_HOST""],\n                user=""your_user_in_cluster"",\n                identity=""path_to_your_ssh_key""\n            )\n\n            exp.add(\n                ""example_a100"",\n                fn=example_to_run,\n                tail_logs=True,\n                executor=a100_cluster,\n            )\n\n            # Runs all the task in the experiment.\n            # By default, all tasks will be run in parallel if all different executors support parallel execution.\n            # You can set sequential=True to run the tasks sequentially.\n            exp.run()\n\n        # Upon exiting the context manager, the Experiment will automatically wait for all tasks to complete,\n        # and optionally tail logs for tasks that have tail_logs=True.\n        # A detach mode (if the executors support it) will be available soon.\n        # Once all tasks have completed,\n        # the Experiment will display a status table and clean up resources like ssh tunnels.\n\n        # You can also manage the experiment at a later point in time\n        exp = run.Experiment.from_title(""example-multiple-gpus"")\n        exp.status()\n        exp.logs(task_id=""example_a100"")\n\n    """"""\n\n    GOODBYE_MESSAGE_PYTHON = """"""\n# The experiment was run with the following tasks: {tasks}\n# You can inspect and reconstruct this experiment at a later point in time using:\nexperiment = run.Experiment.from_id(""{exp_id}"")\nexperiment.status() # Gets the overall status\nexperiment.logs(""{tasks[0]}"") # Gets the log for the provided task\nexperiment.cancel(""{tasks[0]}"") # Cancels the provided task if still running\n""""""\n\n    GOODBYE_MESSAGE_BASH = """"""\n# You can inspect this experiment at a later point in time using the CLI as well:\nnemo experiment status {exp_id}\nnemo experiment logs {exp_id} 0\nnemo experiment cancel {exp_id} 0\n""""""\n    _PARALLEL_SUPPORTED_EXECUTORS = (\n        SlurmExecutor,\n        LocalExecutor,\n        SkypilotExecutor,\n        SkypilotJobsExecutor,\n        DockerExecutor,\n        DGXCloudExecutor,\n        LeptonExecutor,\n    )\n    _DETACH_SUPPORTED_EXECUTORS = (\n        SlurmExecutor,\n        SkypilotExecutor,\n        SkypilotJobsExecutor,\n        DGXCloudExecutor,\n        LeptonExecutor,\n    )\n    _DEPENDENCY_SUPPORTED_EXECUTORS = (SlurmExecutor,)\n    _RUNNER_DEPENDENT_EXECUTORS = (LocalExecutor,)\n    _CONFIG_FILE = ""_CONFIG""\n    _VERSION_FILE = ""_VERSION""\n    _TASK_FILE = ""_TASKS""\n    _DONE_FILE = ""_DONE""\n    _TUNNELS_FILE = ""_TUNNELS""\n    _current_experiment_token: Optional[contextvars.Token]\n\n    @classmethod\n    def catalog(\n        cls: Type[""Experiment""],\n        title: str = """",\n    ) -> list[str]:\n        """"""\n        List all experiments inside get_nemorun_home(), optionally with the provided title.\n        """"""\n        parent_dir = os.path.join(get_nemorun_home(), ""experiments"", title)\n        return _get_sorted_dirs(parent_dir)\n\n    @classmethod\n    def _from_config(cls: Type[""Experiment""], exp_dir: str) -> ""Experiment"":\n        id = os.path.basename(exp_dir)\n        with open(os.path.join(exp_dir, cls._CONFIG_FILE), ""r"") as f:\n            config = f.read()\n\n        if not config:\n            raise ValueError(f""Experiment {id} not found."")\n\n        serializer = ZlibJSONSerializer()\n        cfg: Config[""Experiment""] = fdl.cast(Config, serializer.deserialize(config))\n        if ""id"" not in cfg.__arguments__:\n            cfg.id = id\n\n        cfg._reconstruct = True\n\n        exp: ""Experiment"" = fdl.build(cfg)\n        exp._jobs = exp._load_jobs()\n        try:\n            exp.tunnels = exp._load_tunnels()\n        except Exception as e:\n            exp.console.log(\n                f""Exception {e} loading tunnels for experiment {id}, will continue without loading tunnels.""\n            )\n\n        return exp\n\n    @classmethod\n    def from_id(\n        cls: Type[""Experiment""],\n        id: str,\n    ) -> ""Experiment"":\n        """"""\n        Reconstruct an experiment with the specified id.\n        """"""\n        title, _, _ = id.rpartition(""_"")\n        parent_dir = os.path.join(get_nemorun_home(), ""experiments"", title)\n        exp_dir = os.path.join(parent_dir, id)\n\n        assert os.path.isdir(exp_dir), f""Experiment {id} not found.""\n\n        exp = cls._from_config(exp_dir)\n        return exp\n\n    @classmethod\n    def from_title(\n        cls: Type[""Experiment""],\n        title: str,\n    ) -> ""Experiment"":\n        """"""\n        Reconstruct an experiment with the specified title.\n        """"""\n        parent_dir = os.path.join(get_nemorun_home(), ""experiments"", title)\n        exp_dir = _get_latest_dir(parent_dir)\n\n        assert os.path.isdir(exp_dir), f""Experiment {id} not found.""\n\n        exp = cls._from_config(exp_dir)\n        return exp\n\n    def __init__(\n        self,\n        title: str,\n        executor: Executor | None = None,  # type: ignore\n        id: str | None = None,\n        log_level: str = ""INFO"",\n        _reconstruct: bool = False,\n        jobs: list[Job | JobGroup] | None = None,\n        base_dir: str | None = None,\n        clean_mode: bool = False,\n        enable_goodbye_message: bool = True,\n        threadpool_workers: int = 16,\n        skip_status_at_exit: bool = False,\n        serialize_metadata_for_scripts: bool = True,\n    ) -> None:\n        """"""\n        Initializes an experiment run by creating its metadata directory and saving the experiment config.\n\n        Args:\n            title: Title or name for the experiment\n            executor: Any executor that subclasses run.Executor and is supported by NeMo-Run.\n                This will be used as the default executor for tasks if an explicit one is not specified.\n                Users can also clone this and make task specific executor changes.\n            id (Optional): Unique id for the experiment run.\n                If not specified, will be set automatically based on the current timestamp.\n            log_level: Set log level for the experiment. Defaults to WARN.\n            _reconstruct: Generally, the user does not need to specify this flag.\n                This is only set to True when using run.Experiment.from_dir.\n            clean_mode: If True, disables all console output (logs, progress bars, etc.). Defaults to False.\n            enable_goodbye_message: if True, prints goodbye message after submitting job. Defaults to True.\n        """"""\n        configure_logging(level=log_level)\n        self._reconstruct = _reconstruct\n        if _reconstruct:\n            assert id, ""Cannot reconstruct an experiment without id.""\n\n        self._title = title\n        self._id = id or f""{title}_{int(time.time())}""\n        self._enable_goodbye_message = enable_goodbye_message\n        self._threadpool_workers = threadpool_workers\n        self._skip_status_at_exit = skip_status_at_exit\n        self._serialize_metadata_for_scripts = serialize_metadata_for_scripts\n\n        base_dir = str(base_dir or get_nemorun_home())\n        self._exp_dir = os.path.join(base_dir, ""experiments"", title, self._id)\n\n        self.log_level = log_level\n        self._runner = get_runner(component_defaults=None, experiment=self)\n\n        if not _reconstruct:\n            self.executor = executor if executor else LocalExecutor()\n        else:\n            assert isinstance(executor, Executor)\n            self.executor = executor\n\n        self._jobs: list[Job | JobGroup] = jobs or []\n        self.tunnels: dict[str, Tunnel] = {}\n        self.console = CONSOLE\n        self.clean_mode = clean_mode\n        if self.clean_mode:\n            self.console = DummyConsole()\n        self._launched = False\n        self._live_progress = None\n        self._current_experiment_token = None\n\n    def to_config(self) -> Config:\n        return Config(\n            self.__class__,\n            title=self._title,\n            id=self._id,\n            executor=self.executor.to_config(),\n            log_level=self.log_level,\n            clean_mode=self.clean_mode,\n            threadpool_workers=self._threadpool_workers,\n            enable_goodbye_message=self._enable_goodbye_message,\n            skip_status_at_exit=self._skip_status_at_exit,\n            serialize_metadata_for_scripts=self._serialize_metadata_for_scripts,\n        )\n\n    def _save_experiment(self, exist_ok: bool = False):\n        os.makedirs(self._exp_dir, exist_ok=exist_ok)\n        self._save_config()\n\n    def _save_config(self):\n        with open(os.path.join(self._exp_dir, self.__class__._CONFIG_FILE), ""w+"") as f:\n            f.write(ZlibJSONSerializer().serialize(self.to_config()))\n\n        with open(os.path.join(self._exp_dir, self.__class__._VERSION_FILE), ""w+"") as f:\n            f.write(f""{run.__version__}\n"")\n\n    def _save_tunnels(self):\n        serializer = ZlibJSONSerializer()\n        serialized_tunnels = {\n            k: serializer.serialize(v.to_config()) for k, v in self.tunnels.items()\n        }\n        with open(os.path.join(self._exp_dir, self.__class__._TUNNELS_FILE), ""w+"") as f:\n            json.dump(serialized_tunnels, f)\n\n    def _load_tunnels(self) -> dict[str, Tunnel]:\n        with open(os.path.join(self._exp_dir, self.__class__._TUNNELS_FILE)) as f:\n            serialized_tunnels = json.load(f)\n        serializer = ZlibJSONSerializer()\n        return {k: fdl.build(serializer.deserialize(v)) for k, v in serialized_tunnels.items()}\n\n    def _save_jobs(self):\n        serialized_jobs = list(map(lambda job: job.serialize(), self.jobs))\n        with open(os.path.join(self._exp_dir, self.__class__._TASK_FILE), ""w+"") as f:\n            json.dump(serialized_jobs, f)\n\n        if ""__main__"" in sys.modules:\n            main_module = sys.modules[""__main__""]\n            try:\n                with open(os.path.join(self._exp_dir, ""__main__.py""), ""w+"") as f:\n                    f.write(inspect.getsource(main_module))\n            except TypeError:\n                ...\n\n    def _load_jobs(self) -> list[Job | JobGroup]:\n        with open(os.path.join(self._exp_dir, self._TASK_FILE)) as f:\n            serialized_jobs = json.load(f)\n\n        serializer = ZlibJSONSerializer()\n        jobs = []\n        for job_cfg, task_cfg in serialized_jobs:\n            job_cfg = serializer.deserialize(job_cfg)\n\n            job: Job | JobGroup = fdl.build(job_cfg)\n            if isinstance(job, Job):\n                job.task = task_cfg  # type: ignore\n            elif isinstance(job, JobGroup):\n                job.tasks = task_cfg  # type: ignore\n            else:\n                raise ValueError(f""Unknown task type: {task_cfg.__fn_or_cls__}"")\n\n            jobs.append(job)\n\n        return jobs\n\n    def _prepare(self, exist_ok: bool = False):\n        self._save_experiment(exist_ok=exist_ok)\n\n        for job in self.jobs:\n            job.prepare(serialize_metadata_for_scripts=self._serialize_metadata_for_scripts)\n\n        self._save_jobs()\n\n    def _add_single_job(\n        self,\n        task: Union[Partial, Script],\n        executor: Executor,\n        name: str = """",\n        plugins: Optional[list[ExperimentPlugin]] = None,\n        tail_logs: bool = False,\n        dependencies: Optional[list[str]] = None,\n    ) -> str:\n        if isinstance(task, Script):\n            default_name = task.get_name()\n        else:\n            default_name = get_type_namespace(task.__fn_or_cls__)\n\n        reuse_job_dir = True if name else False\n        name = name or default_name\n        if any(map(lambda job: job.id == name, self.jobs)):\n            task_id = f""{name}_{len(self.jobs)}""\n        else:\n            task_id = name\n\n        self._validate_task(task_info=task_id, task=task)\n\n        executor = executor.clone()\n        executor.assign(\n            self._id,\n            self._exp_dir,\n            task_id=task_id,\n            task_dir=name if reuse_job_dir else task_id,\n        )\n\n        cloned = copy.deepcopy(task) if isinstance(task, Script) else task.clone()\n        job = Job(\n            id=task_id,\n            task=cloned,\n            executor=executor,\n            plugins=plugins,\n            tail_logs=tail_logs,\n            dependencies=dependencies or [],\n        )\n        plugins = plugins or []\n        for plugin in plugins:\n            plugin.assign(self._id)\n            plugin.setup(cloned, executor)\n\n        self._jobs.append(job)\n        return job.id\n\n    def _add_job_group(\n        self,\n        tasks: list[Partial | Script],\n        executor: list[Executor] | Executor,\n        name: str,\n        plugins: Optional[list[ExperimentPlugin]] = None,\n        tail_logs: bool = False,\n        dependencies: Optional[list[str]] = None,\n    ) -> str:\n        if any(map(lambda task: task.id == name, self.jobs)):\n            task_id = f""{name}_{len(self.jobs)}""\n        else:\n            task_id = name\n\n        for i, _task in enumerate(tasks):\n            self._validate_task(task_info=f""Job Group: {task_id}, job index: {i}"", task=_task)\n\n        executors = executor if isinstance(executor, list) else [executor]\n        cloned_executors = []\n        for executor in executors:\n            new_executor = executor.clone()\n            cloned_executors.append(new_executor)\n            new_executor.assign(self._id, self._exp_dir, task_id, task_dir=name)\n\n        cloned_tasks = []\n        for task in tasks:\n            cloned_task = copy.deepcopy(task) if isinstance(task, Script) else task.clone()\n            cloned_tasks.append(cloned_task)\n\n        job_group = JobGroup(\n            id=task_id,\n            tasks=cloned_tasks,\n            executors=cloned_executors,\n            plugins=plugins,\n            tail_logs=tail_logs,\n            dependencies=dependencies or [],\n        )\n        plugins = plugins or []\n        for plugin in plugins:\n            for i, task in enumerate(cloned_tasks):\n                _executor = job_group.executors if job_group._merge else job_group.executors[i]  # type: ignore\n                assert isinstance(_executor, Executor)\n                plugin.setup(task, _executor)\n\n        self._jobs.append(job_group)\n        return job_group.id\n\n    def _validate_task(self, task_info: str, task: Union[Partial, Script]) -> None:\n        valid = True\n        message = """"\n        if isinstance(task, Partial):\n            serializer = ZlibJSONSerializer()\n            serialized = serializer.serialize(task)\n            deserialized = serializer.deserialize(serialized)\n            diff = diffing.build_diff(deserialized, task)\n            diff = {\n                daglish.path_str(d.target): (d.new_value if hasattr(d, ""new_value"") else None)  # type: ignore\n                for d in diff.changes\n            }\n            if deserialized != task:\n                valid = False\n                message += f""""""\nDeserialized task does not match original task. The following paths in your task need to be wrapped in `run.Config` or `run.Partial`:\n\n{pprint.PrettyPrinter(indent=4).pformat(diff)}\n\nFor more information about `run.Config` and `run.Partial`, please refer to https://github.com/NVIDIA-NeMo/Run/blob/main/docs/source/guides/configuration.md.\n""""""\n        if not valid:\n            raise RuntimeError(f""Failed to validate task {task_info}.\n{message}"")\n\n    def add(\n        self,\n        task: Union[Partial, Script] | list[Union[Partial, Script]],\n        executor: Executor | list[Executor] | None = None,\n        name: str = """",\n        plugins: Optional[list[ExperimentPlugin]] = None,\n        tail_logs: bool = False,\n        dependencies: Optional[list[str]] = None,\n    ) -> str:\n        """"""\n        Add a configured function along with its executor config to the experiment.\n        """"""\n        assert _current_experiment.get(None) == self, (\n            ""Using Experiment without it's context manager is not permitted.""\n        )\n\n        job_ids = set([job.id for job in self.jobs])\n        for dep in dependencies or []:\n            assert dep in job_ids, f""Dependency {dep} not found.""\n\n        executor = executor or self.executor\n        if not isinstance(task, list):\n            assert executor and isinstance(executor, Executor)\n            job_id = self._add_single_job(\n                task,\n                executor,\n                name,\n                plugins=plugins,\n                tail_logs=tail_logs,\n                dependencies=dependencies.copy() if dependencies else None,\n            )\n        else:\n            assert name, ""name is required for task group.""\n            job_id = self._add_job_group(\n                task,\n                executor,\n                name,\n                plugins=plugins,\n                tail_logs=tail_logs,\n                dependencies=dependencies.copy() if dependencies else None,\n            )\n\n        return job_id\n\n    def dryrun(self, log: bool = True, exist_ok: bool = False, delete_exp_dir: bool = True):\n        """"""\n        Logs the raw scripts that will be executed for each task.\n        """"""\n        if log:\n            self.console.log(f""[bold magenta]Experiment {self._id} dryrun..."")\n\n        self._prepare(exist_ok=exist_ok)\n\n        for job in self.jobs:\n            if isinstance(job, Job):\n                if log:\n                    self.console.log(f""[bold magenta]Task {job.id}\n"")\n            elif isinstance(job, JobGroup):\n                if log:\n                    self.console.log(f""[bold magenta]Task Group {job.id}\n"")\n            job.launch(wait=False, runner=self._runner, dryrun=True, direct=False, log_dryrun=log)\n\n        if delete_exp_dir:\n            shutil.rmtree(self._exp_dir)\n\n    def run(\n        self,\n        sequential: bool = False,\n        detach: bool = False,\n        tail_logs: bool = False,\n        direct: bool = False,\n    ):\n        """"""\n        Runs all the tasks in the experiment.\n\n        By default, all tasks are run in parallel.\n\n        If sequential=True, all tasks will be run one after the other.\n        The order is based on the order in which they were added.\n\n        Parallel mode only works if all executors in the experiment support it.\n        Currently, all executors support parallel mode.\n\n        In sequential mode, if all executor supports dependencies, then all tasks will be scheduled at once\n        by specifying the correct dependencies to each task.\n        Otherwise, the experiment.run call will block and each task that is scheduled will be executed sequentially.\n        In this particular case, we cannot guarantee the state of the experiment if the process exits in the middle.\n\n        Currently, only the slurm executor supports dependencies.\n\n        Args:\n            sequential: If True, runs all tasks sequentially in the order they were added. Defaults to False.\n            detach: If True, detaches from the process after launching the tasks. Only supported for Slurm and Skypilot. Defaults to False.\n            tail_logs: If True, tails logs from all tasks in the experiment. If False, relies on task specific setting. Defaults to False.\n            direct: If True, runs all tasks in the experiment sequentially in the same process. Note that if direct=True, then sequential also will be True. Defaults to False.\n        """"""\n        assert _current_experiment.get(None) == self, (\n            ""Using Experiment without it's context manager is not permitted.""\n        )\n\n        if self._launched:\n            self.console.log(""[bold magenta]Experiment already running..."")\n            return\n\n        if self._reconstruct:\n            self.console.log(""[bold magenta]Experiment in inspection mode..."")\n            return\n\n        # Prepare experiment before running\n\n        # in case of multi-node execution with LocalExecutor+torchrun+slurm, run only on first rank\n        if int(os.getenv(""SLURM_PROCID"", 0)) == 0:\n            self._prepare()\n\n        if direct:\n            self.console.log(\n                ""[bold magenta]Running the experiment with direct=True. ""\n                ""This will launch all jobs sequentially in the same process.""\n            )\n            if not self.jobs:\n                self.console.log(""[bold red]No jobs to run in this experiment."")\n                return\n\n            assert all(map(lambda job: isinstance(job, Job), self.jobs)), (\n                ""Jobs in this experiment contain JobGroup which cannot be run directly for now.""\n            )\n\n            assert all(map(lambda job: not job.dependencies, self.jobs)), (\n                ""Jobs in this experiment contain dependencies which cannot be run directly for now.""\n            )\n\n            for job in self.jobs:\n                assert isinstance(job, Job)\n                with TeeStdoutStderr(\n                    os.path.join(job.executor.job_dir, f""log_{job.id}_direct_run.out"")\n                ):\n                    job.launch(wait=True, direct=True, runner=self._runner)\n\n            self._save_jobs()\n            self._launched = any(map(lambda job: job.launched, self.jobs))\n            self._direct = True\n            return\n\n        executors = set()\n        for job in self.jobs:\n            if isinstance(job, Job):\n                executors.add(job.executor.__class__)\n            elif isinstance(job, JobGroup):\n                if isinstance(job.executors, list):\n                    for executor in job.executors:\n                        executors.add(executor.__class__)\n                else:\n                    executors.add(job.executors.__class__)\n\n        if detach and any(map(lambda x: x not in self._DETACH_SUPPORTED_EXECUTORS, executors)):\n            self.console.log(\n                ""[bold red] Cannot detach from this experiment. Please keep it running until completion.""\n            )\n            detach = False\n\n        is_dag = any(map(lambda job: len(job.dependencies) > 0, self.jobs))\n        assert not (is_dag and sequential), (\n            ""Jobs in this experiment have dependencies, they cannot be run sequentially. Set sequential=False.""\n        )\n\n        if sequential:\n            for i in range(1, len(self.jobs)):\n                self.jobs[i].dependencies.append(self.jobs[i - 1].id)\n\n        self.dryrun(log=False, exist_ok=True, delete_exp_dir=False)\n        for tunnel in self.tunnels.values():\n            if isinstance(tunnel, SSHTunnel):\n                tunnel.connect()\n                assert tunnel.session, f""SSH tunnel {tunnel.key} failed to connect.""\n                rsync(tunnel.session, self._exp_dir, os.path.dirname(tunnel.job_dir))\n\n            symlink_cmds = []\n            for packaging_job in tunnel.packaging_jobs.values():\n                if packaging_job.symlink:\n                    symlink_cmds.append(packaging_job.symlink_cmd())\n\n            if symlink_cmds:\n                tunnel.run("" && "".join(symlink_cmds))\n\n        self._save_tunnels()\n\n        return self._run_dag(detach=detach, tail_logs=tail_logs, executors=executors)\n\n    def _run_dag(self, detach: bool, tail_logs: bool, executors: set[Executor]):\n        job_map = {job.id: job for job in self._jobs}\n        graph = nx.DiGraph()\n        job_ids = set([job.id for job in self.jobs])\n        for job in self.jobs:\n            graph.add_node(job.id, job=job)\n            for dep in job.dependencies:\n                assert dep in job_ids, f""Dependency {dep} not found in job list {job_ids}.""\n                graph.add_edge(dep, job.id)\n\n        assert nx.is_directed_acyclic_graph(graph), ""Jobs have cyclic dependencies.""\n        order = [sorted(generation) for generation in nx.topological_generations(graph)]\n        add_deps = False\n        if len(order) > 1:\n            if all(map(lambda x: x in self._DEPENDENCY_SUPPORTED_EXECUTORS, executors)):\n                wait = False\n                add_deps = True\n                self.detach = detach\n            else:\n                wait = True\n                if len(self.jobs) > 1:\n                    self.console.log(\n                        f""[bold cyan]Dependencies not supported for atleast one of {executors}.""\n                        ""All jobs will be run one after the other based on their dependencies, please keep the process alive.""\n                    )\n                if detach:\n                    self.console.log(\n                        ""[bold red] Cannot detach from this experiment. Please keep it running until completion.""\n                    )\n        else:\n            # All jobs will be executed in parallel\n            assert all(map(lambda x: x in self._PARALLEL_SUPPORTED_EXECUTORS, executors)), (\n                f""Parallel mode not supported for atleast one of {executors}. Set sequential=True.""\n            )\n            wait = False\n            self.detach = detach\n\n        for level in order:\n            # Launch jobs in this level concurrently since they are independent\n\n            def _set_context(ctx: contextvars.Context):\n                for var, value in ctx.items():\n                    var.set(value)\n\n            ctx = contextvars.copy_context()\n\n            def _launch(node: str):\n                job: Job | JobGroup = job_map[node]\n                self.console.log(f""[bold cyan]Launching job {job.id} for experiment {self._title}"")\n                if tail_logs:\n                    job.tail_logs = True\n\n                try:\n                    if add_deps:\n                        deps = []\n                        for dep_id in job.dependencies:\n                            dep = job_map[dep_id]\n                            handle = dep.handle\n                            assert dep.launched and handle, (\n                                f""Dependency {dep.id} for {job.id} not yet launched.""\n                            )\n                            deps.append(handle)\n\n                        job.executor.dependencies = deps  # type: ignore\n\n                    job.launch(wait=False, runner=self._runner)\n                    return job\n\n                except Exception as e:\n                    self.console.log(f""Error running job {job.id}: {e}"")\n                    raise e\n\n            launched_jobs: list[Job | JobGroup] = []\n            with ThreadPoolExecutor(\n                initializer=_set_context, initargs=(ctx,), max_workers=self._threadpool_workers\n            ) as pool:\n                futures = [pool.submit(_launch, node) for node in level]\n                for future in as_completed(futures):\n                    launched_jobs.append(future.result())\n\n            if wait:\n                self._wait_for_jobs(jobs=launched_jobs)\n\n        self._save_jobs()\n        self._launched = any(map(lambda job: job.launched, self.jobs))\n        self._waited = wait\n\n    def _wait_for_jobs(self, jobs: list[Job | JobGroup]):\n        def set_context(context: contextvars.Context):\n            for var, value in context.items():\n                var.set(value)\n\n        context = contextvars.copy_context()\n        with ThreadPoolExecutor(initializer=set_context, initargs=(context,)) as executor:\n            futures: dict[Future, Job | JobGroup] = {}\n            for job in jobs:\n                if isinstance(job, Job):\n                    handle_exists = job.handle\n                else:\n                    handle_exists = len(job.handles) > 0 and all(job.handles)\n\n                if job.launched and handle_exists:\n                    self._initialize_live_progress()\n                    self._add_progress(job=job)\n                    future = executor.submit(\n                        job.wait,\n                        runner=self._runner\n                        if isinstance(\n                            job.executor,\n                            self._RUNNER_DEPENDENT_EXECUTORS,\n                        )\n                        else get_runner(),\n                    )\n                    futures[future] = job\n\n            for future in as_completed(futures.keys()):\n                job = futures[future]\n                try:\n                    future.result()\n                    self._update_progress(job, job.state)\n                except Exception as e:\n                    self.console.log(f""Exception while waiting for Job {job.id}: {e}"")\n                    self.console.log(*traceback.format_exception(e))\n                    self._update_progress(job, AppState.UNKNOWN)\n                finally:\n                    job.cleanup()\n\n    def _initialize_tunnels(self, extract_from_executors: bool = False):\n        if extract_from_executors:\n            for job in self.jobs:\n                if (\n                    isinstance(job.executor, SlurmExecutor)\n                    and job.executor.tunnel.key not in self.tunnels\n                ):\n                    self.tunnels[job.executor.tunnel.key] = job.executor.tunnel\n\n        for tunnel in self.tunnels.values():\n            if isinstance(tunnel, SSHTunnel):\n                tunnel.connect()\n                assert tunnel.session, f""SSH tunnel {tunnel.key} failed to connect.""\n\n    def status(self, return_dict: bool = False) -> Optional[dict[str, dict[str, str]]]:\n        """"""\n        Prints a table specifying the status of all tasks.\n\n        .. note::\n            status is not supported for local executor\n            and the status for a task using the local executor\n            will be listed as UNKNOWN in most cases\n        """"""\n        _set_current_experiment = False\n        if not self._current_experiment_token:\n            _current_experiment.set(self)\n            _set_current_experiment = True\n\n        def _get_job_info_and_dict(\n            idx: int, job: Job | JobGroup\n        ) -> tuple[list[str], dict[str, str]]:\n            job_info = []\n            job_info.append(f""[bold green]Task {idx}[/bold green]: [bold orange1]{job.id}"")\n            job_info.append(\n                f""- [bold green]Status[/bold green]: {str(job.status(runner=self._runner))}""\n            )\n            job_info.append(f""- [bold green]Executor[/bold green]: {job.executor.info()}"")\n\n            try:\n                _, _, path_str = job.handle.partition(""://"")\n                path = path_str.split(""/"")\n                app_id = path[1]\n            except Exception:\n                app_id = """"\n\n            job_info.append(f""- [bold green]Job id[/bold green]: {app_id}"")\n            directory_info = [\n                ""- [bold green]Local Directory[/bold green]: "" + job.executor.job_dir,\n            ]\n            job_dict = {\n                ""name"": job.id,\n                ""status"": job.status(runner=self._runner),\n                ""executor"": job.executor.info(),\n                ""job_id"": app_id,\n                ""handle"": job.handle,\n                ""local_dir"": job.executor.job_dir,\n            }\n\n            if isinstance(job.executor, SlurmExecutor) and isinstance(\n                job.executor.tunnel, SSHTunnel\n            ):\n                directory_info.extend(\n                    [\n                        ""- [bold green]Remote Directory[/bold green]: ""\n                        + os.path.join(\n                            job.executor.tunnel.job_dir,\n                            Path(job.executor.job_dir).name,\n                        ),\n                    ]\n                )\n                job_dict[""remote_dir""] = os.path.join(\n                    job.executor.tunnel.job_dir,\n                    Path(job.executor.job_dir).name,\n                )\n            job_info.extend(directory_info)\n            return job_info, job_dict\n\n        self._initialize_tunnels(extract_from_executors=True)\n        try:\n            result_dict = {}\n            job_infos: list[Group | None] = [None] * len(self.jobs)\n\n            # Parallelize IO-bound status retrieval across jobs\n            def _collect(arg):\n                idx, job = arg\n                job_info, job_dict = _get_job_info_and_dict(idx, job)\n                return idx, job.id, job_info, job_dict\n\n            # Propagate context variables to worker threads so helpers that rely on them keep working\n            def _set_context(ctx: contextvars.Context):\n                for var, value in ctx.items():\n                    var.set(value)\n\n            ctx = contextvars.copy_context()\n            with ThreadPoolExecutor(\n                initializer=_set_context, initargs=(ctx,), max_workers=self._threadpool_workers\n            ) as pool:\n                futures = [pool.submit(_collect, (idx, job)) for idx, job in enumerate(self.jobs)]\n                for future in as_completed(futures):\n                    idx, job_id, job_info, job_dict = future.result()\n                    job_infos[idx] = Group(*job_info)\n                    result_dict[job_id] = job_dict\n\n            # Remove potential None slots (should not occur)\n            job_infos = [ji for ji in job_infos if ji is not None]\n\n            if return_dict:\n                return result_dict\n\n            self.console.print()\n            self.console.print(\n                f""[bold green]Experiment Status for[/bold green] [bold orange1]{self._id}"",\n                new_line_start=True,\n            )\n            for job_info in job_infos:\n                self.console.print(job_info, soft_wrap=True, new_line_start=True, highlight=False)\n            self.console.print()\n        finally:\n            if _set_current_experiment and self._current_experiment_token:\n                _current_experiment.reset(self._current_experiment_token)\n                self._current_experiment_token = None\n\n    def cancel(self, job_id: str):\n        """"""\n        Cancels an existing job if still running.\n        """"""\n        _set_current_experiment = False\n        if not self._current_experiment_token:\n            _current_experiment.set(self)\n            _set_current_experiment = True\n\n        self.console.log(f""[bold cyan]Cancelling {job_id} if still running"")\n        try:\n            job = next(filter(lambda x: x.id == job_id, self.jobs))\n            job.cancel(runner=self._runner)\n        except StopIteration:\n            self.console.log(f""[bold red]Job {job_id} not found"")\n        except Exception as e:\n            self.console.log(f""[bold red]Failed to cancel {job_id}\nError: {e}\n"")\n            self.console.log(*traceback.format_exception(e))\n        finally:\n            if _set_current_experiment and self._current_experiment_token:\n                _current_experiment.reset(self._current_experiment_token)\n                self._current_experiment_token = None\n\n    def logs(self, job_id: str, regex: str | None = None):\n        """"""\n        Prints the logs of the specified job_id, optionally filtered by regex.\n        """"""\n        _set_current_experiment = False\n        if not self._current_experiment_token:\n            _current_experiment.set(self)\n            _set_current_experiment = True\n\n        self.console.log(f""[bold cyan]Fetching logs for {job_id}"")\n        try:\n            job = next(filter(lambda x: x.id == job_id, self.jobs))\n            if isinstance(job, Job) and job.handle.endswith(""direct_run""):\n                self.console.log(""This job was run with direct=True."")\n                self.console.log(\n                    f""Logs may be present in task directory at:\n[bold]{job.executor.job_dir}.""\n                )\n                return\n\n            try:\n                job.logs(runner=self._runner, regex=regex)\n            except Exception as e:\n                self.console.log(f""[bold red]Failed to get logs for {job_id}\nError: {e}\n"")\n                self.console.log(\n                    f""Logs may be present in job directory at:\n[bold]{job.executor.job_dir}.""\n                )\n        except StopIteration:\n            self.console.log(f""[bold red]Job {job_id} not found"")\n        finally:\n            if _set_current_experiment and self._current_experiment_token:\n                _current_experiment.reset(self._current_experiment_token)\n                self._current_experiment_token = None\n\n    def reset(self) -> ""Experiment"":\n        """"""\n        Resets an experiment to make it ready for a relaunch.\n        Only works if the current experiment run has already been launched.\n        """"""\n        if not self._reconstruct and not os.path.isfile(\n            os.path.join(self._exp_dir, self._DONE_FILE)\n        ):\n            self.console.log(\n                f""[bold magenta]Experiment {self._id} has not run yet, skipping reset...""\n            )\n            return self\n\n        old_id, old_exp_dir, old_launched = self._id, self._exp_dir, self._launched\n        self._id = f""{self._title}_{int(time.time())}""\n        self._exp_dir = os.path.join(get_nemorun_home(), ""experiments"", self._title, self._id)\n        self._launched = False\n        self._live_progress = None\n\n        jobs = self._jobs\n        self._jobs = []\n        serializer = ZlibJSONSerializer()\n        _set_current_experiment = False\n        if not self._current_experiment_token:\n            _current_experiment.set(self)\n            _set_current_experiment = True\n\n        try:\n            if ""__external_main__"" not in sys.modules:\n                maybe_load_external_main(old_exp_dir)\n\n            for job in jobs:\n                if isinstance(job, Job):\n                    if isinstance(job.task, str):\n                        _task = serializer.deserialize(job.task)\n                        if _task.__fn_or_cls__ == Script:\n                            job.task = fdl.build(_task)\n                        else:\n                            job.task = _task  # type: ignore\n\n                    self.add(\n                        job.task,\n                        job.executor,\n                        name=job.id,\n                        tail_logs=job.tail_logs,\n                    )\n                else:\n                    if isinstance(job.tasks, str):\n                        tasks = serializer.deserialize(job.tasks)\n                        job.tasks = [\n                            fdl.build(task) if task.__fn_or_cls__ == Script else task\n                            for task in tasks\n                        ]\n\n                    self.add(\n                        job.tasks,\n                        job.executors,\n                        name=job.id,\n                        tail_logs=job.tail_logs,\n                    )\n        except Exception as e:\n            self.console.log(\n                f""[bold magenta]Failed resetting Experiment {self._id} due to error: {e}""\n            )\n            # Double check exp dir is unchanged\n            new_path = os.path.join(get_nemorun_home(), ""experiments"", self._title, self._id)\n            if self._exp_dir == new_path and new_path != old_exp_dir:\n                shutil.rmtree(self._exp_dir)\n\n            self._id = old_id\n            self._exp_dir = old_exp_dir\n            self._launched = old_launched\n            self._jobs = self._load_jobs()\n        finally:\n            if _set_current_experiment and self._current_experiment_token:\n                _current_experiment.reset(self._current_experiment_token)\n                self._current_experiment_token = None\n\n        self._reconstruct = False\n        return self\n\n    def _initialize_live_progress(self):\n        if not self._live_progress:\n            # Disable live progress if we are tailing logs for any task\n            # as tty output consistency can not be guaranteed as of now\n            if self.clean_mode or any(map(lambda job: job.tail_logs, self.jobs)):\n                return\n\n            assert isinstance(self.console, rich.console.Console)\n            self._progress = Progress(\n                ""{task.description}"",\n                SpinnerColumn(),\n                BarColumn(bar_width=None),\n                TimeElapsedColumn(),\n            )\n            self._exp_panel = Panel(\n                self._progress,\n                title=f""[b]{self._id}"",\n                padding=(1, 3),\n            )\n            self._task_progress: dict[str, TaskID] = {}\n            self._live_progress = Live(self._exp_panel, console=self.console, refresh_per_second=10)\n            self._live_progress.start(refresh=True)\n\n    def _add_progress(self, job: Job | JobGroup):\n        if self._live_progress:\n            self._task_progress[job.id] = self._progress.add_task(\n                f""[bold green]{job.id}"", total=None\n            )\n\n    def _update_progress(self, job: Job | JobGroup, state: AppState):\n        if self._live_progress:\n            color = ""[bold green]"" if state == AppState.SUCCEEDED else ""[bold red]""\n            task_progress_id = self._task_progress[job.id]\n            self._progress.stop_task(task_progress_id)\n            self._progress.update(\n                task_progress_id,\n                description=f""{color}{job.id} {state}"",\n            )\n            progress_task: RichTask = self._progress._tasks[task_progress_id]\n            progress_task.finished_time = progress_task.elapsed\n            progress_task.completed = progress_task.elapsed or 0.0\n            progress_task.total = progress_task.elapsed\n\n            self._progress.refresh()\n\n    def _cleanup(self, tunnels: bool = True):\n        if tunnels and hasattr(self, ""tunnels""):\n            for tunnel in self.tunnels.values():\n                try:\n                    tunnel.cleanup()\n                except Exception:\n                    ...\n\n        self._runner.close()\n\n        if (\n            _current_experiment is not None\n            and _current_experiment.get(None)\n            and self._current_experiment_token\n        ):\n            _current_experiment.reset(self._current_experiment_token)\n            self._current_experiment_token = None\n\n    def __enter__(self) -> ""Experiment"":\n        self._current_experiment_token = _current_experiment.set(self)\n        self.console.rule(\n            f""[bold magenta]Entering Experiment {self._title} with id: {self._id}"",\n        )\n        return self\n\n    def __exit__(self, exc_type, exc_value, tb):\n        try:\n            if hasattr(self, ""detach"") and self.detach:\n                self.console.rule(f""[bold magenta]Detaching from Experiment {self._id}."")\n                self.console.log(\n                    ""Task specific cleanup won't be run.\n""\n                    ""Ephemeral logs and artifacts may be lost."",\n                )\n\n                if self._launched and not self._skip_status_at_exit:\n                    self.status()\n                return\n\n            if self._launched:\n                if hasattr(self, ""_direct"") and self._direct:\n                    self.console.rule(\n                        f""[bold magenta]Direct run Experiment {self._id}"",\n                    )\n                    if not self._skip_status_at_exit:\n                        self.status()\n                    return\n\n                if hasattr(self, ""_waited"") and self._waited:\n                    self.console.rule(\n                        f""[bold magenta]Done waiting for Experiment {self._id}"",\n                    )\n                    if not self._skip_status_at_exit:\n                        self.status()\n                    return\n\n                self.console.rule(\n                    f""[bold magenta]Waiting for Experiment {self._id} to finish"",\n                )\n                if not self._skip_status_at_exit:\n                    self.status()\n\n                self._wait_for_jobs(jobs=self.jobs)\n        finally:\n            if self._live_progress:\n                self._live_progress.stop()\n\n            self._cleanup(tunnels=False)\n            if self._launched:\n                Path(os.path.join(self._exp_dir, self._DONE_FILE)).touch()\n                if self._enable_goodbye_message:\n                    self.console.print(\n                        Syntax(\n                            self.GOODBYE_MESSAGE_PYTHON.format(\n                                exp_id=self._id,\n                                tasks=list(map(lambda job: job.id, self.jobs)),\n                            ),\n                            ""python"",\n                            theme=os.environ.get(""NEMO_RUN_CODE_THEME"", ""monokai""),\n                        )\n                    )\n                    self.console.print(\n                        Syntax(\n                            self.GOODBYE_MESSAGE_BASH.format(\n                                exp_id=self._id,\n                                tasks=list(map(lambda job: job.id, self.jobs)),\n                            ),\n                            ""shell"",\n                            theme=os.environ.get(""NEMO_RUN_CODE_THEME"", ""monokai""),\n                        )\n                    )\n\n    def _repr_svg_(self):\n        return self.to_config()._repr_svg_()\n\n    def __del__(self):\n        try:\n            deconfigure_logging()\n            self._cleanup()\n        except Exception:\n            pass\n\n    @property\n    def jobs(self) -> list[Job | JobGroup]:\n        return Jobs(self._jobs)\n\n    @jobs.setter\n    def jobs(self, jobs: list[Job | JobGroup]):\n        self._jobs = jobs\n\n    @property\n    def tasks(self) -> list[Config]:\n        serializer = ZlibJSONSerializer()\n\n        for job in self._jobs:\n            if isinstance(job, Job):\n                if isinstance(job.task, str):\n                    _task = serializer.deserialize(job.task)\n                    if _task.__fn_or_cls__ == Script:\n                        job.task = fdl.build(_task)\n                    else:\n                        job.task = _task  # type: ignore\n            else:\n                if isinstance(job.tasks, str):\n                    tasks = serializer.deserialize(job.tasks)\n                    job.tasks = [\n                        fdl.build(task) if task.__fn_or_cls__ == Script else task for task in tasks\n                    ]\n\n        return Tasks((job.task if isinstance(job, Job) else job.tasks) for job in self._jobs)\n\n\nclass Tasks(list, ConfigurableMixin): ...\n\n\nclass Jobs(list, ConfigurableMixin): ...\n\n\ndef _get_latest_dir(path) -> str:\n    dirs = [d for d in os.listdir(path) if os.path.isdir(os.path.join(path, d))]\n    latest_dir = max(dirs, key=lambda d: os.path.getctime(os.path.join(path, d)))\n    return os.path.join(path, latest_dir)\n\n\ndef _get_sorted_dirs(path: str) -> list[str]:\n    if not os.path.exists(path):\n        return []\n    dirs = [d for d in os.listdir(path) if os.path.isdir(os.path.join(path, d))]\n    dirs = sorted(dirs, key=lambda d: os.path.getctime(os.path.join(path, d)))\n    return list(dirs)\n\n\n_LOADED_MAINS = set()\n\n\ndef maybe_load_external_main(exp_dir: str):\n    main_file = Path(exp_dir) / ""__main__.py""\n    if main_file.exists() and main_file not in _LOADED_MAINS:\n        _LOADED_MAINS.add(main_file)\n\n        spec = importlib.util.spec_from_file_location(""__external_main__"", main_file)\n        if spec is not None and spec.loader is not None:\n            new_main_module = importlib.util.module_from_spec(spec)\n            spec.loader.exec_module(new_main_module)\n\n            if ""__external_main__"" not in sys.modules:\n                sys.modules[""__external_main__""] = new_main_module\n            else:\n                external = sys.modules[""__external_main__""]\n                for attr in dir(new_main_module):\n                    if not attr.startswith(""__""):\n                        setattr(external, attr, getattr(new_main_module, attr))\n\n            existing_main = sys.modules[""__main__""]\n            for attr in dir(new_main_module):\n                if not attr.startswith(""__""):\n                    setattr(existing_main, attr, getattr(new_main_module, attr))\n",python,tab
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+84,358190,"nemo_run/config.py",0,0,"# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n# SPDX-License-Identifier: Apache-2.0\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\nfrom __future__ import annotations\n\nimport copy\nimport dataclasses\nimport inspect\nimport os\nimport re\nimport sys\nimport typing\nfrom pathlib import Path\nfrom types import MappingProxyType\nfrom typing import Any, Callable, Generic, Iterable, Optional, Type, TypeVar, Union, get_args\n\nimport fiddle as fdl\nimport fiddle._src.experimental.dataclasses as fdl_dc\nimport graphviz\nfrom fiddle._src import config, daglish, daglish_extensions\nfrom fiddle._src.casting import register_supported_cast\nfrom fiddle._src.config import TypeOrCallableProducingT\nfrom fiddle.graphviz import render, render_diff\nfrom typing_extensions import Annotated, ParamSpec, Self\n\nimport nemo_run.exceptions as run_exceptions\n\nParams = ParamSpec(""Params"")\nReturnType = TypeVar(""ReturnType"")\n\n_T = TypeVar(""_T"")\n_BuildableT = TypeVar(""_BuildableT"", bound=fdl.Buildable)\n\nRECURSIVE_TYPES = (typing.Union, typing.Optional)\n_NEMORUN_HOME = os.environ.get(""NEMORUN_HOME"", os.path.expanduser(""~/.nemo_run""))\nRUNDIR_NAME = ""nemo_run""\nRUNDIR_SPECIAL_NAME = ""/$nemo_run""\nSCRIPTS_DIR = ""scripts""\n\n# Metadata keys\nUSE_WITH_RAY_CLUSTER_KEY = ""use_with_ray_cluster""\n\n\ndef get_nemorun_home() -> str:\n    """"""\n    Get the current NEMORUN_HOME directory path.\n\n    Returns:\n        The path to the NEMORUN_HOME directory.\n    """"""\n    return _NEMORUN_HOME\n\n\ndef set_nemorun_home(path: str) -> None:\n    """"""\n    Set the NEMORUN_HOME directory path.\n\n    Args:\n        path: The new path for NEMORUN_HOME.\n    """"""\n    global _NEMORUN_HOME\n    _NEMORUN_HOME = os.path.expanduser(path)\n\n\ndef get_type_namespace(typ: Type | Callable) -> str:\n    """"""\n    Get the namespace of a type or callable.\n\n    Args:\n        typ: The type or callable to get the namespace for.\n\n    Returns:\n        A string representing the namespace of the type or callable.\n\n    Examples:\n        >>> class MyClass:\n        ...     pass\n        >>> get_type_namespace(MyClass)\n        'your_module.MyClass'\n    """"""\n    module = typ.__module__\n    if module == ""__main__"":\n        # Get the filename without extension\n        main_module = sys.modules[""__main__""]\n        filename = os.path.basename(main_module.__file__)\n        module = os.path.splitext(filename)[0]\n\n    if isinstance(typ, fdl.Buildable):\n        typ = typ.__fn_or_cls__\n\n    _name = getattr(typ, ""__qualname__"", str(typ))\n    if _name.startswith(""ForwardRef""):\n        _name = _name.split(""."")[-1]\n    return f""{module}.{_name}""\n\n\ndef get_underlying_types(type_hint: typing.Any) -> typing.Set[typing.Type]:\n    if isinstance(type_hint, typing._GenericAlias):  # type: ignore\n        if str(type_hint).startswith(""typing.Annotated""):\n            origin = type_hint.__origin__\n            if hasattr(origin, ""__origin__""):\n                origin = origin.__origin__\n        else:\n            origin = type_hint.__origin__\n        if origin in RECURSIVE_TYPES:\n            types = set()\n            for arg in type_hint.__args__:\n                types.update(get_underlying_types(arg))\n            return types\n    return {type_hint}\n\n\ndef from_dict(raw_data: dict | list | str | float | int | bool, cls: Type[_T]) -> _T:\n    if isinstance(raw_data, dict):\n        underlying_types = get_underlying_types(cls)\n        underlying_types = [tp for tp in underlying_types if tp is not type(None)]\n        assert len(underlying_types) == 1, (\n            f""Unable to load {cls}. Nested union types are not currently supported.""\n        )\n        cls = underlying_types[0]  # type: ignore\n\n    if dataclasses.is_dataclass(cls):\n        fields_dict = {\n            f.name: from_dict(raw_data.get(f.name), f.type)  # type: ignore\n            for f in dataclasses.fields(cls)\n            if f.init\n        }\n        return cls(**fields_dict)  # type: ignore\n    elif isinstance(raw_data, list):\n        return [from_dict(item, cls.__args__[0]) for item in raw_data]  # type: ignore\n    else:\n        return raw_data  # type: ignore\n\n\ndef set_value(cfg: config.Buildable, key: str, value: Any) -> None:\n    """"""Set an attribute's value.\n\n    Args:\n      cfg: A `fdl.Buildable` whose attribute is to be overridden.\n      assignment: String representing attribute's override expression. Of the form\n        `attribute=value`.\n    """"""\n    *parents, last = _parse_path(key)\n\n    walk = typing.cast(Any, cfg)\n    try:\n        for parent in parents:\n            walk = parent.follow(walk)\n    except Exception as e:\n        raise run_exceptions.SetValueError(f'Invalid path ""{key}"".') from e\n\n    try:\n        if isinstance(last, daglish.Attr):\n            setattr(walk, last.name, value)\n        elif isinstance(last, daglish.Key):\n            walk[last.key] = value\n        else:\n            raise run_exceptions.SetValueError(f""Unexpected path element {last}."")\n    except Exception as e:\n        raise run_exceptions.SetValueError(f'Could not set ""{key}"" to ""{value}"".') from e\n\n\nclass _CloneAndFNMixin:\n    def clone(self):\n        """"""Returns a deep clone of the object.""""""\n        return copy.deepcopy(self)\n\n    def walk(self: _BuildableT, **kwargs) -> _BuildableT:  # type: ignore\n        """"""\n        Recursively applies a transformation function to attributes within the configuration object\n        and its children that match the keys provided in kwargs. Attributes not listed in kwargs\n        are not modified.\n\n        Args:\n            **kwargs (dict): A dictionary where keys are attribute names and values are functions\n                             that take the current attribute value and return a new value.\n\n        Returns\n        -------\n            Config: A new Config instance with selectively modified attributes.\n\n        Examples\n        --------\n            >>> config = Config(model=ModelConfig(seq_length=128))\n            >>> new_config = config.walk(seq_length=lambda cfg: cfg.seq_length * 2)\n            >>> new_config.model.seq_length\n            256\n        """"""\n        return _try_set_all(self, _walk=True, **kwargs)\n\n    def broadcast(self: _BuildableT, **kwargs) -> _BuildableT:  # type: ignore\n        """"""\n        Sets new values to attributes within the configuration object and its children that match\n        the keys provided in kwargs. Attributes not listed in kwargs are not modified.\n\n        Args:\n            **kwargs (dict): A dictionary where keys are attribute names and values are the new\n                             values to be set.\n\n        Returns\n        -------\n            Config: A new Config instance with selectively updated attributes.\n\n        Examples\n        --------\n            >>> config = Config(model=ModelConfig(tensor_model_parallel_size=1))\n            >>> new_config = config.broadcast(tensor_model_parallel_size=2)\n            >>> new_config.model.tensor_model_parallel_size\n            2\n        """"""\n        return _try_set_all(self, **kwargs)\n\n\nclass _VisualizeMixin:\n    def visualize(self, **kwargs) -> graphviz.Graph:\n        return render(self, **kwargs)\n\n    def diff(self, old: Self, trim=True, **kwargs):\n        return render_diff(old=old, new=self, trim=trim, **kwargs)\n\n    def save_config_img(self, path_str: str) -> None:\n        """"""\n        Saves the configuration to a file.\n\n        Args:\n            path (str): The file path where the configuration should be saved.\n            fdl_fn (Partial): The function descriptor library function to save.\n\n        Example:\n            >>> save_config_img(""path/to/dir"", some_fdl_fn)\n        """"""\n        path: Path = Path(path_str)\n\n        if not path.suffix:\n            path = path / ""config.png""\n        elif path.suffix != "".png"":\n            raise ValueError(""The file extension must be .png"")\n\n        path.parent.mkdir(parents=True, exist_ok=True)\n\n        with path.open(""wb"") as f:\n            f.write(self.visualize().pipe(""png""))\n\n    def _repr_svg_(self):\n        """"""Special method used by Jupyter to represent an object as SVG.\n\n        Returns\n        -------\n            str: SVG representation of the Config object if Graphviz can render it.\n            If Graphviz rendering fails or is not available, it returns None.\n        """"""\n        try:\n            # Attempt to render using Graphviz and return SVG representation\n            return self.visualize().pipe(format=""svg"").decode(""utf-8"")\n        except Exception as e:\n            # If rendering fails, log the exception or handle it as needed\n            print(f""Graphviz rendering failed: {e}"")\n            return self.__repr__()\n\n\nclass Config(Generic[_T], fdl.Config[_T], _CloneAndFNMixin, _VisualizeMixin):\n    """"""\n    Wrapper around fdl.Config with nemo_run specific functionality.\n    See `fdl.Config <https://fiddle.readthedocs.io/en/latest/api_reference/core.html#config>`_ for more.\n    """"""\n\n    def __init__(\n        self,\n        fn_or_cls: Union[fdl.Buildable[_T], TypeOrCallableProducingT[_T]],\n        *args,\n        bind_args: bool = True,\n        **kwargs,\n    ):\n        # Handle dict types by converting to _kwargs_to_dict function\n        if fn_or_cls == {} or (hasattr(fn_or_cls, ""__origin__"") and fn_or_cls.__origin__ is dict):\n            fn_or_cls = dict  # type: ignore\n            bind_args = False\n\n        new_kwargs = kwargs\n        if bind_args and not isinstance(fn_or_cls, fdl.Buildable):\n            try:\n                new_kwargs = _bind_args(fn_or_cls, *args, **kwargs)\n            except Exception:\n                new_kwargs = kwargs\n\n        super().__init__(fn_or_cls, *args, **new_kwargs)\n\n    @classmethod\n    def __unflatten__(\n        cls,\n        values: Iterable[Any],\n        metadata: config.BuildableTraverserMetadata,\n    ):\n        # If this is a dictionary config, reconstruct it with the arguments\n        if metadata.fn_or_cls is dict:\n            return cls(**metadata.arguments(values))\n        return super().__unflatten__(values, metadata)\n\n\nclass Partial(Generic[_T], fdl.Partial[_T], _CloneAndFNMixin, _VisualizeMixin):\n    """"""\n    Wrapper around fdl.Partial with nemo_run specific functionality.\n    See `fdl.Partial <https://fiddle.readthedocs.io/en/latest/api_reference/core.html#partial>`_ for more.\n    """"""\n\n    def __init__(\n        self,\n        fn_or_cls: Union[fdl.Buildable[_T], TypeOrCallableProducingT[_T]],\n        *args,\n        bind_args: bool = True,\n        **kwargs,\n    ):\n        new_kwargs = kwargs\n        if bind_args and not isinstance(fn_or_cls, fdl.Buildable):\n            try:\n                new_kwargs = _bind_args(fn_or_cls, **kwargs)\n            except Exception:\n                new_kwargs = kwargs\n\n        super().__init__(fn_or_cls, *args, **new_kwargs)\n\n\nregister_supported_cast(fdl.Config, Config)\nregister_supported_cast(fdl.Partial, Partial)\nregister_supported_cast(Config, Config)\nregister_supported_cast(Partial, Partial)\n\n\nclass ConfigurableMixin(_VisualizeMixin):\n    """"""\n    A mixin class that provides configuration and visualization functionality.\n\n    This mixin adds methods for converting objects to Config instances,\n    visualizing configurations, and comparing configurations.\n\n    For classes that are not dataclasses, the `to_config` method needs to be\n    overridden to provide custom conversion logic to Config instances.\n    """"""\n\n    def diff(self, old: Self, trim=True, **kwargs):\n        """"""\n        Generate a visual difference between this configuration and an old one.\n\n        Args:\n            old (Self): The old configuration to compare against.\n            trim (bool, optional): Whether to trim unchanged parts. Defaults to True.\n            **kwargs: Additional arguments to pass to render_diff.\n\n        Returns:\n            graphviz.Digraph: A graph representing the differences between configurations.\n        """"""\n        return render_diff(old=old.to_config(), new=self.to_config(), trim=trim, **kwargs)\n\n    def to_config(self) -> Config[Self]:\n        """"""\n        Convert the current object to a Config instance.\n\n        This method automatically converts dataclasses to Config instances.\n        For classes that are not dataclasses, this method needs to be overridden\n        to provide custom conversion logic.\n\n        Returns:\n            Config: A Config representation of the current object.\n\n        Raises:\n            NotImplementedError: If the object type cannot be converted to Config\n                                 or if the method is not overridden for non-dataclass types.\n\n        Note:\n            For classes that are not dataclasses, you must override this method\n            to define how the object should be converted to a Config instance.\n        """"""\n        if dataclasses.is_dataclass(self):\n            try:\n                return fdl.cast(\n                    Config, fdl_dc.convert_dataclasses_to_configs(self, allow_post_init=True)\n                )\n            except Exception as e:\n                raise NotImplementedError(\n                    f""Cannot convert type {type(self)} to Config"",\n                    f""Please implement a method `to_config` on {type(self)}."",\n                ) from e\n        elif isinstance(self, (list, tuple, dict)):\n            return self  # type: ignore\n        else:\n            raise NotImplementedError(\n                f""Cannot convert type {type(self)} to Config. ""\n                f""Please override the `to_config` method for {type(self)}.""\n            )\n\n    def _repr_svg_(self):\n        """"""\n        Generate an SVG representation of the object for Jupyter notebooks.\n\n        Returns:\n            str: SVG representation of the object if it can be rendered,\n                 otherwise returns the string representation.\n        """"""\n        if isinstance(self, (list, tuple, dict)):\n            try:\n                return render(self).pipe(format=""svg"").decode(""utf-8"")\n            except Exception as e:\n                print(f""Graphviz rendering failed: {e}"")\n                return self.__repr__()\n\n        return self.to_config()._repr_svg_()\n\n\n@dataclasses.dataclass\nclass Script(ConfigurableMixin):\n    """"""\n    Dataclass to configure raw scripts.\n\n    Examples:\n\n    .. code-block:: python\n\n        file_based_script = run.Script(""./scripts/echo.sh"")\n\n        inline_script = run.Script(\n            inline=\""\""\""\n        env\n        echo ""Hello 1""\n        echo ""Hello 2""\n        \""\""\""\n        )\n\n    """"""\n\n    #: Path to your script\n    path: str = """"\n    #: Inline contents of the script. Either path or inline needs to be set.\n    inline: str = """"\n    #: Args to pass to your scripts, only applicable when path is set.\n    args: list[str] = dataclasses.field(default_factory=list)\n    #: Environment variables to set when running the script.\n    env: dict[str, str] = dataclasses.field(default_factory=dict)\n    #: Entrypoint to use, defaults to bash.\n    entrypoint: str = ""bash""\n    #: Whether to use ``python -m`` when executing via python.\n    m: bool = False\n\n    metadata: dict[str, Any] = dataclasses.field(default_factory=dict)\n\n    def __post_init__(self):\n        assert self.path or self.inline\n        assert self.entrypoint, ""Need to provide an entrypoint for script.""\n        if self.m:\n            assert ""python"" in self.entrypoint, ""-m can only be used with python""\n\n    def get_name(self):\n        if self.inline:\n            name = self.inline.strip()[:10]\n            return re.sub(""[^0-9a-zA-Z]+"", ""_"", name)\n        else:\n            return os.path.basename(self.path)\n\n    def to_command(\n        self, with_entrypoint: bool = False, filename: Optional[str] = None, is_local: bool = False\n    ) -> list[str]:\n        if self.inline:\n            if filename:\n                os.makedirs(os.path.dirname(filename), exist_ok=True)\n                with open(filename, ""w"") as f:\n                    f.write(""#!/usr/bin/bash\n"" + self.inline)\n\n                if is_local:\n                    cmd = [filename]\n                else:\n                    cmd = [os.path.join(f""/{RUNDIR_NAME}"", SCRIPTS_DIR, Path(filename).name)]\n\n                if with_entrypoint:\n                    cmd = [self.entrypoint] + cmd\n\n                return cmd\n\n            inline = self.inline.replace('""', '\\""')\n            cmd = [""-c"", f'""{inline}""']\n            if with_entrypoint:\n                cmd = [self.entrypoint] + cmd\n\n            return cmd\n\n        args = [self.path] + self.args\n        if self.m:\n            cmd = [""-m""] + args\n        else:\n            cmd = args\n\n        if with_entrypoint:\n            if self.entrypoint:\n                cmd = [self.entrypoint] + cmd\n            else:\n                raise ValueError(""Cannot use with_entrypoint=True without specifying entrypoint"")\n\n        return cmd\n\n\n# A type alias for an optional type that is annotated with a Config.\n# This is useful for when you want to specify a type is Optional but\n# always want to provide a default config.\nOptionalDefaultConfig = Annotated[Optional[_T], Config[_T]]\nOptionalDefaultPartial = Annotated[Optional[_T], Partial[_T]]\n\n\ndef _parse_path(path: str) -> daglish.Path:\n    """"""Parses a path into a list of either attributes or index lookups.""""""\n    if not path.startswith(""["") and not path.startswith("".""):\n        path = f"".{path}""  # Add a leading `.` to make parsing work properly.\n\n    return daglish_extensions.parse_path(path)\n\n\ndef _bind_args(\n    fn_or_cls: TypeOrCallableProducingT,\n    *fn_args: fdl.Config | str | Callable,\n    **fn_kwargs: fdl.Config | str | Callable,\n) -> dict[str, fdl.Config | str | Callable]:\n    sig = inspect.signature(fn_or_cls)\n    params = sig.parameters\n\n    if set(fn_kwargs) > set(params):\n        raise TypeError(\n            f""{set(fn_kwargs) - set(params)} does not exist as args in {fn_or_cls.__module__}:{fn_or_cls.__name__}. Please remove them.""\n        )\n\n    final_args = _construct_args(fn_or_cls, params, fn_kwargs)\n    final_args = fn_kwargs | final_args\n    sig.bind(*fn_args, **final_args)\n    return final_args\n\n\ndef _construct_args(\n    fn_or_cls: TypeOrCallableProducingT,\n    params: MappingProxyType[str, inspect.Parameter],\n    kwargs: dict[str, fdl.Config | str | Callable],\n):\n    final_args = {}\n\n    primitive = [str, float, int, bool, bytes]\n    primitive.extend([Optional[t] for t in primitive])\n\n    for name, parameter in params.items():\n        arg = kwargs.get(name, None)\n\n        if arg:\n            if dataclasses.is_dataclass(arg):\n                final_args[name] = fdl.cast(\n                    Config,\n                    fdl_dc.convert_dataclasses_to_configs(arg, allow_post_init=True),\n                )\n            else:\n                final_args[name] = arg\n        elif str(parameter.annotation).startswith(""typing.Annotated""):\n            args = get_args(parameter.annotation)\n            if str(args[0]).startswith(""typing.Optional"") and len(args) > 1:\n                cfg_type = get_args(args[0])[0]\n                buildable = args[1].__origin__\n                if issubclass(buildable, fdl.Buildable):\n                    final_args[name] = buildable(cfg_type)\n\n    return final_args\n\n\nConfigT = TypeVar(""ConfigT"", Config, Partial)\n\n\ndef _try_set_all(config: _BuildableT, _walk: bool = False, **kwargs) -> _BuildableT:\n    for key, val in kwargs.items():\n        if hasattr(config, key):\n            _val = val(config) if _walk else val\n            setattr(config, key, _val)\n\n        for attr_name in dir(config):\n            try:\n                if hasattr(config, attr_name):\n                    attr = getattr(config, attr_name)\n                    if isinstance(attr, (fdl.Config, fdl.Partial)):\n                        _try_set_all(attr, _walk=_walk, **kwargs)\n            except ValueError:\n                pass\n\n    return config\n",python,tab
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4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-f1861737-541e-498a-8209-7a659cb20b861757609758410-2025_09_11-18.56.00.140/source.csv

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4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-faba6583-b2c9-4b94-9ba6-9f240428520a1750722089894-2025_06_23-23.49.28.299/source.csv

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