Add files using upload-large-folder tool

1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-1505f3d0-0cb4-4cc0-84bf-678810d0ac8f1757148592235-2025_09_06-10.49.56.658/source.csv

@@ -0,0 +1,17 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+1,1,"train_tokenizer.py",0,0,"import os\n\nos.environ.setdefault(""XLA_PYTHON_CLIENT_MEM_FRACTION"", ""0.98"")\n\nfrom dataclasses import dataclass, field\nfrom typing import cast, Optional\n\nimport einops\nimport itertools\nfrom jax.sharding import Mesh, PartitionSpec, NamedSharding\nfrom jax.experimental.mesh_utils import create_device_mesh\nimport optax\nimport orbax.checkpoint as ocp\nimport numpy as np\nimport dm_pix as pix\nimport jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\nimport grain\nimport flax.nnx as nnx\n\nfrom models.tokenizer import TokenizerVQVAE\nfrom utils.dataloader import get_dataloader\nfrom utils.train_utils import (\n    get_lr_schedule,\n    count_parameters_by_component,\n    print_mem_stats,\n    print_compiled_memory_stats,\n    print_compiled_cost_analysis,\n)\n\n\n@dataclass\nclass Args:\n    # Experiment\n    num_steps: int = 300_000\n    seed: int = 0\n    seq_len: int = 16\n    image_channels: int = 3\n    image_height: int = 90\n    image_width: int = 160\n    data_dir: str = """"\n    save_ckpt: bool = False\n    restore_ckpt: bool = False\n    # Optimization\n    vq_beta: float = 0.25\n    batch_size: int = 48\n    init_lr: float = 0.0\n    max_lr: float = 3e-4\n    decay_end: float = 0.0\n    wsd_decay_steps: int = (\n        20000  # NOTE: wsd_decay_steps will only be used when using a wsd-schedule\n    )\n    lr_schedule: str = ""wsd""  # supported options: wsd, cos\n    warmup_steps: int = 10000\n    # Tokenizer\n    model_dim: int = 512\n    ffn_dim: int = 2048\n    latent_dim: int = 32\n    num_latents: int = 1024\n    patch_size: int = 4\n    num_blocks: int = 4\n    num_heads: int = 8\n    dropout: float = 0.0\n    codebook_dropout: float = 0.01\n    param_dtype = jnp.float32\n    dtype = jnp.bfloat16\n    # Logging\n    log: bool = False\n    entity: str = """"\n    project: str = """"\n    name: str = ""train_tokenizer""\n    tags: list[str] = field(default_factory=lambda: [""tokenizer""])\n    log_interval: int = 5\n    log_image_interval: int = 250\n    ckpt_dir: str = """"\n    log_checkpoint_interval: int = 10000\n    log_checkpoint_keep_period: int = 20000\n    log_gradients: bool = False\n    wandb_id: str = """"\n    use_flash_attention: bool = True\n\n\ndef build_model(args: Args, rng: jax.Array) -> tuple[TokenizerVQVAE, jax.Array]:\n    rng, _rng = jax.random.split(rng)\n    rngs = nnx.Rngs(_rng)\n    return (\n        TokenizerVQVAE(\n            in_dim=args.image_channels,\n            model_dim=args.model_dim,\n            ffn_dim=args.ffn_dim,\n            latent_dim=args.latent_dim,\n            num_latents=args.num_latents,\n            patch_size=args.patch_size,\n            num_blocks=args.num_blocks,\n            num_heads=args.num_heads,\n            dropout=args.dropout,\n            codebook_dropout=args.codebook_dropout,\n            param_dtype=args.param_dtype,\n            dtype=args.dtype,\n            use_flash_attention=args.use_flash_attention,\n            rngs=rngs,\n        ),\n        rng,\n    )\n\n\ndef build_optimizer(\n    model: TokenizerVQVAE, args: Args\n) -> tuple[nnx.Optimizer, optax.Schedule]:\n    lr_schedule = get_lr_schedule(\n        args.lr_schedule,\n        args.init_lr,\n        args.max_lr,\n        args.decay_end,\n        args.num_steps,\n        args.warmup_steps,\n        args.wsd_decay_steps,\n    )\n    tx = optax.adamw(\n        learning_rate=lr_schedule,\n        b1=0.9,\n        b2=0.9,\n        weight_decay=1e-4,\n        mu_dtype=args.param_dtype,  # moments in full precision\n    )\n    optimizer = nnx.Optimizer(model, tx)\n    return optimizer, lr_schedule\n\n\ndef build_mesh_and_sharding(\n    num_devices: int,\n) -> tuple[Mesh, NamedSharding, NamedSharding]:\n    device_mesh_arr = create_device_mesh((num_devices,))\n    mesh = Mesh(devices=device_mesh_arr, axis_names=(""data"",))\n    replicated_sharding = NamedSharding(mesh, PartitionSpec())\n    videos_sharding = NamedSharding(mesh, PartitionSpec(""data"", None, None, None, None))\n    return mesh, replicated_sharding, videos_sharding\n\n\ndef shard_optimizer_states(\n    optimizer: nnx.Optimizer, replicated_sharding: NamedSharding\n) -> None:\n    model_state = nnx.state(optimizer.model)\n    model_sharded_state = jax.lax.with_sharding_constraint(\n        model_state, replicated_sharding\n    )\n    nnx.update(optimizer.model, model_sharded_state)\n    optimizer_state = nnx.state(optimizer, nnx.optimizer.OptState)\n    optimizer_sharded_state = jax.lax.with_sharding_constraint(\n        optimizer_state, replicated_sharding\n    )\n    nnx.update(optimizer, optimizer_sharded_state)\n\n\ndef build_dataloader(args: Args) -> grain.DataLoaderIterator:\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    return grain_iterator\n\n\ndef build_checkpoint_manager(args: Args) -> ocp.CheckpointManager:\n    handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()\n    handler_registry.add(\n        ""model_state"", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler\n    )\n    handler_registry.add(\n        ""model_state"", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler\n    )\n    handler_registry.add(\n        ""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    checkpoint_options = ocp.CheckpointManagerOptions(\n        save_interval_steps=args.log_checkpoint_interval,\n        max_to_keep=3,\n        keep_period=args.log_checkpoint_keep_period,\n        step_format_fixed_length=6,\n        cleanup_tmp_directories=True,\n    )\n    checkpoint_manager = ocp.CheckpointManager(\n        args.ckpt_dir,\n        options=checkpoint_options,\n        handler_registry=handler_registry,\n    )\n    return checkpoint_manager\n\n\ndef restore_checkpoint_if_needed(\n    args: Args,\n    checkpoint_manager: ocp.CheckpointManager,\n    optimizer: nnx.Optimizer,\n    grain_iterator: grain.DataLoaderIterator,\n    restore_step: Optional[int] = None,\n) -> tuple[int, nnx.Optimizer, grain.DataLoaderIterator]:\n    step = 0\n    if restore_step is None:\n        restore_step = checkpoint_manager.latest_step()\n    if args.restore_ckpt:\n        abstract_optimizer = nnx.eval_shape(lambda: optimizer)\n        abstract_optimizer_state = nnx.state(abstract_optimizer)\n        restored = checkpoint_manager.restore(\n            restore_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 = restore_step or 0\n        print(f""Restored dataloader and model state from step {step}"")\n    return step, optimizer, grain_iterator\n\n\ndef main(args: Args) -> None:\n    jax.distributed.initialize()\n    num_devices = jax.device_count()\n    if num_devices == 0:\n        raise ValueError(""No JAX devices found."")\n    print(f""Running on {num_devices} devices."")\n\n    if args.batch_size % num_devices != 0:\n        raise ValueError(\n            f""Global batch size {args.batch_size} must be divisible by ""\n            f""number of devices {num_devices}.""\n        )\n\n    rng = jax.random.key(args.seed)\n\n    # --- Initialize model ---\n    tokenizer, rng = build_model(args, rng)\n\n    _, params, _ = nnx.split(tokenizer, nnx.Param, ...)\n    param_counts = count_parameters_by_component(params)\n\n    if args.log and jax.process_index() == 0:\n        wandb_init_kwargs = {\n            ""entity"": args.entity,\n            ""project"": args.project,\n            ""name"": args.name,\n            ""tags"": args.tags,\n            ""group"": ""debug"",\n            ""config"": args,\n        }\n\n        if args.wandb_id:\n            wandb_init_kwargs.update(\n                {\n                    ""id"": args.wandb_id,\n                    ""resume"": ""allow"",\n                }\n            )\n        wandb.init(**wandb_init_kwargs)\n\n        wandb.config.update({""model_param_count"": param_counts})\n\n    print(""Parameter counts:"")\n    print(param_counts)\n\n    # --- Initialize optimizer ---\n    optimizer, lr_schedule = build_optimizer(tokenizer, args)\n    del tokenizer\n\n    # FIXME: switch to create_hybrid_device_mesh for runs spanning multiple nodes\n    mesh, replicated_sharding, videos_sharding = build_mesh_and_sharding(num_devices)\n\n    shard_optimizer_states(optimizer, replicated_sharding)\n\n    # --- Initialize checkpoint manager ---\n    checkpoint_manager = build_checkpoint_manager(args)\n\n    # --- Create DataLoaderIterator from dataloader ---\n    grain_iterator = build_dataloader(args)\n\n    # --- Restore checkpoint ---\n    step, optimizer, grain_iterator = restore_checkpoint_if_needed(\n        args, checkpoint_manager, optimizer, grain_iterator\n    )\n\n    # --- Define loss and train step (close over args) ---\n    def tokenizer_loss_fn(\n        model: TokenizerVQVAE, inputs: dict\n    ) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n        gt = jnp.asarray(inputs[""videos""], dtype=jnp.float32) / 255.0\n        inputs[""videos""] = gt.astype(args.dtype)\n        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        gt_clipped = gt.clip(0, 1).reshape(-1, *gt.shape[2:])\n        recon = outputs[""recon""].clip(0, 1).reshape(-1, *outputs[""recon""].shape[2:])\n        psnr = jnp.asarray(pix.psnr(gt_clipped, recon)).mean()\n        ssim = jnp.asarray(pix.ssim(gt_clipped, recon)).mean()\n        _, index_counts = jnp.unique_counts(\n            jnp.ravel(outputs[""indices""]), size=args.num_latents, fill_value=0\n        )\n        codebook_usage = (index_counts != 0).mean()\n        metrics = dict(\n            loss=loss,\n            mse=mse,\n            q_loss=q_loss,\n            commitment_loss=commitment_loss,\n            psnr=psnr,\n            ssim=ssim,\n            codebook_usage=codebook_usage,\n        )\n        return loss, (outputs[""recon""], metrics)\n\n    @nnx.jit(donate_argnums=0)\n    def train_step(\n        optimizer: nnx.Optimizer, inputs: dict\n    ) -> tuple[jax.Array, jax.Array, dict]:\n        def loss_fn(model: TokenizerVQVAE) -> tuple[jax.Array, tuple[jax.Array, dict]]:\n            return tokenizer_loss_fn(model, inputs)\n\n        (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(\n            optimizer.model\n        )\n        optimizer.update(grads)\n        if args.log_gradients:\n            metrics[""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    # --- TRAIN LOOP ---\n    dataloader = (\n        jax.make_array_from_process_local_data(videos_sharding, elem)\n        for elem in grain_iterator\n    )\n    if jax.process_index() == 0:\n        first_videos = next(dataloader)\n        sample_inputs = dict(videos=first_videos)\n        compiled = train_step.lower(optimizer, sample_inputs).compile()\n        print_compiled_memory_stats(compiled.memory_analysis())\n        print_compiled_cost_analysis(compiled.cost_analysis())\n        # Do not skip the first batch during training\n        dataloader = itertools.chain([first_videos], dataloader)\n    print(f""Starting training from step {step}..."")\n    first_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(optimizer, inputs)\n            if step == first_step:\n                print_mem_stats(""After params initialized"")\n            metrics[""lr""] = lr_schedule(step)\n            print(f""Step {step}, loss: {loss}"")\n            step += 1\n\n            # --- 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\n\nif __name__ == ""__main__"":\n    args = tyro.cli(Args)\n    main(args)\n",python,tab
+2,113,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"10:49:56 AM [info] Activating crowd-code\n10:49:56 AM [info] Recording started\n10:49:56 AM [info] Initializing git provider using file system watchers...\n10:49:56 AM [info] Git repository found\n10:49:56 AM [info] Git provider initialized successfully\n",Log,tab
+3,224,"extension-output-pdoom-org.crowd-code-#1-crowd-code",250,0,"10:49:56 AM [info] Initial git state: [object Object]\n",Log,content
+4,188000455,"train_tokenizer.py",0,0,"",python,tab
+5,188012251,"train_tokenizer.py",0,0,"Switched from branch 'main' to 'demo-notebook'",python,git_branch_checkout
+6,188015548,"README.md",0,0,"<h1 align=""center"">🧞‍♀️ Jasmine: A simple, performant and scalable JAX-based world modeling codebase 🧞‍♀️</h1>\n\n<p align=""center"">\n    <a href= ""https://github.com/FLAIROx/jafar/blob/main/LICENSE"">\n        <img src=""https://img.shields.io/badge/license-Apache2.0-blue.svg"" /></a>\n    <a href= ""https://github.com/psf/black"">\n        <img src=""https://img.shields.io/badge/code%20style-black-000000.svg"" /></a>\n</p>\n\nJasmine is a production-ready JAX-based world modeling codebase. It currently implements the high-level architecture of [Genie: Generative Interactive Environments](https://arxiv.org/abs/2402.15391) (Bruce et al., 2024) with [MaskGIT](https://arxiv.org/abs/2202.04200) (Chang et al., 2022), as well as an autoregressive (causal) baseline. A diffusion baseline is coming soon.\n\nJasmine scales from single hosts to hundreds of xPUs thanks to XLA and strives to be an easily hackable, batteries-included foundation for world modeling research.\n\n<h2 name=""overview"" id=""overview"">Overview</h2>\n\n- Asynchronous & distributed checkpointing thanks to [orbax.checkpoint](https://github.com/google/orbax)\n    - Jasmine also supports mixing and matching hardware topologies (e.g. train on four nodes, load the checkpoint on a single node)\n- Optimized dataloading thanks to [Grain](https://github.com/google/grain)\n    - Dataloading scales with the number of processes (i.e. nodes/xPUs)\n- Checkpointing of model weights, optimizer and dataloader states\n- Full reproducibility with **identical** training curves (thanks to seeded dataloading and training, and [JAX' approach to pseudo random numbers](https://docs.jax.dev/en/latest/random-numbers.html))\n- Automatic checkpoint deletion/retention according to specified retention policy thanks to `orbax.checkpoint.CheckpointManager`\n- Mixed precision training using `bfloat16`\n    - `int8` training is on the roadmap via [aqt](https://github.com/google/aqt)\n- FlashAttention thanks to [cuDNN SDPA](https://github.com/jax-ml/jax/blob/a155c5a9997924170e0067d552351a9833c12c11/jax/_src/cudnn/fused_attention_stablehlo.py#L842)\n- Frame-level KV cache resets for accelerated spatiotemporal attention in causal baseline (still in PR)\n- Activation checkpointing (even onto host memory if desired)\n- DDP (changing to FSDP requires changing **a single line of code**)\n- WSD learning rate schedule\n    -  No need to retrain from scratch if you want to train for longer\n- Index-shuffling during dataloading\n- Google-native stack\n    - https://github.com/google/orbax for checkpointing\n    - https://github.com/google/grain for dataloading\n    - https://github.com/google-deepmind/dm_pix for image manipulation\n    - https://github.com/google/array_record as the data format\n- Easy model inspection thanks to [treescope](https://github.com/google-deepmind/treescope)\n- Easy model surgery thanks to the new [flax.nnx](https://flax.readthedocs.io/en/latest/migrating/linen_to_nnx.html) API\n- [Shape suffixes](https://medium.com/@NoamShazeer/shape-suffixes-good-coding-style-f836e72e24fd) throughout the repository\n\n<h2 name=""start"" id=""start"">Setup 🧗</h2>\n\nJasmine requires `python 3.10`, `jax 0.6.2`, and `flax 0.10.7`. To install the requirements, run:\n\n```bash\npip install -r requirements.txt\npre-commit install\n```\n\n---\n\n<h2 name=""dataset"" id=""dataset"">Dataset 📂</h2>\n\nYou can either download our preprocessed dataset from [Hugging Face](https://huggingface.co/datasets/p-doom/open_ai_minecraft_arrayrecords_chunked) or preprocess [OpenAI's VPT dataset](https://github.com/openai/Video-Pre-Training) manually.\n\n### Option 1: Use Preprocessed Dataset (Recommended)\n\nThe easiest way to get started is to download our preprocessed dataset from Hugging Face. This script will handle downloading and extracting it:\n\n```bash\nbash input_pipeline/download/download_array_records.sh\n```\n\n---\n\n### Option 2: Manual Download & Preprocessing of OpenAI's VPT Dataset\n\nIf you prefer to use the raw VPT dataset from OpenAI and preprocess it yourself, follow these steps:\n\n1. **Download index files:**\n   This will download the initial index file:\n\n   ```bash\n   bash input_pipeline/download/openai/download_index_files.sh\n   ```\n\n2. **Download from all index files:**\n   This may take a long time depending on your bandwidth:\n\n   ```bash\n   python input_pipeline/download/openai/download_videos.py --index_file_path data/open_ai_index_files/all_7xx_Apr_6.json\n   python input_pipeline/download/openai/download_videos.py --index_file_path data/open_ai_index_files/all_8xx_Jun_29.json\n   python input_pipeline/download/openai/download_videos.py --index_file_path data/open_ai_index_files/all_9xx_Jun_29.json\n   python input_pipeline/download/openai/download_videos.py --index_file_path data/open_ai_index_files/all_10xx_Jun_29.json\n   ```\n\n3. **Preprocess videos into ArrayRecords:**\n   For efficient distributed training, convert the raw videos into the arrayrecord format (make sure to have [ffmpeg](https://github.com/FFmpeg/FFmpeg) installed on your machine):\n\n   ```bash\n   python input_pipeline/preprocess/video_to_array_records.py\n   ```\n\n> **Note:** This is a large dataset and may take considerable time and storage to download and process.\n\n\n<h2 name=""train"" id=""train"">Quick Start 🚀 </h2>\n\nGenie has three components: a [video tokenizer](models/tokenizer.py), a [latent action model](models/lam.py), and a [dynamics model](models/dynamics.py). Each of these components are trained separately, however, the dynamics model requires a pre-trained video tokenizer (and latent action model).\n\nTo train the video tokenizer, run:\n\n```bash\npython train_tokenizer.py --ckpt_dir <path>\n```\n\nTo train the latent action model, run:\n\n```bash\npython train_lam.py --ckpt_dir <path>\n```\n\nOnce the tokenizer and LAM are trained, the dynamics model can be trained with:\n\n```bash\npython train_dynamics.py --tokenizer_checkpoint <path> --lam_checkpoint <path>\n```\n\nLogging with `wandb` is supported. To enable logging, set the `WANDB_API_KEY` environment variable or run:\n\n```bash\nwandb login\n```\n\nTraining can then be logged by setting the `--log` flag:\n\n```bash\npython train_tokenizer.py --log --entity <wandb-entity> --project <wandb-project>\n```\n\n<h2 name=""cite"" id=""cite"">Citing 📜 </h2>\n\nJasmine was built by [Mihir Mahajan](https://maharajamihir.github.io/), [Alfred Nguyen](https://avocadoali.github.io/) and [Franz Srambical](https://srambical.fr/), but started as a fork of [Jafar](https://github.com/flairox/jafar), built by [Matthew Jackson](https://matthewtjackson.com) and [Timon Willi](https://www.timonwilli.com).\n\nIf you use Jasmine in your work, please cite us, Jafar, and the original Genie paper as follows:\n\n```\n@article{\n    mahajan2025jasmine,\n    title={Jasmine: A simple, performant and scalable JAX-based world modeling codebase},\n    author={Mihir Mahajan and Alfred Nguyen and Franz Srambical and Stefan Bauer},\n    journal = {p(doom) blog},\n    year={2025},\n    url={https://pdoom.org/jasmine.html},\n    note = {https://pdoom.org/blog.html}\n}\n```\n```\n@inproceedings{\n    willi2024jafar,\n    title={Jafar: An Open-Source Genie Reimplemention in Jax},\n    author={Timon Willi and Matthew Thomas Jackson and Jakob Nicolaus Foerster},\n    booktitle={First Workshop on Controllable Video Generation @ ICML 2024},\n    year={2024},\n    url={https://openreview.net/forum?id=ZZGaQHs9Jb}\n}\n```\n```\n@inproceedings{\n    bruce2024genie,\n    title={Genie: Generative Interactive Environments},\n    author={Jake Bruce and Michael D Dennis and Ashley Edwards and Jack Parker-Holder and Yuge Shi and Edward Hughes and Matthew Lai and Aditi Mavalankar and Richie Steigerwald and Chris Apps and Yusuf Aytar and Sarah Maria Elisabeth Bechtle and Feryal Behbahani and Stephanie C.Y. Chan and Nicolas Heess and Lucy Gonzalez and Simon Osindero and Sherjil Ozair and Scott Reed and Jingwei Zhang and Konrad Zolna and Jeff Clune and Nando de Freitas and Satinder Singh and Tim Rockt{\""a}schel},\n    booktitle={Forty-first International Conference on Machine Learning},\n    year={2024},\n    url={https://openreview.net/forum?id=bJbSbJskOS}\n}\n```\n",markdown,tab
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+14,188033002,"README.md",0,0,"<h1 align=""center"">🧞‍♀️ Jasmine: A simple, performant and scalable JAX-based world modeling codebase 🧞‍♀️</h1>\n\n<p align=""center"">\n    <a href= ""https://github.com/FLAIROx/jafar/blob/main/LICENSE"">\n        <img src=""https://img.shields.io/badge/license-Apache2.0-blue.svg"" /></a>\n    <a href= ""https://github.com/psf/black"">\n        <img src=""https://img.shields.io/badge/code%20style-black-000000.svg"" /></a>\n</p>\n\nJasmine is a production-ready JAX-based world modeling codebase. It currently implements the high-level architecture of [Genie: Generative Interactive Environments](https://arxiv.org/abs/2402.15391) (Bruce et al., 2024) with [MaskGIT](https://arxiv.org/abs/2202.04200) (Chang et al., 2022), as well as an autoregressive (causal) baseline. A diffusion baseline is coming soon.\n\nJasmine scales from single hosts to hundreds of xPUs thanks to XLA and strives to be an easily hackable, batteries-included foundation for world modeling research.\n\n<h2 name=""overview"" id=""overview"">Overview</h2>\n\n- Asynchronous & distributed checkpointing thanks to [orbax.checkpoint](https://github.com/google/orbax)\n    - Jasmine also supports mixing and matching hardware topologies (e.g. train on four nodes, load the checkpoint on a single node)\n- Optimized dataloading thanks to [Grain](https://github.com/google/grain)\n    - Dataloading scales with the number of processes (i.e. nodes/xPUs)\n- Checkpointing of model weights, optimizer and dataloader states\n- Full reproducibility with **identical** training curves (thanks to seeded dataloading and training, and [JAX' approach to pseudo random numbers](https://docs.jax.dev/en/latest/random-numbers.html))\n- Automatic checkpoint deletion/retention according to specified retention policy thanks to `orbax.checkpoint.CheckpointManager`\n- Mixed precision training using `bfloat16`\n    - `int8` training is on the roadmap via [aqt](https://github.com/google/aqt)\n- FlashAttention thanks to [cuDNN SDPA](https://github.com/jax-ml/jax/blob/a155c5a9997924170e0067d552351a9833c12c11/jax/_src/cudnn/fused_attention_stablehlo.py#L842)\n- Frame-level KV cache resets for accelerated spatiotemporal attention in causal baseline (still in PR)\n- Activation checkpointing (even onto host memory if desired)\n- DDP (changing to FSDP requires changing **a single line of code**)\n- WSD learning rate schedule\n    -  No need to retrain from scratch if you want to train for longer\n- Index-shuffling during dataloading\n- Google-native stack\n    - https://github.com/google/orbax for checkpointing\n    - https://github.com/google/grain for dataloading\n    - https://github.com/google-deepmind/dm_pix for image manipulation\n    - https://github.com/google/array_record as the data format\n- Easy model inspection thanks to [treescope](https://github.com/google-deepmind/treescope)\n- Modularized training script for easy inspection using notebooks ([demo notebook](https://colab.research.google.com/drive/1zHkciFIZxXloJgue9F5LtFlA0m00rJIf?usp=sharing))\n- Easy model surgery thanks to the new [flax.nnx](https://flax.readthedocs.io/en/latest/migrating/linen_to_nnx.html) API\n- [Shape suffixes](https://medium.com/@NoamShazeer/shape-suffixes-good-coding-style-f836e72e24fd) throughout the repository\n\n<h2 name=""start"" id=""start"">Setup 🧗</h2>\n\nJasmine requires `python 3.10`, `jax 0.6.2`, and `flax 0.10.7`. To install the requirements, run:\n\n```bash\npip install -r requirements.txt\npre-commit install\n```\n\n---\n\n<h2 name=""dataset"" id=""dataset"">Dataset 📂</h2>\n\nYou can either download our preprocessed dataset from [Hugging Face](https://huggingface.co/datasets/p-doom/open_ai_minecraft_arrayrecords_chunked) or preprocess [OpenAI's VPT dataset](https://github.com/openai/Video-Pre-Training) manually.\n\n### Option 1: Use Preprocessed Dataset (Recommended)\n\nThe easiest way to get started is to download our preprocessed dataset from Hugging Face. This script will handle downloading and extracting it:\n\n```bash\nbash input_pipeline/download/download_array_records.sh\n```\n\n---\n\n### Option 2: Manual Download & Preprocessing of OpenAI's VPT Dataset\n\nIf you prefer to use the raw VPT dataset from OpenAI and preprocess it yourself, follow these steps:\n\n1. **Download index files:**\n   This will download the initial index file:\n\n   ```bash\n   bash input_pipeline/download/openai/download_index_files.sh\n   ```\n\n2. **Download from all index files:**\n   This may take a long time depending on your bandwidth:\n\n   ```bash\n   python input_pipeline/download/openai/download_videos.py --index_file_path data/open_ai_index_files/all_7xx_Apr_6.json\n   python input_pipeline/download/openai/download_videos.py --index_file_path data/open_ai_index_files/all_8xx_Jun_29.json\n   python input_pipeline/download/openai/download_videos.py --index_file_path data/open_ai_index_files/all_9xx_Jun_29.json\n   python input_pipeline/download/openai/download_videos.py --index_file_path data/open_ai_index_files/all_10xx_Jun_29.json\n   ```\n\n3. **Preprocess videos into ArrayRecords:**\n   For efficient distributed training, convert the raw videos into the arrayrecord format (make sure to have [ffmpeg](https://github.com/FFmpeg/FFmpeg) installed on your machine):\n\n   ```bash\n   python input_pipeline/preprocess/video_to_array_records.py\n   ```\n\n> **Note:** This is a large dataset and may take considerable time and storage to download and process.\n\n\n<h2 name=""train"" id=""train"">Quick Start 🚀 </h2>\n\nGenie has three components: a [video tokenizer](models/tokenizer.py), a [latent action model](models/lam.py), and a [dynamics model](models/dynamics.py). Each of these components are trained separately, however, the dynamics model requires a pre-trained video tokenizer (and latent action model).\n\nTo train the video tokenizer, run:\n\n```bash\npython train_tokenizer.py --ckpt_dir <path>\n```\n\nTo train the latent action model, run:\n\n```bash\npython train_lam.py --ckpt_dir <path>\n```\n\nOnce the tokenizer and LAM are trained, the dynamics model can be trained with:\n\n```bash\npython train_dynamics.py --tokenizer_checkpoint <path> --lam_checkpoint <path>\n```\n\nLogging with `wandb` is supported. To enable logging, set the `WANDB_API_KEY` environment variable or run:\n\n```bash\nwandb login\n```\n\nTraining can then be logged by setting the `--log` flag:\n\n```bash\npython train_tokenizer.py --log --entity <wandb-entity> --project <wandb-project>\n```\n\n<h2 name=""cite"" id=""cite"">Citing 📜 </h2>\n\nJasmine was built by [Mihir Mahajan](https://maharajamihir.github.io/), [Alfred Nguyen](https://avocadoali.github.io/) and [Franz Srambical](https://srambical.fr/), but started as a fork of [Jafar](https://github.com/flairox/jafar), built by [Matthew Jackson](https://matthewtjackson.com) and [Timon Willi](https://www.timonwilli.com).\n\nIf you use Jasmine in your work, please cite us, Jafar, and the original Genie paper as follows:\n\n```\n@article{\n    mahajan2025jasmine,\n    title={Jasmine: A simple, performant and scalable JAX-based world modeling codebase},\n    author={Mihir Mahajan and Alfred Nguyen and Franz Srambical and Stefan Bauer},\n    journal = {p(doom) blog},\n    year={2025},\n    url={https://pdoom.org/jasmine.html},\n    note = {https://pdoom.org/blog.html}\n}\n```\n```\n@inproceedings{\n    willi2024jafar,\n    title={Jafar: An Open-Source Genie Reimplemention in Jax},\n    author={Timon Willi and Matthew Thomas Jackson and Jakob Nicolaus Foerster},\n    booktitle={First Workshop on Controllable Video Generation @ ICML 2024},\n    year={2024},\n    url={https://openreview.net/forum?id=ZZGaQHs9Jb}\n}\n```\n```\n@inproceedings{\n    bruce2024genie,\n    title={Genie: Generative Interactive Environments},\n    author={Jake Bruce and Michael D Dennis and Ashley Edwards and Jack Parker-Holder and Yuge Shi and Edward Hughes and Matthew Lai and Aditi Mavalankar and Richie Steigerwald and Chris Apps and Yusuf Aytar and Sarah Maria Elisabeth Bechtle and Feryal Behbahani and Stephanie C.Y. Chan and Nicolas Heess and Lucy Gonzalez and Simon Osindero and Sherjil Ozair and Scott Reed and Jingwei Zhang and Konrad Zolna and Jeff Clune and Nando de Freitas and Satinder Singh and Tim Rockt{\""a}schel},\n    booktitle={Forty-first International Conference on Machine Learning},\n    year={2024},\n    url={https://openreview.net/forum?id=bJbSbJskOS}\n}\n```\n",markdown,tab
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1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-1f52a67d-2cca-4352-b39c-6cd9f3effae01765648091845-2025_12_13-18.48.27.228/source.csv

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+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 an embedded Python interpreter to load HuggingFace\n//! tokenizers for accurate token counting.\n\nuse std::path::PathBuf;\n\nuse clap::Parser;\nuse pyo3::prelude::*;\nuse pyo3::types::PyModule;\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 Python tokenizer for exact token counting and truncation.\nstruct PythonTokenizer {\n    tokenizer: Py<PyAny>,\n}\n\nimpl PythonTokenizer {\n    /// Load a HuggingFace tokenizer.\n    fn load(model_name: &str) -> PyResult<Self> {\n        Python::with_gil(|py| {\n            let transformers = PyModule::import(py, ""transformers"")?;\n            let auto_tokenizer = transformers.getattr(""AutoTokenizer"")?;\n            let tokenizer = auto_tokenizer.call_method1(""from_pretrained"", (model_name,))?;\n            Ok(Self {\n                tokenizer: tokenizer.into(),\n            })\n        })\n    }\n}\n\nimpl Tokenizer for PythonTokenizer {\n    fn count_tokens(&self, text: &str) -> usize {\n        Python::with_gil(|py| {\n            let tokenizer = self.tokenizer.as_ref(py);\n            let tokens = tokenizer\n                .call_method1(""encode"", (text,))\n                .expect(""Failed to encode text with tokenizer"");\n            tokens.len().unwrap()\n        })\n    }\n\n    fn truncate_to_max_tokens(&self, text: &str, max_tokens: usize) -> String {\n        Python::with_gil(|py| {\n            let tokenizer = self.tokenizer.as_ref(py);\n            let kwargs = pyo3::types::PyDict::new(py);\n            kwargs.set_item(""max_length"", max_tokens).unwrap();\n            kwargs.set_item(""truncation"", true).unwrap();\n            \n            let tokens = tokenizer\n                .call_method(""encode"", (text,), Some(kwargs))\n                .expect(""Failed to encode text with tokenizer"");\n            \n            tokenizer\n                .call_method1(""decode"", (tokens,))\n                .expect(""Failed to decode tokens"")\n                .extract()\n                .unwrap()\n        })\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 = PythonTokenizer::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\n",rust,tab
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+7,5680,"TERMINAL",0,0,"]633;C  JOBID       USER PARTITION NODES  CPUS ST         SUBMIT_TIME          START_TIME     TIME TIME_LIMIT NODELIST(REASON)\r\n  36313   xiao.liu interacti     1   128  R 2025-12-13T12:56:48 2025-12-13T12:56:48  5:51:44   23:59:00 hai005\r\n  36303   xiao.liu interacti     1   128  R 2025-12-12T23:25:52 2025-12-12T23:25:52 19:22:40   23:59:00 hai006\r\n  36317 mihir.maha  standard     1    10  R 2025-12-13T15:27:10 2025-12-13T15:27:10  3:21:22 1-00:00:00 hai004\r\n  36314   xiao.liu  standard     1   128  R 2025-12-13T13:27:57 2025-12-13T15:13:56  3:34:36   23:59:00 hai007\r\n  36304 nishant.ku  standard     3   624  R 2025-12-13T07:56:43 2025-12-13T07:56:43 10:51:49 1-00:00:00 hai[001-003]\r\n]0;franz.srambical@hai-login1:~/crowd-pilot-serializer",,terminal_output
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+36,32907,"TERMINAL",0,0,"    Cleaning [===>                     ]  18.07%                                                                     \r",,terminal_output
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+46,34019,"TERMINAL",0,0,"    Cleaning [=====>                   ]  26.02%                                                                     \r",,terminal_output
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+48,34297,"TERMINAL",0,0,"    Cleaning [======>                  ]  28.02%                                                                     \r",,terminal_output
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+54,34910,"TERMINAL",0,0,"    Cleaning [=======>                 ]  32.07%                                                                     \r",,terminal_output
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+56,35073,"TERMINAL",0,0,"    Cleaning [=======>                 ]  33.33%                                                                     \rerror: failed to remove file `/fast/home/franz.srambical/crowd-pilot-serializer/target/debug/deps/.nfs000000012d584f9d00000407`\r\n\r\nCaused by:\r\n    Device or resource busy (os error 16)\r\n\r\nError: failed to remove directory `/fast/home/franz.srambical/crowd-pilot-serializer/target/debug/deps`\r\n\r\nCaused by:\r\n  Device or resource busy (os error 16)\r\n]0;franz.srambical@hai-login1:~/crowd-pilot-serializer",,terminal_output
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+62,52574,"TERMINAL",0,0,"]633;Crm: cannot remove 'target/debug/deps/.nfs000000012d584f9d00000407': Device or resource busy\r\nrm: cannot remove 'target/debug/deps/.nfs000000012ce413230000040b': Device or resource busy\r\nrm: cannot remove 'target/debug/deps/.nfs000000012cf90e1000000408': Device or resource busy\r\nrm: cannot remove 'target/debug/deps/.nfs000000012cb3183b0000040a': Device or resource busy\r\nrm: cannot remove 'target/debug/deps/.nfs000000012c0f154900000406': Device or resource busy\r\nrm: cannot remove 'target/debug/deps/.nfs000000012c166c5400000409': Device or resource busy\r\nrm: cannot remove 'target/debug/deps/.nfs000000012d1819c400000405': Device or resource busy\r\n",,terminal_output
+63,56197,"TERMINAL",0,0,"^C\r\n]0;franz.srambical@hai-login1:~/crowd-pilot-serializer",,terminal_output
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+65,59616,"TERMINAL",0,0,"]633;C  JOBID       USER PARTITION NODES  CPUS ST         SUBMIT_TIME          START_TIME     TIME TIME_LIMIT NODELIST(REASON)\r\n  36313   xiao.liu interacti     1   128  R 2025-12-13T12:56:48 2025-12-13T12:56:48  5:52:38   23:59:00 hai005\r\n  36303   xiao.liu interacti     1   128  R 2025-12-12T23:25:52 2025-12-12T23:25:52 19:23:34   23:59:00 hai006\r\n  36317 mihir.maha  standard     1    10  R 2025-12-13T15:27:10 2025-12-13T15:27:10  3:22:16 1-00:00:00 hai004\r\n  36314   xiao.liu  standard     1   128  R 2025-12-13T13:27:57 2025-12-13T15:13:56  3:35:30   23:59:00 hai007\r\n  36304 nishant.ku  standard     3   624  R 2025-12-13T07:56:43 2025-12-13T07:56:43 10:52:43 1-00:00:00 hai[001-003]\r\n]0;franz.srambical@hai-login1:~/crowd-pilot-serializer",,terminal_output

1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-9543d8e2-2376-4957-873e-df7016d502961763465687199-2025_11_18-12.34.49.442/source.csv

@@ -0,0 +1,245 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+2,67,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"12:34:49 PM [info] Activating crowd-code\n12:34:49 PM [info] Recording started\n12:34:49 PM [info] Initializing git provider using file system watchers...\n12:34:49 PM [info] Git repository found\n12:34:49 PM [info] Git provider initialized successfully\n",Log,tab
+3,116,"extension-output-pdoom-org.crowd-code-#1-crowd-code",250,0,"12:34:49 PM [info] Initial git state: [object Object]\n",Log,content
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+5,13030,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",0,0,"# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Optional\n\nimport lightning.pytorch as pl\nimport nemo_run as run\nimport torch\nfrom nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer\n\nfrom nemo.collections.llm.api import finetune, pretrain\nfrom nemo.collections.llm.gpt.data.mock import MockDataModule\nfrom nemo.collections.llm.peft import PEFT_STR2CLS\nfrom nemo.collections.llm.recipes.finetune_default import default_finetune_recipe\nfrom nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger\nfrom nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing\nfrom nemo.collections.llm.recipes.qwen3 import qwen3_model, qwen3_trainer\nfrom nemo.utils.exp_manager import TimingCallback\n\nNAME = ""qwen3_30b_a3b""\n\n\n@run.cli.factory(name=NAME)\ndef model() -> run.Config[pl.LightningModule]:\n    """"""\n    Factory function to create a Qwen3 30B-A3B model configuration.\n    This is a MoE (Mixture of Experts) model with 128 experts.\n\n    Returns:\n        run.Config[pl.LightningModule]: Configuration for the Qwen3 30B-A3B model.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain model=qwen3_30b_a3b ...\n\n        Python API usage:\n            >>> model_config = model()\n            >>> print(model_config)\n    """"""\n    return qwen3_model(version=NAME)\n\n\n@run.cli.factory(target=pretrain, name=NAME)\ndef pretrain_recipe(\n    # General\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    # Trainer\n    tensor_parallelism: int = 4,  # Default for 30B-A3B model\n    pipeline_parallelism: int = 2,\n    pipeline_parallelism_type: Optional[torch.dtype] = None,\n    virtual_pipeline_parallelism: Optional[int] = None,\n    context_parallelism: int = 1,\n    expert_parallelism: Optional[int] = 4,\n    sequence_parallelism: bool = True,\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    max_steps: int = 300000,\n    precision: str = ""bf16-mixed"",\n    accumulate_grad_batches: int = 1,\n    gradient_clip_val: float = 1.0,\n    limit_test_batches: int = 32,\n    limit_val_batches: int = 32,\n    log_every_n_steps: int = 10,\n    val_check_interval: int = 500,\n    # Data\n    global_batch_size=32,\n    micro_batch_size=2,\n    seq_length=4096,\n    # Optimizer\n    warmup_steps=500,\n    constant_steps=0,\n    min_lr=3e-5,\n    max_lr=3e-4,\n    # Training function\n    fn=pretrain,\n) -> run.Partial:\n    """"""\n    Create a pre-training recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for pre-training, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the pre-training run.\n        tensor_parallelism (int): Degree of tensor model parallelism.\n        pipeline_parallelism (int): Degree of pipeline model parallelism.\n        pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism.\n        virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism.\n        context_parallelism (int): Degree of context parallelism.\n        sequence_parallelism (bool): Whether to use sequence parallelism.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        max_steps (int): Maximum number of training steps.\n        precision (str): Precision configuration, one of fp32, 16-mixed or bf16-mixed.\n        accumulate_grad_batches (int): Number of steps per gradient accumulation.\n        gradient_clip_val (float): Value for gradient clipping.\n        limit_test_batches (int): Limit the number of test batches.\n        limit_val_batches (int): Limit the number of validation batches.\n        log_every_n_steps (int): Log every n steps.\n        val_check_interval (int): Run validation every N steps.\n        global_batch_size (int): Global batch size.\n        micro_batch_size (int): Micro batch size.\n        seq_length (int): Sequence length.\n        warmup_steps (int): Number of warmup steps.\n        constant_steps (int): Number of constant steps.\n        min_lr (float): Minimum learning rate.\n        max_lr (float): Maximum learning rate.\n        fn (Callable): The pre-training function to use.\n\n    Returns:\n        run.Partial: Partial configuration for pre-training.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain --factory qwen3_30b_a3b\n            $ nemo llm pretrain --factory ""qwen3_30b_a3b(num_nodes=1, name='my_qwen3_pretrain')""\n\n        Python API usage:\n            >>> recipe = pretrain_recipe(name=""qwen3_pretrain"", num_nodes=1)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses a mock dataset, look for the finetune examples to see how to change the dataset.\n    """"""\n    recipe = run.Partial(\n        fn,\n        model=model(),\n        trainer=qwen3_trainer(\n            tensor_parallelism=tensor_parallelism,\n            pipeline_parallelism=pipeline_parallelism,\n            pipeline_parallelism_type=pipeline_parallelism_type,\n            virtual_pipeline_parallelism=virtual_pipeline_parallelism,\n            context_parallelism=context_parallelism,\n            sequence_parallelism=sequence_parallelism,\n            expert_parallelism=expert_parallelism,\n            num_nodes=num_nodes,\n            num_gpus_per_node=num_gpus_per_node,\n            max_steps=max_steps,\n            precision=precision,\n            accumulate_grad_batches=accumulate_grad_batches,\n            limit_test_batches=limit_test_batches,\n            limit_val_batches=limit_val_batches,\n            log_every_n_steps=log_every_n_steps,\n            val_check_interval=val_check_interval,\n            callbacks=[run.Config(TimingCallback)],\n        ),\n        data=run.Config(\n            MockDataModule,\n            seq_length=seq_length,\n            global_batch_size=global_batch_size,\n            micro_batch_size=micro_batch_size,\n            tokenizer=run.Config(AutoTokenizer, ""Qwen/Qwen3-30B-A3B""),\n        ),\n        log=default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),\n        optim=distributed_fused_adam_with_cosine_annealing(\n            precision=precision,\n            warmup_steps=warmup_steps,\n            constant_steps=constant_steps,\n            min_lr=min_lr,\n            max_lr=max_lr,\n            clip_grad=gradient_clip_val,\n        ),\n        resume=default_resume(),\n    )\n    recipe.model.config.recompute_granularity = ""full""\n    recipe.model.config.recompute_method = ""uniform""\n    recipe.model.config.recompute_num_layers = 1\n    return recipe\n\n\n@run.cli.factory(target=finetune, name=NAME)\ndef finetune_recipe(\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    peft_scheme: Optional[str] = 'lora',\n    packed_sequence: bool = False,\n) -> run.Partial:\n    """"""\n    Create a fine-tuning recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for fine-tuning, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    The recipe uses LoRA (Low-Rank Adaptation) for efficient fine-tuning, unless peft_scheme is set to None.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the fine-tuning run.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        peft_scheme (Optional[str]): Name of the peft scheme to use for fine-tuning.\n            Allowed values: 'lora'/'dora'/'none'/None.\n        packed_sequence (Optional[bool]): Packing multiple training sequences into one long sequence for training\n            efficiency. Default sequence length is 2048.\n\n    Returns:\n        run.Partial: Partial configuration for fine-tuning.\n\n    Examples:\n        CLI usage:\n            $ nemo llm finetune --factory qwen3_30b_a3b\n\n        Python API usage:\n            >>> recipe = finetune_recipe(name=""qwen3_30b_a3b_finetune"", num_nodes=2)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses the SQuAD dataset for fine-tuning.\n    """"""\n    recipe = default_finetune_recipe(\n        model(), ""Qwen/Qwen3-30B-A3B"", dir, name, num_nodes, num_gpus_per_node, packed_sequence\n    )\n    if peft_scheme is None or peft_scheme.lower() == 'none':\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.pipeline_model_parallel_size = 2\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.optim.config.lr = 5e-6\n    elif peft_scheme.lower() in ['lora', 'dora']:\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.peft = run.Config(PEFT_STR2CLS[peft_scheme.lower()])\n        recipe.peft.target_modules = ['linear_qkv', 'linear_proj']\n        recipe.optim.config.lr = 1e-4\n    else:\n        raise ValueError(f""Unrecognized peft scheme: {peft_scheme}"")\n    return recipe\n",python,tab
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+70,3052561,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",0,10041,"# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Optional\n\nimport lightning.pytorch as pl\nimport nemo_run as run\nimport torch\nfrom nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer\n\nfrom nemo.collections.llm.api import finetune, pretrain\nfrom nemo.collections.llm.gpt.data.mock import MockDataModule\nfrom nemo.collections.llm.peft import PEFT_STR2CLS\nfrom nemo.collections.llm.recipes.finetune_default import default_finetune_recipe\nfrom nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger\nfrom nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing\nfrom nemo.collections.llm.recipes.qwen3 import qwen3_model, qwen3_trainer\nfrom nemo.utils.exp_manager import TimingCallback\n\nNAME = ""qwen3_30b_a3b""\n\n\n@run.cli.factory(name=NAME)\ndef model() -> run.Config[pl.LightningModule]:\n    """"""\n    Factory function to create a Qwen3 30B-A3B model configuration.\n    This is a MoE (Mixture of Experts) model with 128 experts.\n\n    Returns:\n        run.Config[pl.LightningModule]: Configuration for the Qwen3 30B-A3B model.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain model=qwen3_30b_a3b ...\n\n        Python API usage:\n            >>> model_config = model()\n            >>> print(model_config)\n    """"""\n    return qwen3_model(version=NAME)\n\n\n@run.cli.factory(target=pretrain, name=NAME)\ndef pretrain_recipe(\n    # General\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    # Trainer\n    tensor_parallelism: int = 4,  # Default for 30B-A3B model\n    pipeline_parallelism: int = 2,\n    pipeline_parallelism_type: Optional[torch.dtype] = None,\n    virtual_pipeline_parallelism: Optional[int] = None,\n    context_parallelism: int = 1,\n    expert_parallelism: Optional[int] = 4,\n    sequence_parallelism: bool = True,\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    max_steps: int = 300000,\n    precision: str = ""bf16-mixed"",\n    accumulate_grad_batches: int = 1,\n    gradient_clip_val: float = 1.0,\n    limit_test_batches: int = 32,\n    limit_val_batches: int = 32,\n    log_every_n_steps: int = 10,\n    val_check_interval: int = 500,\n    # Data\n    global_batch_size=32,\n    micro_batch_size=2,\n    seq_length=4096,\n    # Optimizer\n    warmup_steps=500,\n    constant_steps=0,\n    min_lr=3e-5,\n    max_lr=3e-4,\n    # Training function\n    fn=pretrain,\n) -> run.Partial:\n    """"""\n    Create a pre-training recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for pre-training, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the pre-training run.\n        tensor_parallelism (int): Degree of tensor model parallelism.\n        pipeline_parallelism (int): Degree of pipeline model parallelism.\n        pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism.\n        virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism.\n        context_parallelism (int): Degree of context parallelism.\n        sequence_parallelism (bool): Whether to use sequence parallelism.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        max_steps (int): Maximum number of training steps.\n        precision (str): Precision configuration, one of fp32, 16-mixed or bf16-mixed.\n        accumulate_grad_batches (int): Number of steps per gradient accumulation.\n        gradient_clip_val (float): Value for gradient clipping.\n        limit_test_batches (int): Limit the number of test batches.\n        limit_val_batches (int): Limit the number of validation batches.\n        log_every_n_steps (int): Log every n steps.\n        val_check_interval (int): Run validation every N steps.\n        global_batch_size (int): Global batch size.\n        micro_batch_size (int): Micro batch size.\n        seq_length (int): Sequence length.\n        warmup_steps (int): Number of warmup steps.\n        constant_steps (int): Number of constant steps.\n        min_lr (float): Minimum learning rate.\n        max_lr (float): Maximum learning rate.\n        fn (Callable): The pre-training function to use.\n\n    Returns:\n        run.Partial: Partial configuration for pre-training.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain --factory qwen3_30b_a3b\n            $ nemo llm pretrain --factory ""qwen3_30b_a3b(num_nodes=1, name='my_qwen3_pretrain')""\n\n        Python API usage:\n            >>> recipe = pretrain_recipe(name=""qwen3_pretrain"", num_nodes=1)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses a mock dataset, look for the finetune examples to see how to change the dataset.\n    """"""\n    recipe = run.Partial(\n        fn,\n        model=model(),\n        trainer=qwen3_trainer(\n            tensor_parallelism=tensor_parallelism,\n            pipeline_parallelism=pipeline_parallelism,\n            pipeline_parallelism_type=pipeline_parallelism_type,\n            virtual_pipeline_parallelism=virtual_pipeline_parallelism,\n            context_parallelism=context_parallelism,\n            sequence_parallelism=sequence_parallelism,\n            expert_parallelism=expert_parallelism,\n            num_nodes=num_nodes,\n            num_gpus_per_node=num_gpus_per_node,\n            max_steps=max_steps,\n            precision=precision,\n            accumulate_grad_batches=accumulate_grad_batches,\n            limit_test_batches=limit_test_batches,\n            limit_val_batches=limit_val_batches,\n            log_every_n_steps=log_every_n_steps,\n            val_check_interval=val_check_interval,\n            callbacks=[run.Config(TimingCallback)],\n        ),\n        data=run.Config(\n            MockDataModule,\n            seq_length=seq_length,\n            global_batch_size=global_batch_size,\n            micro_batch_size=micro_batch_size,\n            tokenizer=run.Config(AutoTokenizer, ""Qwen/Qwen3-30B-A3B""),\n        ),\n        log=default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),\n        optim=distributed_fused_adam_with_cosine_annealing(\n            precision=precision,\n            warmup_steps=warmup_steps,\n            constant_steps=constant_steps,\n            min_lr=min_lr,\n            max_lr=max_lr,\n            clip_grad=gradient_clip_val,\n        ),\n        resume=default_resume(),\n    )\n    recipe.model.config.recompute_granularity = ""full""\n    recipe.model.config.recompute_method = ""uniform""\n    recipe.model.config.recompute_num_layers = 1\n    return recipe\n\n\n@run.cli.factory(target=finetune, name=NAME)\ndef finetune_recipe(\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    peft_scheme: Optional[str] = 'lora',\n    packed_sequence: bool = False,\n) -> run.Partial:\n    """"""\n    Create a fine-tuning recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for fine-tuning, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    The recipe uses LoRA (Low-Rank Adaptation) for efficient fine-tuning, unless peft_scheme is set to None.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the fine-tuning run.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        peft_scheme (Optional[str]): Name of the peft scheme to use for fine-tuning.\n            Allowed values: 'lora'/'dora'/'none'/None.\n        packed_sequence (Optional[bool]): Packing multiple training sequences into one long sequence for training\n            efficiency. Default sequence length is 2048.\n\n    Returns:\n        run.Partial: Partial configuration for fine-tuning.\n\n    Examples:\n        CLI usage:\n            $ nemo llm finetune --factory qwen3_30b_a3b\n\n        Python API usage:\n            >>> recipe = finetune_recipe(name=""qwen3_30b_a3b_finetune"", num_nodes=2)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses the SQuAD dataset for fine-tuning.\n    """"""\n    recipe = default_finetune_recipe(\n        model(), ""Qwen/Qwen3-30B-A3B"", dir, name, num_nodes, num_gpus_per_node, packed_sequence\n    )\n    if peft_scheme is None or peft_scheme.lower() == 'none':\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.pipeline_model_parallel_size = 2\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.optim.config.lr = 5e-6\n    elif peft_scheme.lower() in ['lora', 'dora']:\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.peft = run.Config(PEFT_STR2CLS[peft_scheme.lower()])\n        recipe.peft.target_modules = ['linear_qkv', 'linear_proj']\n        recipe.optim.config.lr = 1e-4\n    else:\n        raise ValueError(f""Unrecognized peft scheme: {peft_scheme}"")\n    return recipe\n",python,selection_command
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+155,4549882,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",0,10041,"# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Optional\n\nimport lightning.pytorch as pl\nimport nemo_run as run\nimport torch\nfrom nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer\n\nfrom nemo.collections.llm.api import finetune, pretrain\nfrom nemo.collections.llm.gpt.data.mock import MockDataModule\nfrom nemo.collections.llm.peft import PEFT_STR2CLS\nfrom nemo.collections.llm.recipes.finetune_default import default_finetune_recipe\nfrom nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger\nfrom nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing\nfrom nemo.collections.llm.recipes.qwen3 import qwen3_model, qwen3_trainer\nfrom nemo.utils.exp_manager import TimingCallback\n\nNAME = ""qwen3_30b_a3b""\n\n\n@run.cli.factory(name=NAME)\ndef model() -> run.Config[pl.LightningModule]:\n    """"""\n    Factory function to create a Qwen3 30B-A3B model configuration.\n    This is a MoE (Mixture of Experts) model with 128 experts.\n\n    Returns:\n        run.Config[pl.LightningModule]: Configuration for the Qwen3 30B-A3B model.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain model=qwen3_30b_a3b ...\n\n        Python API usage:\n            >>> model_config = model()\n            >>> print(model_config)\n    """"""\n    return qwen3_model(version=NAME)\n\n\n@run.cli.factory(target=pretrain, name=NAME)\ndef pretrain_recipe(\n    # General\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    # Trainer\n    tensor_parallelism: int = 4,  # Default for 30B-A3B model\n    pipeline_parallelism: int = 2,\n    pipeline_parallelism_type: Optional[torch.dtype] = None,\n    virtual_pipeline_parallelism: Optional[int] = None,\n    context_parallelism: int = 1,\n    expert_parallelism: Optional[int] = 4,\n    sequence_parallelism: bool = True,\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    max_steps: int = 300000,\n    precision: str = ""bf16-mixed"",\n    accumulate_grad_batches: int = 1,\n    gradient_clip_val: float = 1.0,\n    limit_test_batches: int = 32,\n    limit_val_batches: int = 32,\n    log_every_n_steps: int = 10,\n    val_check_interval: int = 500,\n    # Data\n    global_batch_size=32,\n    micro_batch_size=2,\n    seq_length=4096,\n    # Optimizer\n    warmup_steps=500,\n    constant_steps=0,\n    min_lr=3e-5,\n    max_lr=3e-4,\n    # Training function\n    fn=pretrain,\n) -> run.Partial:\n    """"""\n    Create a pre-training recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for pre-training, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the pre-training run.\n        tensor_parallelism (int): Degree of tensor model parallelism.\n        pipeline_parallelism (int): Degree of pipeline model parallelism.\n        pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism.\n        virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism.\n        context_parallelism (int): Degree of context parallelism.\n        sequence_parallelism (bool): Whether to use sequence parallelism.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        max_steps (int): Maximum number of training steps.\n        precision (str): Precision configuration, one of fp32, 16-mixed or bf16-mixed.\n        accumulate_grad_batches (int): Number of steps per gradient accumulation.\n        gradient_clip_val (float): Value for gradient clipping.\n        limit_test_batches (int): Limit the number of test batches.\n        limit_val_batches (int): Limit the number of validation batches.\n        log_every_n_steps (int): Log every n steps.\n        val_check_interval (int): Run validation every N steps.\n        global_batch_size (int): Global batch size.\n        micro_batch_size (int): Micro batch size.\n        seq_length (int): Sequence length.\n        warmup_steps (int): Number of warmup steps.\n        constant_steps (int): Number of constant steps.\n        min_lr (float): Minimum learning rate.\n        max_lr (float): Maximum learning rate.\n        fn (Callable): The pre-training function to use.\n\n    Returns:\n        run.Partial: Partial configuration for pre-training.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain --factory qwen3_30b_a3b\n            $ nemo llm pretrain --factory ""qwen3_30b_a3b(num_nodes=1, name='my_qwen3_pretrain')""\n\n        Python API usage:\n            >>> recipe = pretrain_recipe(name=""qwen3_pretrain"", num_nodes=1)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses a mock dataset, look for the finetune examples to see how to change the dataset.\n    """"""\n    recipe = run.Partial(\n        fn,\n        model=model(),\n        trainer=qwen3_trainer(\n            tensor_parallelism=tensor_parallelism,\n            pipeline_parallelism=pipeline_parallelism,\n            pipeline_parallelism_type=pipeline_parallelism_type,\n            virtual_pipeline_parallelism=virtual_pipeline_parallelism,\n            context_parallelism=context_parallelism,\n            sequence_parallelism=sequence_parallelism,\n            expert_parallelism=expert_parallelism,\n            num_nodes=num_nodes,\n            num_gpus_per_node=num_gpus_per_node,\n            max_steps=max_steps,\n            precision=precision,\n            accumulate_grad_batches=accumulate_grad_batches,\n            limit_test_batches=limit_test_batches,\n            limit_val_batches=limit_val_batches,\n            log_every_n_steps=log_every_n_steps,\n            val_check_interval=val_check_interval,\n            callbacks=[run.Config(TimingCallback)],\n        ),\n        data=run.Config(\n            MockDataModule,\n            seq_length=seq_length,\n            global_batch_size=global_batch_size,\n            micro_batch_size=micro_batch_size,\n            tokenizer=run.Config(AutoTokenizer, ""Qwen/Qwen3-30B-A3B""),\n        ),\n        log=default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),\n        optim=distributed_fused_adam_with_cosine_annealing(\n            precision=precision,\n            warmup_steps=warmup_steps,\n            constant_steps=constant_steps,\n            min_lr=min_lr,\n            max_lr=max_lr,\n            clip_grad=gradient_clip_val,\n        ),\n        resume=default_resume(),\n    )\n    recipe.model.config.recompute_granularity = ""full""\n    recipe.model.config.recompute_method = ""uniform""\n    recipe.model.config.recompute_num_layers = 1\n    return recipe\n\n\n@run.cli.factory(target=finetune, name=NAME)\ndef finetune_recipe(\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    peft_scheme: Optional[str] = 'lora',\n    packed_sequence: bool = False,\n) -> run.Partial:\n    """"""\n    Create a fine-tuning recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for fine-tuning, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    The recipe uses LoRA (Low-Rank Adaptation) for efficient fine-tuning, unless peft_scheme is set to None.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the fine-tuning run.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        peft_scheme (Optional[str]): Name of the peft scheme to use for fine-tuning.\n            Allowed values: 'lora'/'dora'/'none'/None.\n        packed_sequence (Optional[bool]): Packing multiple training sequences into one long sequence for training\n            efficiency. Default sequence length is 2048.\n\n    Returns:\n        run.Partial: Partial configuration for fine-tuning.\n\n    Examples:\n        CLI usage:\n            $ nemo llm finetune --factory qwen3_30b_a3b\n\n        Python API usage:\n            >>> recipe = finetune_recipe(name=""qwen3_30b_a3b_finetune"", num_nodes=2)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses the SQuAD dataset for fine-tuning.\n    """"""\n    recipe = default_finetune_recipe(\n        model(), ""Qwen/Qwen3-30B-A3B"", dir, name, num_nodes, num_gpus_per_node, packed_sequence\n    )\n    if peft_scheme is None or peft_scheme.lower() == 'none':\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.pipeline_model_parallel_size = 2\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.optim.config.lr = 5e-6\n    elif peft_scheme.lower() in ['lora', 'dora']:\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.peft = run.Config(PEFT_STR2CLS[peft_scheme.lower()])\n        recipe.peft.target_modules = ['linear_qkv', 'linear_proj']\n        recipe.optim.config.lr = 1e-4\n    else:\n        raise ValueError(f""Unrecognized peft scheme: {peft_scheme}"")\n    return recipe\n",python,selection_command
+156,4550471,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",10041,0,"",python,selection_command
+157,4559897,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",0,10041,"# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Optional\n\nimport lightning.pytorch as pl\nimport nemo_run as run\nimport torch\nfrom nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer\n\nfrom nemo.collections.llm.api import finetune, pretrain\nfrom nemo.collections.llm.gpt.data.mock import MockDataModule\nfrom nemo.collections.llm.peft import PEFT_STR2CLS\nfrom nemo.collections.llm.recipes.finetune_default import default_finetune_recipe\nfrom nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger\nfrom nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing\nfrom nemo.collections.llm.recipes.qwen3 import qwen3_model, qwen3_trainer\nfrom nemo.utils.exp_manager import TimingCallback\n\nNAME = ""qwen3_30b_a3b""\n\n\n@run.cli.factory(name=NAME)\ndef model() -> run.Config[pl.LightningModule]:\n    """"""\n    Factory function to create a Qwen3 30B-A3B model configuration.\n    This is a MoE (Mixture of Experts) model with 128 experts.\n\n    Returns:\n        run.Config[pl.LightningModule]: Configuration for the Qwen3 30B-A3B model.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain model=qwen3_30b_a3b ...\n\n        Python API usage:\n            >>> model_config = model()\n            >>> print(model_config)\n    """"""\n    return qwen3_model(version=NAME)\n\n\n@run.cli.factory(target=pretrain, name=NAME)\ndef pretrain_recipe(\n    # General\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    # Trainer\n    tensor_parallelism: int = 4,  # Default for 30B-A3B model\n    pipeline_parallelism: int = 2,\n    pipeline_parallelism_type: Optional[torch.dtype] = None,\n    virtual_pipeline_parallelism: Optional[int] = None,\n    context_parallelism: int = 1,\n    expert_parallelism: Optional[int] = 4,\n    sequence_parallelism: bool = True,\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    max_steps: int = 300000,\n    precision: str = ""bf16-mixed"",\n    accumulate_grad_batches: int = 1,\n    gradient_clip_val: float = 1.0,\n    limit_test_batches: int = 32,\n    limit_val_batches: int = 32,\n    log_every_n_steps: int = 10,\n    val_check_interval: int = 500,\n    # Data\n    global_batch_size=32,\n    micro_batch_size=2,\n    seq_length=4096,\n    # Optimizer\n    warmup_steps=500,\n    constant_steps=0,\n    min_lr=3e-5,\n    max_lr=3e-4,\n    # Training function\n    fn=pretrain,\n) -> run.Partial:\n    """"""\n    Create a pre-training recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for pre-training, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the pre-training run.\n        tensor_parallelism (int): Degree of tensor model parallelism.\n        pipeline_parallelism (int): Degree of pipeline model parallelism.\n        pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism.\n        virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism.\n        context_parallelism (int): Degree of context parallelism.\n        sequence_parallelism (bool): Whether to use sequence parallelism.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        max_steps (int): Maximum number of training steps.\n        precision (str): Precision configuration, one of fp32, 16-mixed or bf16-mixed.\n        accumulate_grad_batches (int): Number of steps per gradient accumulation.\n        gradient_clip_val (float): Value for gradient clipping.\n        limit_test_batches (int): Limit the number of test batches.\n        limit_val_batches (int): Limit the number of validation batches.\n        log_every_n_steps (int): Log every n steps.\n        val_check_interval (int): Run validation every N steps.\n        global_batch_size (int): Global batch size.\n        micro_batch_size (int): Micro batch size.\n        seq_length (int): Sequence length.\n        warmup_steps (int): Number of warmup steps.\n        constant_steps (int): Number of constant steps.\n        min_lr (float): Minimum learning rate.\n        max_lr (float): Maximum learning rate.\n        fn (Callable): The pre-training function to use.\n\n    Returns:\n        run.Partial: Partial configuration for pre-training.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain --factory qwen3_30b_a3b\n            $ nemo llm pretrain --factory ""qwen3_30b_a3b(num_nodes=1, name='my_qwen3_pretrain')""\n\n        Python API usage:\n            >>> recipe = pretrain_recipe(name=""qwen3_pretrain"", num_nodes=1)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses a mock dataset, look for the finetune examples to see how to change the dataset.\n    """"""\n    recipe = run.Partial(\n        fn,\n        model=model(),\n        trainer=qwen3_trainer(\n            tensor_parallelism=tensor_parallelism,\n            pipeline_parallelism=pipeline_parallelism,\n            pipeline_parallelism_type=pipeline_parallelism_type,\n            virtual_pipeline_parallelism=virtual_pipeline_parallelism,\n            context_parallelism=context_parallelism,\n            sequence_parallelism=sequence_parallelism,\n            expert_parallelism=expert_parallelism,\n            num_nodes=num_nodes,\n            num_gpus_per_node=num_gpus_per_node,\n            max_steps=max_steps,\n            precision=precision,\n            accumulate_grad_batches=accumulate_grad_batches,\n            limit_test_batches=limit_test_batches,\n            limit_val_batches=limit_val_batches,\n            log_every_n_steps=log_every_n_steps,\n            val_check_interval=val_check_interval,\n            callbacks=[run.Config(TimingCallback)],\n        ),\n        data=run.Config(\n            MockDataModule,\n            seq_length=seq_length,\n            global_batch_size=global_batch_size,\n            micro_batch_size=micro_batch_size,\n            tokenizer=run.Config(AutoTokenizer, ""Qwen/Qwen3-30B-A3B""),\n        ),\n        log=default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),\n        optim=distributed_fused_adam_with_cosine_annealing(\n            precision=precision,\n            warmup_steps=warmup_steps,\n            constant_steps=constant_steps,\n            min_lr=min_lr,\n            max_lr=max_lr,\n            clip_grad=gradient_clip_val,\n        ),\n        resume=default_resume(),\n    )\n    recipe.model.config.recompute_granularity = ""full""\n    recipe.model.config.recompute_method = ""uniform""\n    recipe.model.config.recompute_num_layers = 1\n    return recipe\n\n\n@run.cli.factory(target=finetune, name=NAME)\ndef finetune_recipe(\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    peft_scheme: Optional[str] = 'lora',\n    packed_sequence: bool = False,\n) -> run.Partial:\n    """"""\n    Create a fine-tuning recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for fine-tuning, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    The recipe uses LoRA (Low-Rank Adaptation) for efficient fine-tuning, unless peft_scheme is set to None.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the fine-tuning run.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        peft_scheme (Optional[str]): Name of the peft scheme to use for fine-tuning.\n            Allowed values: 'lora'/'dora'/'none'/None.\n        packed_sequence (Optional[bool]): Packing multiple training sequences into one long sequence for training\n            efficiency. Default sequence length is 2048.\n\n    Returns:\n        run.Partial: Partial configuration for fine-tuning.\n\n    Examples:\n        CLI usage:\n            $ nemo llm finetune --factory qwen3_30b_a3b\n\n        Python API usage:\n            >>> recipe = finetune_recipe(name=""qwen3_30b_a3b_finetune"", num_nodes=2)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses the SQuAD dataset for fine-tuning.\n    """"""\n    recipe = default_finetune_recipe(\n        model(), ""Qwen/Qwen3-30B-A3B"", dir, name, num_nodes, num_gpus_per_node, packed_sequence\n    )\n    if peft_scheme is None or peft_scheme.lower() == 'none':\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.pipeline_model_parallel_size = 2\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.optim.config.lr = 5e-6\n    elif peft_scheme.lower() in ['lora', 'dora']:\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.peft = run.Config(PEFT_STR2CLS[peft_scheme.lower()])\n        recipe.peft.target_modules = ['linear_qkv', 'linear_proj']\n        recipe.optim.config.lr = 1e-4\n    else:\n        raise ValueError(f""Unrecognized peft scheme: {peft_scheme}"")\n    return recipe\n",python,selection_command
+158,4560021,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",10041,0,"",python,selection_command
+159,4560890,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",10041,0,"# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Optional\n\nimport lightning.pytorch as pl\nimport nemo_run as run\nimport torch\nfrom nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer\n\nfrom nemo.collections.llm.api import finetune, pretrain\nfrom nemo.collections.llm.gpt.data.mock import MockDataModule\nfrom nemo.collections.llm.peft import PEFT_STR2CLS\nfrom nemo.collections.llm.recipes.finetune_default import default_finetune_recipe\nfrom nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger\nfrom nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing\nfrom nemo.collections.llm.recipes.qwen3 import qwen3_model, qwen3_trainer\nfrom nemo.utils.exp_manager import TimingCallback\n\nNAME = ""qwen3_30b_a3b""\n\n\n@run.cli.factory(name=NAME)\ndef model() -> run.Config[pl.LightningModule]:\n    """"""\n    Factory function to create a Qwen3 30B-A3B model configuration.\n    This is a MoE (Mixture of Experts) model with 128 experts.\n\n    Returns:\n        run.Config[pl.LightningModule]: Configuration for the Qwen3 30B-A3B model.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain model=qwen3_30b_a3b ...\n\n        Python API usage:\n            >>> model_config = model()\n            >>> print(model_config)\n    """"""\n    return qwen3_model(version=NAME)\n\n\n@run.cli.factory(target=pretrain, name=NAME)\ndef pretrain_recipe(\n    # General\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    # Trainer\n    tensor_parallelism: int = 4,  # Default for 30B-A3B model\n    pipeline_parallelism: int = 2,\n    pipeline_parallelism_type: Optional[torch.dtype] = None,\n    virtual_pipeline_parallelism: Optional[int] = None,\n    context_parallelism: int = 1,\n    expert_parallelism: Optional[int] = 4,\n    sequence_parallelism: bool = True,\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    max_steps: int = 300000,\n    precision: str = ""bf16-mixed"",\n    accumulate_grad_batches: int = 1,\n    gradient_clip_val: float = 1.0,\n    limit_test_batches: int = 32,\n    limit_val_batches: int = 32,\n    log_every_n_steps: int = 10,\n    val_check_interval: int = 500,\n    # Data\n    global_batch_size=32,\n    micro_batch_size=2,\n    seq_length=4096,\n    # Optimizer\n    warmup_steps=500,\n    constant_steps=0,\n    min_lr=3e-5,\n    max_lr=3e-4,\n    # Training function\n    fn=pretrain,\n) -> run.Partial:\n    """"""\n    Create a pre-training recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for pre-training, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the pre-training run.\n        tensor_parallelism (int): Degree of tensor model parallelism.\n        pipeline_parallelism (int): Degree of pipeline model parallelism.\n        pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism.\n        virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism.\n        context_parallelism (int): Degree of context parallelism.\n        sequence_parallelism (bool): Whether to use sequence parallelism.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        max_steps (int): Maximum number of training steps.\n        precision (str): Precision configuration, one of fp32, 16-mixed or bf16-mixed.\n        accumulate_grad_batches (int): Number of steps per gradient accumulation.\n        gradient_clip_val (float): Value for gradient clipping.\n        limit_test_batches (int): Limit the number of test batches.\n        limit_val_batches (int): Limit the number of validation batches.\n        log_every_n_steps (int): Log every n steps.\n        val_check_interval (int): Run validation every N steps.\n        global_batch_size (int): Global batch size.\n        micro_batch_size (int): Micro batch size.\n        seq_length (int): Sequence length.\n        warmup_steps (int): Number of warmup steps.\n        constant_steps (int): Number of constant steps.\n        min_lr (float): Minimum learning rate.\n        max_lr (float): Maximum learning rate.\n        fn (Callable): The pre-training function to use.\n\n    Returns:\n        run.Partial: Partial configuration for pre-training.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain --factory qwen3_30b_a3b\n            $ nemo llm pretrain --factory ""qwen3_30b_a3b(num_nodes=1, name='my_qwen3_pretrain')""\n\n        Python API usage:\n            >>> recipe = pretrain_recipe(name=""qwen3_pretrain"", num_nodes=1)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses a mock dataset, look for the finetune examples to see how to change the dataset.\n    """"""\n    recipe = run.Partial(\n        fn,\n        model=model(),\n        trainer=qwen3_trainer(\n            tensor_parallelism=tensor_parallelism,\n            pipeline_parallelism=pipeline_parallelism,\n            pipeline_parallelism_type=pipeline_parallelism_type,\n            virtual_pipeline_parallelism=virtual_pipeline_parallelism,\n            context_parallelism=context_parallelism,\n            sequence_parallelism=sequence_parallelism,\n            expert_parallelism=expert_parallelism,\n            num_nodes=num_nodes,\n            num_gpus_per_node=num_gpus_per_node,\n            max_steps=max_steps,\n            precision=precision,\n            accumulate_grad_batches=accumulate_grad_batches,\n            limit_test_batches=limit_test_batches,\n            limit_val_batches=limit_val_batches,\n            log_every_n_steps=log_every_n_steps,\n            val_check_interval=val_check_interval,\n            callbacks=[run.Config(TimingCallback)],\n        ),\n        data=run.Config(\n            MockDataModule,\n            seq_length=seq_length,\n            global_batch_size=global_batch_size,\n            micro_batch_size=micro_batch_size,\n            tokenizer=run.Config(AutoTokenizer, ""Qwen/Qwen3-30B-A3B""),\n        ),\n        log=default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),\n        optim=distributed_fused_adam_with_cosine_annealing(\n            precision=precision,\n            warmup_steps=warmup_steps,\n            constant_steps=constant_steps,\n            min_lr=min_lr,\n            max_lr=max_lr,\n            clip_grad=gradient_clip_val,\n        ),\n        resume=default_resume(),\n    )\n    recipe.model.config.recompute_granularity = ""full""\n    recipe.model.config.recompute_method = ""uniform""\n    recipe.model.config.recompute_num_layers = 1\n    return recipe\n\n\n@run.cli.factory(target=finetune, name=NAME)\ndef finetune_recipe(\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    peft_scheme: Optional[str] = 'lora',\n    packed_sequence: bool = False,\n) -> run.Partial:\n    """"""\n    Create a fine-tuning recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for fine-tuning, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    The recipe uses LoRA (Low-Rank Adaptation) for efficient fine-tuning, unless peft_scheme is set to None.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the fine-tuning run.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        peft_scheme (Optional[str]): Name of the peft scheme to use for fine-tuning.\n            Allowed values: 'lora'/'dora'/'none'/None.\n        packed_sequence (Optional[bool]): Packing multiple training sequences into one long sequence for training\n            efficiency. Default sequence length is 2048.\n\n    Returns:\n        run.Partial: Partial configuration for fine-tuning.\n\n    Examples:\n        CLI usage:\n            $ nemo llm finetune --factory qwen3_30b_a3b\n\n        Python API usage:\n            >>> recipe = finetune_recipe(name=""qwen3_30b_a3b_finetune"", num_nodes=2)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses the SQuAD dataset for fine-tuning.\n    """"""\n    recipe = default_finetune_recipe(\n        model(), ""Qwen/Qwen3-30B-A3B"", dir, name, num_nodes, num_gpus_per_node, packed_sequence\n    )\n    if peft_scheme is None or peft_scheme.lower() == 'none':\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.pipeline_model_parallel_size = 2\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.optim.config.lr = 5e-6\n    elif peft_scheme.lower() in ['lora', 'dora']:\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.peft = run.Config(PEFT_STR2CLS[peft_scheme.lower()])\n        recipe.peft.target_modules = ['linear_qkv', 'linear_proj']\n        recipe.optim.config.lr = 1e-4\n    else:\n        raise ValueError(f""Unrecognized peft scheme: {peft_scheme}"")\n    return recipe\n",python,content
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+222,8520791,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",0,10041,"# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Optional\n\nimport lightning.pytorch as pl\nimport nemo_run as run\nimport torch\nfrom nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer\n\nfrom nemo.collections.llm.api import finetune, pretrain\nfrom nemo.collections.llm.gpt.data.mock import MockDataModule\nfrom nemo.collections.llm.peft import PEFT_STR2CLS\nfrom nemo.collections.llm.recipes.finetune_default import default_finetune_recipe\nfrom nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger\nfrom nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing\nfrom nemo.collections.llm.recipes.qwen3 import qwen3_model, qwen3_trainer\nfrom nemo.utils.exp_manager import TimingCallback\n\nNAME = ""qwen3_30b_a3b""\n\n\n@run.cli.factory(name=NAME)\ndef model() -> run.Config[pl.LightningModule]:\n    """"""\n    Factory function to create a Qwen3 30B-A3B model configuration.\n    This is a MoE (Mixture of Experts) model with 128 experts.\n\n    Returns:\n        run.Config[pl.LightningModule]: Configuration for the Qwen3 30B-A3B model.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain model=qwen3_30b_a3b ...\n\n        Python API usage:\n            >>> model_config = model()\n            >>> print(model_config)\n    """"""\n    return qwen3_model(version=NAME)\n\n\n@run.cli.factory(target=pretrain, name=NAME)\ndef pretrain_recipe(\n    # General\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    # Trainer\n    tensor_parallelism: int = 4,  # Default for 30B-A3B model\n    pipeline_parallelism: int = 2,\n    pipeline_parallelism_type: Optional[torch.dtype] = None,\n    virtual_pipeline_parallelism: Optional[int] = None,\n    context_parallelism: int = 1,\n    expert_parallelism: Optional[int] = 4,\n    sequence_parallelism: bool = True,\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    max_steps: int = 300000,\n    precision: str = ""bf16-mixed"",\n    accumulate_grad_batches: int = 1,\n    gradient_clip_val: float = 1.0,\n    limit_test_batches: int = 32,\n    limit_val_batches: int = 32,\n    log_every_n_steps: int = 10,\n    val_check_interval: int = 500,\n    # Data\n    global_batch_size=32,\n    micro_batch_size=2,\n    seq_length=4096,\n    # Optimizer\n    warmup_steps=500,\n    constant_steps=0,\n    min_lr=3e-5,\n    max_lr=3e-4,\n    # Training function\n    fn=pretrain,\n) -> run.Partial:\n    """"""\n    Create a pre-training recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for pre-training, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the pre-training run.\n        tensor_parallelism (int): Degree of tensor model parallelism.\n        pipeline_parallelism (int): Degree of pipeline model parallelism.\n        pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism.\n        virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism.\n        context_parallelism (int): Degree of context parallelism.\n        sequence_parallelism (bool): Whether to use sequence parallelism.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        max_steps (int): Maximum number of training steps.\n        precision (str): Precision configuration, one of fp32, 16-mixed or bf16-mixed.\n        accumulate_grad_batches (int): Number of steps per gradient accumulation.\n        gradient_clip_val (float): Value for gradient clipping.\n        limit_test_batches (int): Limit the number of test batches.\n        limit_val_batches (int): Limit the number of validation batches.\n        log_every_n_steps (int): Log every n steps.\n        val_check_interval (int): Run validation every N steps.\n        global_batch_size (int): Global batch size.\n        micro_batch_size (int): Micro batch size.\n        seq_length (int): Sequence length.\n        warmup_steps (int): Number of warmup steps.\n        constant_steps (int): Number of constant steps.\n        min_lr (float): Minimum learning rate.\n        max_lr (float): Maximum learning rate.\n        fn (Callable): The pre-training function to use.\n\n    Returns:\n        run.Partial: Partial configuration for pre-training.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain --factory qwen3_30b_a3b\n            $ nemo llm pretrain --factory ""qwen3_30b_a3b(num_nodes=1, name='my_qwen3_pretrain')""\n\n        Python API usage:\n            >>> recipe = pretrain_recipe(name=""qwen3_pretrain"", num_nodes=1)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses a mock dataset, look for the finetune examples to see how to change the dataset.\n    """"""\n    recipe = run.Partial(\n        fn,\n        model=model(),\n        trainer=qwen3_trainer(\n            tensor_parallelism=tensor_parallelism,\n            pipeline_parallelism=pipeline_parallelism,\n            pipeline_parallelism_type=pipeline_parallelism_type,\n            virtual_pipeline_parallelism=virtual_pipeline_parallelism,\n            context_parallelism=context_parallelism,\n            sequence_parallelism=sequence_parallelism,\n            expert_parallelism=expert_parallelism,\n            num_nodes=num_nodes,\n            num_gpus_per_node=num_gpus_per_node,\n            max_steps=max_steps,\n            precision=precision,\n            accumulate_grad_batches=accumulate_grad_batches,\n            limit_test_batches=limit_test_batches,\n            limit_val_batches=limit_val_batches,\n            log_every_n_steps=log_every_n_steps,\n            val_check_interval=val_check_interval,\n            callbacks=[run.Config(TimingCallback)],\n        ),\n        data=run.Config(\n            MockDataModule,\n            seq_length=seq_length,\n            global_batch_size=global_batch_size,\n            micro_batch_size=micro_batch_size,\n            tokenizer=run.Config(AutoTokenizer, ""Qwen/Qwen3-30B-A3B""),\n        ),\n        log=default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),\n        optim=distributed_fused_adam_with_cosine_annealing(\n            precision=precision,\n            warmup_steps=warmup_steps,\n            constant_steps=constant_steps,\n            min_lr=min_lr,\n            max_lr=max_lr,\n            clip_grad=gradient_clip_val,\n        ),\n        resume=default_resume(),\n    )\n    recipe.model.config.recompute_granularity = ""full""\n    recipe.model.config.recompute_method = ""uniform""\n    recipe.model.config.recompute_num_layers = 1\n    return recipe\n\n\n@run.cli.factory(target=finetune, name=NAME)\ndef finetune_recipe(\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    peft_scheme: Optional[str] = 'lora',\n    packed_sequence: bool = False,\n) -> run.Partial:\n    """"""\n    Create a fine-tuning recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for fine-tuning, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    The recipe uses LoRA (Low-Rank Adaptation) for efficient fine-tuning, unless peft_scheme is set to None.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the fine-tuning run.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        peft_scheme (Optional[str]): Name of the peft scheme to use for fine-tuning.\n            Allowed values: 'lora'/'dora'/'none'/None.\n        packed_sequence (Optional[bool]): Packing multiple training sequences into one long sequence for training\n            efficiency. Default sequence length is 2048.\n\n    Returns:\n        run.Partial: Partial configuration for fine-tuning.\n\n    Examples:\n        CLI usage:\n            $ nemo llm finetune --factory qwen3_30b_a3b\n\n        Python API usage:\n            >>> recipe = finetune_recipe(name=""qwen3_30b_a3b_finetune"", num_nodes=2)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses the SQuAD dataset for fine-tuning.\n    """"""\n    recipe = default_finetune_recipe(\n        model(), ""Qwen/Qwen3-30B-A3B"", dir, name, num_nodes, num_gpus_per_node, packed_sequence\n    )\n    if peft_scheme is None or peft_scheme.lower() == 'none':\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.pipeline_model_parallel_size = 2\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.optim.config.lr = 5e-6\n    elif peft_scheme.lower() in ['lora', 'dora']:\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.peft = run.Config(PEFT_STR2CLS[peft_scheme.lower()])\n        recipe.peft.target_modules = ['linear_qkv', 'linear_proj']\n        recipe.optim.config.lr = 1e-4\n    else:\n        raise ValueError(f""Unrecognized peft scheme: {peft_scheme}"")\n    return recipe\n",python,selection_command
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+239,15925176,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",1016,0,"",python,selection_command
+240,15925334,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",1023,0,"",python,selection_command
+241,15925510,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",1047,0,"",python,selection_command
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+243,15926409,"nemo/collections/llm/recipes/finetune_default.py",0,0,"# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import TYPE_CHECKING, Any, Optional\n\nimport lightning.pytorch as pl\nimport nemo_run as run\nimport torch\n\nimport nemo.lightning as nl\nfrom nemo.collections import llm\nfrom nemo.collections.llm.gpt.data.packed_sequence import PackedSequenceSpecs\nfrom nemo.collections.llm.peft import DoRA, LoRA\nfrom nemo.collections.llm.recipes.log.default import tensorboard_logger\nfrom nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing\nfrom nemo.collections.llm.recipes.precision.mixed_precision import bf16_mixed\nfrom nemo.lightning.pytorch.callbacks import PEFT\nfrom nemo.utils.exp_manager import TimingCallback\n\nif TYPE_CHECKING:\n    from lightning.pytorch.loggers import TensorBoardLogger, WandbLogger\n\nTokenizerType = Any\n\n\ndef default_finetune_recipe(\n    model: run.Config[pl.LightningModule],\n    resume_path: str,\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    packed_sequence: bool = False,  # once packing recipe is well tested, change this default to true\n    tokenizer: Optional[TokenizerType] = ""model"",\n) -> run.Partial:\n    """"""\n    Create a default fine-tuning recipe for any model.\n\n    This function sets up a template for a complete configuration for fine-tuning, including\n    model, trainer, data, logging, optimization, and resumption settings.\n\n    Args:\n        model (run.Config[pl.LightningModule]): Configuration for a NeMo model.\n        resume_path (str): Path to the Huggingface model or pretrained distributed checkpoint for resume\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the fine-tuning run.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        packed_sequence (bool): Whether to use packed sequence.\n        tokenizer (Optional[TokenizerType]): Tokenizer setting to be applied. Can be 'data' or 'model'\n            or an instance of TokenizerSpec.\n\n    Returns:\n        run.Partial: Partial configuration for fine-tuning.\n\n    See usages of this recipe for further details.\n    """"""\n    if packed_sequence:\n        datamodule = run.Config(\n            llm.SquadDataModule,\n            seq_length=2048,\n            global_batch_size=8,\n            micro_batch_size=1,\n            packed_sequence_specs=PackedSequenceSpecs(packed_sequence_size=2048),\n        )\n    else:\n        datamodule = run.Config(llm.SquadDataModule, seq_length=2048, global_batch_size=128, micro_batch_size=1)\n    recipe = run.Partial(\n        llm.finetune,\n        model=model,\n        trainer=default_finetune_trainer(\n            num_nodes=num_nodes,\n            num_gpus_per_node=num_gpus_per_node,\n        ),\n        data=datamodule,\n        log=default_finetune_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),\n        optim=distributed_fused_adam_with_cosine_annealing(max_lr=1e-4, min_lr=0, warmup_steps=50, adam_beta2=0.98),\n        resume=nemo_resume(resume_path),\n        tokenizer=tokenizer,\n    )\n\n    return recipe\n\n\ndef default_finetune_trainer(\n    tensor_parallelism=1,\n    pipeline_parallelism=1,\n    pipeline_parallelism_type=torch.bfloat16,\n    virtual_pipeline_parallelism=None,\n    context_parallelism=1,\n    sequence_parallelism=False,\n    num_nodes=1,\n    num_gpus_per_node=8,\n    max_steps=1000,\n    limit_test_batches=None,\n    limit_val_batches=None,\n    val_check_interval=30,\n):\n    """"""\n    Create a default fine-tuning trainer for any model.\n\n    This function sets up a template for strategy and trainer.\n\n    Args:\n        See docstrings of MegatronStrategy and Trainer.\n\n    Returns:\n        run.Config: Config for a finetuning trainer.\n\n    See usages of this in recipes for further details.\n    """"""\n    strategy = run.Config(\n        nl.MegatronStrategy,\n        tensor_model_parallel_size=tensor_parallelism,\n        pipeline_model_parallel_size=pipeline_parallelism,\n        pipeline_dtype=pipeline_parallelism_type,\n        virtual_pipeline_model_parallel_size=virtual_pipeline_parallelism,\n        context_parallel_size=context_parallelism,\n        sequence_parallel=sequence_parallelism,\n        gradient_as_bucket_view=True,\n        ckpt_load_strictness=""log_all"",\n    )\n\n    trainer = run.Config(\n        nl.Trainer,\n        accelerator=""gpu"",\n        accumulate_grad_batches=1,\n        devices=num_gpus_per_node,\n        limit_test_batches=limit_test_batches,\n        limit_val_batches=limit_val_batches,\n        log_every_n_steps=1,\n        max_steps=max_steps,\n        num_nodes=num_nodes,\n        plugins=bf16_mixed(),\n        strategy=strategy,\n        use_distributed_sampler=False,\n        val_check_interval=val_check_interval,\n        callbacks=[run.Config(TimingCallback)],\n    )\n\n    return trainer\n\n\ndef default_finetune_log(\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    tensorboard_logger: Optional[run.Config['TensorBoardLogger']] = None,\n    wandb_logger: Optional[run.Config['WandbLogger']] = None,\n) -> run.Config[nl.NeMoLogger]:\n    """"""\n    Create a default fine-tuning logger for any model.\n\n    This function sets up a template for ModelCheckpoint and NeMoLogger.\n\n    Args:\n        See docstrings of ModelCheckpoint and NeMoLogger.\n\n    Returns:\n        run.Config: Config for a finetuning NeMoLogger.\n\n    See usages of this in recipes for further details.\n    """"""\n\n    ckpt = run.Config(\n        nl.ModelCheckpoint,\n        save_last=""link"",\n        save_top_k=2,\n        every_n_train_steps=50,\n        filename=""{model_name}--{val_loss:.2f}-{step}-{consumed_samples}"",\n    )\n\n    return run.Config(\n        nl.NeMoLogger,\n        ckpt=ckpt,\n        name=name,\n        tensorboard=tensorboard_logger,\n        wandb=wandb_logger,\n        log_dir=dir,\n    )\n\n\ndef nemo_resume(model_id: str) -> run.Config[nl.AutoResume]:\n    """"""\n    Configure automatic resumption from a NeMo checkpoint converted from Huggingface for\n    https://huggingface.co/{model_id}.\n\n    This NeMo checkpoint should be converted from Huggingface beforehand, using nemo.collections.llm.import_ckpt.\n    When converting the checkpoint, the NeMo checkpoint will be saved in NEMO_HOME (set to ~/.cache/nemo by default).\n\n    This function sets up the configuration to resume training from path nemo://{model_id}.\n    This translates to the full path {NEMO_HOME}/models/{model_id}.\n\n    Args:\n        model_id (str): Path to the Huggingface model or pretrained distributed checkpoint for resume\n\n    Returns:\n        run.Config[nl.AutoResume]: Configuration for resuming from NeMo checkpoint.\n    """"""\n    return run.Config(\n        nl.AutoResume,\n        restore_config=run.Config(nl.RestoreConfig, path=f""nemo://{model_id}""),\n    )\n\n\n@run.cli.factory(name='lora')\ndef lora() -> run.Config[PEFT]:\n    """"""\n    Factory function to create a LoRA configuration.\n\n    Returns:\n        run.Config[PEFT]: Configuration for the LoRA class.\n\n    Examples:\n        CLI usage:\n            $ nemo llm finetune -f llama3_8b peft=lora\n\n        Python API usage:\n            >>> lora_config = lora()\n            >>> print(lora_config)\n    """"""\n    return run.Config(LoRA)\n\n\n@run.cli.factory(name='dora')\ndef dora() -> run.Config[PEFT]:\n    """"""\n    Factory function to create a DoRA configuration.\n\n    Returns:\n        run.Config[PEFT]: Configuration for the DoRA class.\n\n    Examples:\n        CLI usage:\n            $ nemo llm finetune -f llama3_8b peft=dora\n\n        Python API usage:\n            >>> dora_config = dora()\n            >>> print(dora_config)\n    """"""\n    return run.Config(DoRA)\n",python,tab
+244,15926417,"nemo/collections/llm/recipes/finetune_default.py",1382,0,"",python,selection_command
+245,15929448,"nemo/collections/llm/recipes/finetune_default.py",3372,0,"",python,selection_keyboard

1f15334ab7e6820c9fda17c961659882ef9853cc80f7356b9a9b22f286fd7389/crowd-code-ac4f665d-1bc1-467a-98b3-5da2178968731760857710663-2025_10_19-09.08.50.386/source.csv

@@ -0,0 +1,6 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+1,3,"slurm/dev/franz/berlin/coinrun/sample/maskgit/sample_mila_submission_case_study_vanilla.sh",0,0,"#!/usr/bin/env bash\n\n#SBATCH --nodes=1\n#SBATCH --ntasks-per-node=1\n#SBATCH --time=24:00:00\n#SBATCH --cpus-per-task=8\n#SBATCH --gres=gpu:1\n#SBATCH --output=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/coinrun/dynamics_sample/%x_%j.log\n#SBATCH --error=/fast/project/HFMI_SynergyUnit/jafar_ws/logs/franz/coinrun/dynamics_sample/%x_%j.log\n#SBATCH --job-name=coinrun_sample_maskgit_mila_submission_case_study_vanilla\n\n# Activate virtual environment\nsource .venv/bin/activate\n\narray_records_dir=""/fast/project/HFMI_SynergyUnit/jafar_ws/data/coinrun/array_records_10M_npy_arr_rec/array_record/test""\nCHECKPOINT_PATH=""/fast/project/HFMI_SynergyUnit/jafar_ws/checkpoints/coinrun/dynamics/dynamics_case_study_dataset_10M_30031""\n\ncurrent_branch=$(git rev-parse --abbrev-ref HEAD)\nif [ ""$current_branch"" != ""main"" ]; then\n    echo ""This script must be run from the main branch. Current branch is $current_branch. Exiting.""\n    exit 1\nfi\n\necho ""Sampling from checkpoint: $CHECKPOINT_PATH""\n\nsrun python jasmine/sample.py \\n    --seed=1 \\n    --maskgit_steps=1 \\n    --tokenizer_ffn_dim=512 \\n    --tokenizer_num_blocks=8 \\n    --dyna_ffn_dim=512 \\n    --dyna_num_blocks=12 \\n    --output_dir=gifs/dynamics_case_study_dataset_10M_vanilla \\n    --checkpoint $CHECKPOINT_PATH \\n    --data_dir=$array_records_dir \\n    --seq_len=16 \\n    --batch_size=32 \\n    --patch_size=4 \\n    --start_frame=4 \\n    --image_height=64 \\n    --image_width=64 \\n    --dyna_type=maskgit\n",shellscript,tab
+2,262,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"9:08:50 AM [info] Activating crowd-code\n9:08:50 AM [info] Recording started\n9:08:50 AM [info] Initializing git provider using file system watchers...\n9:08:50 AM [info] Git repository found\n9:08:50 AM [info] Git provider initialized successfully\n9:08:50 AM [info] Initial git state: [object Object]\n",Log,tab
+3,4665,"TERMINAL",0,0,"",,terminal_command
+4,11496,"TERMINAL",0,0,"",,terminal_command
+5,106853,"TERMINAL",0,0,"",,terminal_command

4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-1fa9b85d-3794-4f3b-b7a0-5170b7d2faaa1762362332596-2025_11_05-18.05.39.648/source.csv

@@ -0,0 +1,19 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+1,3,"src/extension/completions-core/vscode-node/lib/src/prompt/completionsPromptFactory/componentsCompletionsPromptFactory.tsx",0,0,"/*---------------------------------------------------------------------------------------------\n *  Copyright (c) Microsoft Corporation. All rights reserved.\n *  Licensed under the MIT License. See License.txt in the project root for license information.\n *--------------------------------------------------------------------------------------------*/\n\n/** @jsxRuntime automatic */\n/** @jsxImportSource ../../../../prompt/jsx-runtime/ */\nimport { CopilotContentExclusionManager, StatusBarEvent } from '../../contentExclusion/contentExclusionManager';\nimport { ICompletionsContextService } from '../../context';\nimport { logger, LogTarget } from '../../logger';\n\nimport { IInstantiationService, ServicesAccessor } from '../../../../../../../util/vs/platform/instantiation/common/instantiation';\nimport { ICompletionsTelemetryService } from '../../../../bridge/src/completionsTelemetryServiceBridge';\nimport { DataPipe, VirtualPrompt } from '../../../../prompt/src/components/virtualPrompt';\nimport { TokenizerName } from '../../../../prompt/src/tokenization';\nimport { CancellationToken, Position } from '../../../../types/src';\nimport { CompletionState } from '../../completionState';\nimport { telemetryException, TelemetryWithExp } from '../../telemetry';\nimport { TextDocumentContents } from '../../textDocument';\nimport { CodeSnippets } from '../components/codeSnippets';\nimport { CompletionsContext } from '../components/completionsContext';\nimport { CompletionsPromptOk, CompletionsPromptRenderer } from '../components/completionsPromptRenderer';\nimport { ContextProviderBridge } from '../components/contextProviderBridge';\nimport { CurrentFile } from '../components/currentFile';\nimport { DocumentMarker } from '../components/marker';\nimport { RecentEdits } from '../components/recentEdits';\nimport { SimilarFiles } from '../components/similarFiles';\nimport { splitContextCompletionsPrompt } from '../components/splitContextPrompt';\nimport { SplitContextPromptRenderer } from '../components/splitContextPromptRenderer';\nimport { Traits } from '../components/traits';\nimport {\n\tContextProviderTelemetry,\n\tmatchContextItems,\n\tResolvedContextItem,\n\ttelemetrizeContextItems,\n\tuseContextProviderAPI,\n} from '../contextProviderRegistry';\nimport { getCodeSnippetsFromContextItems } from '../contextProviders/codeSnippets';\nimport {\n\tCodeSnippetWithId,\n\tSupportedContextItemWithId,\n\tTraitWithId,\n} from '../contextProviders/contextItemSchemas';\nimport { getTraitsFromContextItems, ReportTraitsTelemetry } from '../contextProviders/traits';\nimport { componentStatisticsToPromptMatcher, ContextProviderStatistics } from '../contextProviderStatistics';\nimport {\n\t_contextTooShort,\n\t_copilotContentExclusion,\n\t_promptCancelled,\n\t_promptError,\n\tgetPromptOptions,\n\tMIN_PROMPT_CHARS,\n\tPromptResponse,\n\ttrimLastLine,\n} from '../prompt';\nimport { isIncludeNeighborFilesActive } from '../similarFiles/neighborFiles';\nimport {\n\tCompletionsPromptFactory,\n\tCompletionsPromptOptions,\n\tPromptOpts,\n} from './completionsPromptFactory';\n\nexport type CompletionRequestDocument = TextDocumentContents;\n\nexport type CompletionRequestData = {\n\tdocument: CompletionRequestDocument;\n\tposition: Position;\n\ttelemetryData: TelemetryWithExp;\n\tcancellationToken?: CancellationToken;\n\t// see inlineCompletions data param\n\tdata?: unknown;\n\t// Context provider items\n\ttraits?: TraitWithId[];\n\tcodeSnippets?: CodeSnippetWithId[];\n\tturnOffSimilarFiles?: boolean;\n\tsuffixMatchThreshold?: number;\n\tmaxPromptTokens: number;\n\ttokenizer?: TokenizerName;\n};\n\nexport function isCompletionRequestData(data: unknown): data is CompletionRequestData {\n\tif (!data || typeof data !== 'object') { return false; }\n\n\tconst req = data as Partial<CompletionRequestData>;\n\n\t// Check document\n\tif (!req.document) { return false; }\n\n\t// Check position\n\tif (!req.position) { return false; }\n\tif (req.position.line === undefined) { return false; }\n\tif (req.position.character === undefined) { return false; }\n\n\t// Check telemetryData\n\tif (!req.telemetryData) { return false; }\n\n\treturn true;\n}\n\nexport enum PromptOrdering {\n\tDefault = 'default',\n\tSplitContext = 'splitContext',\n}\n\ntype DeclarativePromptFunction = typeof defaultCompletionsPrompt;\ntype AvailableDeclarativePrompts = {\n\t[K in PromptOrdering]: {\n\t\tpromptFunction: DeclarativePromptFunction;\n\t\trenderer: typeof CompletionsPromptRenderer;\n\t};\n};\n\nconst availableDeclarativePrompts: AvailableDeclarativePrompts = {\n\t[PromptOrdering.Default]: {\n\t\tpromptFunction: defaultCompletionsPrompt,\n\t\trenderer: CompletionsPromptRenderer,\n\t},\n\t[PromptOrdering.SplitContext]: {\n\t\tpromptFunction: splitContextCompletionsPrompt,\n\t\trenderer: SplitContextPromptRenderer,\n\t},\n};\n\n// The weights mimic the PromptPriorityList from prompt/src/wishlist.ts\nfunction defaultCompletionsPrompt(accessor: ServicesAccessor) {\n\tconst ctx = accessor.get(ICompletionsContextService);\n\treturn (\n\t\t<>\n\t\t\t<CompletionsContext>\n\t\t\t\t<DocumentMarker ctx={ctx} weight={0.7} />\n\t\t\t\t<Traits weight={0.6} />\n\t\t\t\t<CodeSnippets ctx={ctx} weight={0.9} />\n\t\t\t\t<SimilarFiles ctx={ctx} weight={0.8} />\n\t\t\t\t<RecentEdits ctx={ctx} weight={0.99} />\n\t\t\t</CompletionsContext>\n\t\t\t<CurrentFile weight={1} />\n\t\t</>\n\t);\n}\n\n// Exported for testing\nexport class ComponentsCompletionsPromptFactory implements CompletionsPromptFactory {\n\tprivate virtualPrompt: VirtualPrompt;\n\tprivate pipe: DataPipe;\n\tprivate renderer: CompletionsPromptRenderer;\n\tprivate promptOrdering: PromptOrdering;\n\tprivate logTarget;\n\n\tconstructor(\n\t\tvirtualPrompt: VirtualPrompt | undefined = undefined,\n\t\tordering: PromptOrdering | undefined = undefined,\n\t\t@ICompletionsContextService private readonly ctx: ICompletionsContextService,\n\t\t@IInstantiationService private readonly instantiationService: IInstantiationService,\n\t\t@ICompletionsTelemetryService private readonly completionsTelemetryService: ICompletionsTelemetryService,\n\t) {\n\t\tthis.logTarget = this.ctx.get(LogTarget);\n\t\tthis.promptOrdering = ordering ?? PromptOrdering.Default;\n\t\tthis.virtualPrompt = virtualPrompt ?? new VirtualPrompt(this.completionsPrompt());\n\t\tthis.pipe = this.virtualPrompt.createPipe();\n\t\tthis.renderer = this.getRenderer();\n\t}\n\n\tasync prompt(opts: CompletionsPromptOptions, cancellationToken?: CancellationToken): Promise<PromptResponse> {\n\t\ttry {\n\t\t\treturn await this.createPromptUnsafe(opts, cancellationToken);\n\t\t} catch (e) {\n\t\t\treturn this.errorPrompt(e as Error);\n\t\t}\n\t}\n\n\tasync createPromptUnsafe(\n\t\t{ completionId, completionState, telemetryData, promptOpts }: CompletionsPromptOptions,\n\t\tcancellationToken?: CancellationToken\n\t): Promise<PromptResponse> {\n\t\tconst { maxPromptLength, suffixPercent, suffixMatchThreshold } = this.instantiationService.invokeFunction(getPromptOptions,\n\t\t\ttelemetryData,\n\t\t\tcompletionState.textDocument.detectedLanguageId\n\t\t);\n\n\t\tconst failFastPrompt = await this.failFastPrompt(\n\t\t\tcompletionState.textDocument,\n\t\t\tcompletionState.position,\n\t\t\tsuffixPercent,\n\t\t\tcancellationToken\n\t\t);\n\t\tif (failFastPrompt) {\n\t\t\treturn failFastPrompt;\n\t\t}\n\n\t\t// TODO: Prompt ordering changes are triggered by ExP changes.\n\t\t// TODO@benibenj remove this as its always true (except in tests)\n\t\tconst promptOrdering = promptOpts?.separateContext ? PromptOrdering.SplitContext : PromptOrdering.Default;\n\t\tthis.setPromptOrdering(promptOrdering);\n\n\t\tconst start = performance.now();\n\n\t\tconst { traits, codeSnippets, turnOffSimilarFiles, resolvedContextItems } = await this.resolveContext(\n\t\t\tcompletionId,\n\t\t\tcompletionState,\n\t\t\ttelemetryData,\n\t\t\tcancellationToken,\n\t\t\tpromptOpts\n\t\t);\n\n\t\tawait this.updateComponentData(\n\t\t\tcompletionState.textDocument,\n\t\t\tcompletionState.position,\n\t\t\ttraits,\n\t\t\tcodeSnippets,\n\t\t\ttelemetryData,\n\t\t\tturnOffSimilarFiles,\n\t\t\tmaxPromptLength,\n\t\t\tcancellationToken,\n\t\t\tpromptOpts,\n\t\t\tsuffixMatchThreshold,\n\t\t\tpromptOpts?.tokenizer\n\t\t);\n\n\t\tif (cancellationToken?.isCancellationRequested) {\n\t\t\treturn _promptCancelled;\n\t\t}\n\n\t\tconst snapshot = this.virtualPrompt.snapshot(cancellationToken);\n\t\tconst snapshotStatus = snapshot.status;\n\t\tif (snapshotStatus === 'cancelled') {\n\t\t\treturn _promptCancelled;\n\t\t} else if (snapshotStatus === 'error') {\n\t\t\treturn this.errorPrompt(snapshot.error);\n\t\t}\n\n\t\tconst rendered = this.renderer.render(\n\t\t\tsnapshot.snapshot!,\n\t\t\t{\n\t\t\t\tdelimiter: '\n',\n\t\t\t\ttokenizer: promptOpts?.tokenizer,\n\t\t\t\tpromptTokenLimit: maxPromptLength,\n\t\t\t\tsuffixPercent: suffixPercent,\n\t\t\t\tlanguageId: completionState.textDocument.detectedLanguageId,\n\t\t\t},\n\t\t\tcancellationToken\n\t\t);\n\t\tif (rendered.status === 'cancelled') {\n\t\t\treturn _promptCancelled;\n\t\t} else if (rendered.status === 'error') {\n\t\t\treturn this.errorPrompt(rendered.error);\n\t\t}\n\n\t\tconst [prefix, trailingWs] = trimLastLine(rendered.prefix);\n\t\tconst renderedTrimmed = { ...rendered, prefix };\n\n\t\tlet contextProvidersTelemetry: ContextProviderTelemetry[] | undefined = undefined;\n\t\tconst languageId = completionState.textDocument.detectedLanguageId;\n\t\tif (this.instantiationService.invokeFunction(useContextProviderAPI, languageId, telemetryData)) {\n\t\t\tconst promptMatcher = componentStatisticsToPromptMatcher(rendered.metadata.componentStatistics);\n\t\t\tthis.ctx\n\t\t\t\t.get(ContextProviderStatistics)\n\t\t\t\t.getStatisticsForCompletion(completionId)\n\t\t\t\t.computeMatch(promptMatcher);\n\t\t\tcontextProvidersTelemetry = telemetrizeContextItems(this.ctx, completionId, resolvedContextItems);\n\t\t\t// To support generating context provider metrics of completion in COffE.\n\t\t\tlogger.debug(this.logTarget, `Context providers telemetry: '${JSON.stringify(contextProvidersTelemetry)}'`);\n\t\t}\n\t\tconst end = performance.now();\n\t\tthis.resetIfEmpty(rendered);\n\t\treturn this.successPrompt(renderedTrimmed, end, start, trailingWs, contextProvidersTelemetry);\n\t}\n\n\tprivate async updateComponentData(\n\t\ttextDocument: CompletionRequestDocument,\n\t\tposition: Position,\n\t\ttraits: TraitWithId[] | undefined,\n\t\tcodeSnippets: CodeSnippetWithId[] | undefined,\n\t\ttelemetryData: TelemetryWithExp,\n\t\tturnOffSimilarFiles: boolean,\n\t\tmaxPromptLength: number,\n\t\tcancellationToken?: CancellationToken,\n\t\topts: PromptOpts = {},\n\t\tsuffixMatchThreshold?: number,\n\t\ttokenizer?: TokenizerName\n\t) {\n\t\tconst completionRequestData = this.createRequestData(\n\t\t\ttextDocument,\n\t\t\tposition,\n\t\t\ttelemetryData,\n\t\t\tcancellationToken,\n\t\t\topts,\n\t\t\tmaxPromptLength,\n\t\t\ttraits,\n\t\t\tcodeSnippets,\n\t\t\tturnOffSimilarFiles,\n\t\t\tsuffixMatchThreshold,\n\t\t\ttokenizer\n\t\t);\n\t\tawait this.pipe.pump(completionRequestData);\n\t}\n\n\tprivate async resolveContext(\n\t\tcompletionId: string,\n\t\tcompletionState: CompletionState,\n\t\ttelemetryData: TelemetryWithExp,\n\t\tcancellationToken?: CancellationToken,\n\t\topts: PromptOpts = {}\n\t): Promise<{\n\t\ttraits: TraitWithId[] | undefined;\n\t\tcodeSnippets: CodeSnippetWithId[] | undefined;\n\t\tturnOffSimilarFiles: boolean;\n\t\tresolvedContextItems: ResolvedContextItem[];\n\t}> {\n\t\tlet resolvedContextItems: ResolvedContextItem[] = [];\n\t\tlet traits: TraitWithId[] | undefined;\n\t\tlet codeSnippets: CodeSnippetWithId[] | undefined;\n\t\tlet turnOffSimilarFiles = false;\n\t\tif (this.instantiationService.invokeFunction(useContextProviderAPI, completionState.textDocument.detectedLanguageId, telemetryData)) {\n\t\t\tresolvedContextItems = await this.ctx.get(ContextProviderBridge).resolution(completionId);\n\t\t\tconst { textDocument } = completionState;\n\t\t\t// Turn off neighboring files if:\n\t\t\t// - it's not explicitly enabled via EXP flag\n\t\t\t// - there are matched context providers\n\t\t\tconst matchedContextItems = resolvedContextItems.filter(matchContextItems);\n\t\t\tif (!this.instantiationService.invokeFunction(similarFilesEnabled, textDocument.detectedLanguageId, matchedContextItems, telemetryData)) {\n\t\t\t\tturnOffSimilarFiles = true;\n\t\t\t}\n\n\t\t\ttraits = await this.instantiationService.invokeFunction(getTraitsFromContextItems, completionId, matchedContextItems);\n\t\t\tvoid this.instantiationService.invokeFunction(ReportTraitsTelemetry,\n\t\t\t\t`contextProvider.traits`,\n\t\t\t\ttraits,\n\t\t\t\ttextDocument.detectedLanguageId,\n\t\t\t\ttextDocument.detectedLanguageId, // TextDocumentContext does not have clientLanguageId\n\t\t\t\ttelemetryData\n\t\t\t);\n\n\t\t\tcodeSnippets = await this.instantiationService.invokeFunction(getCodeSnippetsFromContextItems,\n\t\t\t\tcompletionId,\n\t\t\t\tmatchedContextItems,\n\t\t\t\ttextDocument.detectedLanguageId\n\t\t\t);\n\t\t}\n\t\treturn { traits, codeSnippets, turnOffSimilarFiles, resolvedContextItems };\n\t}\n\n\tprivate async failFastPrompt(\n\t\ttextDocument: TextDocumentContents,\n\t\tposition: Position,\n\t\tsuffixPercent: number,\n\t\tcancellationToken: CancellationToken | undefined\n\t) {\n\t\tif (cancellationToken?.isCancellationRequested) {\n\t\t\treturn _promptCancelled;\n\t\t}\n\t\tif (\n\t\t\t(\n\t\t\t\tawait this.ctx\n\t\t\t\t\t.get(CopilotContentExclusionManager)\n\t\t\t\t\t.evaluate(textDocument.uri, textDocument.getText(), StatusBarEvent.UPDATE)\n\t\t\t).isBlocked\n\t\t) {\n\t\t\treturn _copilotContentExclusion;\n\t\t}\n\n\t\tconst eligibleChars = suffixPercent > 0 ? textDocument.getText().length : textDocument.offsetAt(position);\n\t\tif (eligibleChars < MIN_PROMPT_CHARS) {\n\t\t\t// Too short context\n\t\t\treturn _contextTooShort;\n\t\t}\n\t}\n\n\tprivate createRequestData(\n\t\ttextDocument: CompletionRequestDocument,\n\t\tposition: Position,\n\t\ttelemetryData: TelemetryWithExp,\n\t\tcancellationToken: CancellationToken | undefined,\n\t\topts: PromptOpts,\n\t\tmaxPromptLength: number,\n\t\ttraits?: TraitWithId[],\n\t\tcodeSnippets?: CodeSnippetWithId[],\n\t\tturnOffSimilarFiles?: boolean,\n\t\tsuffixMatchThreshold?: number,\n\t\ttokenizer?: TokenizerName\n\t): CompletionRequestData {\n\t\treturn {\n\t\t\tdocument: textDocument,\n\t\t\tposition,\n\t\t\ttelemetryData,\n\t\t\tcancellationToken,\n\t\t\tdata: opts.data,\n\t\t\ttraits,\n\t\t\tcodeSnippets,\n\t\t\tturnOffSimilarFiles,\n\t\t\tsuffixMatchThreshold,\n\t\t\tmaxPromptTokens: maxPromptLength,\n\t\t\ttokenizer,\n\t\t};\n\t}\n\n\tprivate resetIfEmpty(rendered: CompletionsPromptOk) {\n\t\tif (rendered.prefix.length === 0 && rendered.suffix.length === 0) {\n\t\t\tthis.reset();\n\t\t}\n\t}\n\n\tprivate successPrompt(\n\t\trendered: CompletionsPromptOk,\n\t\tend: number,\n\t\tstart: number,\n\t\ttrailingWs: string,\n\t\tcontextProvidersTelemetry?: ContextProviderTelemetry[]\n\t): PromptResponse {\n\t\treturn {\n\t\t\ttype: 'prompt',\n\t\t\tprompt: {\n\t\t\t\tprefix: rendered.prefix,\n\t\t\t\tprefixTokens: rendered.prefixTokens,\n\t\t\t\tsuffix: rendered.suffix,\n\t\t\t\tsuffixTokens: rendered.suffixTokens,\n\t\t\t\tcontext: rendered.context,\n\t\t\t\tisFimEnabled: rendered.suffix.length > 0,\n\t\t\t},\n\t\t\tcomputeTimeMs: end - start,\n\t\t\ttrailingWs,\n\t\t\tneighborSource: new Map(),\n\t\t\tmetadata: rendered.metadata,\n\t\t\tcontextProvidersTelemetry,\n\t\t};\n\t}\n\n\tprivate errorPrompt(error: Error): PromptResponse {\n\t\ttelemetryException(this.completionsTelemetryService, error, 'PromptComponents.CompletionsPromptFactory');\n\t\tthis.reset();\n\t\treturn _promptError;\n\t}\n\n\tprivate reset() {\n\t\tthis.renderer = this.getRenderer();\n\t\tthis.virtualPrompt = new VirtualPrompt(this.completionsPrompt());\n\t\tthis.pipe = this.virtualPrompt.createPipe();\n\t}\n\n\tprivate setPromptOrdering(ordering: PromptOrdering) {\n\t\tif (this.promptOrdering !== ordering) {\n\t\t\tthis.promptOrdering = ordering;\n\t\t\tthis.reset();\n\t\t}\n\t}\n\n\tprivate completionsPrompt() {\n\t\tconst promptFunction =\n\t\t\tavailableDeclarativePrompts[this.promptOrdering]?.promptFunction ?? defaultCompletionsPrompt;\n\t\treturn this.instantiationService.invokeFunction(promptFunction);\n\t}\n\n\tprivate getRenderer() {\n\t\tconst promptInfo =\n\t\t\tavailableDeclarativePrompts[this.promptOrdering] ?? availableDeclarativePrompts[PromptOrdering.Default];\n\t\treturn new promptInfo.renderer();\n\t}\n}\n\n// Similar files is enabled if:\n// - the languageId is C/C++.\n// - it's explicitly enabled via EXP flag or config.\n// - no code snippets are provided (which includes the case when all providers error).\nfunction similarFilesEnabled(\n\taccessor: ServicesAccessor,\n\tdetectedLanguageId: string,\n\tmatchedContextItems: ResolvedContextItem<SupportedContextItemWithId>[],\n\ttelemetryData: TelemetryWithExp\n) {\n\tconst cppLanguageIds = ['cpp', 'c'];\n\tconst includeNeighboringFiles =\n\t\tisIncludeNeighborFilesActive(accessor, detectedLanguageId, telemetryData) || cppLanguageIds.includes(detectedLanguageId);\n\treturn (\n\t\tincludeNeighboringFiles || !matchedContextItems.some(ci => ci.data.some(item => item.type === 'CodeSnippet'))\n\t);\n}\n",typescriptreact,tab
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+12,1005293,"src/extension/completions-core/vscode-node/lib/src/prompt/completionsPromptFactory/componentsCompletionsPromptFactory.tsx",4908,0,"",typescriptreact,selection_command
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+16,2093995,"TERMINAL",0,0,"cd ..",,terminal_command
+17,2094451,"TERMINAL",0,0,"ls",,terminal_command
+18,2094466,"TERMINAL",0,0,"]633;Ccleanrl  crowd-code-player  crowd-pilot  jafar  jax_cache  maxtext  npm-global  oai-compatible-copilot  sbatch-runner  Stoix  vscode-crowd-pilot-chat\r\n]0;franz.srambical@hai-login2:~",,terminal_output

4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-4b7a193b-6fd0-48b6-a605-a1ce6ba179221764439942282-2025_11_29-19.12.25.371/source.csv

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+2,106,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"7:12:25 PM [info] Activating crowd-code\n7:12:25 PM [info] Recording started\n7:12:25 PM [info] Initializing git provider using file system watchers...\n7:12:25 PM [info] No workspace folder found\n",Log,tab
+3,2027,"extension-output-pdoom-org.crowd-code-#1-crowd-code",194,0,"7:12:27 PM [info] Retrying git provider initialization...\n7:12:27 PM [info] No workspace folder found\n",Log,content
+4,9498,"Untitled-1",0,0,"",plaintext,tab
+5,11149,"TERMINAL",0,0,"Test",,terminal_focus
+6,11153,"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-549b7320-9591-428d-ba6a-d3d8fb65a0001764416891089-2025_11_29-12.48.19.667/source.csv

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+4,4333,"TERMINAL",0,0,"zsh",,terminal_focus
+5,20864,"TERMINAL",0,0,"cd webview-ui/sto",,terminal_command
+6,20866,"TERMINAL",0,0,"]633;Ccd: no such file or directory: webview-ui/sto\r\n%                                                                                                                                                                      \r \r",,terminal_output
+7,22604,"TERMINAL",0,0,"cd webview-ui/sto",,terminal_command
+8,22605,"TERMINAL",0,0,"]633;Ccd: no such file or directory: webview-ui/sto\r\n%                                                                                                                                                                      \r \r",,terminal_output
+9,28427,"TERMINAL",0,0,"cd webview-ui/.storybook",,terminal_command
+10,28428,"TERMINAL",0,0,"]633;C%                                                                                                                                                                      \r \r",,terminal_output
+11,31719,"TERMINAL",0,0,"npm run storybook",,terminal_command
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+13,32548,"TERMINAL",0,0,"\r\n> webview-ui@0.3.0 storybook\r\n> storybook dev -p 6006\r\n\r\nsh: storybook: command not found\r\n⠙%                                                                                                                                                                      \r \r",,terminal_output
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+15,201179,"TERMINAL",0,0,"]633;C%                                                                                                                                                                      \r \r",,terminal_output
+16,202166,"TERMINAL",0,0,"cd ..",,terminal_command
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+18,208467,"TERMINAL",0,0,"npm install",,terminal_command
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+39,214040,"TERMINAL",0,0,"⠋npm warn deprecated rimraf@3.0.2: Rimraf versions prior to v4 are no longer supported\r\n⠋npm warn deprecated rimraf@3.0.2: Rimraf versions prior to v4 are no longer supported\r\n⠋npm warn deprecated rimraf@3.0.2: Rimraf versions prior to v4 are no longer supported\r\n⠋",,terminal_output
+40,214240,"TERMINAL",0,0,"⠙npm warn deprecated glob@7.2.3: Glob versions prior to v9 are no longer supported\r\n⠙npm warn deprecated glob@7.2.3: Glob versions prior to v9 are no longer supported\r\n⠙npm warn deprecated glob@7.2.3: Glob versions prior to v9 are no longer supported\r\n⠙npm warn deprecated glob@7.2.3: Glob versions prior to v9 are no longer supported\r\n⠙",,terminal_output
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+924,301052,"TERMINAL",0,0,"⠋\r\nadded 978 packages, and audited 984 packages in 18s\r\n⠋\r\n⠋187 packages are looking for funding\r\n⠋  run `npm fund` for details\r\n⠋\r\nfound 0 vulnerabilities\r\n⠋%                                                                                                                                                                      \r \r",,terminal_output
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+927,374749,"TERMINAL",0,0,"\r\n> webview-ui@0.3.0 storybook\r\n> storybook dev -p 6006\r\n\r\n",,terminal_output
+928,375925,"TERMINAL",0,0,"storybook v9.1.7\r\n\r\n",,terminal_output
+929,376672,"TERMINAL",0,0,"(node:36013) ExperimentalWarning: Type Stripping is an experimental feature and might change at any time\r\n(Use `node --trace-warnings ...` to show where the warning was created)\r\n",,terminal_output
+930,376730,"TERMINAL",0,0,"Attention: Storybook now collects completely anonymous telemetry regarding usage. This information is used to shape Storybook's roadmap and prioritize features.\r\nYou can learn more, including how to opt-out if you'd not like to participate in this anonymous program, by visiting the following URL:\r\nhttps://storybook.js.org/telemetry\r\n\r\n",,terminal_output
+931,377210,"TERMINAL",0,0,"info => Starting manager..\r\n",,terminal_output
+932,377331,"TERMINAL",0,0,"No story files found for the specified pattern: src/**/*.mdx\r\n",,terminal_output
+933,377521,"TERMINAL",0,0,"info => Starting preview..\r\n",,terminal_output
+934,379138,"TERMINAL",0,0,"Building webview for vscode\r\n",,terminal_output
+935,380938,"TERMINAL",0,0,"info Using tsconfig paths for react-docgen\r\nThe `define` option contains an object with ""PATH"" for ""process.env"" key. It looks like you may have passed the entire `process.env` object to `define`, which can unintentionally expose all environment variables. This poses a security risk and is discouraged.\r\n",,terminal_output
+936,387648,"TERMINAL",0,0,"╭──────────────────────────────────────────────────────────────────────────────────────────╮\r\n│                                                                                          │\r\n│   Storybook 9.1.7 for react-vite started                                                 │\r\n│   312 ms for manager and 10 s for preview                                                │\r\n│                                                                                          │\r\n│    Local:            http://localhost:6006/                                              │\r\n│    On your network:  http://192.168.178.68:6006/                                         │\r\n│                                                                                          │\r\n│   A new version (10.1.0) is available!                                                   │\r\n│                                                                                          │\r\n│   Upgrade now: npx storybook@latest upgrade                                              │\r\n│                                                                                          │\r\n│   Read full changelog: https://github.com/storybookjs/storybook/blob/main/CHANGELOG.md   │\r\n│                                                                                          │\r\n╰──────────────────────────────────────────────────────────────────────────────────────────╯\r\n",,terminal_output
+937,390868,"TERMINAL",0,0,"[baseline-browser-mapping] The data in this module is over two months old.  To ensure accurate Baseline data, please update: `npm i baseline-browser-mapping@latest -D`\r\n",,terminal_output
+938,395179,"TERMINAL",0,0,"12:54:54 PM [vite] (client) Pre-transform error: Failed to resolve import ""@shared/proto/cline/common"" from ""src/context/ClineAuthContext.tsx"". Does the file exist?\r\n  Plugin: vite:import-analysis\r\n  File: /Users/franzsrambical/Documents/pdoom/cline/webview-ui/src/context/ClineAuthContext.tsx:2:29\r\n  20 |  import { jsxDEV as _jsxDEV } from ""react/jsx-dev-runtime"";\r\n  21 |  var _s = $RefreshSig$(), _s1 = $RefreshSig$();\r\n  22 |  import { EmptyRequest } from ""@shared/proto/cline/common"";\r\n     |                                ^\r\n  23 |  import deepEqual from ""fast-deep-equal"";\r\n  24 |  import { createContext, useCallback, useContext, useEffect, useMemo, useState } from ""react"";\r\n12:54:54 PM [vite] (client) Pre-transform error: Failed to resolve import ""../services/grpc-client"" from ""src/context/ExtensionStateContext.tsx"". Does the file exist?\r\n  Plugin: vite:import-analysis\r\n  File: /Users/franzsrambical/Documents/pdoom/cline/webview-ui/src/context/ExtensionStateContext.tsx:31:91\r\n  34 |  import { Environment } from ""../../../src/config"";\r\n  35 |  import { basetenDefaultModelId, basetenModels, groqDefaultModelId, groqModels, openRouterDefaultModelId, openRouterDe...\r\n  36 |  import { McpServiceClient, ModelsServiceClient, StateServiceClient, UiServiceClient } from ""../services/grpc-client"";\r\n     |                                                                                              ^\r\n  37 |  export const ExtensionStateContext = /*#__PURE__*/ createContext(undefined);\r\n  38 |  export const ExtensionStateContextProvider = ({ children })=>{\r\n",,terminal_output
+939,395343,"TERMINAL",0,0,"12:54:54 PM [vite] (client) Pre-transform error: Failed to resolve import ""@shared/proto/cline/ui"" from ""../src/shared/proto-conversions/cline-message.ts"". Does the file exist?\r\n  Plugin: vite:import-analysis\r\n  File: /Users/franzsrambical/Documents/pdoom/cline/src/shared/proto-conversions/cline-message.ts:3:88\r\n  1  |  import { ClineAsk, ClineMessageType, ClineSay } from ""@shared/proto/cline/ui"";\r\n     |                                                        ^\r\n  2  |  // Helper function to convert ClineAsk string to enum\r\n  3  |  function convertClineAskToProtoEnum(ask) {\r\n12:54:54 PM [vite] (client) Pre-transform error: Failed to resolve import ""@shared/proto/cline/mcp"" from ""../src/shared/proto-conversions/mcp/mcp-server-conversion.ts"". Does the file exist?\r\n  Plugin: vite:import-analysis\r\n  File: /Users/franzsrambical/Documents/pdoom/cline/src/shared/proto-conversions/mcp/mcp-server-conversion.ts:7:7\r\n  1  |  import { McpServerStatus } from ""@shared/proto/cline/mcp"";\r\n     |                                   ^\r\n  2  |  // Helper to convert TS status to Proto enum\r\n  3  |  function convertMcpStatusToProto(status) {\r\n12:54:54 PM [vite] (client) Pre-transform error: Failed to resolve import ""@shared/proto/cline/models"" from ""../src/shared/proto-conversions/models/typeConversion.ts"". Does the file exist?\r\n  Plugin: vite:import-analysis\r\n  File: /Users/franzsrambical/Documents/pdoom/cline/src/shared/proto-conversions/models/typeConversion.ts:8:7\r\n  1  |  import { OpenRouterModelInfo, ThinkingConfig } from ""@shared/proto/cline/models"";\r\n     |                                                       ^\r\n  2  |  /**\r\n  3  |   * Convert protobuf ThinkingConfig to application ThinkingConfig\r\n",,terminal_output
+940,398882,"TERMINAL",0,0,"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n12:54:58 PM [vite] Internal server error: Failed to resolve import ""@shared/proto/cline/common"" from ""src/context/ClineAuthContext.tsx"". Does the file exist?\r\n  Plugin: vite:import-analysis\r\n  File: /Users/franzsrambical/Documents/pdoom/cline/webview-ui/src/context/ClineAuthContext.tsx:2:29\r\n  20 |  import { jsxDEV as _jsxDEV } from ""react/jsx-dev-runtime"";\r\n  21 |  var _s = $RefreshSig$(), _s1 = $RefreshSig$();\r\n  22 |  import { EmptyRequest } from ""@shared/proto/cline/common"";\r\n     |                                ^\r\n  23 |  import deepEqual from ""fast-deep-equal"";\r\n  24 |  import { createContext, useCallback, useContext, useEffect, useMemo, useState } from ""react"";\r\n      at TransformPluginContext._formatLog (file:///Users/franzsrambical/Documents/pdoom/cline/webview-ui/node_modules/vite/dist/node/chunks/config.js:29618:43)\r\n      at TransformPluginContext.error (file:///Users/franzsrambical/Documents/pdoom/cline/webview-ui/node_modules/vite/dist/node/chunks/config.js:29615:14)\r\n      at normalizeUrl (file:///Users/franzsrambical/Documents/pdoom/cline/webview-ui/node_modules/vite/dist/node/chunks/config.js:27738:18)\r\n      at async file:///Users/franzsrambical/Documents/pdoom/cline/webview-ui/node_modules/vite/dist/node/chunks/config.js:27796:32\r\n      at async Promise.all (index 3)\r\n      at async TransformPluginContext.transform (file:///Users/franzsrambical/Documents/pdoom/cline/webview-ui/node_modules/vite/dist/node/chunks/config.js:27764:4)\r\n      at async EnvironmentPluginContainer.transform (file:///Users/franzsrambical/Documents/pdoom/cline/webview-ui/node_modules/vite/dist/node/chunks/config.js:29416:14)\r\n      at async loadAndTransform (file:///Users/franzsrambical/Documents/pdoom/cline/webview-ui/node_modules/vite/dist/node/chunks/config.js:23287:26)\r\n      at async viteTransformMiddleware (file:///Users/franzsrambical/Documents/pdoom/cline/webview-ui/node_modules/vite/dist/node/chunks/config.js:25159:20)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n12:54:58 PM [vite] Internal server error: Failed to resolve import ""../services/grpc-client"" from ""src/context/ExtensionStateContext.tsx"". Does the file exist?\r\n  Plugin: vite:import-analysis\r\n  File: /Users/franzsrambical/Documents/pdoom/cline/webview-ui/src/context/ExtensionStateContext.tsx:31:91\r\n  34 |  import { Environment } from ""../../../src/config"";\r\n  35 |  import { basetenDefaultModelId, basetenModels, groqDefaultModelId, groqModels, openRouterDefaultModelId, openRouterDe...\r\n  36 |  import { McpServiceClient, ModelsServiceClient, StateServiceClient, UiServiceClient } from ""../services/grpc-client"";\r\n     |                                                                                              ^\r\n  37 |  export const ExtensionStateContext = /*#__PURE__*/ createContext(undefined);\r\n  38 |  export const ExtensionStateContextProvider = ({ children })=>{\r\n      at TransformPluginContext._formatLog (file:///Users/franzsrambical/Documents/pdoom/cline/webview-ui/node_modules/vite/dist/node/chunks/config.js:29618:43)\r\n      at TransformPluginContext.error (file:///Users/franzsrambical/Documents/pdoom/cline/webview-ui/node_modules/vite/dist/node/chunks/config.js:29615:14)\r\n      at normalizeUrl (file:///Users/franzsrambical/Documents/pdoom/cline/webview-ui/node_modules/vite/dist/node/chunks/config.js:27738:18)\r\n      at async file:///Users/franzsrambical/Documents/pdoom/cline/webview-ui/node_modules/vite/dist/node/chunks/config.js:27796:32\r\n      at async Promise.all (index 17)\r\n      at async TransformPluginContext.transform (file:///Users/franzsrambical/Documents/pdoom/cline/webview-ui/node_modules/vite/dist/node/chunks/config.js:27764:4)\r\n      at async EnvironmentPluginContainer.transform (file:///Users/franzsrambical/Documents/pdoom/cline/webview-ui/node_modules/vite/dist/node/chunks/config.js:29416:14)\r\n      at async loadAndTransform (file:///Users/franzsrambical/Documents/pdoom/cline/webview-ui/node_modules/vite/dist/node/chunks/config.js:23287:26)\r\n      at async viteTransformMiddleware (file:///Users/franzsrambical/Documents/pdoom/cline/webview-ui/node_modules/vite/dist/node/chunks/config.js:25159:20)\r\n12:54:58 PM [vite] (client) Pre-transform error: Failed to resolve import ""@shared/proto/cline/mcp"" from ""../src/shared/proto-conversions/mcp/mcp-server-conversion.ts"". Does the file exist?\r\n  Plugin: vite:import-analysis\r\n  File: /Users/franzsrambical/Documents/pdoom/cline/src/shared/proto-conversions/mcp/mcp-server-conversion.ts:7:7\r\n  1  |  import { McpServerStatus } from ""@shared/proto/cline/mcp"";\r\n     |                                   ^\r\n  2  |  // Helper to convert TS status to Proto enum\r\n  3  |  function convertMcpStatusToProto(status) {\r\n12:54:58 PM [vite] (client) Pre-transform error: Failed to resolve import ""@shared/proto/cline/models"" from ""../src/shared/proto-conversions/models/typeConversion.ts"". Does the file exist?\r\n  Plugin: vite:import-analysis\r\n  File: /Users/franzsrambical/Documents/pdoom/cline/src/shared/proto-conversions/models/typeConversion.ts:8:7\r\n  1  |  import { OpenRouterModelInfo, ThinkingConfig } from ""@shared/proto/cline/models"";\r\n     |                                                       ^\r\n  2  |  /**\r\n  3  |   * Convert protobuf ThinkingConfig to application ThinkingConfig\r\n12:54:58 PM [vite] (client) Pre-transform error: Failed to resolve import ""@shared/proto/cline/ui"" from ""../src/shared/proto-conversions/cline-message.ts"". Does the file exist?\r\n  Plugin: vite:import-analysis\r\n  File: /Users/franzsrambical/Documents/pdoom/cline/src/shared/proto-conversions/cline-message.ts:3:88\r\n  1  |  import { ClineAsk, ClineMessageType, ClineSay } from ""@shared/proto/cline/ui"";\r\n     |                                                        ^\r\n  2  |  // Helper function to convert ClineAsk string to enum\r\n  3  |  function convertClineAskToProtoEnum(ask) {\r\n",,terminal_output
+941,2981449,"TERMINAL",0,0,"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n1:38:01 PM [vite] (client) page reload src/services/grpc-client.ts\r\n",,terminal_output
+942,2987755,"TERMINAL",0,0,"1:38:07 PM [vite] (client) ✨ new dependencies optimized: @bufbuild/protobuf/wire, uuid\r\n1:38:07 PM [vite] (client) ✨ optimized dependencies changed. reloading\r\n",,terminal_output
+943,2992777,"TERMINAL",0,0,"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n1:38:12 PM [vite] (client) hmr update /src/index.css\r\n",,terminal_output
+944,3008542,"TERMINAL",0,0,"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n1:38:28 PM [vite] (client) hmr update /src/index.css (x2)\r\n",,terminal_output
+945,3026313,"TERMINAL",0,0,"\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n1:38:45 PM [vite] (client) hmr update /src/index.css (x3)\r\n",,terminal_output
+946,3034948,"TERMINAL",0,0,"^C",,terminal_output
+947,3035530,"TERMINAL",0,0,"⠙%                                                                                                                                           \r \r",,terminal_output
+948,3036908,"TERMINAL",0,0,"npm run storybook",,terminal_command
+949,3036960,"TERMINAL",0,0,"]633;C",,terminal_output
+950,3037124,"TERMINAL",0,0,"\r\n> webview-ui@0.3.0 storybook\r\n> storybook dev -p 6006\r\n\r\n",,terminal_output
+951,3038143,"TERMINAL",0,0,"storybook v9.1.7\r\n\r\n",,terminal_output
+952,3038311,"TERMINAL",0,0,"(node:43008) ExperimentalWarning: Type Stripping is an experimental feature and might change at any time\r\n(Use `node --trace-warnings ...` to show where the warning was created)\r\n",,terminal_output
+953,3038770,"TERMINAL",0,0,"info => Starting manager..\r\n",,terminal_output
+954,3038852,"TERMINAL",0,0,"No story files found for the specified pattern: src/**/*.mdx\r\n",,terminal_output
+955,3039062,"TERMINAL",0,0,"info => Starting preview..\r\n",,terminal_output
+956,3040101,"TERMINAL",0,0,"Building webview for vscode\r\n",,terminal_output
+957,3042195,"TERMINAL",0,0,"info Using tsconfig paths for react-docgen\r\n=> Failed to build the preview\r\nReferenceError: __dirname is not defined\r\n    at Object.viteFinal (./.storybook/main.ts:13:17)\r\n    at ./node_modules/storybook/dist/common/index.cjs:23650:18\r\n    at async createViteServer (./node_modules/@storybook/builder-vite/dist/index.js:92:25)\r\n    at async Module.start (./node_modules/@storybook/builder-vite/dist/index.js:92:658)\r\n    at async storybookDevServer (./node_modules/storybook/dist/core-server/index.cjs:42917:79)\r\n    at async buildOrThrow (./node_modules/storybook/dist/core-server/index.cjs:39285:12)\r\n    at async buildDevStandalone (./node_modules/storybook/dist/core-server/index.cjs:44170:78)\r\n    at async withTelemetry (./node_modules/storybook/dist/core-server/index.cjs:42279:12)\r\n    at async dev (./node_modules/storybook/dist/cli/bin/index.cjs:5905:3)\r\n    at async r.<anonymous> (./node_modules/storybook/dist/cli/bin/index.cjs:6015:74)\r\n\r\nBroken build, fix the error above.\r\nYou may need to refresh the browser.\r\n\r\n[?25l? Would you like to help improve Storybook by sending anonymous crash reports? › (Y/n)",,terminal_output
+958,3056937,"TERMINAL",0,0,"✔ Would you like to help improve Storybook by sending anonymous crash reports? … no\r\n[?25h",,terminal_output
+959,3057351,"TERMINAL",0,0,"⠙%                                                                                                                                           \r \r",,terminal_output
+960,3058995,"TERMINAL",0,0,"npm run storybook",,terminal_command
+961,3059046,"TERMINAL",0,0,"]633;C",,terminal_output
+962,3059206,"TERMINAL",0,0,"\r\n> webview-ui@0.3.0 storybook\r\n> storybook dev -p 6006\r\n\r\n",,terminal_output
+963,3059534,"TERMINAL",0,0,"storybook v9.1.7\r\n\r\n",,terminal_output
+964,3059688,"TERMINAL",0,0,"(node:43148) ExperimentalWarning: Type Stripping is an experimental feature and might change at any time\r\n(Use `node --trace-warnings ...` to show where the warning was created)\r\n",,terminal_output
+965,3060001,"TERMINAL",0,0,"info => Starting manager..\r\nNo story files found for the specified pattern: src/**/*.mdx\r\n",,terminal_output
+966,3060120,"TERMINAL",0,0,"info => Starting preview..\r\n",,terminal_output
+967,3060243,"TERMINAL",0,0,"Building webview for vscode\r\n",,terminal_output
+968,3060516,"TERMINAL",0,0,"info Using tsconfig paths for react-docgen\r\n=> Failed to build the preview\r\nReferenceError: __dirname is not defined\r\n    at Object.viteFinal (./.storybook/main.ts:13:17)\r\n    at ./node_modules/storybook/dist/common/index.cjs:23650:18\r\n    at async createViteServer (./node_modules/@storybook/builder-vite/dist/index.js:92:25)\r\n    at async Module.start (./node_modules/@storybook/builder-vite/dist/index.js:92:658)\r\n    at async storybookDevServer (./node_modules/storybook/dist/core-server/index.cjs:42917:79)\r\n    at async buildOrThrow (./node_modules/storybook/dist/core-server/index.cjs:39285:12)\r\n    at async buildDevStandalone (./node_modules/storybook/dist/core-server/index.cjs:44170:78)\r\n    at async withTelemetry (./node_modules/storybook/dist/core-server/index.cjs:42279:12)\r\n    at async dev (./node_modules/storybook/dist/cli/bin/index.cjs:5905:3)\r\n    at async r.<anonymous> (./node_modules/storybook/dist/cli/bin/index.cjs:6015:74)\r\n\r\nBroken build, fix the error above.\r\nYou may need to refresh the browser.\r\n\r\n",,terminal_output
+969,3060956,"TERMINAL",0,0,"⠙%                                                                                                                                           \r \r",,terminal_output
+970,3078567,"TERMINAL",0,0,"npm run storybook",,terminal_command
+971,3078619,"TERMINAL",0,0,"]633;C",,terminal_output
+972,3078759,"TERMINAL",0,0,"\r\n> webview-ui@0.3.0 storybook\r\n> storybook dev -p 6006\r\n\r\n",,terminal_output
+973,3079085,"TERMINAL",0,0,"storybook v9.1.7\r\n\r\n",,terminal_output
+974,3079242,"TERMINAL",0,0,"(node:43265) ExperimentalWarning: Type Stripping is an experimental feature and might change at any time\r\n(Use `node --trace-warnings ...` to show where the warning was created)\r\n",,terminal_output
+975,3079508,"TERMINAL",0,0,"info => Starting manager..\r\nNo story files found for the specified pattern: src/**/*.mdx\r\n",,terminal_output
+976,3079609,"TERMINAL",0,0,"info => Starting preview..\r\n",,terminal_output
+977,3079857,"TERMINAL",0,0,"Building webview for vscode\r\n",,terminal_output
+978,3080137,"TERMINAL",0,0,"info Using tsconfig paths for react-docgen\r\n=> Failed to build the preview\r\nReferenceError: __dirname is not defined\r\n    at Object.viteFinal (./.storybook/main.ts:13:17)\r\n    at ./node_modules/storybook/dist/common/index.cjs:23650:18\r\n    at async createViteServer (./node_modules/@storybook/builder-vite/dist/index.js:92:25)\r\n    at async Module.start (./node_modules/@storybook/builder-vite/dist/index.js:92:658)\r\n    at async storybookDevServer (./node_modules/storybook/dist/core-server/index.cjs:42917:79)\r\n    at async buildOrThrow (./node_modules/storybook/dist/core-server/index.cjs:39285:12)\r\n    at async buildDevStandalone (./node_modules/storybook/dist/core-server/index.cjs:44170:78)\r\n    at async withTelemetry (./node_modules/storybook/dist/core-server/index.cjs:42279:12)\r\n    at async dev (./node_modules/storybook/dist/cli/bin/index.cjs:5905:3)\r\n    at async r.<anonymous> (./node_modules/storybook/dist/cli/bin/index.cjs:6015:74)\r\n\r\nBroken build, fix the error above.\r\nYou may need to refresh the browser.\r\n\r\n",,terminal_output
+979,3080559,"TERMINAL",0,0,"⠙%                                                                                                                                           \r \r",,terminal_output
+980,3082050,"TERMINAL",0,0,"cd ..",,terminal_command
+981,3082051,"TERMINAL",0,0,"]633;C%                                                                                                                                           \r \r",,terminal_output
+982,3087158,"TERMINAL",0,0,"npm run storybook",,terminal_command
+983,3087209,"TERMINAL",0,0,"]633;C",,terminal_output
+984,3087345,"TERMINAL",0,0,"\r\n> claude-dev@3.38.3 storybook\r\n> cd webview-ui && npm run storybook\r\n\r\n",,terminal_output
+985,3087427,"TERMINAL",0,0,"\r\n> webview-ui@0.3.0 storybook\r\n> storybook dev -p 6006\r\n\r\n",,terminal_output
+986,3087717,"TERMINAL",0,0,"storybook v9.1.7\r\n\r\n",,terminal_output
+987,3087864,"TERMINAL",0,0,"(node:43453) ExperimentalWarning: Type Stripping is an experimental feature and might change at any time\r\n(Use `node --trace-warnings ...` to show where the warning was created)\r\n",,terminal_output
+988,3088122,"TERMINAL",0,0,"info => Starting manager..\r\nNo story files found for the specified pattern: src/**/*.mdx\r\n",,terminal_output
+989,3088223,"TERMINAL",0,0,"info => Starting preview..\r\n",,terminal_output
+990,3088362,"TERMINAL",0,0,"Building webview for vscode\r\n",,terminal_output
+991,3088601,"TERMINAL",0,0,"info Using tsconfig paths for react-docgen\r\n=> Failed to build the preview\r\nReferenceError: __dirname is not defined\r\n    at Object.viteFinal (./.storybook/main.ts:13:17)\r\n    at ./node_modules/storybook/dist/common/index.cjs:23650:18\r\n    at async createViteServer (./node_modules/@storybook/builder-vite/dist/index.js:92:25)\r\n    at async Module.start (./node_modules/@storybook/builder-vite/dist/index.js:92:658)\r\n    at async storybookDevServer (./node_modules/storybook/dist/core-server/index.cjs:42917:79)\r\n    at async buildOrThrow (./node_modules/storybook/dist/core-server/index.cjs:39285:12)\r\n    at async buildDevStandalone (./node_modules/storybook/dist/core-server/index.cjs:44170:78)\r\n    at async withTelemetry (./node_modules/storybook/dist/core-server/index.cjs:42279:12)\r\n    at async dev (./node_modules/storybook/dist/cli/bin/index.cjs:5905:3)\r\n    at async r.<anonymous> (./node_modules/storybook/dist/cli/bin/index.cjs:6015:74)\r\n\r\nBroken build, fix the error above.\r\nYou may need to refresh the browser.\r\n\r\n",,terminal_output
+992,3088992,"TERMINAL",0,0,"⠙⠙%                                                                                                                                           \r \r",,terminal_output
+993,3161357,"TERMINAL",0,0,"cd webview-ui",,terminal_command
+994,3161357,"TERMINAL",0,0,"]633;C%                                                                                                                                           \r \r",,terminal_output
+995,3162625,"TERMINAL",0,0,"npm run storybook",,terminal_command
+996,3162677,"TERMINAL",0,0,"]633;C",,terminal_output
+997,3162941,"TERMINAL",0,0,"\r\n> webview-ui@0.3.0 storybook\r\n> storybook dev -p 6006\r\n\r\n",,terminal_output
+998,3163284,"TERMINAL",0,0,"storybook v9.1.7\r\n\r\n",,terminal_output
+999,3163477,"TERMINAL",0,0,"(node:43708) ExperimentalWarning: Type Stripping is an experimental feature and might change at any time\r\n(Use `node --trace-warnings ...` to show where the warning was created)\r\n",,terminal_output
+1000,3163764,"TERMINAL",0,0,"info => Starting manager..\r\nNo story files found for the specified pattern: src/**/*.mdx\r\n",,terminal_output
+1001,3163883,"TERMINAL",0,0,"info => Starting preview..\r\n",,terminal_output
+1002,3164047,"TERMINAL",0,0,"Building webview for vscode\r\n",,terminal_output
+1003,3164319,"TERMINAL",0,0,"info Using tsconfig paths for react-docgen\r\nThe `define` option contains an object with ""PATH"" for ""process.env"" key. It looks like you may have passed the entire `process.env` object to `define`, which can unintentionally expose all environment variables. This poses a security risk and is discouraged.\r\n",,terminal_output
+1004,3169884,"TERMINAL",0,0,"╭──────────────────────────────────────────────────────────────────────────────────────────╮\r\n│                                                                                          │\r\n│   Storybook 9.1.7 for react-vite started                                                 │\r\n│   157 ms for manager and 5.95 s for preview                                              │\r\n│                                                                                          │\r\n│    Local:            http://localhost:6006/                                              │\r\n│    On your network:  http://192.168.178.68:6006/                                         │\r\n│                                                                                          │\r\n│   A new version (10.1.0) is available!                                                   │\r\n│                                                                                          │\r\n│   Upgrade now: npx storybook@latest upgrade                                              │\r\n│                                                                                          │\r\n│   Read full changelog: https://github.com/storybookjs/storybook/blob/main/CHANGELOG.md   │\r\n│                                                                                          │\r\n╰──────────────────────────────────────────────────────────────────────────────────────────╯\r\n",,terminal_output
+1005,3170751,"TERMINAL",0,0,"[baseline-browser-mapping] The data in this module is over two months old.  To ensure accurate Baseline data, please update: `npm i baseline-browser-mapping@latest -D`\r\n",,terminal_output
+1006,3210633,"TERMINAL",0,0,"The request id ""/Users/franzsrambical/Documents/pdoom/cline/node_modules/@vscode/codicons/dist/codicon.ttf"" is outside of Vite serving allow list.\r\n\r\n- /Users/franzsrambical/Documents/pdoom/cline/webview-ui\r\n\r\nRefer to docs https://vite.dev/config/server-options.html#server-fs-allow for configurations and more details.\r\n\r\n",,terminal_output

4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-9543d8e2-2376-4957-873e-df7016d502961763465687199-2025_11_18-12.34.49.442/source.csv

@@ -0,0 +1,226 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+2,67,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"12:34:49 PM [info] Activating crowd-code\n12:34:49 PM [info] Recording started\n12:34:49 PM [info] Initializing git provider using file system watchers...\n12:34:49 PM [info] Git repository found\n12:34:49 PM [info] Git provider initialized successfully\n",Log,tab
+3,116,"extension-output-pdoom-org.crowd-code-#1-crowd-code",250,0,"12:34:49 PM [info] Initial git state: [object Object]\n",Log,content
+4,2975,"extension-output-pdoom-org.crowd-code-#1-crowd-code",304,0,"",Log,selection_mouse
+5,13030,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",0,0,"# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Optional\n\nimport lightning.pytorch as pl\nimport nemo_run as run\nimport torch\nfrom nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer\n\nfrom nemo.collections.llm.api import finetune, pretrain\nfrom nemo.collections.llm.gpt.data.mock import MockDataModule\nfrom nemo.collections.llm.peft import PEFT_STR2CLS\nfrom nemo.collections.llm.recipes.finetune_default import default_finetune_recipe\nfrom nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger\nfrom nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing\nfrom nemo.collections.llm.recipes.qwen3 import qwen3_model, qwen3_trainer\nfrom nemo.utils.exp_manager import TimingCallback\n\nNAME = ""qwen3_30b_a3b""\n\n\n@run.cli.factory(name=NAME)\ndef model() -> run.Config[pl.LightningModule]:\n    """"""\n    Factory function to create a Qwen3 30B-A3B model configuration.\n    This is a MoE (Mixture of Experts) model with 128 experts.\n\n    Returns:\n        run.Config[pl.LightningModule]: Configuration for the Qwen3 30B-A3B model.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain model=qwen3_30b_a3b ...\n\n        Python API usage:\n            >>> model_config = model()\n            >>> print(model_config)\n    """"""\n    return qwen3_model(version=NAME)\n\n\n@run.cli.factory(target=pretrain, name=NAME)\ndef pretrain_recipe(\n    # General\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    # Trainer\n    tensor_parallelism: int = 4,  # Default for 30B-A3B model\n    pipeline_parallelism: int = 2,\n    pipeline_parallelism_type: Optional[torch.dtype] = None,\n    virtual_pipeline_parallelism: Optional[int] = None,\n    context_parallelism: int = 1,\n    expert_parallelism: Optional[int] = 4,\n    sequence_parallelism: bool = True,\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    max_steps: int = 300000,\n    precision: str = ""bf16-mixed"",\n    accumulate_grad_batches: int = 1,\n    gradient_clip_val: float = 1.0,\n    limit_test_batches: int = 32,\n    limit_val_batches: int = 32,\n    log_every_n_steps: int = 10,\n    val_check_interval: int = 500,\n    # Data\n    global_batch_size=32,\n    micro_batch_size=2,\n    seq_length=4096,\n    # Optimizer\n    warmup_steps=500,\n    constant_steps=0,\n    min_lr=3e-5,\n    max_lr=3e-4,\n    # Training function\n    fn=pretrain,\n) -> run.Partial:\n    """"""\n    Create a pre-training recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for pre-training, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the pre-training run.\n        tensor_parallelism (int): Degree of tensor model parallelism.\n        pipeline_parallelism (int): Degree of pipeline model parallelism.\n        pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism.\n        virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism.\n        context_parallelism (int): Degree of context parallelism.\n        sequence_parallelism (bool): Whether to use sequence parallelism.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        max_steps (int): Maximum number of training steps.\n        precision (str): Precision configuration, one of fp32, 16-mixed or bf16-mixed.\n        accumulate_grad_batches (int): Number of steps per gradient accumulation.\n        gradient_clip_val (float): Value for gradient clipping.\n        limit_test_batches (int): Limit the number of test batches.\n        limit_val_batches (int): Limit the number of validation batches.\n        log_every_n_steps (int): Log every n steps.\n        val_check_interval (int): Run validation every N steps.\n        global_batch_size (int): Global batch size.\n        micro_batch_size (int): Micro batch size.\n        seq_length (int): Sequence length.\n        warmup_steps (int): Number of warmup steps.\n        constant_steps (int): Number of constant steps.\n        min_lr (float): Minimum learning rate.\n        max_lr (float): Maximum learning rate.\n        fn (Callable): The pre-training function to use.\n\n    Returns:\n        run.Partial: Partial configuration for pre-training.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain --factory qwen3_30b_a3b\n            $ nemo llm pretrain --factory ""qwen3_30b_a3b(num_nodes=1, name='my_qwen3_pretrain')""\n\n        Python API usage:\n            >>> recipe = pretrain_recipe(name=""qwen3_pretrain"", num_nodes=1)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses a mock dataset, look for the finetune examples to see how to change the dataset.\n    """"""\n    recipe = run.Partial(\n        fn,\n        model=model(),\n        trainer=qwen3_trainer(\n            tensor_parallelism=tensor_parallelism,\n            pipeline_parallelism=pipeline_parallelism,\n            pipeline_parallelism_type=pipeline_parallelism_type,\n            virtual_pipeline_parallelism=virtual_pipeline_parallelism,\n            context_parallelism=context_parallelism,\n            sequence_parallelism=sequence_parallelism,\n            expert_parallelism=expert_parallelism,\n            num_nodes=num_nodes,\n            num_gpus_per_node=num_gpus_per_node,\n            max_steps=max_steps,\n            precision=precision,\n            accumulate_grad_batches=accumulate_grad_batches,\n            limit_test_batches=limit_test_batches,\n            limit_val_batches=limit_val_batches,\n            log_every_n_steps=log_every_n_steps,\n            val_check_interval=val_check_interval,\n            callbacks=[run.Config(TimingCallback)],\n        ),\n        data=run.Config(\n            MockDataModule,\n            seq_length=seq_length,\n            global_batch_size=global_batch_size,\n            micro_batch_size=micro_batch_size,\n            tokenizer=run.Config(AutoTokenizer, ""Qwen/Qwen3-30B-A3B""),\n        ),\n        log=default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),\n        optim=distributed_fused_adam_with_cosine_annealing(\n            precision=precision,\n            warmup_steps=warmup_steps,\n            constant_steps=constant_steps,\n            min_lr=min_lr,\n            max_lr=max_lr,\n            clip_grad=gradient_clip_val,\n        ),\n        resume=default_resume(),\n    )\n    recipe.model.config.recompute_granularity = ""full""\n    recipe.model.config.recompute_method = ""uniform""\n    recipe.model.config.recompute_num_layers = 1\n    return recipe\n\n\n@run.cli.factory(target=finetune, name=NAME)\ndef finetune_recipe(\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    peft_scheme: Optional[str] = 'lora',\n    packed_sequence: bool = False,\n) -> run.Partial:\n    """"""\n    Create a fine-tuning recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for fine-tuning, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    The recipe uses LoRA (Low-Rank Adaptation) for efficient fine-tuning, unless peft_scheme is set to None.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the fine-tuning run.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        peft_scheme (Optional[str]): Name of the peft scheme to use for fine-tuning.\n            Allowed values: 'lora'/'dora'/'none'/None.\n        packed_sequence (Optional[bool]): Packing multiple training sequences into one long sequence for training\n            efficiency. Default sequence length is 2048.\n\n    Returns:\n        run.Partial: Partial configuration for fine-tuning.\n\n    Examples:\n        CLI usage:\n            $ nemo llm finetune --factory qwen3_30b_a3b\n\n        Python API usage:\n            >>> recipe = finetune_recipe(name=""qwen3_30b_a3b_finetune"", num_nodes=2)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses the SQuAD dataset for fine-tuning.\n    """"""\n    recipe = default_finetune_recipe(\n        model(), ""Qwen/Qwen3-30B-A3B"", dir, name, num_nodes, num_gpus_per_node, packed_sequence\n    )\n    if peft_scheme is None or peft_scheme.lower() == 'none':\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.pipeline_model_parallel_size = 2\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.optim.config.lr = 5e-6\n    elif peft_scheme.lower() in ['lora', 'dora']:\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.peft = run.Config(PEFT_STR2CLS[peft_scheme.lower()])\n        recipe.peft.target_modules = ['linear_qkv', 'linear_proj']\n        recipe.optim.config.lr = 1e-4\n    else:\n        raise ValueError(f""Unrecognized peft scheme: {peft_scheme}"")\n    return recipe\n",python,tab
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+70,3052561,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",0,10041,"# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Optional\n\nimport lightning.pytorch as pl\nimport nemo_run as run\nimport torch\nfrom nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer\n\nfrom nemo.collections.llm.api import finetune, pretrain\nfrom nemo.collections.llm.gpt.data.mock import MockDataModule\nfrom nemo.collections.llm.peft import PEFT_STR2CLS\nfrom nemo.collections.llm.recipes.finetune_default import default_finetune_recipe\nfrom nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger\nfrom nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing\nfrom nemo.collections.llm.recipes.qwen3 import qwen3_model, qwen3_trainer\nfrom nemo.utils.exp_manager import TimingCallback\n\nNAME = ""qwen3_30b_a3b""\n\n\n@run.cli.factory(name=NAME)\ndef model() -> run.Config[pl.LightningModule]:\n    """"""\n    Factory function to create a Qwen3 30B-A3B model configuration.\n    This is a MoE (Mixture of Experts) model with 128 experts.\n\n    Returns:\n        run.Config[pl.LightningModule]: Configuration for the Qwen3 30B-A3B model.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain model=qwen3_30b_a3b ...\n\n        Python API usage:\n            >>> model_config = model()\n            >>> print(model_config)\n    """"""\n    return qwen3_model(version=NAME)\n\n\n@run.cli.factory(target=pretrain, name=NAME)\ndef pretrain_recipe(\n    # General\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    # Trainer\n    tensor_parallelism: int = 4,  # Default for 30B-A3B model\n    pipeline_parallelism: int = 2,\n    pipeline_parallelism_type: Optional[torch.dtype] = None,\n    virtual_pipeline_parallelism: Optional[int] = None,\n    context_parallelism: int = 1,\n    expert_parallelism: Optional[int] = 4,\n    sequence_parallelism: bool = True,\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    max_steps: int = 300000,\n    precision: str = ""bf16-mixed"",\n    accumulate_grad_batches: int = 1,\n    gradient_clip_val: float = 1.0,\n    limit_test_batches: int = 32,\n    limit_val_batches: int = 32,\n    log_every_n_steps: int = 10,\n    val_check_interval: int = 500,\n    # Data\n    global_batch_size=32,\n    micro_batch_size=2,\n    seq_length=4096,\n    # Optimizer\n    warmup_steps=500,\n    constant_steps=0,\n    min_lr=3e-5,\n    max_lr=3e-4,\n    # Training function\n    fn=pretrain,\n) -> run.Partial:\n    """"""\n    Create a pre-training recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for pre-training, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the pre-training run.\n        tensor_parallelism (int): Degree of tensor model parallelism.\n        pipeline_parallelism (int): Degree of pipeline model parallelism.\n        pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism.\n        virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism.\n        context_parallelism (int): Degree of context parallelism.\n        sequence_parallelism (bool): Whether to use sequence parallelism.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        max_steps (int): Maximum number of training steps.\n        precision (str): Precision configuration, one of fp32, 16-mixed or bf16-mixed.\n        accumulate_grad_batches (int): Number of steps per gradient accumulation.\n        gradient_clip_val (float): Value for gradient clipping.\n        limit_test_batches (int): Limit the number of test batches.\n        limit_val_batches (int): Limit the number of validation batches.\n        log_every_n_steps (int): Log every n steps.\n        val_check_interval (int): Run validation every N steps.\n        global_batch_size (int): Global batch size.\n        micro_batch_size (int): Micro batch size.\n        seq_length (int): Sequence length.\n        warmup_steps (int): Number of warmup steps.\n        constant_steps (int): Number of constant steps.\n        min_lr (float): Minimum learning rate.\n        max_lr (float): Maximum learning rate.\n        fn (Callable): The pre-training function to use.\n\n    Returns:\n        run.Partial: Partial configuration for pre-training.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain --factory qwen3_30b_a3b\n            $ nemo llm pretrain --factory ""qwen3_30b_a3b(num_nodes=1, name='my_qwen3_pretrain')""\n\n        Python API usage:\n            >>> recipe = pretrain_recipe(name=""qwen3_pretrain"", num_nodes=1)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses a mock dataset, look for the finetune examples to see how to change the dataset.\n    """"""\n    recipe = run.Partial(\n        fn,\n        model=model(),\n        trainer=qwen3_trainer(\n            tensor_parallelism=tensor_parallelism,\n            pipeline_parallelism=pipeline_parallelism,\n            pipeline_parallelism_type=pipeline_parallelism_type,\n            virtual_pipeline_parallelism=virtual_pipeline_parallelism,\n            context_parallelism=context_parallelism,\n            sequence_parallelism=sequence_parallelism,\n            expert_parallelism=expert_parallelism,\n            num_nodes=num_nodes,\n            num_gpus_per_node=num_gpus_per_node,\n            max_steps=max_steps,\n            precision=precision,\n            accumulate_grad_batches=accumulate_grad_batches,\n            limit_test_batches=limit_test_batches,\n            limit_val_batches=limit_val_batches,\n            log_every_n_steps=log_every_n_steps,\n            val_check_interval=val_check_interval,\n            callbacks=[run.Config(TimingCallback)],\n        ),\n        data=run.Config(\n            MockDataModule,\n            seq_length=seq_length,\n            global_batch_size=global_batch_size,\n            micro_batch_size=micro_batch_size,\n            tokenizer=run.Config(AutoTokenizer, ""Qwen/Qwen3-30B-A3B""),\n        ),\n        log=default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),\n        optim=distributed_fused_adam_with_cosine_annealing(\n            precision=precision,\n            warmup_steps=warmup_steps,\n            constant_steps=constant_steps,\n            min_lr=min_lr,\n            max_lr=max_lr,\n            clip_grad=gradient_clip_val,\n        ),\n        resume=default_resume(),\n    )\n    recipe.model.config.recompute_granularity = ""full""\n    recipe.model.config.recompute_method = ""uniform""\n    recipe.model.config.recompute_num_layers = 1\n    return recipe\n\n\n@run.cli.factory(target=finetune, name=NAME)\ndef finetune_recipe(\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    peft_scheme: Optional[str] = 'lora',\n    packed_sequence: bool = False,\n) -> run.Partial:\n    """"""\n    Create a fine-tuning recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for fine-tuning, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    The recipe uses LoRA (Low-Rank Adaptation) for efficient fine-tuning, unless peft_scheme is set to None.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the fine-tuning run.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        peft_scheme (Optional[str]): Name of the peft scheme to use for fine-tuning.\n            Allowed values: 'lora'/'dora'/'none'/None.\n        packed_sequence (Optional[bool]): Packing multiple training sequences into one long sequence for training\n            efficiency. Default sequence length is 2048.\n\n    Returns:\n        run.Partial: Partial configuration for fine-tuning.\n\n    Examples:\n        CLI usage:\n            $ nemo llm finetune --factory qwen3_30b_a3b\n\n        Python API usage:\n            >>> recipe = finetune_recipe(name=""qwen3_30b_a3b_finetune"", num_nodes=2)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses the SQuAD dataset for fine-tuning.\n    """"""\n    recipe = default_finetune_recipe(\n        model(), ""Qwen/Qwen3-30B-A3B"", dir, name, num_nodes, num_gpus_per_node, packed_sequence\n    )\n    if peft_scheme is None or peft_scheme.lower() == 'none':\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.pipeline_model_parallel_size = 2\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.optim.config.lr = 5e-6\n    elif peft_scheme.lower() in ['lora', 'dora']:\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.peft = run.Config(PEFT_STR2CLS[peft_scheme.lower()])\n        recipe.peft.target_modules = ['linear_qkv', 'linear_proj']\n        recipe.optim.config.lr = 1e-4\n    else:\n        raise ValueError(f""Unrecognized peft scheme: {peft_scheme}"")\n    return recipe\n",python,selection_command
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+155,4549882,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",0,10041,"# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Optional\n\nimport lightning.pytorch as pl\nimport nemo_run as run\nimport torch\nfrom nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer\n\nfrom nemo.collections.llm.api import finetune, pretrain\nfrom nemo.collections.llm.gpt.data.mock import MockDataModule\nfrom nemo.collections.llm.peft import PEFT_STR2CLS\nfrom nemo.collections.llm.recipes.finetune_default import default_finetune_recipe\nfrom nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger\nfrom nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing\nfrom nemo.collections.llm.recipes.qwen3 import qwen3_model, qwen3_trainer\nfrom nemo.utils.exp_manager import TimingCallback\n\nNAME = ""qwen3_30b_a3b""\n\n\n@run.cli.factory(name=NAME)\ndef model() -> run.Config[pl.LightningModule]:\n    """"""\n    Factory function to create a Qwen3 30B-A3B model configuration.\n    This is a MoE (Mixture of Experts) model with 128 experts.\n\n    Returns:\n        run.Config[pl.LightningModule]: Configuration for the Qwen3 30B-A3B model.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain model=qwen3_30b_a3b ...\n\n        Python API usage:\n            >>> model_config = model()\n            >>> print(model_config)\n    """"""\n    return qwen3_model(version=NAME)\n\n\n@run.cli.factory(target=pretrain, name=NAME)\ndef pretrain_recipe(\n    # General\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    # Trainer\n    tensor_parallelism: int = 4,  # Default for 30B-A3B model\n    pipeline_parallelism: int = 2,\n    pipeline_parallelism_type: Optional[torch.dtype] = None,\n    virtual_pipeline_parallelism: Optional[int] = None,\n    context_parallelism: int = 1,\n    expert_parallelism: Optional[int] = 4,\n    sequence_parallelism: bool = True,\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    max_steps: int = 300000,\n    precision: str = ""bf16-mixed"",\n    accumulate_grad_batches: int = 1,\n    gradient_clip_val: float = 1.0,\n    limit_test_batches: int = 32,\n    limit_val_batches: int = 32,\n    log_every_n_steps: int = 10,\n    val_check_interval: int = 500,\n    # Data\n    global_batch_size=32,\n    micro_batch_size=2,\n    seq_length=4096,\n    # Optimizer\n    warmup_steps=500,\n    constant_steps=0,\n    min_lr=3e-5,\n    max_lr=3e-4,\n    # Training function\n    fn=pretrain,\n) -> run.Partial:\n    """"""\n    Create a pre-training recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for pre-training, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the pre-training run.\n        tensor_parallelism (int): Degree of tensor model parallelism.\n        pipeline_parallelism (int): Degree of pipeline model parallelism.\n        pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism.\n        virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism.\n        context_parallelism (int): Degree of context parallelism.\n        sequence_parallelism (bool): Whether to use sequence parallelism.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        max_steps (int): Maximum number of training steps.\n        precision (str): Precision configuration, one of fp32, 16-mixed or bf16-mixed.\n        accumulate_grad_batches (int): Number of steps per gradient accumulation.\n        gradient_clip_val (float): Value for gradient clipping.\n        limit_test_batches (int): Limit the number of test batches.\n        limit_val_batches (int): Limit the number of validation batches.\n        log_every_n_steps (int): Log every n steps.\n        val_check_interval (int): Run validation every N steps.\n        global_batch_size (int): Global batch size.\n        micro_batch_size (int): Micro batch size.\n        seq_length (int): Sequence length.\n        warmup_steps (int): Number of warmup steps.\n        constant_steps (int): Number of constant steps.\n        min_lr (float): Minimum learning rate.\n        max_lr (float): Maximum learning rate.\n        fn (Callable): The pre-training function to use.\n\n    Returns:\n        run.Partial: Partial configuration for pre-training.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain --factory qwen3_30b_a3b\n            $ nemo llm pretrain --factory ""qwen3_30b_a3b(num_nodes=1, name='my_qwen3_pretrain')""\n\n        Python API usage:\n            >>> recipe = pretrain_recipe(name=""qwen3_pretrain"", num_nodes=1)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses a mock dataset, look for the finetune examples to see how to change the dataset.\n    """"""\n    recipe = run.Partial(\n        fn,\n        model=model(),\n        trainer=qwen3_trainer(\n            tensor_parallelism=tensor_parallelism,\n            pipeline_parallelism=pipeline_parallelism,\n            pipeline_parallelism_type=pipeline_parallelism_type,\n            virtual_pipeline_parallelism=virtual_pipeline_parallelism,\n            context_parallelism=context_parallelism,\n            sequence_parallelism=sequence_parallelism,\n            expert_parallelism=expert_parallelism,\n            num_nodes=num_nodes,\n            num_gpus_per_node=num_gpus_per_node,\n            max_steps=max_steps,\n            precision=precision,\n            accumulate_grad_batches=accumulate_grad_batches,\n            limit_test_batches=limit_test_batches,\n            limit_val_batches=limit_val_batches,\n            log_every_n_steps=log_every_n_steps,\n            val_check_interval=val_check_interval,\n            callbacks=[run.Config(TimingCallback)],\n        ),\n        data=run.Config(\n            MockDataModule,\n            seq_length=seq_length,\n            global_batch_size=global_batch_size,\n            micro_batch_size=micro_batch_size,\n            tokenizer=run.Config(AutoTokenizer, ""Qwen/Qwen3-30B-A3B""),\n        ),\n        log=default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),\n        optim=distributed_fused_adam_with_cosine_annealing(\n            precision=precision,\n            warmup_steps=warmup_steps,\n            constant_steps=constant_steps,\n            min_lr=min_lr,\n            max_lr=max_lr,\n            clip_grad=gradient_clip_val,\n        ),\n        resume=default_resume(),\n    )\n    recipe.model.config.recompute_granularity = ""full""\n    recipe.model.config.recompute_method = ""uniform""\n    recipe.model.config.recompute_num_layers = 1\n    return recipe\n\n\n@run.cli.factory(target=finetune, name=NAME)\ndef finetune_recipe(\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    peft_scheme: Optional[str] = 'lora',\n    packed_sequence: bool = False,\n) -> run.Partial:\n    """"""\n    Create a fine-tuning recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for fine-tuning, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    The recipe uses LoRA (Low-Rank Adaptation) for efficient fine-tuning, unless peft_scheme is set to None.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the fine-tuning run.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        peft_scheme (Optional[str]): Name of the peft scheme to use for fine-tuning.\n            Allowed values: 'lora'/'dora'/'none'/None.\n        packed_sequence (Optional[bool]): Packing multiple training sequences into one long sequence for training\n            efficiency. Default sequence length is 2048.\n\n    Returns:\n        run.Partial: Partial configuration for fine-tuning.\n\n    Examples:\n        CLI usage:\n            $ nemo llm finetune --factory qwen3_30b_a3b\n\n        Python API usage:\n            >>> recipe = finetune_recipe(name=""qwen3_30b_a3b_finetune"", num_nodes=2)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses the SQuAD dataset for fine-tuning.\n    """"""\n    recipe = default_finetune_recipe(\n        model(), ""Qwen/Qwen3-30B-A3B"", dir, name, num_nodes, num_gpus_per_node, packed_sequence\n    )\n    if peft_scheme is None or peft_scheme.lower() == 'none':\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.pipeline_model_parallel_size = 2\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.optim.config.lr = 5e-6\n    elif peft_scheme.lower() in ['lora', 'dora']:\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.peft = run.Config(PEFT_STR2CLS[peft_scheme.lower()])\n        recipe.peft.target_modules = ['linear_qkv', 'linear_proj']\n        recipe.optim.config.lr = 1e-4\n    else:\n        raise ValueError(f""Unrecognized peft scheme: {peft_scheme}"")\n    return recipe\n",python,selection_command
+156,4550471,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",10041,0,"",python,selection_command
+157,4559897,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",0,10041,"# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Optional\n\nimport lightning.pytorch as pl\nimport nemo_run as run\nimport torch\nfrom nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer\n\nfrom nemo.collections.llm.api import finetune, pretrain\nfrom nemo.collections.llm.gpt.data.mock import MockDataModule\nfrom nemo.collections.llm.peft import PEFT_STR2CLS\nfrom nemo.collections.llm.recipes.finetune_default import default_finetune_recipe\nfrom nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger\nfrom nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing\nfrom nemo.collections.llm.recipes.qwen3 import qwen3_model, qwen3_trainer\nfrom nemo.utils.exp_manager import TimingCallback\n\nNAME = ""qwen3_30b_a3b""\n\n\n@run.cli.factory(name=NAME)\ndef model() -> run.Config[pl.LightningModule]:\n    """"""\n    Factory function to create a Qwen3 30B-A3B model configuration.\n    This is a MoE (Mixture of Experts) model with 128 experts.\n\n    Returns:\n        run.Config[pl.LightningModule]: Configuration for the Qwen3 30B-A3B model.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain model=qwen3_30b_a3b ...\n\n        Python API usage:\n            >>> model_config = model()\n            >>> print(model_config)\n    """"""\n    return qwen3_model(version=NAME)\n\n\n@run.cli.factory(target=pretrain, name=NAME)\ndef pretrain_recipe(\n    # General\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    # Trainer\n    tensor_parallelism: int = 4,  # Default for 30B-A3B model\n    pipeline_parallelism: int = 2,\n    pipeline_parallelism_type: Optional[torch.dtype] = None,\n    virtual_pipeline_parallelism: Optional[int] = None,\n    context_parallelism: int = 1,\n    expert_parallelism: Optional[int] = 4,\n    sequence_parallelism: bool = True,\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    max_steps: int = 300000,\n    precision: str = ""bf16-mixed"",\n    accumulate_grad_batches: int = 1,\n    gradient_clip_val: float = 1.0,\n    limit_test_batches: int = 32,\n    limit_val_batches: int = 32,\n    log_every_n_steps: int = 10,\n    val_check_interval: int = 500,\n    # Data\n    global_batch_size=32,\n    micro_batch_size=2,\n    seq_length=4096,\n    # Optimizer\n    warmup_steps=500,\n    constant_steps=0,\n    min_lr=3e-5,\n    max_lr=3e-4,\n    # Training function\n    fn=pretrain,\n) -> run.Partial:\n    """"""\n    Create a pre-training recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for pre-training, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the pre-training run.\n        tensor_parallelism (int): Degree of tensor model parallelism.\n        pipeline_parallelism (int): Degree of pipeline model parallelism.\n        pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism.\n        virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism.\n        context_parallelism (int): Degree of context parallelism.\n        sequence_parallelism (bool): Whether to use sequence parallelism.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        max_steps (int): Maximum number of training steps.\n        precision (str): Precision configuration, one of fp32, 16-mixed or bf16-mixed.\n        accumulate_grad_batches (int): Number of steps per gradient accumulation.\n        gradient_clip_val (float): Value for gradient clipping.\n        limit_test_batches (int): Limit the number of test batches.\n        limit_val_batches (int): Limit the number of validation batches.\n        log_every_n_steps (int): Log every n steps.\n        val_check_interval (int): Run validation every N steps.\n        global_batch_size (int): Global batch size.\n        micro_batch_size (int): Micro batch size.\n        seq_length (int): Sequence length.\n        warmup_steps (int): Number of warmup steps.\n        constant_steps (int): Number of constant steps.\n        min_lr (float): Minimum learning rate.\n        max_lr (float): Maximum learning rate.\n        fn (Callable): The pre-training function to use.\n\n    Returns:\n        run.Partial: Partial configuration for pre-training.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain --factory qwen3_30b_a3b\n            $ nemo llm pretrain --factory ""qwen3_30b_a3b(num_nodes=1, name='my_qwen3_pretrain')""\n\n        Python API usage:\n            >>> recipe = pretrain_recipe(name=""qwen3_pretrain"", num_nodes=1)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses a mock dataset, look for the finetune examples to see how to change the dataset.\n    """"""\n    recipe = run.Partial(\n        fn,\n        model=model(),\n        trainer=qwen3_trainer(\n            tensor_parallelism=tensor_parallelism,\n            pipeline_parallelism=pipeline_parallelism,\n            pipeline_parallelism_type=pipeline_parallelism_type,\n            virtual_pipeline_parallelism=virtual_pipeline_parallelism,\n            context_parallelism=context_parallelism,\n            sequence_parallelism=sequence_parallelism,\n            expert_parallelism=expert_parallelism,\n            num_nodes=num_nodes,\n            num_gpus_per_node=num_gpus_per_node,\n            max_steps=max_steps,\n            precision=precision,\n            accumulate_grad_batches=accumulate_grad_batches,\n            limit_test_batches=limit_test_batches,\n            limit_val_batches=limit_val_batches,\n            log_every_n_steps=log_every_n_steps,\n            val_check_interval=val_check_interval,\n            callbacks=[run.Config(TimingCallback)],\n        ),\n        data=run.Config(\n            MockDataModule,\n            seq_length=seq_length,\n            global_batch_size=global_batch_size,\n            micro_batch_size=micro_batch_size,\n            tokenizer=run.Config(AutoTokenizer, ""Qwen/Qwen3-30B-A3B""),\n        ),\n        log=default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),\n        optim=distributed_fused_adam_with_cosine_annealing(\n            precision=precision,\n            warmup_steps=warmup_steps,\n            constant_steps=constant_steps,\n            min_lr=min_lr,\n            max_lr=max_lr,\n            clip_grad=gradient_clip_val,\n        ),\n        resume=default_resume(),\n    )\n    recipe.model.config.recompute_granularity = ""full""\n    recipe.model.config.recompute_method = ""uniform""\n    recipe.model.config.recompute_num_layers = 1\n    return recipe\n\n\n@run.cli.factory(target=finetune, name=NAME)\ndef finetune_recipe(\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    peft_scheme: Optional[str] = 'lora',\n    packed_sequence: bool = False,\n) -> run.Partial:\n    """"""\n    Create a fine-tuning recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for fine-tuning, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    The recipe uses LoRA (Low-Rank Adaptation) for efficient fine-tuning, unless peft_scheme is set to None.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the fine-tuning run.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        peft_scheme (Optional[str]): Name of the peft scheme to use for fine-tuning.\n            Allowed values: 'lora'/'dora'/'none'/None.\n        packed_sequence (Optional[bool]): Packing multiple training sequences into one long sequence for training\n            efficiency. Default sequence length is 2048.\n\n    Returns:\n        run.Partial: Partial configuration for fine-tuning.\n\n    Examples:\n        CLI usage:\n            $ nemo llm finetune --factory qwen3_30b_a3b\n\n        Python API usage:\n            >>> recipe = finetune_recipe(name=""qwen3_30b_a3b_finetune"", num_nodes=2)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses the SQuAD dataset for fine-tuning.\n    """"""\n    recipe = default_finetune_recipe(\n        model(), ""Qwen/Qwen3-30B-A3B"", dir, name, num_nodes, num_gpus_per_node, packed_sequence\n    )\n    if peft_scheme is None or peft_scheme.lower() == 'none':\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.pipeline_model_parallel_size = 2\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.optim.config.lr = 5e-6\n    elif peft_scheme.lower() in ['lora', 'dora']:\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.peft = run.Config(PEFT_STR2CLS[peft_scheme.lower()])\n        recipe.peft.target_modules = ['linear_qkv', 'linear_proj']\n        recipe.optim.config.lr = 1e-4\n    else:\n        raise ValueError(f""Unrecognized peft scheme: {peft_scheme}"")\n    return recipe\n",python,selection_command
+158,4560021,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",10041,0,"",python,selection_command
+159,4560890,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",10041,0,"# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Optional\n\nimport lightning.pytorch as pl\nimport nemo_run as run\nimport torch\nfrom nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer\n\nfrom nemo.collections.llm.api import finetune, pretrain\nfrom nemo.collections.llm.gpt.data.mock import MockDataModule\nfrom nemo.collections.llm.peft import PEFT_STR2CLS\nfrom nemo.collections.llm.recipes.finetune_default import default_finetune_recipe\nfrom nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger\nfrom nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing\nfrom nemo.collections.llm.recipes.qwen3 import qwen3_model, qwen3_trainer\nfrom nemo.utils.exp_manager import TimingCallback\n\nNAME = ""qwen3_30b_a3b""\n\n\n@run.cli.factory(name=NAME)\ndef model() -> run.Config[pl.LightningModule]:\n    """"""\n    Factory function to create a Qwen3 30B-A3B model configuration.\n    This is a MoE (Mixture of Experts) model with 128 experts.\n\n    Returns:\n        run.Config[pl.LightningModule]: Configuration for the Qwen3 30B-A3B model.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain model=qwen3_30b_a3b ...\n\n        Python API usage:\n            >>> model_config = model()\n            >>> print(model_config)\n    """"""\n    return qwen3_model(version=NAME)\n\n\n@run.cli.factory(target=pretrain, name=NAME)\ndef pretrain_recipe(\n    # General\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    # Trainer\n    tensor_parallelism: int = 4,  # Default for 30B-A3B model\n    pipeline_parallelism: int = 2,\n    pipeline_parallelism_type: Optional[torch.dtype] = None,\n    virtual_pipeline_parallelism: Optional[int] = None,\n    context_parallelism: int = 1,\n    expert_parallelism: Optional[int] = 4,\n    sequence_parallelism: bool = True,\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    max_steps: int = 300000,\n    precision: str = ""bf16-mixed"",\n    accumulate_grad_batches: int = 1,\n    gradient_clip_val: float = 1.0,\n    limit_test_batches: int = 32,\n    limit_val_batches: int = 32,\n    log_every_n_steps: int = 10,\n    val_check_interval: int = 500,\n    # Data\n    global_batch_size=32,\n    micro_batch_size=2,\n    seq_length=4096,\n    # Optimizer\n    warmup_steps=500,\n    constant_steps=0,\n    min_lr=3e-5,\n    max_lr=3e-4,\n    # Training function\n    fn=pretrain,\n) -> run.Partial:\n    """"""\n    Create a pre-training recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for pre-training, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the pre-training run.\n        tensor_parallelism (int): Degree of tensor model parallelism.\n        pipeline_parallelism (int): Degree of pipeline model parallelism.\n        pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism.\n        virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism.\n        context_parallelism (int): Degree of context parallelism.\n        sequence_parallelism (bool): Whether to use sequence parallelism.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        max_steps (int): Maximum number of training steps.\n        precision (str): Precision configuration, one of fp32, 16-mixed or bf16-mixed.\n        accumulate_grad_batches (int): Number of steps per gradient accumulation.\n        gradient_clip_val (float): Value for gradient clipping.\n        limit_test_batches (int): Limit the number of test batches.\n        limit_val_batches (int): Limit the number of validation batches.\n        log_every_n_steps (int): Log every n steps.\n        val_check_interval (int): Run validation every N steps.\n        global_batch_size (int): Global batch size.\n        micro_batch_size (int): Micro batch size.\n        seq_length (int): Sequence length.\n        warmup_steps (int): Number of warmup steps.\n        constant_steps (int): Number of constant steps.\n        min_lr (float): Minimum learning rate.\n        max_lr (float): Maximum learning rate.\n        fn (Callable): The pre-training function to use.\n\n    Returns:\n        run.Partial: Partial configuration for pre-training.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain --factory qwen3_30b_a3b\n            $ nemo llm pretrain --factory ""qwen3_30b_a3b(num_nodes=1, name='my_qwen3_pretrain')""\n\n        Python API usage:\n            >>> recipe = pretrain_recipe(name=""qwen3_pretrain"", num_nodes=1)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses a mock dataset, look for the finetune examples to see how to change the dataset.\n    """"""\n    recipe = run.Partial(\n        fn,\n        model=model(),\n        trainer=qwen3_trainer(\n            tensor_parallelism=tensor_parallelism,\n            pipeline_parallelism=pipeline_parallelism,\n            pipeline_parallelism_type=pipeline_parallelism_type,\n            virtual_pipeline_parallelism=virtual_pipeline_parallelism,\n            context_parallelism=context_parallelism,\n            sequence_parallelism=sequence_parallelism,\n            expert_parallelism=expert_parallelism,\n            num_nodes=num_nodes,\n            num_gpus_per_node=num_gpus_per_node,\n            max_steps=max_steps,\n            precision=precision,\n            accumulate_grad_batches=accumulate_grad_batches,\n            limit_test_batches=limit_test_batches,\n            limit_val_batches=limit_val_batches,\n            log_every_n_steps=log_every_n_steps,\n            val_check_interval=val_check_interval,\n            callbacks=[run.Config(TimingCallback)],\n        ),\n        data=run.Config(\n            MockDataModule,\n            seq_length=seq_length,\n            global_batch_size=global_batch_size,\n            micro_batch_size=micro_batch_size,\n            tokenizer=run.Config(AutoTokenizer, ""Qwen/Qwen3-30B-A3B""),\n        ),\n        log=default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),\n        optim=distributed_fused_adam_with_cosine_annealing(\n            precision=precision,\n            warmup_steps=warmup_steps,\n            constant_steps=constant_steps,\n            min_lr=min_lr,\n            max_lr=max_lr,\n            clip_grad=gradient_clip_val,\n        ),\n        resume=default_resume(),\n    )\n    recipe.model.config.recompute_granularity = ""full""\n    recipe.model.config.recompute_method = ""uniform""\n    recipe.model.config.recompute_num_layers = 1\n    return recipe\n\n\n@run.cli.factory(target=finetune, name=NAME)\ndef finetune_recipe(\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    peft_scheme: Optional[str] = 'lora',\n    packed_sequence: bool = False,\n) -> run.Partial:\n    """"""\n    Create a fine-tuning recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for fine-tuning, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    The recipe uses LoRA (Low-Rank Adaptation) for efficient fine-tuning, unless peft_scheme is set to None.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the fine-tuning run.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        peft_scheme (Optional[str]): Name of the peft scheme to use for fine-tuning.\n            Allowed values: 'lora'/'dora'/'none'/None.\n        packed_sequence (Optional[bool]): Packing multiple training sequences into one long sequence for training\n            efficiency. Default sequence length is 2048.\n\n    Returns:\n        run.Partial: Partial configuration for fine-tuning.\n\n    Examples:\n        CLI usage:\n            $ nemo llm finetune --factory qwen3_30b_a3b\n\n        Python API usage:\n            >>> recipe = finetune_recipe(name=""qwen3_30b_a3b_finetune"", num_nodes=2)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses the SQuAD dataset for fine-tuning.\n    """"""\n    recipe = default_finetune_recipe(\n        model(), ""Qwen/Qwen3-30B-A3B"", dir, name, num_nodes, num_gpus_per_node, packed_sequence\n    )\n    if peft_scheme is None or peft_scheme.lower() == 'none':\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.pipeline_model_parallel_size = 2\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.optim.config.lr = 5e-6\n    elif peft_scheme.lower() in ['lora', 'dora']:\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.peft = run.Config(PEFT_STR2CLS[peft_scheme.lower()])\n        recipe.peft.target_modules = ['linear_qkv', 'linear_proj']\n        recipe.optim.config.lr = 1e-4\n    else:\n        raise ValueError(f""Unrecognized peft scheme: {peft_scheme}"")\n    return recipe\n",python,content
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+222,8520791,"nemo/collections/llm/recipes/qwen3_30b_a3b.py",0,10041,"# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the ""License"");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an ""AS IS"" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Optional\n\nimport lightning.pytorch as pl\nimport nemo_run as run\nimport torch\nfrom nemo.collections.common.tokenizers.huggingface.auto_tokenizer import AutoTokenizer\n\nfrom nemo.collections.llm.api import finetune, pretrain\nfrom nemo.collections.llm.gpt.data.mock import MockDataModule\nfrom nemo.collections.llm.peft import PEFT_STR2CLS\nfrom nemo.collections.llm.recipes.finetune_default import default_finetune_recipe\nfrom nemo.collections.llm.recipes.log.default import default_log, default_resume, tensorboard_logger\nfrom nemo.collections.llm.recipes.optim.adam import distributed_fused_adam_with_cosine_annealing\nfrom nemo.collections.llm.recipes.qwen3 import qwen3_model, qwen3_trainer\nfrom nemo.utils.exp_manager import TimingCallback\n\nNAME = ""qwen3_30b_a3b""\n\n\n@run.cli.factory(name=NAME)\ndef model() -> run.Config[pl.LightningModule]:\n    """"""\n    Factory function to create a Qwen3 30B-A3B model configuration.\n    This is a MoE (Mixture of Experts) model with 128 experts.\n\n    Returns:\n        run.Config[pl.LightningModule]: Configuration for the Qwen3 30B-A3B model.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain model=qwen3_30b_a3b ...\n\n        Python API usage:\n            >>> model_config = model()\n            >>> print(model_config)\n    """"""\n    return qwen3_model(version=NAME)\n\n\n@run.cli.factory(target=pretrain, name=NAME)\ndef pretrain_recipe(\n    # General\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    # Trainer\n    tensor_parallelism: int = 4,  # Default for 30B-A3B model\n    pipeline_parallelism: int = 2,\n    pipeline_parallelism_type: Optional[torch.dtype] = None,\n    virtual_pipeline_parallelism: Optional[int] = None,\n    context_parallelism: int = 1,\n    expert_parallelism: Optional[int] = 4,\n    sequence_parallelism: bool = True,\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    max_steps: int = 300000,\n    precision: str = ""bf16-mixed"",\n    accumulate_grad_batches: int = 1,\n    gradient_clip_val: float = 1.0,\n    limit_test_batches: int = 32,\n    limit_val_batches: int = 32,\n    log_every_n_steps: int = 10,\n    val_check_interval: int = 500,\n    # Data\n    global_batch_size=32,\n    micro_batch_size=2,\n    seq_length=4096,\n    # Optimizer\n    warmup_steps=500,\n    constant_steps=0,\n    min_lr=3e-5,\n    max_lr=3e-4,\n    # Training function\n    fn=pretrain,\n) -> run.Partial:\n    """"""\n    Create a pre-training recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for pre-training, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the pre-training run.\n        tensor_parallelism (int): Degree of tensor model parallelism.\n        pipeline_parallelism (int): Degree of pipeline model parallelism.\n        pipeline_parallelism_type (Optional[torch.dtype]): Data type for pipeline parallelism.\n        virtual_pipeline_parallelism (Optional[int]): Size of virtual pipeline parallelism.\n        context_parallelism (int): Degree of context parallelism.\n        sequence_parallelism (bool): Whether to use sequence parallelism.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        max_steps (int): Maximum number of training steps.\n        precision (str): Precision configuration, one of fp32, 16-mixed or bf16-mixed.\n        accumulate_grad_batches (int): Number of steps per gradient accumulation.\n        gradient_clip_val (float): Value for gradient clipping.\n        limit_test_batches (int): Limit the number of test batches.\n        limit_val_batches (int): Limit the number of validation batches.\n        log_every_n_steps (int): Log every n steps.\n        val_check_interval (int): Run validation every N steps.\n        global_batch_size (int): Global batch size.\n        micro_batch_size (int): Micro batch size.\n        seq_length (int): Sequence length.\n        warmup_steps (int): Number of warmup steps.\n        constant_steps (int): Number of constant steps.\n        min_lr (float): Minimum learning rate.\n        max_lr (float): Maximum learning rate.\n        fn (Callable): The pre-training function to use.\n\n    Returns:\n        run.Partial: Partial configuration for pre-training.\n\n    Examples:\n        CLI usage:\n            $ nemo llm pretrain --factory qwen3_30b_a3b\n            $ nemo llm pretrain --factory ""qwen3_30b_a3b(num_nodes=1, name='my_qwen3_pretrain')""\n\n        Python API usage:\n            >>> recipe = pretrain_recipe(name=""qwen3_pretrain"", num_nodes=1)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses a mock dataset, look for the finetune examples to see how to change the dataset.\n    """"""\n    recipe = run.Partial(\n        fn,\n        model=model(),\n        trainer=qwen3_trainer(\n            tensor_parallelism=tensor_parallelism,\n            pipeline_parallelism=pipeline_parallelism,\n            pipeline_parallelism_type=pipeline_parallelism_type,\n            virtual_pipeline_parallelism=virtual_pipeline_parallelism,\n            context_parallelism=context_parallelism,\n            sequence_parallelism=sequence_parallelism,\n            expert_parallelism=expert_parallelism,\n            num_nodes=num_nodes,\n            num_gpus_per_node=num_gpus_per_node,\n            max_steps=max_steps,\n            precision=precision,\n            accumulate_grad_batches=accumulate_grad_batches,\n            limit_test_batches=limit_test_batches,\n            limit_val_batches=limit_val_batches,\n            log_every_n_steps=log_every_n_steps,\n            val_check_interval=val_check_interval,\n            callbacks=[run.Config(TimingCallback)],\n        ),\n        data=run.Config(\n            MockDataModule,\n            seq_length=seq_length,\n            global_batch_size=global_batch_size,\n            micro_batch_size=micro_batch_size,\n            tokenizer=run.Config(AutoTokenizer, ""Qwen/Qwen3-30B-A3B""),\n        ),\n        log=default_log(dir=dir, name=name, tensorboard_logger=tensorboard_logger(name=name)),\n        optim=distributed_fused_adam_with_cosine_annealing(\n            precision=precision,\n            warmup_steps=warmup_steps,\n            constant_steps=constant_steps,\n            min_lr=min_lr,\n            max_lr=max_lr,\n            clip_grad=gradient_clip_val,\n        ),\n        resume=default_resume(),\n    )\n    recipe.model.config.recompute_granularity = ""full""\n    recipe.model.config.recompute_method = ""uniform""\n    recipe.model.config.recompute_num_layers = 1\n    return recipe\n\n\n@run.cli.factory(target=finetune, name=NAME)\ndef finetune_recipe(\n    dir: Optional[str] = None,\n    name: str = ""default"",\n    num_nodes: int = 1,\n    num_gpus_per_node: int = 8,\n    peft_scheme: Optional[str] = 'lora',\n    packed_sequence: bool = False,\n) -> run.Partial:\n    """"""\n    Create a fine-tuning recipe for Qwen3 30B-A3B model.\n\n    This function sets up a complete configuration for fine-tuning, including\n    model, trainer, data, logging, optimization, and resumption settings.\n    The recipe uses LoRA (Low-Rank Adaptation) for efficient fine-tuning, unless peft_scheme is set to None.\n    This model uses Mixture of Experts (MoE) architecture with 128 experts.\n\n    Args:\n        dir (Optional[str]): Directory for saving logs and checkpoints.\n        name (str): Name of the fine-tuning run.\n        num_nodes (int): Number of compute nodes to use.\n        num_gpus_per_node (int): Number of GPUs per node.\n        peft_scheme (Optional[str]): Name of the peft scheme to use for fine-tuning.\n            Allowed values: 'lora'/'dora'/'none'/None.\n        packed_sequence (Optional[bool]): Packing multiple training sequences into one long sequence for training\n            efficiency. Default sequence length is 2048.\n\n    Returns:\n        run.Partial: Partial configuration for fine-tuning.\n\n    Examples:\n        CLI usage:\n            $ nemo llm finetune --factory qwen3_30b_a3b\n\n        Python API usage:\n            >>> recipe = finetune_recipe(name=""qwen3_30b_a3b_finetune"", num_nodes=2)\n            >>> print(recipe)\n\n    Note:\n        This recipe uses the SQuAD dataset for fine-tuning.\n    """"""\n    recipe = default_finetune_recipe(\n        model(), ""Qwen/Qwen3-30B-A3B"", dir, name, num_nodes, num_gpus_per_node, packed_sequence\n    )\n    if peft_scheme is None or peft_scheme.lower() == 'none':\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.pipeline_model_parallel_size = 2\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.optim.config.lr = 5e-6\n    elif peft_scheme.lower() in ['lora', 'dora']:\n        recipe.trainer.strategy.tensor_model_parallel_size = 4\n        recipe.trainer.strategy.expert_model_parallel_size = 4\n        recipe.trainer.strategy.expert_tensor_parallel_size = 1\n        recipe.trainer.strategy.sequence_parallel = True\n        recipe.peft = run.Config(PEFT_STR2CLS[peft_scheme.lower()])\n        recipe.peft.target_modules = ['linear_qkv', 'linear_proj']\n        recipe.optim.config.lr = 1e-4\n    else:\n        raise ValueError(f""Unrecognized peft scheme: {peft_scheme}"")\n    return recipe\n",python,selection_command
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4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-a7b808c2-b1d0-43a0-a38c-8b82cd2886711764488770794-2025_11_30-08.46.18.897/source.csv

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4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-aeed47b9-f6ef-4272-b0ca-0c15ab4c25021758266694991-2025_09_19-09.25.04.660/source.csv

@@ -0,0 +1,151 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+1,3,"cleanrl/ppo_atari_envpool.py",0,0,"# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_atari_envpoolpy\nimport os\nimport random\nimport time\nfrom collections import deque\nfrom dataclasses import dataclass\n\nimport envpool\nimport gym\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport tyro\nfrom torch.distributions.categorical import Categorical\nfrom torch.utils.tensorboard import SummaryWriter\n\n\n@dataclass\nclass Args:\n    exp_name: str = os.path.basename(__file__)[: -len("".py"")]\n    """"""the name of this experiment""""""\n    seed: int = 1\n    """"""seed of the experiment""""""\n    torch_deterministic: bool = True\n    """"""if toggled, `torch.backends.cudnn.deterministic=False`""""""\n    cuda: bool = True\n    """"""if toggled, cuda will be enabled by default""""""\n    track: bool = False\n    """"""if toggled, this experiment will be tracked with Weights and Biases""""""\n    wandb_project_name: str = ""cleanRL""\n    """"""the wandb's project name""""""\n    wandb_entity: str = None\n    """"""the entity (team) of wandb's project""""""\n    capture_video: bool = False\n    """"""whether to capture videos of the agent performances (check out `videos` folder)""""""\n\n    # Algorithm specific arguments\n    env_id: str = ""Breakout-v5""\n    """"""the id of the environment""""""\n    total_timesteps: int = 10000000\n    """"""total timesteps of the experiments""""""\n    learning_rate: float = 2.5e-4\n    """"""the learning rate of the optimizer""""""\n    num_envs: int = 8\n    """"""the number of parallel game environments""""""\n    num_steps: int = 128\n    """"""the number of steps to run in each environment per policy rollout""""""\n    anneal_lr: bool = True\n    """"""Toggle learning rate annealing for policy and value networks""""""\n    gamma: float = 0.99\n    """"""the discount factor gamma""""""\n    gae_lambda: float = 0.95\n    """"""the lambda for the general advantage estimation""""""\n    num_minibatches: int = 4\n    """"""the number of mini-batches""""""\n    update_epochs: int = 4\n    """"""the K epochs to update the policy""""""\n    norm_adv: bool = True\n    """"""Toggles advantages normalization""""""\n    clip_coef: float = 0.1\n    """"""the surrogate clipping coefficient""""""\n    clip_vloss: bool = True\n    """"""Toggles whether or not to use a clipped loss for the value function, as per the paper.""""""\n    ent_coef: float = 0.01\n    """"""coefficient of the entropy""""""\n    vf_coef: float = 0.5\n    """"""coefficient of the value function""""""\n    max_grad_norm: float = 0.5\n    """"""the maximum norm for the gradient clipping""""""\n    target_kl: float = None\n    """"""the target KL divergence threshold""""""\n\n    # to be filled in runtime\n    batch_size: int = 0\n    """"""the batch size (computed in runtime)""""""\n    minibatch_size: int = 0\n    """"""the mini-batch size (computed in runtime)""""""\n    num_iterations: int = 0\n    """"""the number of iterations (computed in runtime)""""""\n\n\nclass RecordEpisodeStatistics(gym.Wrapper):\n    def __init__(self, env, deque_size=100):\n        super().__init__(env)\n        self.num_envs = getattr(env, ""num_envs"", 1)\n        self.episode_returns = None\n        self.episode_lengths = None\n\n    def reset(self, **kwargs):\n        observations = super().reset(**kwargs)\n        self.episode_returns = np.zeros(self.num_envs, dtype=np.float32)\n        self.episode_lengths = np.zeros(self.num_envs, dtype=np.int32)\n        self.lives = np.zeros(self.num_envs, dtype=np.int32)\n        self.returned_episode_returns = np.zeros(self.num_envs, dtype=np.float32)\n        self.returned_episode_lengths = np.zeros(self.num_envs, dtype=np.int32)\n        return observations\n\n    def step(self, action):\n        observations, rewards, dones, infos = super().step(action)\n        self.episode_returns += infos[""reward""]\n        self.episode_lengths += 1\n        self.returned_episode_returns[:] = self.episode_returns\n        self.returned_episode_lengths[:] = self.episode_lengths\n        self.episode_returns *= 1 - infos[""terminated""]\n        self.episode_lengths *= 1 - infos[""terminated""]\n        infos[""r""] = self.returned_episode_returns\n        infos[""l""] = self.returned_episode_lengths\n        return (\n            observations,\n            rewards,\n            dones,\n            infos,\n        )\n\n\ndef layer_init(layer, std=np.sqrt(2), bias_const=0.0):\n    torch.nn.init.orthogonal_(layer.weight, std)\n    torch.nn.init.constant_(layer.bias, bias_const)\n    return layer\n\n\nclass Agent(nn.Module):\n    def __init__(self, envs):\n        super().__init__()\n        self.network = nn.Sequential(\n            layer_init(nn.Conv2d(4, 32, 8, stride=4)),\n            nn.ReLU(),\n            layer_init(nn.Conv2d(32, 64, 4, stride=2)),\n            nn.ReLU(),\n            layer_init(nn.Conv2d(64, 64, 3, stride=1)),\n            nn.ReLU(),\n            nn.Flatten(),\n            layer_init(nn.Linear(64 * 7 * 7, 512)),\n            nn.ReLU(),\n        )\n        self.actor = layer_init(nn.Linear(512, envs.single_action_space.n), std=0.01)\n        self.critic = layer_init(nn.Linear(512, 1), std=1)\n\n    def get_value(self, x):\n        return self.critic(self.network(x / 255.0))\n\n    def get_action_and_value(self, x, action=None):\n        hidden = self.network(x / 255.0)\n        logits = self.actor(hidden)\n        probs = Categorical(logits=logits)\n        if action is None:\n            action = probs.sample()\n        return action, probs.log_prob(action), probs.entropy(), self.critic(hidden)\n\n\nif __name__ == ""__main__"":\n    args = tyro.cli(Args)\n    args.batch_size = int(args.num_envs * args.num_steps)\n    args.minibatch_size = int(args.batch_size // args.num_minibatches)\n    args.num_iterations = args.total_timesteps // args.batch_size\n    run_name = f""{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}""\n    if args.track:\n        import wandb\n\n        wandb.init(\n            project=args.wandb_project_name,\n            entity=args.wandb_entity,\n            sync_tensorboard=True,\n            config=vars(args),\n            name=run_name,\n            monitor_gym=True,\n            save_code=True,\n        )\n    writer = SummaryWriter(f""runs/{run_name}"")\n    writer.add_text(\n        ""hyperparameters"",\n        ""|param|value|\n|-|-|\n%s"" % (""\n"".join([f""|{key}|{value}|"" for key, value in vars(args).items()])),\n    )\n\n    # TRY NOT TO MODIFY: seeding\n    random.seed(args.seed)\n    np.random.seed(args.seed)\n    torch.manual_seed(args.seed)\n    torch.backends.cudnn.deterministic = args.torch_deterministic\n\n    device = torch.device(""cuda"" if torch.cuda.is_available() and args.cuda else ""cpu"")\n\n    # env setup\n    envs = envpool.make(\n        args.env_id,\n        env_type=""gym"",\n        num_envs=args.num_envs,\n        episodic_life=True,\n        reward_clip=True,\n        seed=args.seed,\n    )\n    envs.num_envs = args.num_envs\n    envs.single_action_space = envs.action_space\n    envs.single_observation_space = envs.observation_space\n    envs = RecordEpisodeStatistics(envs)\n    assert isinstance(envs.action_space, gym.spaces.Discrete), ""only discrete action space is supported""\n\n    agent = Agent(envs).to(device)\n    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)\n\n    # ALGO Logic: Storage setup\n    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)\n    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)\n    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)\n    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)\n    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)\n    values = torch.zeros((args.num_steps, args.num_envs)).to(device)\n    avg_returns = deque(maxlen=20)\n\n    # TRY NOT TO MODIFY: start the game\n    global_step = 0\n    start_time = time.time()\n    next_obs = torch.Tensor(envs.reset()).to(device)\n    next_done = torch.zeros(args.num_envs).to(device)\n\n    for iteration in range(1, args.num_iterations + 1):\n        # Annealing the rate if instructed to do so.\n        if args.anneal_lr:\n            frac = 1.0 - (iteration - 1.0) / args.num_iterations\n            lrnow = frac * args.learning_rate\n            optimizer.param_groups[0][""lr""] = lrnow\n\n        for step in range(0, args.num_steps):\n            global_step += args.num_envs\n            obs[step] = next_obs\n            dones[step] = next_done\n\n            # ALGO LOGIC: action logic\n            with torch.no_grad():\n                action, logprob, _, value = agent.get_action_and_value(next_obs)\n                values[step] = value.flatten()\n            actions[step] = action\n            logprobs[step] = logprob\n\n            # TRY NOT TO MODIFY: execute the game and log data.\n            next_obs, reward, next_done, info = envs.step(action.cpu().numpy())\n            rewards[step] = torch.tensor(reward).to(device).view(-1)\n            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(next_done).to(device)\n\n            for idx, d in enumerate(next_done):\n                if d and info[""lives""][idx] == 0:\n                    print(f""global_step={global_step}, episodic_return={info['r'][idx]}"")\n                    avg_returns.append(info[""r""][idx])\n                    writer.add_scalar(""charts/avg_episodic_return"", np.average(avg_returns), global_step)\n                    writer.add_scalar(""charts/episodic_return"", info[""r""][idx], global_step)\n                    writer.add_scalar(""charts/episodic_length"", info[""l""][idx], global_step)\n\n        # bootstrap value if not done\n        with torch.no_grad():\n            next_value = agent.get_value(next_obs).reshape(1, -1)\n            advantages = torch.zeros_like(rewards).to(device)\n            lastgaelam = 0\n            for t in reversed(range(args.num_steps)):\n                if t == args.num_steps - 1:\n                    nextnonterminal = 1.0 - next_done\n                    nextvalues = next_value\n                else:\n                    nextnonterminal = 1.0 - dones[t + 1]\n                    nextvalues = values[t + 1]\n                delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]\n                advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam\n            returns = advantages + values\n\n        # flatten the batch\n        b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)\n        b_logprobs = logprobs.reshape(-1)\n        b_actions = actions.reshape((-1,) + envs.single_action_space.shape)\n        b_advantages = advantages.reshape(-1)\n        b_returns = returns.reshape(-1)\n        b_values = values.reshape(-1)\n\n        # Optimizing the policy and value network\n        b_inds = np.arange(args.batch_size)\n        clipfracs = []\n        for epoch in range(args.update_epochs):\n            np.random.shuffle(b_inds)\n            for start in range(0, args.batch_size, args.minibatch_size):\n                end = start + args.minibatch_size\n                mb_inds = b_inds[start:end]\n\n                _, newlogprob, entropy, newvalue = agent.get_action_and_value(b_obs[mb_inds], b_actions.long()[mb_inds])\n                logratio = newlogprob - b_logprobs[mb_inds]\n                ratio = logratio.exp()\n\n                with torch.no_grad():\n                    # calculate approx_kl http://joschu.net/blog/kl-approx.html\n                    old_approx_kl = (-logratio).mean()\n                    approx_kl = ((ratio - 1) - logratio).mean()\n                    clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean().item()]\n\n                mb_advantages = b_advantages[mb_inds]\n                if args.norm_adv:\n                    mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)\n\n                # Policy loss\n                pg_loss1 = -mb_advantages * ratio\n                pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1 + args.clip_coef)\n                pg_loss = torch.max(pg_loss1, pg_loss2).mean()\n\n                # Value loss\n                newvalue = newvalue.view(-1)\n                if args.clip_vloss:\n                    v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2\n                    v_clipped = b_values[mb_inds] + torch.clamp(\n                        newvalue - b_values[mb_inds],\n                        -args.clip_coef,\n                        args.clip_coef,\n                    )\n                    v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2\n                    v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)\n                    v_loss = 0.5 * v_loss_max.mean()\n                else:\n                    v_loss = 0.5 * ((newvalue - b_returns[mb_inds]) ** 2).mean()\n\n                entropy_loss = entropy.mean()\n                loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef\n\n                optimizer.zero_grad()\n                loss.backward()\n                nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)\n                optimizer.step()\n\n            if args.target_kl is not None and approx_kl > args.target_kl:\n                break\n\n        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()\n        var_y = np.var(y_true)\n        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y\n\n        # TRY NOT TO MODIFY: record rewards for plotting purposes\n        writer.add_scalar(""charts/learning_rate"", optimizer.param_groups[0][""lr""], global_step)\n        writer.add_scalar(""losses/value_loss"", v_loss.item(), global_step)\n        writer.add_scalar(""losses/policy_loss"", pg_loss.item(), global_step)\n        writer.add_scalar(""losses/entropy"", entropy_loss.item(), global_step)\n        writer.add_scalar(""losses/old_approx_kl"", old_approx_kl.item(), global_step)\n        writer.add_scalar(""losses/approx_kl"", approx_kl.item(), global_step)\n        writer.add_scalar(""losses/clipfrac"", np.mean(clipfracs), global_step)\n        writer.add_scalar(""losses/explained_variance"", explained_var, global_step)\n        print(""SPS:"", int(global_step / (time.time() - start_time)))\n        writer.add_scalar(""charts/SPS"", int(global_step / (time.time() - start_time)), global_step)\n\n    envs.close()\n    writer.close()\n",python,tab
+2,142,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"9:25:04 AM [info] Activating crowd-code\n9:25:04 AM [info] Recording started\n9:25:04 AM [info] Initializing git provider using file system watchers...\n",Log,tab
+3,155,"extension-output-pdoom-org.crowd-code-#1-crowd-code",40,0,"",Log,selection_command
+4,197,"extension-output-pdoom-org.crowd-code-#1-crowd-code",150,0,"9:25:04 AM [info] Git repository found\n9:25:04 AM [info] Git provider initialized successfully\n9:25:04 AM [info] Initial git state: [object Object]\n",Log,content
+5,2189,"cleanrl/ppo_atari_envpool.py",0,0,"",python,tab
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+7,2213,"TERMINAL",0,0,"",,terminal_command
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+9,18113,"TERMINAL",0,0,"source .venv/bin/activate",,terminal_command
+10,24112,"TERMINAL",0,0,"python cleanrl/ppo_atari_envpool.py",,terminal_command
+11,24163,"TERMINAL",0,0,"]633;C",,terminal_output
+12,26014,"TERMINAL",0,0,"/fast/home/franz.srambical/cleanrl/.venv/lib/python3.10/site-packages/treevalue/tree/integration/torch.py:23: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.\r\n  register_for_torch(TreeValue)\r\n/fast/home/franz.srambical/cleanrl/.venv/lib/python3.10/site-packages/treevalue/tree/integration/torch.py:24: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.\r\n  register_for_torch(FastTreeValue)\r\n",,terminal_output
+13,26264,"TERMINAL",0,0,"Gym has been unmaintained since 2022 and does not support NumPy 2.0 amongst other critical functionality.\r\nPlease upgrade to Gymnasium, the maintained drop-in replacement of Gym, or contact the authors of your software and request that they upgrade.\r\nSee the migration guide at https://gymnasium.farama.org/introduction/migration_guide/ for additional information.\r\n",,terminal_output
+14,26759,"TERMINAL",0,0,"/fast/home/franz.srambical/cleanrl/.venv/lib/python3.10/site-packages/tyro/_parsers.py:379: UserWarning: The field `wandb-entity` is annotated with type `<class 'str'>`, but the default value `None` has type `<class 'NoneType'>`. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n  warnings.warn(message)\r\n/fast/home/franz.srambical/cleanrl/.venv/lib/python3.10/site-packages/tyro/_parsers.py:379: UserWarning: The field `target-kl` is annotated with type `<class 'float'>`, but the default value `None` has type `<class 'NoneType'>`. We'll try to handle this gracefully, but it may cause unexpected behavior.\r\n  warnings.warn(message)\r\n",,terminal_output
+15,28466,"TERMINAL",0,0,"global_step=992, episodic_return=0.0\r\nglobal_step=992, episodic_return=0.0\r\nglobal_step=992, episodic_return=0.0\r\nglobal_step=992, episodic_return=0.0\r\nglobal_step=992, episodic_return=0.0\r\nglobal_step=992, episodic_return=0.0\r\nglobal_step=992, episodic_return=0.0\r\nglobal_step=992, episodic_return=0.0\r\n",,terminal_output
+16,28939,"TERMINAL",0,0,"SPS: 1339\r\n",,terminal_output
+17,29746,"TERMINAL",0,0,"SPS: 1302\r\n",,terminal_output
+18,29996,"TERMINAL",0,0,"global_step=2776, episodic_return=2.0\r\nglobal_step=2776, episodic_return=2.0\r\nglobal_step=2776, episodic_return=2.0\r\nglobal_step=2776, episodic_return=2.0\r\nglobal_step=2776, episodic_return=2.0\r\nglobal_step=2776, episodic_return=2.0\r\nglobal_step=2776, episodic_return=2.0\r\nglobal_step=2776, episodic_return=2.0\r\n",,terminal_output
+19,30574,"TERMINAL",0,0,"SPS: 1280\r\n",,terminal_output
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+27,34109,"TERMINAL",0,0,"^CTraceback (most recent call last):\r\n  File ""/fast/home/franz.srambical/cleanrl/cleanrl/ppo_atari_envpool.py"", line 237, in <module>\r\n    next_obs, reward, next_done, info = envs.step(action.cpu().numpy())\r\n  File ""/fast/home/franz.srambical/cleanrl/cleanrl/ppo_atari_envpool.py"", line 100, in step\r\n    observations, rewards, dones, infos = super().step(action)\r\n  File ""/fast/home/franz.srambical/cleanrl/.venv/lib/python3.10/site-packages/gym/core.py"", line 280, in step\r\n    return self.env.step(action)\r\n  File ""/fast/home/franz.srambical/cleanrl/.venv/lib/python3.10/site-packages/envpool/python/envpool.py"", line 144, in step\r\n    return self.recv(reset=False, return_info=True)\r\n  File ""/fast/home/franz.srambical/cleanrl/.venv/lib/python3.10/site-packages/envpool/python/envpool.py"", line 130, in recv\r\n    state_list = self._recv()\r\nKeyboardInterrupt\r\n",,terminal_output
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+33,290061,"/home/franz.srambical/jafar/input_pipeline/generate_coinrun_dataset.py",0,0,"""""""\nGenerates a dataset of random-action CoinRun episodes.\nEpisodes are saved individually as memory-mapped files for efficient loading.\n""""""\n\nfrom dataclasses import dataclass\n\nfrom gym3 import types_np\nimport numpy as np\nfrom procgen import ProcgenGym3Env\nimport tyro\nimport json\nimport os\nfrom utils import save_chunks\n\n\n@dataclass\nclass Args:\n    num_episodes_train: int = 10000\n    num_episodes_val: int = 500\n    num_episodes_test: int = 500\n    output_dir: str = ""data/coinrun_episodes""\n    min_episode_length: int = 1000\n    max_episode_length: int = 1000\n    chunk_size: int = 100\n    chunks_per_file: int = 100\n    seed: int = 0\n\n\nargs = tyro.cli(Args)\nassert (\n    args.max_episode_length >= args.min_episode_length\n), ""Maximum episode length must be greater than or equal to minimum episode length.""\n\nif args.min_episode_length < args.chunk_size:\n    print(\n        ""Warning: Minimum episode length is smaller than chunk size. Note that episodes shorter than the chunk size will be discarded.""\n    )\n\n\n# --- Generate episodes ---\ndef generate_episodes(num_episodes, split):\n    episode_idx = 0\n    episode_metadata = []\n    obs_chunks = []\n    act_chunks = []\n    file_idx = 0\n    output_dir_split = os.path.join(args.output_dir, split)\n    while episode_idx < num_episodes:\n        seed = np.random.randint(0, 10000)\n        env = ProcgenGym3Env(num=1, env_name=""coinrun"", start_level=seed)\n\n        observations_seq = []\n        actions_seq = []\n        episode_obs_chunks = []\n        episode_act_chunks = []\n\n        # --- Run episode ---\n        step_t = 0\n        for step_t in range(args.max_episode_length):\n            action = types_np.sample(env.ac_space, bshape=(env.num,))\n            env.act(action)\n            _, obs, first = env.observe()\n            observations_seq.append(obs[""rgb""])\n            actions_seq.append(action)\n            if len(observations_seq) == args.chunk_size:\n                episode_obs_chunks.append(observations_seq)\n                episode_act_chunks.append(actions_seq)\n                observations_seq = []\n                actions_seq = []\n            if first:\n                break\n\n        # --- Save episode ---\n        if step_t + 1 >= args.min_episode_length:\n            if observations_seq:\n                if len(observations_seq) < args.chunk_size:\n                    print(\n                        f""Warning: Inconsistent chunk_sizes. Episode has {len(observations_seq)} frames, ""\n                        f""which is smaller than the requested chunk_size: {args.chunk_size}. ""\n                        ""This might lead to performance degradation during training.""\n                    )\n                episode_obs_chunks.append(observations_seq)\n                episode_act_chunks.append(actions_seq)\n\n            obs_chunks_data = [\n                np.concatenate(seq, axis=0).astype(np.uint8)\n                for seq in episode_obs_chunks\n            ]\n            act_chunks_data = [\n                np.concatenate(act, axis=0) for act in episode_act_chunks\n            ]\n            obs_chunks.extend(obs_chunks_data)\n            act_chunks.extend(act_chunks_data)\n\n            ep_metadata, obs_chunks, file_idx, act_chunks = save_chunks(\n                obs_chunks, file_idx, args.chunks_per_file, output_dir_split, act_chunks\n            )\n            episode_metadata.extend(ep_metadata)\n\n            print(f""Episode {episode_idx} completed, length: {step_t + 1}."")\n            episode_idx += 1\n        else:\n            print(f""Episode too short ({step_t + 1}), resampling..."")\n\n    if len(obs_chunks) > 0:\n        print(\n            f""Warning: Dropping {len(obs_chunks)} chunks for consistent number of chunks per file."",\n            ""Consider changing the chunk_size and chunks_per_file parameters to prevent data-loss."",\n        )\n\n    print(f""Done generating {split} split"")\n    return episode_metadata\n\n\ndef get_action_space():\n    env = ProcgenGym3Env(num=1, env_name=""coinrun"", start_level=0)\n    return env.ac_space.eltype.n\n\n\ndef main():\n    # Set random seed and create dataset directories\n    np.random.seed(args.seed)\n    # --- Generate episodes ---\n    train_episode_metadata = generate_episodes(args.num_episodes_train, ""train"")\n    val_episode_metadata = generate_episodes(args.num_episodes_val, ""val"")\n    test_episode_metadata = generate_episodes(args.num_episodes_test, ""test"")\n\n    # --- Save metadata ---\n    metadata = {\n        ""env"": ""coinrun"",\n        ""num_actions"": get_action_space(),\n        ""num_episodes_train"": args.num_episodes_train,\n        ""num_episodes_val"": args.num_episodes_val,\n        ""num_episodes_test"": args.num_episodes_test,\n        ""avg_episode_len_train"": np.mean(\n            [ep[""avg_seq_len""] for ep in train_episode_metadata]\n        ),\n        ""avg_episode_len_val"": np.mean(\n            [ep[""avg_seq_len""] for ep in val_episode_metadata]\n        ),\n        ""avg_episode_len_test"": np.mean(\n            [ep[""avg_seq_len""] for ep in test_episode_metadata]\n        ),\n        ""episode_metadata_train"": train_episode_metadata,\n        ""episode_metadata_val"": val_episode_metadata,\n        ""episode_metadata_test"": test_episode_metadata,\n    }\n    with open(os.path.join(args.output_dir, ""metadata.json""), ""w"") as f:\n        json.dump(metadata, f)\n\n    print(f""Done generating dataset."")\n\n\nif __name__ == ""__main__"":\n    main()\n",python,tab
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+47,541842,"TERMINAL",0,0,"source /home/franz.srambical/cleanrl/.venv/bin/activate",,terminal_command
+48,544327,"TERMINAL",0,0,"cd ../jafar/",,terminal_command
+49,554362,"TERMINAL",0,0,"git checkout gt-actions",,terminal_command
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+51,554473,"TERMINAL",0,0,"M\t.gitignore\r\nAlready on 'gt-actions'\r\nYour branch is behind 'origin/gt-actions' by 1 commit, and can be fast-forwarded.\r\n  (use ""git pull"" to update your local branch)\r\n]0;franz.srambical@hai-login2:~/jafar",,terminal_output
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+54,557974,"TERMINAL",0,0,"Updating 96d560e..1b6b878\r\nFast-forward\r\n input_pipeline/generate_coinrun_dataset.py | 2 +-\r\n 1 file changed, 1 insertion(+), 1 deletion(-)\r\n]0;franz.srambical@hai-login2:~/jafar",,terminal_output
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+66,782305,"cleanrl/trajectory_saver.py",0,0,"import os\nimport json\nfrom typing import List, Dict, Any\n\nimport numpy as np\n\n\nclass TrajectorySaver:\n    """"""\n    Collects per-environment observation frames and actions, chunks them,\n    and writes chunked files to disk. Designed to be lightweight and only\n    enabled when trajectory capture is requested.\n\n    Behavior mirrors the high level flow in `generate_coinrun_dataset.py`:\n    - Build sequences of length `chunk_size` from steps\n    - At episode end, if a partial sequence exists (< chunk_size), include it\n      with a warning about inconsistent chunk sizes\n    - Group `chunks_per_file` chunks per output file. Any remainder at the\n      very end is dropped with a warning\n    - Store both observation chunks and action chunks side-by-side\n    """"""\n\n    def __init__(\n        self,\n        output_dir: str,\n        num_envs: int,\n        chunk_size: int = 160,\n        chunks_per_file: int = 100,\n    ) -> None:\n        self.output_dir = output_dir\n        self.chunks_dir = os.path.join(output_dir, ""chunks"")\n        os.makedirs(self.chunks_dir, exist_ok=True)\n        self.num_envs = num_envs\n        self.chunk_size = int(chunk_size)\n        self.chunks_per_file = int(chunks_per_file)\n\n        # Per-env rolling buffers for the current episode\n        self.env_buffers: List[Dict[str, Any]] = [\n            {""obs_seq"": [], ""act_seq"": [], ""episode_obs_chunks"": [], ""episode_act_chunks"": []}\n            for _ in range(self.num_envs)\n        ]\n\n        # Global chunk buffers waiting to be flushed to disk\n        self.obs_chunks: List[np.ndarray] = []\n        self.act_chunks: List[np.ndarray] = []\n        self.file_idx: int = 0\n\n        # Metadata (optional). We keep a minimal JSONL with per-file info\n        self.metadata_path = os.path.join(self.output_dir, ""metadata.jsonl"")\n\n    def _finalize_episode_for_env(self, env_idx: int) -> None:\n        buf = self.env_buffers[env_idx]\n        obs_seq: List[np.ndarray] = buf[""obs_seq""]\n        act_seq: List[np.ndarray] = buf[""act_seq""]\n\n        if len(obs_seq) > 0:\n            # Partial chunk at episode end\n            if len(obs_seq) < self.chunk_size:\n                print(\n                    f""Warning: Inconsistent chunk sizes. Env {env_idx} episode ended with ""\n                    f""{len(obs_seq)} frames (< chunk_size={self.chunk_size}). ""\n                    ""Including the partial chunk may impact training I/O performance.""\n                )\n            self._append_chunk_from_sequences(env_idx)\n\n        # Move episode chunks to global buffers\n        if len(buf[""episode_obs_chunks""]) > 0:\n            self.obs_chunks.extend(buf[""episode_obs_chunks""])\n            self.act_chunks.extend(buf[""episode_act_chunks""])\n            buf[""episode_obs_chunks""].clear()\n            buf[""episode_act_chunks""].clear()\n\n        self._save_if_ready()\n\n    def _append_chunk_from_sequences(self, env_idx: int) -> None:\n        buf = self.env_buffers[env_idx]\n        obs_seq: List[np.ndarray] = buf[""obs_seq""]\n        act_seq: List[np.ndarray] = buf[""act_seq""]\n\n        if len(obs_seq) == 0:\n            return\n\n        # Stack along time dimension. Shapes become:\n        #   obs_chunk: (T, C, H, W)  dtype=uint8\n        #   act_chunk: (T,)          dtype=int64 (or int)\n        obs_chunk = np.stack(obs_seq, axis=0).astype(np.uint8)\n        act_chunk = np.asarray(act_seq, dtype=np.int64)\n\n        buf[""episode_obs_chunks""].append(obs_chunk)\n        buf[""episode_act_chunks""].append(act_chunk)\n\n        # Reset rolling sequences\n        buf[""obs_seq""] = []\n        buf[""act_seq""] = []\n\n    def _save_if_ready(self) -> None:\n        while len(self.obs_chunks) >= self.chunks_per_file:\n            obs_to_write = self.obs_chunks[: self.chunks_per_file]\n            act_to_write = self.act_chunks[: self.chunks_per_file]\n\n            # Remove from buffers\n            self.obs_chunks = self.obs_chunks[self.chunks_per_file :]\n            self.act_chunks = self.act_chunks[self.chunks_per_file :]\n\n            file_stem = f""chunks_{self.file_idx:06d}""\n            out_path = os.path.join(self.chunks_dir, f""{file_stem}.npz"")\n\n            # Write as a .npz with numbered arrays to preserve variable chunk lengths\n            save_dict: Dict[str, Any] = {}\n            for idx, (o, a) in enumerate(zip(obs_to_write, act_to_write)):\n                save_dict[f""obs_{idx:03d}""] = o\n                save_dict[f""acts_{idx:03d}""] = a\n\n            np.savez_compressed(out_path, **save_dict)\n\n            # Minimal metadata per file for bookkeeping\n            meta = {\n                ""file"": os.path.basename(out_path),\n                ""num_chunks"": len(obs_to_write),\n                ""chunk_size"": self.chunk_size,\n                ""chunk_indices"": [i for i in range(len(obs_to_write))],\n            }\n            with open(self.metadata_path, ""a"") as f:\n                f.write(json.dumps(meta) + ""\n"")\n\n            self.file_idx += 1\n\n    def add_step(\n        self,\n        obs_batch: np.ndarray,\n        actions_batch: np.ndarray,\n        episode_done_batch: np.ndarray,\n    ) -> None:\n        """"""\n        Add a single environment step across all envs.\n\n        Args:\n            obs_batch: np.ndarray with shape (num_envs, C, H, W). Values should be [0, 255].\n            actions_batch: np.ndarray with shape (num_envs,).\n            episode_done_batch: np.ndarray with shape (num_envs,), boolean flags indicating end-of-episode.\n        """"""\n        assert obs_batch.shape[0] == self.num_envs\n        assert actions_batch.shape[0] == self.num_envs\n        assert episode_done_batch.shape[0] == self.num_envs\n\n        for env_idx in range(self.num_envs):\n            buf = self.env_buffers[env_idx]\n            buf[""obs_seq""].append(obs_batch[env_idx])\n            buf[""act_seq""].append(int(actions_batch[env_idx]))\n\n            if len(buf[""obs_seq""]) == self.chunk_size:\n                self._append_chunk_from_sequences(env_idx)\n\n            # Episode ended: finalize the episode\n            if bool(episode_done_batch[env_idx]):\n                self._finalize_episode_for_env(env_idx)\n\n    def close(self) -> None:\n        # Drop remainder to keep consistent chunks-per-file contract\n        remainder = len(self.obs_chunks)\n        if remainder > 0:\n            print(\n                f""Warning: Dropping {remainder} chunks to keep a consistent number of chunks per file. ""\n                ""Consider adjusting chunk_size and chunks_per_file to reduce data loss.""\n            )\n        # Nothing else to do; per-episode buffers do not carry data unless an episode just ended\n\n\n",python,tab
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+70,877159,"cleanrl/ppo_atari_envpool.py",8628,0,"            # Capture current observation and action prior to stepping the envs\n            if traj_saver is not None:\n                obs_to_save = next_obs.detach().cpu().numpy().astype(np.uint8)\n                actions_to_save = action.detach().cpu().numpy()\n",python,content
+71,877159,"cleanrl/ppo_atari_envpool.py",6967,0,"    # Optional: trajectory saver\n    traj_saver = None\n    if args.capture_trajectories:\n        traj_saver = TrajectorySaver(\n            output_dir=args.trajectories_output_dir,\n            num_envs=args.num_envs,\n            chunk_size=args.trajectories_chunk_size,\n            chunks_per_file=args.trajectories_chunks_per_file,\n        )\n\n",python,content
+72,877159,"cleanrl/ppo_atari_envpool.py",1167,0,"    # Trajectory capture\n    capture_trajectories: bool = False\n    """"""if toggled, save observation frames and actions during training""""""\n    trajectories_output_dir: str = ""data/atari_trajectories""\n    """"""directory to store trajectory chunks (.npz)""""""\n    trajectories_chunk_size: int = 160\n    """"""number of time steps per chunk before writing""""""\n    trajectories_chunks_per_file: int = 100\n    """"""number of chunks grouped per npz file""""""\n\n",python,content
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+81,913354,"cleanrl/ppo_atari_envpool.py",10671,0,"        # Initialize for type-checkers\n        approx_kl = torch.tensor(0.0)\n        old_approx_kl = torch.tensor(0.0)\n        v_loss = torch.tensor(0.0)\n        pg_loss = torch.tensor(0.0)\n        entropy_loss = torch.tensor(0.0)\n",python,content
+82,913354,"cleanrl/ppo_atari_envpool.py",8882,0,"            if traj_saver is not None and obs_to_save is not None and actions_to_save is not None:\n                # Use true termination flags when available\n                terminated = np.asarray(info.get(""terminated"", next_done.detach().cpu().numpy()), dtype=bool)\n                traj_saver.add_step(obs_to_save, actions_to_save, terminated)\n\n",python,content
+83,913354,"cleanrl/ppo_atari_envpool.py",8628,0,"            # Capture current observation and action prior to stepping the envs\n            obs_to_save = None\n            actions_to_save = None\n            if traj_saver is not None:\n                obs_to_save = next_obs.detach().cpu().numpy().astype(np.uint8)\n                actions_to_save = action.detach().cpu().numpy()\n",python,content
+84,913354,"cleanrl/ppo_atari_envpool.py",6861,104,"    assert isinstance(envs.action_space, spaces.Discrete), ""only discrete action space is supported""\n\n    # Optional: trajectory saver\n    traj_saver = None\n    if args.capture_trajectories:\n        traj_saver = TrajectorySaver(\n            output_dir=args.trajectories_output_dir,\n            num_envs=args.num_envs,\n            chunk_size=args.trajectories_chunk_size,\n            chunks_per_file=args.trajectories_chunks_per_file,\n        )",python,content
+85,913354,"cleanrl/ppo_atari_envpool.py",2469,27,"    target_kl: Optional[float] = None",python,content
+86,913355,"cleanrl/ppo_atari_envpool.py",1167,0,"    # Trajectory capture\n    capture_trajectories: bool = False\n    """"""if toggled, save observation frames and actions during training""""""\n    trajectories_output_dir: str = ""data/atari_trajectories""\n    """"""directory to store trajectory chunks (.npz)""""""\n    trajectories_chunk_size: int = 160\n    """"""number of time steps per chunk before writing""""""\n    trajectories_chunks_per_file: int = 100\n    """"""number of chunks grouped per npz file""""""\n\n",python,content
+87,913355,"cleanrl/ppo_atari_envpool.py",968,28,"    wandb_entity: Optional[str] = None",python,content
+88,913355,"cleanrl/ppo_atari_envpool.py",387,49,"from torch.utils.tensorboard.writer import SummaryWriter\nfrom typing import Optional\nfrom gym import spaces\nfrom .trajectory_saver import TrajectorySaver",python,content
+89,961375,"cleanrl/ppo_atari_envpool.py",15958,58,"",python,content
+90,961375,"cleanrl/ppo_atari_envpool.py",12251,231,"",python,content
+91,961375,"cleanrl/ppo_atari_envpool.py",10114,348,"",python,content
+92,961375,"cleanrl/ppo_atari_envpool.py",9532,328,"",python,content
+93,961375,"cleanrl/ppo_atari_envpool.py",7426,443,"    assert isinstance(envs.action_space, gym.spaces.Discrete), ""only discrete action space is supported""",python,content
+94,961375,"cleanrl/ppo_atari_envpool.py",3024,37,"    target_kl: float = None",python,content
+95,961375,"cleanrl/ppo_atari_envpool.py",1281,441,"",python,content
+96,961375,"cleanrl/ppo_atari_envpool.py",1072,38,"    wandb_entity: str = None",python,content
+97,961375,"cleanrl/ppo_atari_envpool.py",387,153,"from torch.utils.tensorboard import SummaryWriter",python,content
+98,961438,"cleanrl/ppo_atari_envpool.py",14147,0,"    if traj_saver is not None:\n        traj_saver.close()\n",python,content
+99,961438,"cleanrl/ppo_atari_envpool.py",10671,0,"        # Initialize for type-checkers\n        approx_kl = torch.tensor(0.0)\n        old_approx_kl = torch.tensor(0.0)\n        v_loss = torch.tensor(0.0)\n        pg_loss = torch.tensor(0.0)\n        entropy_loss = torch.tensor(0.0)\n",python,content
+100,961438,"cleanrl/ppo_atari_envpool.py",8882,0,"            if traj_saver is not None and obs_to_save is not None and actions_to_save is not None:\n                # Use true termination flags when available\n                terminated = np.asarray(info.get(""terminated"", next_done.detach().cpu().numpy()), dtype=bool)\n                traj_saver.add_step(obs_to_save, actions_to_save, terminated)\n\n",python,content
+101,961438,"cleanrl/ppo_atari_envpool.py",8628,0,"            # Capture current observation and action prior to stepping the envs\n            obs_to_save = None\n            actions_to_save = None\n            if traj_saver is not None:\n                obs_to_save = next_obs.detach().cpu().numpy().astype(np.uint8)\n                actions_to_save = action.detach().cpu().numpy()\n",python,content
+102,961438,"cleanrl/ppo_atari_envpool.py",7002,79,"    optimizer = Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)",python,content
+103,961438,"cleanrl/ppo_atari_envpool.py",6861,104,"    assert isinstance(envs.action_space, spaces.Discrete), ""only discrete action space is supported""\n\n    # Optional: trajectory saver\n    traj_saver = None\n    if args.capture_trajectories:\n        traj_saver = TrajectorySaver(\n            output_dir=args.trajectories_output_dir,\n            num_envs=args.num_envs,\n            chunk_size=args.trajectories_chunk_size,\n            chunks_per_file=args.trajectories_chunks_per_file,\n        )",python,content
+104,961438,"cleanrl/ppo_atari_envpool.py",2982,71,"        self.episode_returns = np.zeros(self.num_envs, dtype=np.float32)\n        self.episode_lengths = np.zeros(self.num_envs, dtype=np.int32)",python,content
+105,961438,"cleanrl/ppo_atari_envpool.py",2469,27,"    target_kl: Optional[float] = None",python,content
+106,961438,"cleanrl/ppo_atari_envpool.py",1167,0,"    # Trajectory capture\n    capture_trajectories: bool = False\n    """"""if toggled, save observation frames and actions during training""""""\n    trajectories_output_dir: str = ""data/atari_trajectories""\n    """"""directory to store trajectory chunks (.npz)""""""\n    trajectories_chunk_size: int = 160\n    """"""number of time steps per chunk before writing""""""\n    trajectories_chunks_per_file: int = 100\n    """"""number of chunks grouped per npz file""""""\n\n",python,content
+107,961438,"cleanrl/ppo_atari_envpool.py",968,28,"    wandb_entity: Optional[str] = None",python,content
+108,961438,"cleanrl/ppo_atari_envpool.py",387,49,"from torch.utils.tensorboard.writer import SummaryWriter\nfrom typing import Optional\nfrom gym import spaces\nfrom .trajectory_saver import TrajectorySaver",python,content
+109,961438,"cleanrl/ppo_atari_envpool.py",291,27,"from torch.optim import Adam",python,content
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+111,968030,"cleanrl/ppo_atari_envpool.py",267,0,"",python,selection_command
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+115,978956,"cleanrl/ppo_atari_envpool.py",7978,73,"    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)",python,content
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+122,1027883,"/home/franz.srambical/jafar/input_pipeline/generate_coinrun_dataset.py",0,0,"",python,tab
+123,1037912,"/home/franz.srambical/jafar/input_pipeline/generate_coinrun_dataset.py",3201,0,"",python,selection_mouse
+124,1043895,"/home/franz.srambical/jafar/input_pipeline/generate_coinrun_dataset.py",0,0,"",python,selection_command
+125,1063321,"/home/franz.srambical/jafar/input_pipeline/utils.py",0,0,"import os\nimport pickle\nimport numpy as np\nfrom array_record.python.array_record_module import ArrayRecordWriter\n\n\ndef save_chunks(obs_chunks, file_idx, chunks_per_file, output_dir, act_chunks=None):\n    os.makedirs(output_dir, exist_ok=True)\n\n    metadata = []\n    while len(obs_chunks) >= chunks_per_file:\n        chunk_batch = obs_chunks[:chunks_per_file]\n        obs_chunks = obs_chunks[chunks_per_file:]\n        act_chunk_batch = None\n        if act_chunks:\n            act_chunk_batch = act_chunks[:chunks_per_file]\n            act_chunks = act_chunks[chunks_per_file:]\n        episode_path = os.path.join(output_dir, f""data_{file_idx:04d}.array_record"")\n        writer = ArrayRecordWriter(str(episode_path), ""group_size:1"")\n        seq_lens = []\n        for idx, chunk in enumerate(chunk_batch):\n            seq_len = chunk.shape[0]\n            seq_lens.append(seq_len)\n            chunk_record = {\n                ""raw_video"": chunk.tobytes(),\n                ""sequence_length"": seq_len,\n            }\n            if act_chunk_batch:\n                assert len(chunk) == len(\n                    act_chunk_batch[idx]\n                ), f""Observation data length and action sequence length do not match: {len(chunk)} != {len(act_chunk_batch[idx])}""\n                chunk_record[""actions""] = act_chunk_batch[idx]\n            writer.write(pickle.dumps(chunk_record))\n        writer.close()\n        file_idx += 1\n        metadata.append(\n            {\n                ""path"": episode_path,\n                ""num_chunks"": len(chunk_batch),\n                ""avg_seq_len"": np.mean(seq_lens),\n            }\n        )\n        print(f""Created {episode_path} with {len(chunk_batch)} video chunks"")\n\n    return metadata, obs_chunks, file_idx, act_chunks\n",python,tab
+126,1077626,"/home/franz.srambical/jafar/input_pipeline/generate_coinrun_dataset.py",0,0,"",python,tab
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4de8d861ed2563988d5f1871647ebc5fe70861b32d24a4b32f9363518653a328/crowd-code-d9cdf338-0ddd-4679-853a-6d7bdf2b18581751046137722-2025_06_27-10.42.19.354/source.csv

@@ -0,0 +1,167 @@
+Sequence,Time,File,RangeOffset,RangeLength,Text,Language,Type
+1,1,"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
+2,38,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"",Log,tab
+3,67,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"10:42:19 AM [info] Activating crowd-code\n10:42:19 AM [info] Recording started\n10:42:19 AM [info] Initializing git provider using file system watchers...\n10:42:19 AM [info] Git repository found\n10:42:19 AM [info] Git provider initialized successfully\n10:42:19 AM [info] Initial git state: [object Object]\n",Log,content
+4,905,"utils/dataloader.py",0,0,"",python,tab
+5,19039,"utils/dataloader.py",2752,0,"",python,selection_command
+6,19130,"utils/dataloader.py",2715,0,"",python,selection_command
+7,30009,"models/dynamics.py",0,0,"from typing import Dict, Any\n\nimport jax\nimport jax.numpy as jnp\nimport flax.linen as nn\n\nfrom utils.nn import STTransformer\n\n\nclass DynamicsMaskGIT(nn.Module):\n    """"""MaskGIT dynamics model""""""\n\n    model_dim: int\n    num_latents: int\n    num_blocks: int\n    num_heads: int\n    dropout: float\n    mask_limit: float\n\n    def setup(self):\n        self.dynamics = STTransformer(\n            self.model_dim,\n            self.num_latents,\n            self.num_blocks,\n            self.num_heads,\n            self.dropout,\n        )\n        self.patch_embed = nn.Embed(self.num_latents, self.model_dim)\n        self.mask_token = self.param(\n            ""mask_token"",\n            nn.initializers.lecun_uniform(),\n            (1, 1, 1, self.model_dim),\n        )\n        self.action_up = nn.Dense(self.model_dim)\n\n    def __call__(self, batch: Dict[str, Any], training: bool = True) -> Dict[str, Any]:\n        # --- Mask videos ---\n        vid_embed = self.patch_embed(batch[""video_tokens""])\n        if training:\n            rng1, rng2 = jax.random.split(batch[""mask_rng""])\n            mask_prob = jax.random.uniform(rng1, minval=self.mask_limit)\n            mask = jax.random.bernoulli(rng2, mask_prob, vid_embed.shape[:-1])\n            mask = mask.at[:, 0].set(False)\n            vid_embed = jnp.where(jnp.expand_dims(mask, -1), self.mask_token, vid_embed)\n        else:\n            mask = None\n\n        # --- Predict transition ---\n        act_embed = self.action_up(batch[""latent_actions""])\n        vid_embed += jnp.pad(act_embed, ((0, 0), (1, 0), (0, 0), (0, 0)))\n        logits = self.dynamics(vid_embed)\n        return dict(token_logits=logits, mask=mask)\n",python,tab
+8,30937,"models/dynamics.py",416,0,"",python,selection_command
+9,31509,"models/dynamics.py",1359,0,"",python,selection_command
+10,31790,"models/dynamics.py",1655,0,"",python,selection_command
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+13,32898,"models/dynamics.py",0,0,"",python,selection_command
+14,35425,"train_dynamics.py",0,0,"from dataclasses import dataclass, field\nimport os\nimport time\n\nimport einops\nfrom flax.training import orbax_utils\nfrom flax.training.train_state import TrainState\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 jax\nimport jax.numpy as jnp\nimport tyro\nimport wandb\n\nfrom genie import Genie, restore_genie_components\nfrom models.tokenizer import TokenizerVQVAE\nfrom models.lam import LatentActionModel\nfrom utils.dataloader import get_dataloader\n\nts = int(time.time())\n\n\n@dataclass\nclass Args:\n    # Experiment\n    num_steps: int = 200_000\n    seed: int = 0\n    seq_len: int = 16\n    image_channels: int = 3\n    image_height: int = 90\n    image_width: int = 160\n    data_dir: str = ""data_tfrecords/coinrun""\n    # Optimization\n    batch_size: int = 36\n    min_lr: float = 3e-6\n    max_lr: float = 3e-5\n    warmup_steps: int = 5000\n    # Tokenizer\n    tokenizer_dim: int = 512\n    latent_patch_dim: int = 32\n    num_patch_latents: int = 1024\n    patch_size: int = 4\n    tokenizer_num_blocks: int = 8\n    tokenizer_num_heads: int = 8\n    tokenizer_checkpoint: str = """"\n    # LAM\n    lam_dim: int = 512\n    latent_action_dim: int = 32\n    num_latent_actions: int = 6\n    lam_patch_size: int = 16\n    lam_num_blocks: int = 8\n    lam_num_heads: int = 8\n    lam_checkpoint: str = """"\n    # Dynamics\n    dyna_dim: int = 512\n    dyna_num_blocks: int = 12\n    dyna_num_heads: int = 8\n    dropout: float = 0.0\n    mask_limit: float = 0.5\n    # Logging\n    log: bool = False\n    entity: str = """"\n    project: str = """"\n    name: str = ""train_dynamics""\n    tags: list[str] = field(default_factory=lambda: [""dynamics""])\n    log_interval: int = 5\n    log_image_interval: int = 250\n    ckpt_dir: str = """"\n    log_checkpoint_interval: int = 25000\n    log_gradients: bool = False\n\n\nargs = tyro.cli(Args)\n\n\ndef dynamics_loss_fn(params, state, inputs):\n    """"""Compute masked dynamics loss""""""\n    outputs = state.apply_fn(\n        params,\n        inputs,\n        training=True,\n        rngs={""params"": inputs[""rng""], ""dropout"": inputs[""dropout_rng""]},\n    )\n    mask = outputs[""mask""]\n    ce_loss = optax.softmax_cross_entropy_with_integer_labels(\n        outputs[""token_logits""], outputs[""video_tokens""]\n    )\n    ce_loss = (mask * ce_loss).sum() / mask.sum()\n    acc = outputs[""token_logits""].argmax(-1) == outputs[""video_tokens""]\n    acc = (mask * acc).sum() / mask.sum()\n    select_probs = jax.nn.softmax(outputs[""token_logits""])\n    metrics = dict(\n        cross_entropy_loss=ce_loss,\n        masked_token_accuracy=acc,\n        select_logit=outputs[""token_logits""].max(-1).mean(),\n        select_p=select_probs.max(-1).mean(),\n        entropy=jax.scipy.special.entr(select_probs).sum(-1).mean(),\n    )\n    return ce_loss, (outputs[""recon""], metrics)\n\n\n@jax.jit\ndef train_step(state, inputs):\n    """"""Update state and compute metrics""""""\n    grad_fn = jax.value_and_grad(dynamics_loss_fn, has_aux=True, allow_int=True)\n    (loss, (recon, metrics)), grads = grad_fn(state.params, state, inputs)\n    state = state.apply_gradients(grads=grads)\n    if args.log_gradients:\n        metrics[""gradients_std/""] = jax.tree.map(\n            lambda x: x.std(), grads[""params""][""dynamics""]\n        )\n    return state, loss, recon, metrics\n\n\nif __name__ == ""__main__"":\n    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    genie = Genie(\n        # Tokenizer\n        in_dim=args.image_channels,\n        tokenizer_dim=args.tokenizer_dim,\n        latent_patch_dim=args.latent_patch_dim,\n        num_patch_latents=args.num_patch_latents,\n        patch_size=args.patch_size,\n        tokenizer_num_blocks=args.tokenizer_num_blocks,\n        tokenizer_num_heads=args.tokenizer_num_heads,\n        # LAM\n        lam_dim=args.lam_dim,\n        latent_action_dim=args.latent_action_dim,\n        num_latent_actions=args.num_latent_actions,\n        lam_patch_size=args.lam_patch_size,\n        lam_num_blocks=args.lam_num_blocks,\n        lam_num_heads=args.lam_num_heads,\n        # Dynamics\n        dyna_dim=args.dyna_dim,\n        dyna_num_blocks=args.dyna_num_blocks,\n        dyna_num_heads=args.dyna_num_heads,\n        dropout=args.dropout,\n        mask_limit=args.mask_limit,\n    )\n    rng, _rng = jax.random.split(rng)\n    image_shape = (args.image_height, args.image_width, args.image_channels)\n    dummy_inputs = dict(\n        videos=jnp.zeros(\n            (per_device_batch_size_for_init, args.seq_len, *image_shape),\n            dtype=jnp.float32,\n        ),\n        action=jnp.zeros(\n            (per_device_batch_size_for_init, args.seq_len), dtype=jnp.float32\n        ),\n        mask_rng=_rng,\n    )\n    rng, _rng = jax.random.split(rng)\n    init_params = genie.init(_rng, dummy_inputs)\n\n    # --- Initialize optimizer ---\n    lr_schedule = optax.warmup_cosine_decay_schedule(\n        args.min_lr, args.max_lr, args.warmup_steps, args.num_steps\n    )\n    tx = optax.adamw(learning_rate=lr_schedule, b1=0.9, b2=0.9, weight_decay=1e-4)\n    train_state = TrainState.create(apply_fn=genie.apply, params=init_params, tx=tx)\n\n    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    # --- Restore checkpoint ---\n    train_state = restore_genie_components(\n        train_state, replicated_sharding, dummy_inputs, rng, args\n    )\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    step = 0\n    while step < args.num_steps:\n        for videos in dataloader:\n            # --- Train step ---\n            rng, _rng, _rng_dropout, _rng_mask = jax.random.split(rng, 4)\n\n            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(\n                videos=videos,\n                rng=_rng,\n                dropout_rng=_rng_dropout,\n                mask_rng=_rng_mask,\n            )\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                    if jax.process_index() == 0:\n                        log_images = dict(\n                            image=wandb.Image(np.asarray(gt_seq[args.seq_len - 1])),\n                            recon=wandb.Image(np.asarray(recon_seq[args.seq_len - 1])),\n                            true_vs_recon=wandb.Image(\n                                np.asarray(comparison_seq.astype(np.uint8))\n                            ),\n                        )\n                        wandb.log(log_images)\n            if step % args.log_checkpoint_interval == 0:\n                ckpt = {""model"": train_state}\n                orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n                save_args = orbax_utils.save_args_from_target(ckpt)\n                orbax_checkpointer.save(\n                    os.path.join(os.getcwd(), args.ckpt_dir, f""genie_{ts}_{step}""),\n                    ckpt,\n                    save_args=save_args,\n                )\n            if step >= args.num_steps:\n                break\n",python,tab
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+1,6,"scripts/rename_mp4_files.py",0,0,"#!/usr/bin/env python3\n""""""\nScript to rename all MP4 files in a directory with a custom prefix.\n""""""\n\nimport os\nimport argparse\nimport re\nfrom pathlib import Path\n\n\ndef rename_mp4_files(directory, prefix, dry_run=False, pattern=None, start_number=1):\n    """"""\n    Rename all MP4 files in the specified directory with a custom prefix.\n    \n    Args:\n        directory (str): Path to the directory containing MP4 files\n        prefix (str): Custom prefix to add to filenames\n        dry_run (bool): If True, only show what would be renamed without actually renaming\n        pattern (str): Optional regex pattern to filter files\n        start_number (int): Starting number for sequential naming\n    """"""\n    directory_path = Path(directory)\n    \n    if not directory_path.exists():\n        print(f""Error: Directory '{directory}' does not exist."")\n        return\n    \n    if not directory_path.is_dir():\n        print(f""Error: '{directory}' is not a directory."")\n        return\n    \n    # Find all MP4 files\n    mp4_files = list(directory_path.glob(""*.mp4""))\n    \n    if not mp4_files:\n        print(f""No MP4 files found in '{directory}'"")\n        return\n    \n    # Filter by pattern if provided\n    if pattern:\n        regex = re.compile(pattern)\n        mp4_files = [f for f in mp4_files if regex.search(f.name)]\n    \n    if not mp4_files:\n        print(f""No MP4 files match the pattern '{pattern}' in '{directory}'"")\n        return\n    \n    print(f""Found {len(mp4_files)} MP4 files to rename:"")\n    \n    # Sort files for consistent ordering\n    mp4_files.sort()\n    \n    for i, file_path in enumerate(mp4_files, start=start_number):\n        # Get the original filename without extension\n        original_name = file_path.stem\n        extension = file_path.suffix\n        \n        # Create new filename with prefix\n        new_name = f""{prefix}_{i:03d}_{original_name}{extension}""\n        new_path = file_path.parent / new_name\n        \n        # Check if new filename already exists\n        if new_path.exists() and not dry_run:\n            print(f""Warning: '{new_name}' already exists, skipping '{file_path.name}'"")\n            continue\n        \n        if dry_run:\n            print(f""Would rename: '{file_path.name}' -> '{new_name}'"")\n        else:\n            try:\n                file_path.rename(new_path)\n                print(f""Renamed: '{file_path.name}' -> '{new_name}'"")\n            except OSError as e:\n                print(f""Error renaming '{file_path.name}': {e}"")\n    \n    if dry_run:\n        print(f""\nDry run completed. {len(mp4_files)} files would be renamed."")\n    else:\n        print(f""\nRenaming completed. {len(mp4_files)} files renamed."")\n\n\ndef main():\n    parser = argparse.ArgumentParser(\n        description=""Rename all MP4 files in a directory with a custom prefix"",\n        formatter_class=argparse.RawDescriptionHelpFormatter,\n        epilog=""""""\nExamples:\n  %(prog)s /path/to/videos minecraft\n  %(prog)s /path/to/videos player --dry-run\n  %(prog)s /path/to/videos episode --pattern ""joost"" --start-number 10\n        """"""\n    )\n    \n    parser.add_argument(\n        ""directory"",\n        help=""Directory containing MP4 files to rename""\n    )\n    \n    parser.add_argument(\n        ""prefix"",\n        help=""Custom prefix to add to filenames""\n    )\n    \n    parser.add_argument(\n        ""--dry-run"",\n        action=""store_true"",\n        help=""Show what would be renamed without actually renaming files""\n    )\n    \n    parser.add_argument(\n        ""--pattern"",\n        help=""Regex pattern to filter files (only rename files matching this pattern)""\n    )\n    \n    parser.add_argument(\n        ""--start-number"",\n        type=int,\n        default=1,\n        help=""Starting number for sequential naming (default: 1)""\n    )\n    \n    args = parser.parse_args()\n    \n    # Validate start number\n    if args.start_number < 1:\n        print(""Error: start-number must be at least 1"")\n        return\n    \n    rename_mp4_files(\n        directory=args.directory,\n        prefix=args.prefix,\n        dry_run=args.dry_run,\n        pattern=args.pattern,\n        start_number=args.start_number\n    )\n\n\nif __name__ == ""__main__"":\n    main() ",python,tab
+2,319,"extension-output-pdoom-org.crowd-code-#1-crowd-code",0,0,"6:09:57 PM [info] Activating crowd-code\n6:09:57 PM [info] Welcome back tum_ind3695. Your user-id is '507ab0ec0dfe0c18ad7778dd15e072f92367194c94623114de802c8ed9c52e20'. Happy coding!\n6:09:57 PM [info] Recording started\n",Log,tab
+3,2485,"scripts/rename_mp4_files.py",0,0,"",python,tab
+4,2488,"scripts/rename_mp4_files.py",1333,0,"",python,selection_mouse
+5,2493,"scripts/rename_mp4_files.py",1332,0,"",python,selection_command
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+8,419551,"TERMINAL",0,0,"cd /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared//checkpoints/dyn/3289577",,terminal_command
+9,419570,"TERMINAL",0,0,"]633;E;2025-06-25 18:16:56 cd /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared//checkpoints/dyn/3289577;ef772368-fdbb-4ff2-ad99-2db92190992c]633;C]0;tum_ind3695@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/dyn/3289577]633;D;0",,terminal_output
+10,420075,"TERMINAL",0,0,"ls",,terminal_command
+11,420099,"TERMINAL",0,0,"]633;E;2025-06-25 18:16:57 ls;ef772368-fdbb-4ff2-ad99-2db92190992c]633;Cgenie_1750786631_1000   genie_1750786631_11000  genie_1750786631_12500  genie_1750786631_2500  genie_1750786631_4000  genie_1750786631_5000  genie_1750786631_6500  genie_1750786631_8000  genie_1750786631_9500\r\ngenie_1750786631_10000  genie_1750786631_11500  genie_1750786631_1500   genie_1750786631_3000  genie_1750786631_4500  genie_1750786631_5500  genie_1750786631_7000  genie_1750786631_8500\r\ngenie_1750786631_10500  genie_1750786631_12000  genie_1750786631_2000   genie_1750786631_3500  genie_1750786631_500   genie_1750786631_6000  genie_1750786631_7500  genie_1750786631_9000\r\n]0;tum_ind3695@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/dyn/3289577]633;D;0",,terminal_output
+12,439480,"TERMINAL",0,0,"cd genie_1750786631_12500",,terminal_command
+13,440497,"TERMINAL",0,0,"pwd",,terminal_command
+14,440512,"TERMINAL",0,0,"]633;E;2025-06-25 18:17:17 pwd;ef772368-fdbb-4ff2-ad99-2db92190992c]633;C/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/dyn/3289577/genie_1750786631_12500\r\n]0;tum_ind3695@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/dyn/3289577/genie_1750786631_12500]633;D;0",,terminal_output
+15,615482,"TERMINAL",0,0,"cd ../../..",,terminal_command
+16,615494,"TERMINAL",0,0,"]633;E;2025-06-25 18:20:12 cd ../../..;ef772368-fdbb-4ff2-ad99-2db92190992c]633;C]0;tum_ind3695@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints]633;D;0",,terminal_output
+17,616076,"TERMINAL",0,0,"ls",,terminal_command
+18,616111,"TERMINAL",0,0,"]633;E;2025-06-25 18:20:13 ls;ef772368-fdbb-4ff2-ad99-2db92190992c]633;C0000  3290283  3290284  3290295  3290296  3290366  3290367  3290391  3290392  3290439  3290440  3291405  dyn  lam  tokenizer\r\n]0;tum_ind3695@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints]633;D;0",,terminal_output
+19,619493,"TERMINAL",0,0,"cd tokenizer/",,terminal_command
+20,619506,"TERMINAL",0,0,"]633;E;2025-06-25 18:20:16 cd tokenizer/;ef772368-fdbb-4ff2-ad99-2db92190992c]633;C]0;tum_ind3695@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer]633;D;0",,terminal_output
+21,619896,"TERMINAL",0,0,"ls",,terminal_command
+22,619948,"TERMINAL",0,0,"]633;E;2025-06-25 18:20:17 ls;ef772368-fdbb-4ff2-ad99-2db92190992c]633;C",,terminal_output
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+24,649663,"TERMINAL",0,0,"cd 3255052/",,terminal_command
+25,649679,"TERMINAL",0,0,"]633;E;2025-06-25 18:20:47 cd 3255052/;ef772368-fdbb-4ff2-ad99-2db92190992c]633;C]0;tum_ind3695@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/3255052]633;D;0",,terminal_output
+26,650176,"TERMINAL",0,0,"ls",,terminal_command
+27,650194,"TERMINAL",0,0,"]633;E;2025-06-25 18:20:47 ls;ef772368-fdbb-4ff2-ad99-2db92190992c]633;C]0;tum_ind3695@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer/3255052]633;D;0",,terminal_output
+28,652601,"TERMINAL",0,0,"cd ..",,terminal_command
+29,652609,"TERMINAL",0,0,"]633;E;2025-06-25 18:20:50 cd ..;ef772368-fdbb-4ff2-ad99-2db92190992c]633;C]0;tum_ind3695@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/tokenizer]633;D;0",,terminal_output
+30,654392,"TERMINAL",0,0,"cd ..",,terminal_command
+31,654398,"TERMINAL",0,0,"]633;E;2025-06-25 18:20:51 cd ..;ef772368-fdbb-4ff2-ad99-2db92190992c]633;C]0;tum_ind3695@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints]633;D;0",,terminal_output
+32,655918,"TERMINAL",0,0,"cd ..",,terminal_command
+33,655926,"TERMINAL",0,0,"]633;E;2025-06-25 18:20:53 cd ..;ef772368-fdbb-4ff2-ad99-2db92190992c]633;C]0;tum_ind3695@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared]633;D;0",,terminal_output
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+35,1148124,"TERMINAL",0,0,"]633;E;2025-06-25 18:29:05 queue;ef772368-fdbb-4ff2-ad99-2db92190992c]633;C",,terminal_output
+36,1148182,"TERMINAL",0,0,"[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Wed Jun 25 18:29:05 2025JOBID PARTITION     NAME     USER ST\tTIME  NODES NODELIST(REASON)3292119 accelerat train_to tum_ind3 PD\t0:00\t  1 (Priority)3289577 accelerat train_dy tum_ind3  R   22:52:29\t  1 hkn0712",,terminal_output
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+41,1150481,"TERMINAL",0,0,"[?1049h(B[?7hEvery 1.0s: squeue --mehkn1993.localdomain: Wed Jun 25 18:29:07 2025JOBID PARTITION     NAME     USER ST\tTIME  NODES NODELIST(REASON)3292119 accelerat train_to tum_ind3 PD\t0:00\t  1 (Priority)3289577 accelerat train_dy tum_ind3  R   22:52:31\t  1 hkn0712",,terminal_output
+42,1151212,"TERMINAL",0,0,"[?1049l\r[?1l>]0;tum_ind3695@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared]633;D;0",,terminal_output
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+44,1152616,"TERMINAL",0,0,"]633;E;2025-06-25 18:29:10 squeue;ef772368-fdbb-4ff2-ad99-2db92190992c]633;C             JOBID PARTITION     NAME     USER ST       TIME  NODES NODELIST(REASON)\r\n           3292119 accelerat train_to tum_ind3 PD       0:00      1 (Priority)\r\n           3289577 accelerat train_dy tum_ind3  R   22:52:34      1 hkn0712\r\n]0;tum_ind3695@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared]633;D;0]633;P;Cwd=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared",,terminal_output
+45,2263248,"TERMINAL",0,0,"cd /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared//checkpoints/dyn/3289577",,terminal_command
+46,2263255,"TERMINAL",0,0,"]633;E;2025-06-25 18:47:40 cd /hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared//checkpoints/dyn/3289577;ef772368-fdbb-4ff2-ad99-2db92190992c]633;C]0;tum_ind3695@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/dyn/3289577]633;D;0",,terminal_output
+47,2264288,"TERMINAL",0,0,"ls",,terminal_command
+48,2264341,"TERMINAL",0,0,"]633;E;2025-06-25 18:47:41 ls;ef772368-fdbb-4ff2-ad99-2db92190992c]633;C",,terminal_output
+49,2264401,"TERMINAL",0,0,"genie_1750786631_1000   genie_1750786631_11000  genie_1750786631_12500  genie_1750786631_2000  genie_1750786631_3500  genie_1750786631_500   genie_1750786631_6000  genie_1750786631_7500  genie_1750786631_9000\r\ngenie_1750786631_10000  genie_1750786631_11500  genie_1750786631_13000  genie_1750786631_2500  genie_1750786631_4000  genie_1750786631_5000  genie_1750786631_6500  genie_1750786631_8000  genie_1750786631_9500\r\ngenie_1750786631_10500  genie_1750786631_12000  genie_1750786631_1500   genie_1750786631_3000  genie_1750786631_4500  genie_1750786631_5500  genie_1750786631_7000  genie_1750786631_8500\r\n]0;tum_ind3695@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/dyn/3289577]633;D;0",,terminal_output
+50,2269289,"TERMINAL",0,0,"cd genie_1750786631_13000",,terminal_command
+51,2269296,"TERMINAL",0,0,"]633;E;2025-06-25 18:47:46 cd genie_1750786631_13000;ef772368-fdbb-4ff2-ad99-2db92190992c]633;C]0;tum_ind3695@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/dyn/3289577/genie_1750786631_13000]633;D;0",,terminal_output
+52,2270468,"TERMINAL",0,0,"pwd",,terminal_command
+53,2270477,"TERMINAL",0,0,"]633;E;2025-06-25 18:47:47 pwd;ef772368-fdbb-4ff2-ad99-2db92190992c]633;C/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/dyn/3289577/genie_1750786631_13000\r\n]0;tum_ind3695@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/dyn/3289577/genie_1750786631_13000]633;D;0",,terminal_output
+54,2272125,"TERMINAL",0,0,"os",,terminal_command
+55,2272199,"TERMINAL",0,0,"]633;E;2025-06-25 18:47:49 os;ef772368-fdbb-4ff2-ad99-2db92190992c]633;Cbash: os: command not found...\r\n",,terminal_output
+56,2274189,"TERMINAL",0,0,"ls",,terminal_command
+57,2274240,"TERMINAL",0,0,"]633;E;2025-06-25 18:47:51 ls;ef772368-fdbb-4ff2-ad99-2db92190992c]633;C",,terminal_output
+58,2274298,"TERMINAL",0,0,"array_metadatas  _CHECKPOINT_METADATA  d  manifest.ocdbt  _METADATA  ocdbt.process_0  ocdbt.process_1  ocdbt.process_2  ocdbt.process_3  _sharding\r\n]0;tum_ind3695@hkn1993:/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/checkpoints/dyn/3289577/genie_1750786631_13000]633;D;0",,terminal_output
+59,2708521,"scripts/file_duplicate_checker.py",0,0,"import os\nfrom collections import defaultdict\nfrom tqdm import tqdm\n\ndef find_duplicate_filenames(root_dir):\n    filenames = defaultdict(list)\n    file_count = 0\n\n    # Use tqdm with manual update and no percentage/ETA bar\n    pbar = tqdm(desc=""Files scanned"", unit=""file"", dynamic_ncols=True, bar_format=""{desc}: {n_fmt}"")\n\n    # Walk the directory recursively\n    for dirpath, _, files in os.walk(root_dir):\n        for file in files:\n            full_path = os.path.join(dirpath, file)\n            if os.path.isfile(full_path):\n                filenames[file].append(full_path)\n                file_count += 1\n                pbar.update(1)\n\n    pbar.close()\n\n    # Print duplicates\n    duplicates = {name: paths for name, paths in filenames.items() if len(paths) > 1}\n    if duplicates:\n        print(""\nDuplicate filenames found:\n"")\n        for name, paths in duplicates.items():\n            print(f""Filename: {name}"")\n            for path in paths:\n                print(f"" - {path}"")\n            print()\n    else:\n        print(""\nNo duplicate filenames found."")\n\nif __name__ == ""__main__"":\n    import sys\n    if len(sys.argv) < 2:\n        print(""Usage: python find_duplicates.py <directory_path>"")\n    else:\n        find_duplicate_filenames(sys.argv[1])\n\n",python,tab
+60,2729693,"scripts/file_duplicate_checker.py",991,0,"",python,selection_mouse
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