Add files using upload-large-folder tool

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import json
import os
import time
from dataclasses import asdict, dataclass, field
from functools import partial
from typing import Any, Dict, List, Literal, Tuple, Optional

import torch
import torch.distributed as dist
import wandb
from tqdm import trange

from veomni.checkpoint import build_checkpointer, ckpt_to_state_dict
from veomni.data import (
    build_dataloader,
    build_iterative_dataset,
    build_mapping_dataset,
)
from veomni.distributed.offloading import build_activation_offloading_context
from veomni.distributed.parallel_state import get_parallel_state, init_parallel_state
from veomni.distributed.torch_parallelize import build_parallelize_model
from veomni.models import build_foundation_model, build_tokenizer, save_model_assets, save_model_weights
from veomni.optim import build_lr_scheduler, build_optimizer
from veomni.utils import helper
from veomni.utils.arguments import DataArguments, ModelArguments, TrainingArguments, parse_args, save_args
from veomni.utils.device import (
    get_device_type,
    get_nccl_backend,
    get_torch_device,
    synchronize,
)
from veomni.utils.dist_utils import all_reduce
from veomni.models.registry import ModelRegistry
ModelRegistry.register_modeling_path("models.llada2_moe")
from dataset.data_transform import process_mdm_tokenized_example, process_mdm_sft_example
from dataset import build_local_dataset


logger = helper.create_logger(__name__)

@dataclass
class LLaDA2ModelArguments(ModelArguments):
    attn_implementation: Optional[Literal["eager", "sdpa", "flex_attention"]] = field(
        default="sdpa",
        metadata={"help": "Attention implementation to use."},
    )


@dataclass
class LLaDA2DataArguments(DataArguments):
    data_type: Literal["conversation", "tokenid"] = field(
        default="conversation",
        metadata={"help": "Type of the training data."},
    )
    datasets_type: Literal["mapping", "local"] = field(
        default="mapping",
        metadata={"help": "Type of the datasets."},
    )
    text_keys: str = field(
        default="messages",
        metadata={"help": "Key to get text from the training data."},
    )
    noise_range_low: float = field(
        default=0.3,
        metadata={"help": "Noise level for random flip input_ids to mask_ids"}
    )
    noise_range_high: float = field(
        default=0.8,
        metadata={"help": "Noise level for random flip input_ids to mask_ids"}
    )

    def __post_init__(self):
        super().__post_init__()
        if self.noise_range_low > self.noise_range_high:
            raise ValueError(
                f"noise_range_low ({self.noise_range_low}) "
                f"cannot be greater than noise_range_high ({self.noise_range_high})."
            )

        if not (0.0 <= self.noise_range_low <= 1.0):
            raise ValueError(
                f"noise_range_low must be between 0.0 and 1.0, but got {self.noise_range_low}."
            )

        if not (0.0 <= self.noise_range_high <= 1.0):
            raise ValueError(
                f"noise_range_high must be between 0.0 and 1.0, but got {self.noise_range_high}."
            )


@dataclass
class LLaDA2TrainingArguments(TrainingArguments):
    beta1: float = field(
        default=0.9,
        metadata={"help": "AdamW optimizer beta1."},
    )
    beta2: float = field(
        default=0.999,
        metadata={"help": "AdamW optimizer beta2"},
    )
    block_diffusion_mode: bool = field(
        default=False,
        metadata={"help": "If train MDM in block_diffusion mode. True: use block_diffusion, False: full_attention"}
    )
    block_size: int = field(
        default=32,
        metadata={"help": "The block size for block diffusion block size"}
    )
    same_token_labels: bool = field(
        default=False,
        metadata={"help": "If use same token location labels. True: no shift, False: use next-token prediction shift."}
    )


@dataclass
class Arguments:
    model: "LLaDA2ModelArguments" = field(default_factory=LLaDA2ModelArguments)
    data: "LLaDA2DataArguments" = field(default_factory=LLaDA2DataArguments)
    train: "LLaDA2TrainingArguments" = field(default_factory=LLaDA2TrainingArguments)


def block_diffusion_mask(b, h, q_idx, kv_idx, block_size=None, n=None):
    """
    Constructs the specialized block diffusion attention mask for training
    composed of three masks:
    - **Block Diagonal Mask (M_BD)**: Self-attention within noised blocks
    - **Offset Block Causal Mask (M_OBC)**: Cross-attention for conditional context
    - **Block Causal Mask (M_BC)**: Attention to update x0
    Args:
        b, h: Batch and head indices (ignored for mask logic).
        q_idx, kv_idx: Query and Key indices.
        seq_len: Total sequence length.
        block_size: Defines the block structure.
    Returns:
        A boolean attention mask.
    """

    # Indicate whether token belongs to xt or x0
    x0_flag_q = (q_idx >= n)
    x0_flag_kv = (kv_idx >= n)

    # Compute block indices
    block_q = torch.where(x0_flag_q == 1,
                          (q_idx - n) // block_size,
                          q_idx // block_size)
    block_kv = torch.where(x0_flag_kv == 1,
                           (kv_idx - n) // block_size,
                           kv_idx // block_size)

    # **1. Block Diagonal Mask (M_BD) **
    block_diagonal = (block_q == block_kv) & (x0_flag_q == x0_flag_kv)

    # **2. Offset Block-Causal Mask (M_OBC) **
    offset_block_causal = (
        (block_q > block_kv)
        & (x0_flag_kv == 1)
        & (x0_flag_q == 0)
    )

    # **3. Block-Causal Mask (M_BC) **
    block_causal = (block_q >= block_kv) & (x0_flag_kv == 1) & (x0_flag_q == 1)

    # **4. Combine Masks **
    return block_diagonal | offset_block_causal | block_causal


def main():
    dist.init_process_group(backend=get_nccl_backend())
    args = parse_args(Arguments)
    logger.info(f"Process rank: {args.train.global_rank}, world size: {args.train.world_size}")
    logger.info_rank0(json.dumps(asdict(args), indent=2))
    get_torch_device().set_device(f"{get_device_type()}:{args.train.local_rank}")
    helper.set_seed(args.train.seed, args.train.enable_full_determinism)
    if args.train.local_rank == 0:
        helper.enable_third_party_logging()

    if args.train.global_rank == 0:
        save_args(args, args.train.output_dir)

    Checkpointer = build_checkpointer(dist_backend=args.train.data_parallel_mode, ckpt_manager=args.train.ckpt_manager)

    init_parallel_state(
        dp_size=args.train.data_parallel_size,
        dp_replicate_size=args.train.data_parallel_replicate_size,
        dp_shard_size=args.train.data_parallel_shard_size,
        tp_size=args.train.tensor_parallel_size,
        ep_size=args.train.expert_parallel_size,
        pp_size=args.train.pipeline_parallel_size,
        cp_size=args.train.context_parallel_size,
        ulysses_size=args.train.ulysses_parallel_size,
        dp_mode=args.train.data_parallel_mode,
    )

    logger.info_rank0("Prepare data")
    tokenizer = build_tokenizer(args.model.tokenizer_path)
    if args.data.data_type == "conversation":
        if not tokenizer.chat_template:
            raise ValueError(f"No chat template found in the tokenizer.")

        transform = partial(
            process_mdm_sft_example,
            tokenizer=tokenizer,
            max_seq_len=args.data.max_seq_len,
            text_keys=args.data.text_keys,
            noise_range=(args.data.noise_range_low, args.data.noise_range_high),
            mask_token_id=156895, 
        )
    elif args.data.data_type == "tokenid":
        transform = partial(
            process_mdm_tokenized_example,
            max_seq_len=args.data.max_seq_len,
            text_keys=args.data.text_keys,
            noise_range=(args.data.noise_range_low, args.data.noise_range_high),
            mask_token_id=156895, 
        )
    else:
        raise NotImplementedError(f"Unsupported data type: {args.data.data_type}.")

    if args.data.dataloader_type == "native":
        if args.data.datasets_type == "iterable":
            logger.info_rank0("Start building iterative dataset")
            train_dataset = build_iterative_dataset(args.data.train_path, transform=transform, seed=args.train.seed)
        elif args.data.datasets_type == "mapping":
            logger.info_rank0("Start building mapping dataset")
            train_dataset = build_mapping_dataset(args.data.train_path, transform=transform)
        elif args.data.datasets_type == "local":
            logger.info_rank0("Start building local dataset")
            train_dataset = build_local_dataset(args.data.train_path, transform=transform)
        
        dataset_length = None if not hasattr(train_dataset, "__len__") else len(train_dataset)
        if args.data.datasets_type == "mapping" or args.data.datasets_type == "local":
            dataset_length = dataset_length / args.train.data_parallel_size
        args.train.compute_train_steps(args.data.max_seq_len, args.data.train_size, dataset_length)

        train_dataloader = build_dataloader(
            dataset=train_dataset,
            micro_batch_size=args.train.micro_batch_size,
            global_batch_size=args.train.global_batch_size,
            dataloader_batch_size=args.train.dataloader_batch_size,
            seed=args.train.seed,
            max_seq_len=args.data.max_seq_len,
            train_steps=args.train.train_steps,
            rmpad=args.train.rmpad,
            rmpad_with_pos_ids=args.train.rmpad_with_pos_ids,
            bsz_warmup_ratio=args.train.bsz_warmup_ratio,
            bsz_warmup_init_mbtoken=args.train.bsz_warmup_init_mbtoken,
            dyn_bsz_margin=args.train.dyn_bsz_margin,
            dyn_bsz_buffer_size=args.train.dyn_bsz_buffer_size,
            num_workers=args.data.num_workers,
            drop_last=args.data.drop_last,
            pin_memory=args.data.pin_memory,
            prefetch_factor=args.data.prefetch_factor,
        )
    else:
        raise NotImplementedError(f"Unsupported dataloader type: {args.data.dataloader_type}.")

    logger.info_rank0("Prepare model")
    model = build_foundation_model(
        config_path=args.model.config_path,
        weights_path=args.model.model_path,
        torch_dtype="float32" if args.train.enable_mixed_precision else "bfloat16",
        attn_implementation=args.model.attn_implementation,
        moe_implementation=args.model.moe_implementation,
        init_device=args.train.init_device,
        force_use_huggingface=args.model.force_use_huggingface,
    )
    model_config = model.config
    helper.print_device_mem_info("VRAM usage after building model")

    get_optimizer_pre_hook = getattr(model, "get_optimizer_pre_hook", None)
    model = build_parallelize_model(
        model,
        init_device=args.train.init_device,
        weights_path=args.model.model_path,
        enable_full_shard=args.train.enable_full_shard,
        enable_mixed_precision=args.train.enable_mixed_precision,
        enable_gradient_checkpointing=args.train.enable_gradient_checkpointing,
        enable_fsdp_offload=args.train.enable_fsdp_offload,
        basic_modules=model._no_split_modules + args.model.basic_modules,
        enable_reentrant=args.train.enable_reentrant,
        enable_forward_prefetch=args.train.enable_forward_prefetch,
        broadcast_model_weights_from_rank0=args.train.broadcast_model_weights_from_rank0
    )

    optimizer = build_optimizer(
        model,
        lr=args.train.lr,
        betas=(args.train.beta1, args.train.beta2),
        weight_decay=args.train.weight_decay,
        fused=True,
        optimizer_type=args.train.optimizer,
    )

    if get_optimizer_pre_hook is not None:
        optimizer_pre_hook = get_optimizer_pre_hook(model, model_config, args.train.data_parallel_mode)
        optimizer.register_step_pre_hook(optimizer_pre_hook)

    lr_scheduler = build_lr_scheduler(
        optimizer,
        train_steps=args.train.train_steps * args.train.num_train_epochs,
        lr=args.train.lr,
        lr_min=args.train.lr_min,
        lr_decay_style=args.train.lr_decay_style,
        lr_decay_ratio=args.train.lr_decay_ratio,
        lr_warmup_ratio=args.train.lr_warmup_ratio,
        lr_start=args.train.lr_start,
    )

    if args.train.global_rank == 0:
        if args.train.use_wandb:
            wandb.init(
                project=args.train.wandb_project,
                name=args.train.wandb_name,
                config={**vars(args.model), **vars(args.data), **vars(args.train)},  # flatten dict
            )

        # save model_assets before training
        model_assets = [model_config, tokenizer]
        save_model_assets(args.train.model_assets_dir, model_assets)

    if args.train.profile_this_rank:
        profiler = helper.create_profiler(
            start_step=args.train.profile_start_step,
            end_step=args.train.profile_end_step,
            trace_dir=args.train.profile_trace_dir,
            record_shapes=args.train.profile_record_shapes,
            profile_memory=args.train.profile_profile_memory,
            with_stack=args.train.profile_with_stack,
            global_rank=args.train.global_rank,
        )
        profiler.start()

    start_epoch, start_step, global_step = 0, 0, 0
    save_checkpoint_path = None
    environ_meter = helper.EnvironMeter(
        config=model_config,
        global_batch_size=args.train.global_batch_size,
        rmpad=args.train.rmpad,
        rmpad_with_pos_ids=args.train.rmpad_with_pos_ids,
        empty_cache_steps=args.train.empty_cache_steps,
        enable_multisource=args.data.enable_multisource,
        dataloader=train_dataloader,
        data_path=args.data.train_path,
    )

    if args.train.load_checkpoint_path:
        state = {"model": model, "optimizer": optimizer, "extra_state": {}}  # cannot be None
        Checkpointer.load(args.train.load_checkpoint_path, state)
        global_step = state["extra_state"]["global_step"]
        start_epoch = global_step // args.train.train_steps
        start_step = global_step % args.train.train_steps
        lr_scheduler.load_state_dict(state["extra_state"]["lr_scheduler"])
        train_dataloader.load_state_dict(state["extra_state"]["train_dataloader"])
        environ_meter.load_state_dict(state["extra_state"]["environ_meter"])
        torch.set_rng_state(state["extra_state"]["torch_rng_state"])
        if start_step == 0:  # resume at the end of epoch
            iter(train_dataloader)  # clear resume state and prefetch data

        dist.barrier()
        logger.info_rank0(f"Load distributed checkpoint from {args.train.load_checkpoint_path} successfully!")

    # Build block diffusion attention mask
    if args.train.block_diffusion_mode:
        bd_attn_full_len = args.data.max_seq_len * 2
        block_size = args.train.block_size
        # NOTE: Boolean dtype block diffusion attention mask
        block_diffusion_attn_mask_flag = block_diffusion_mask(
            b=None, h=None,
            q_idx=torch.arange(bd_attn_full_len)[:, None],
            kv_idx=torch.arange(bd_attn_full_len)[None, :],
            block_size=block_size,
            n=args.data.max_seq_len
        ).unsqueeze(0).unsqueeze(0)
        
        block_diffusion_attn_mask_prototype = torch.zeros_like(
            block_diffusion_attn_mask_flag, 
            dtype=torch.float32 if args.train.enable_mixed_precision else torch.bfloat16
        )
        block_diffusion_attn_mask_prototype.masked_fill_(block_diffusion_attn_mask_flag.logical_not(), float("-inf"))

    helper.empty_cache()
    model_fwd_context, model_bwd_context = build_activation_offloading_context(
        args.train.enable_activation_offload, args.train.enable_gradient_checkpointing, args.train.activation_gpu_limit
    )
    model.train()
    logger.info(
        f"rank{args.train.local_rank} Start training, train_steps: {args.train.train_steps}, epochs: {args.train.num_train_epochs}"
    )
    for epoch in range(start_epoch, args.train.num_train_epochs):
        if hasattr(train_dataloader, "set_epoch"):
            train_dataloader.set_epoch(epoch)

        data_loader_tqdm = trange(
            args.train.train_steps,
            desc=f"Epoch {epoch + 1}/{args.train.num_train_epochs}",
            total=args.train.train_steps,
            initial=start_step,
            disable=args.train.local_rank != 0,
        )
        data_iterator = iter(train_dataloader)
        for _ in range(start_step, args.train.train_steps):
            global_step += 1

            try:
                micro_batches: List[Dict[str, Any]] = next(data_iterator)
            except StopIteration:
                logger.info(f"epoch:{epoch} Dataloader finished with drop_last {args.data.drop_last}")
                break

            if global_step == 1:
                helper.print_example(example=micro_batches[0], rank=args.train.local_rank)

            total_loss = 0
            synchronize()
            start_time = time.time()
            for micro_batch in micro_batches:
                environ_meter.add(micro_batch)
                if args.data.enable_multisource:
                    micro_batch.pop("ds_idx", None)
                    micro_batch.pop("source_name", None)

                if args.train.block_diffusion_mode:
                    noisy_input_ids = micro_batch["noisy_input_ids"]
                    clean_input_ids = micro_batch["input_ids"]
                    batch_size = noisy_input_ids.shape[0]
                    full_input_ids = torch.cat([noisy_input_ids, clean_input_ids], dim=1)
                    noisy_position_ids = torch.arange(noisy_input_ids.shape[1], device=get_device_type(), dtype=torch.long)
                    clean_position_ids = torch.arange(clean_input_ids.shape[1], device=get_device_type(), dtype=torch.long)
                    position_ids = torch.cat([noisy_position_ids, clean_position_ids], dim=0).unsqueeze(0).expand(batch_size, -1).clone()
                    micro_batch["input_ids"] = full_input_ids
                    micro_batch["position_ids"] = position_ids
                    micro_batch["attention_mask"] = block_diffusion_attn_mask_prototype.expand(batch_size, -1, -1, -1)
                else:
                    micro_batch["attention_mask"] = None

                micro_batch = {
                    k: v.to(get_device_type(), non_blocking=True) if isinstance(v, torch.Tensor) else v
                    for k, v in micro_batch.items()
                }

                labels = micro_batch.pop("labels", None)

                with model_fwd_context:
                    logits: "torch.Tensor" = model(**micro_batch, use_cache=False, output_router_logits=False).logits
                    if args.train.block_diffusion_mode:
                        noisy_logits = logits[:, :noisy_input_ids.shape[1]].contiguous()
                    else:
                        noisy_logits = logits

                    if args.train.same_token_labels:
                        unscaled_loss = torch.nn.functional.cross_entropy(
                            noisy_logits.view(-1, noisy_logits.shape[-1]),
                            labels.view(-1),
                            reduction="none",
                        )
                        loss = unscaled_loss.sum() / (labels != -100).sum() / len(micro_batches)
                    else:
                        shifted_noisy_logits = noisy_logits[:, :-1, :].contiguous()
                        shifted_labels = labels[:, 1:].contiguous()
                        unscaled_loss = torch.nn.functional.cross_entropy(
                            shifted_noisy_logits.view(-1, shifted_noisy_logits.shape[-1]),
                            shifted_labels.view(-1),
                            reduction="none",
                        ).view(shifted_noisy_logits.shape[0], -1)
                        loss = unscaled_loss.sum() / (shifted_labels != -100).sum() / len(micro_batches)

                with model_bwd_context:
                    loss.backward()

                total_loss += loss.item()
                del micro_batch

            # Prefer model-provided clip_grad_norm_ (now both FSDP1 and FSDP2 registers custom grad norm clipping)
            if hasattr(model, "clip_grad_norm_"):
                _gn = model.clip_grad_norm_(args.train.max_grad_norm)
                grad_norm = _gn.item() if hasattr(_gn, "item") else float(_gn)
            else:
                logger.info_rank0(
                    "Can NOT find regitsered clip_grad_norm_ method in the model, using PyTorch default implementation.."
                )
                grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), args.train.max_grad_norm)

            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()
            if hasattr(grad_norm, "full_tensor"):
                grad_norm = grad_norm.full_tensor().item()

            # collect mean loss across data parallel group
            total_loss, grad_norm = all_reduce((total_loss, grad_norm), group=get_parallel_state().fsdp_group)
            synchronize()
            delta_time = time.time() - start_time
            lr = max(lr_scheduler.get_last_lr())
            train_metrics = environ_meter.step(delta_time, global_step=global_step)

            data_loader_tqdm.set_postfix_str(f"loss: {total_loss:.2f}, grad_norm: {grad_norm:.2f}, lr: {lr:.2e}")
            data_loader_tqdm.update()

            if args.train.global_rank == 0:
                if args.train.use_wandb:
                    train_metrics.update(
                        {"training/loss": total_loss, "training/grad_norm": grad_norm, "training/lr": lr}
                    )
                    wandb.log(train_metrics, step=global_step)

            if args.train.profile_this_rank and global_step <= args.train.profile_end_step:
                profiler.step()
                if global_step == args.train.profile_end_step:
                    profiler.stop()

            if args.train.save_steps and global_step % args.train.save_steps == 0:
                helper.empty_cache()
                save_checkpoint_path = os.path.join(args.train.save_checkpoint_path, f"global_step_{global_step}")
                state = {
                    "model": model,
                    # "optimizer": optimizer,
                    # "extra_state": {
                    #     "global_step": global_step,
                    #     "lr_scheduler": lr_scheduler.state_dict(),
                    #     "train_dataloader": train_dataloader.state_dict(),
                    #     "environ_meter": environ_meter.state_dict(),
                    #     "torch_rng_state": torch.get_rng_state(),
                    # },
                }
                Checkpointer.save(args.train.save_checkpoint_path, state, global_steps=global_step)

                dist.barrier()
                logger.info_rank0(f"Distributed checkpoint saved at {save_checkpoint_path} successfully!")

        data_loader_tqdm.close()
        start_step = 0
        helper.print_device_mem_info(f"VRAM usage after epoch {epoch + 1}")
        if args.train.save_epochs and (epoch + 1) % args.train.save_epochs == 0:
            helper.empty_cache()
            save_checkpoint_path = os.path.join(args.train.save_checkpoint_path, f"global_step_{global_step}")
            state = {
                "model": model,
                # "optimizer": optimizer,
                # "extra_state": {
                #     "global_step": global_step,
                #     "lr_scheduler": lr_scheduler.state_dict(),
                #     "train_dataloader": train_dataloader.state_dict(),
                #     "environ_meter": environ_meter.state_dict(),
                #     "torch_rng_state": torch.get_rng_state(),
                # },
            }
            Checkpointer.save(args.train.save_checkpoint_path, state, global_steps=global_step)
            dist.barrier()
            logger.info_rank0(f"Distributed checkpoint saved at {save_checkpoint_path} successfully!")

    synchronize()
    # release memory
    del optimizer, lr_scheduler
    helper.empty_cache()
    # save model in huggingface's format
    if args.train.global_rank == 0 and args.train.save_hf_weights and save_checkpoint_path is not None:
        hf_weights_path = os.path.join(save_checkpoint_path, "hf_ckpt")
        model_state_dict = ckpt_to_state_dict(
            save_checkpoint_path=save_checkpoint_path,
            output_dir=args.train.output_dir,
            ckpt_manager=args.train.ckpt_manager,
        )
        save_model_weights(hf_weights_path, model_state_dict, model_assets=model_assets)
        logger.info_rank0(f"Huggingface checkpoint saved at {hf_weights_path} successfully!")

    dist.barrier()
    dist.destroy_process_group()


if __name__ == "__main__":
    main()

	import json
	import os
	import time
	from dataclasses import asdict, dataclass, field
	from functools import partial
	from typing import Any, Dict, List, Literal, Tuple, Optional

	import torch
	import torch.distributed as dist
	import wandb
	from tqdm import trange

	from veomni.checkpoint import build_checkpointer, ckpt_to_state_dict
	from veomni.data import (
	build_dataloader,
	build_iterative_dataset,
	build_mapping_dataset,
	)
	from veomni.distributed.offloading import build_activation_offloading_context
	from veomni.distributed.parallel_state import get_parallel_state, init_parallel_state
	from veomni.distributed.torch_parallelize import build_parallelize_model
	from veomni.models import build_foundation_model, build_tokenizer, save_model_assets, save_model_weights
	from veomni.optim import build_lr_scheduler, build_optimizer
	from veomni.utils import helper
	from veomni.utils.arguments import DataArguments, ModelArguments, TrainingArguments, parse_args, save_args
	from veomni.utils.device import (
	get_device_type,
	get_nccl_backend,
	get_torch_device,
	synchronize,
	)
	from veomni.utils.dist_utils import all_reduce
	from veomni.models.registry import ModelRegistry
	ModelRegistry.register_modeling_path("models.llada2_moe")
	from dataset.data_transform import process_mdm_tokenized_example, process_mdm_sft_example
	from dataset import build_local_dataset


	logger = helper.create_logger(__name__)

	@dataclass
	class LLaDA2ModelArguments(ModelArguments):
	attn_implementation: Optional[Literal["eager", "sdpa", "flex_attention"]] = field(
	default="sdpa",
	metadata={"help": "Attention implementation to use."},
	)


	@dataclass
	class LLaDA2DataArguments(DataArguments):
	data_type: Literal["conversation", "tokenid"] = field(
	default="conversation",
	metadata={"help": "Type of the training data."},
	)
	datasets_type: Literal["mapping", "local"] = field(
	default="mapping",
	metadata={"help": "Type of the datasets."},
	)
	text_keys: str = field(
	default="messages",
	metadata={"help": "Key to get text from the training data."},
	)
	noise_range_low: float = field(
	default=0.3,
	metadata={"help": "Noise level for random flip input_ids to mask_ids"}
	)
	noise_range_high: float = field(
	default=0.8,
	metadata={"help": "Noise level for random flip input_ids to mask_ids"}
	)

	def __post_init__(self):
	super().__post_init__()
	if self.noise_range_low > self.noise_range_high:
	raise ValueError(
	f"noise_range_low ({self.noise_range_low}) "
	f"cannot be greater than noise_range_high ({self.noise_range_high})."
	)

	if not (0.0 <= self.noise_range_low <= 1.0):
	raise ValueError(
	f"noise_range_low must be between 0.0 and 1.0, but got {self.noise_range_low}."
	)

	if not (0.0 <= self.noise_range_high <= 1.0):
	raise ValueError(
	f"noise_range_high must be between 0.0 and 1.0, but got {self.noise_range_high}."
	)


	@dataclass
	class LLaDA2TrainingArguments(TrainingArguments):
	beta1: float = field(
	default=0.9,
	metadata={"help": "AdamW optimizer beta1."},
	)
	beta2: float = field(
	default=0.999,
	metadata={"help": "AdamW optimizer beta2"},
	)
	block_diffusion_mode: bool = field(
	default=False,
	metadata={"help": "If train MDM in block_diffusion mode. True: use block_diffusion, False: full_attention"}
	)
	block_size: int = field(
	default=32,
	metadata={"help": "The block size for block diffusion block size"}
	)
	same_token_labels: bool = field(
	default=False,
	metadata={"help": "If use same token location labels. True: no shift, False: use next-token prediction shift."}
	)


	@dataclass
	class Arguments:
	model: "LLaDA2ModelArguments" = field(default_factory=LLaDA2ModelArguments)
	data: "LLaDA2DataArguments" = field(default_factory=LLaDA2DataArguments)
	train: "LLaDA2TrainingArguments" = field(default_factory=LLaDA2TrainingArguments)


	def block_diffusion_mask(b, h, q_idx, kv_idx, block_size=None, n=None):
	"""
	Constructs the specialized block diffusion attention mask for training
	composed of three masks:
	- Block Diagonal Mask (M_BD): Self-attention within noised blocks
	- Offset Block Causal Mask (M_OBC): Cross-attention for conditional context
	- Block Causal Mask (M_BC): Attention to update x0

	Args:
	b, h: Batch and head indices (ignored for mask logic).
	q_idx, kv_idx: Query and Key indices.
	seq_len: Total sequence length.
	block_size: Defines the block structure.

	Returns:
	A boolean attention mask.
	"""

	# Indicate whether token belongs to xt or x0
	x0_flag_q = (q_idx >= n)
	x0_flag_kv = (kv_idx >= n)

	# Compute block indices
	block_q = torch.where(x0_flag_q == 1,
	(q_idx - n) // block_size,
	q_idx // block_size)
	block_kv = torch.where(x0_flag_kv == 1,
	(kv_idx - n) // block_size,
	kv_idx // block_size)

	# 1. Block Diagonal Mask (M_BD)
	block_diagonal = (block_q == block_kv) & (x0_flag_q == x0_flag_kv)

	# 2. Offset Block-Causal Mask (M_OBC)
	offset_block_causal = (
	(block_q > block_kv)
	& (x0_flag_kv == 1)
	& (x0_flag_q == 0)
	)

	# 3. Block-Causal Mask (M_BC)
	block_causal = (block_q >= block_kv) & (x0_flag_kv == 1) & (x0_flag_q == 1)

	# 4. Combine Masks
	return block_diagonal \| offset_block_causal \| block_causal


	def main():
	dist.init_process_group(backend=get_nccl_backend())
	args = parse_args(Arguments)
	logger.info(f"Process rank: {args.train.global_rank}, world size: {args.train.world_size}")
	logger.info_rank0(json.dumps(asdict(args), indent=2))
	get_torch_device().set_device(f"{get_device_type()}:{args.train.local_rank}")
	helper.set_seed(args.train.seed, args.train.enable_full_determinism)
	if args.train.local_rank == 0:
	helper.enable_third_party_logging()

	if args.train.global_rank == 0:
	save_args(args, args.train.output_dir)

	Checkpointer = build_checkpointer(dist_backend=args.train.data_parallel_mode, ckpt_manager=args.train.ckpt_manager)

	init_parallel_state(
	dp_size=args.train.data_parallel_size,
	dp_replicate_size=args.train.data_parallel_replicate_size,
	dp_shard_size=args.train.data_parallel_shard_size,
	tp_size=args.train.tensor_parallel_size,
	ep_size=args.train.expert_parallel_size,
	pp_size=args.train.pipeline_parallel_size,
	cp_size=args.train.context_parallel_size,
	ulysses_size=args.train.ulysses_parallel_size,
	dp_mode=args.train.data_parallel_mode,
	)

	logger.info_rank0("Prepare data")
	tokenizer = build_tokenizer(args.model.tokenizer_path)
	if args.data.data_type == "conversation":
	if not tokenizer.chat_template:
	raise ValueError(f"No chat template found in the tokenizer.")

	transform = partial(
	process_mdm_sft_example,
	tokenizer=tokenizer,
	max_seq_len=args.data.max_seq_len,
	text_keys=args.data.text_keys,
	noise_range=(args.data.noise_range_low, args.data.noise_range_high),
	mask_token_id=156895,
	)
	elif args.data.data_type == "tokenid":
	transform = partial(
	process_mdm_tokenized_example,
	max_seq_len=args.data.max_seq_len,
	text_keys=args.data.text_keys,
	noise_range=(args.data.noise_range_low, args.data.noise_range_high),
	mask_token_id=156895,
	)
	else:
	raise NotImplementedError(f"Unsupported data type: {args.data.data_type}.")

	if args.data.dataloader_type == "native":
	if args.data.datasets_type == "iterable":
	logger.info_rank0("Start building iterative dataset")
	train_dataset = build_iterative_dataset(args.data.train_path, transform=transform, seed=args.train.seed)
	elif args.data.datasets_type == "mapping":
	logger.info_rank0("Start building mapping dataset")
	train_dataset = build_mapping_dataset(args.data.train_path, transform=transform)
	elif args.data.datasets_type == "local":
	logger.info_rank0("Start building local dataset")
	train_dataset = build_local_dataset(args.data.train_path, transform=transform)

	dataset_length = None if not hasattr(train_dataset, "__len__") else len(train_dataset)
	if args.data.datasets_type == "mapping" or args.data.datasets_type == "local":
	dataset_length = dataset_length / args.train.data_parallel_size
	args.train.compute_train_steps(args.data.max_seq_len, args.data.train_size, dataset_length)

	train_dataloader = build_dataloader(
	dataset=train_dataset,
	micro_batch_size=args.train.micro_batch_size,
	global_batch_size=args.train.global_batch_size,
	dataloader_batch_size=args.train.dataloader_batch_size,
	seed=args.train.seed,
	max_seq_len=args.data.max_seq_len,
	train_steps=args.train.train_steps,
	rmpad=args.train.rmpad,
	rmpad_with_pos_ids=args.train.rmpad_with_pos_ids,
	bsz_warmup_ratio=args.train.bsz_warmup_ratio,
	bsz_warmup_init_mbtoken=args.train.bsz_warmup_init_mbtoken,
	dyn_bsz_margin=args.train.dyn_bsz_margin,
	dyn_bsz_buffer_size=args.train.dyn_bsz_buffer_size,
	num_workers=args.data.num_workers,
	drop_last=args.data.drop_last,
	pin_memory=args.data.pin_memory,
	prefetch_factor=args.data.prefetch_factor,
	)
	else:
	raise NotImplementedError(f"Unsupported dataloader type: {args.data.dataloader_type}.")

	logger.info_rank0("Prepare model")
	model = build_foundation_model(
	config_path=args.model.config_path,
	weights_path=args.model.model_path,
	torch_dtype="float32" if args.train.enable_mixed_precision else "bfloat16",
	attn_implementation=args.model.attn_implementation,
	moe_implementation=args.model.moe_implementation,
	init_device=args.train.init_device,
	force_use_huggingface=args.model.force_use_huggingface,
	)
	model_config = model.config
	helper.print_device_mem_info("VRAM usage after building model")

	get_optimizer_pre_hook = getattr(model, "get_optimizer_pre_hook", None)
	model = build_parallelize_model(
	model,
	init_device=args.train.init_device,
	weights_path=args.model.model_path,
	enable_full_shard=args.train.enable_full_shard,
	enable_mixed_precision=args.train.enable_mixed_precision,
	enable_gradient_checkpointing=args.train.enable_gradient_checkpointing,
	enable_fsdp_offload=args.train.enable_fsdp_offload,
	basic_modules=model._no_split_modules + args.model.basic_modules,
	enable_reentrant=args.train.enable_reentrant,
	enable_forward_prefetch=args.train.enable_forward_prefetch,
	broadcast_model_weights_from_rank0=args.train.broadcast_model_weights_from_rank0
	)

	optimizer = build_optimizer(
	model,
	lr=args.train.lr,
	betas=(args.train.beta1, args.train.beta2),
	weight_decay=args.train.weight_decay,
	fused=True,
	optimizer_type=args.train.optimizer,
	)

	if get_optimizer_pre_hook is not None:
	optimizer_pre_hook = get_optimizer_pre_hook(model, model_config, args.train.data_parallel_mode)
	optimizer.register_step_pre_hook(optimizer_pre_hook)

	lr_scheduler = build_lr_scheduler(
	optimizer,
	train_steps=args.train.train_steps * args.train.num_train_epochs,
	lr=args.train.lr,
	lr_min=args.train.lr_min,
	lr_decay_style=args.train.lr_decay_style,
	lr_decay_ratio=args.train.lr_decay_ratio,
	lr_warmup_ratio=args.train.lr_warmup_ratio,
	lr_start=args.train.lr_start,
	)

	if args.train.global_rank == 0:
	if args.train.use_wandb:
	wandb.init(
	project=args.train.wandb_project,
	name=args.train.wandb_name,
	config={vars(args.model), vars(args.data), **vars(args.train)}, # flatten dict
	)

	# save model_assets before training
	model_assets = [model_config, tokenizer]
	save_model_assets(args.train.model_assets_dir, model_assets)

	if args.train.profile_this_rank:
	profiler = helper.create_profiler(
	start_step=args.train.profile_start_step,
	end_step=args.train.profile_end_step,
	trace_dir=args.train.profile_trace_dir,
	record_shapes=args.train.profile_record_shapes,
	profile_memory=args.train.profile_profile_memory,
	with_stack=args.train.profile_with_stack,
	global_rank=args.train.global_rank,
	)
	profiler.start()

	start_epoch, start_step, global_step = 0, 0, 0
	save_checkpoint_path = None
	environ_meter = helper.EnvironMeter(
	config=model_config,
	global_batch_size=args.train.global_batch_size,
	rmpad=args.train.rmpad,
	rmpad_with_pos_ids=args.train.rmpad_with_pos_ids,
	empty_cache_steps=args.train.empty_cache_steps,
	enable_multisource=args.data.enable_multisource,
	dataloader=train_dataloader,
	data_path=args.data.train_path,
	)

	if args.train.load_checkpoint_path:
	state = {"model": model, "optimizer": optimizer, "extra_state": {}} # cannot be None
	Checkpointer.load(args.train.load_checkpoint_path, state)
	global_step = state["extra_state"]["global_step"]
	start_epoch = global_step // args.train.train_steps
	start_step = global_step % args.train.train_steps
	lr_scheduler.load_state_dict(state["extra_state"]["lr_scheduler"])
	train_dataloader.load_state_dict(state["extra_state"]["train_dataloader"])
	environ_meter.load_state_dict(state["extra_state"]["environ_meter"])
	torch.set_rng_state(state["extra_state"]["torch_rng_state"])
	if start_step == 0: # resume at the end of epoch
	iter(train_dataloader) # clear resume state and prefetch data

	dist.barrier()
	logger.info_rank0(f"Load distributed checkpoint from {args.train.load_checkpoint_path} successfully!")

	# Build block diffusion attention mask
	if args.train.block_diffusion_mode:
	bd_attn_full_len = args.data.max_seq_len * 2
	block_size = args.train.block_size
	# NOTE: Boolean dtype block diffusion attention mask
	block_diffusion_attn_mask_flag = block_diffusion_mask(
	b=None, h=None,
	q_idx=torch.arange(bd_attn_full_len)[:, None],
	kv_idx=torch.arange(bd_attn_full_len)[None, :],
	block_size=block_size,
	n=args.data.max_seq_len
	).unsqueeze(0).unsqueeze(0)

	block_diffusion_attn_mask_prototype = torch.zeros_like(
	block_diffusion_attn_mask_flag,
	dtype=torch.float32 if args.train.enable_mixed_precision else torch.bfloat16
	)
	block_diffusion_attn_mask_prototype.masked_fill_(block_diffusion_attn_mask_flag.logical_not(), float("-inf"))

	helper.empty_cache()
	model_fwd_context, model_bwd_context = build_activation_offloading_context(
	args.train.enable_activation_offload, args.train.enable_gradient_checkpointing, args.train.activation_gpu_limit
	)
	model.train()
	logger.info(
	f"rank{args.train.local_rank} Start training, train_steps: {args.train.train_steps}, epochs: {args.train.num_train_epochs}"
	)
	for epoch in range(start_epoch, args.train.num_train_epochs):
	if hasattr(train_dataloader, "set_epoch"):
	train_dataloader.set_epoch(epoch)

	data_loader_tqdm = trange(
	args.train.train_steps,
	desc=f"Epoch {epoch + 1}/{args.train.num_train_epochs}",
	total=args.train.train_steps,
	initial=start_step,
	disable=args.train.local_rank != 0,
	)
	data_iterator = iter(train_dataloader)
	for _ in range(start_step, args.train.train_steps):
	global_step += 1

	try:
	micro_batches: List[Dict[str, Any]] = next(data_iterator)
	except StopIteration:
	logger.info(f"epoch:{epoch} Dataloader finished with drop_last {args.data.drop_last}")
	break

	if global_step == 1:
	helper.print_example(example=micro_batches[0], rank=args.train.local_rank)

	total_loss = 0
	synchronize()
	start_time = time.time()
	for micro_batch in micro_batches:
	environ_meter.add(micro_batch)
	if args.data.enable_multisource:
	micro_batch.pop("ds_idx", None)
	micro_batch.pop("source_name", None)

	if args.train.block_diffusion_mode:
	noisy_input_ids = micro_batch["noisy_input_ids"]
	clean_input_ids = micro_batch["input_ids"]
	batch_size = noisy_input_ids.shape[0]
	full_input_ids = torch.cat([noisy_input_ids, clean_input_ids], dim=1)
	noisy_position_ids = torch.arange(noisy_input_ids.shape[1], device=get_device_type(), dtype=torch.long)
	clean_position_ids = torch.arange(clean_input_ids.shape[1], device=get_device_type(), dtype=torch.long)
	position_ids = torch.cat([noisy_position_ids, clean_position_ids], dim=0).unsqueeze(0).expand(batch_size, -1).clone()
	micro_batch["input_ids"] = full_input_ids
	micro_batch["position_ids"] = position_ids
	micro_batch["attention_mask"] = block_diffusion_attn_mask_prototype.expand(batch_size, -1, -1, -1)
	else:
	micro_batch["attention_mask"] = None

	micro_batch = {
	k: v.to(get_device_type(), non_blocking=True) if isinstance(v, torch.Tensor) else v
	for k, v in micro_batch.items()
	}

	labels = micro_batch.pop("labels", None)

	with model_fwd_context:
	logits: "torch.Tensor" = model(**micro_batch, use_cache=False, output_router_logits=False).logits
	if args.train.block_diffusion_mode:
	noisy_logits = logits[:, :noisy_input_ids.shape[1]].contiguous()
	else:
	noisy_logits = logits

	if args.train.same_token_labels:
	unscaled_loss = torch.nn.functional.cross_entropy(
	noisy_logits.view(-1, noisy_logits.shape[-1]),
	labels.view(-1),
	reduction="none",
	)
	loss = unscaled_loss.sum() / (labels != -100).sum() / len(micro_batches)
	else:
	shifted_noisy_logits = noisy_logits[:, :-1, :].contiguous()
	shifted_labels = labels[:, 1:].contiguous()
	unscaled_loss = torch.nn.functional.cross_entropy(
	shifted_noisy_logits.view(-1, shifted_noisy_logits.shape[-1]),
	shifted_labels.view(-1),
	reduction="none",
	).view(shifted_noisy_logits.shape[0], -1)
	loss = unscaled_loss.sum() / (shifted_labels != -100).sum() / len(micro_batches)

	with model_bwd_context:
	loss.backward()

	total_loss += loss.item()
	del micro_batch

	# Prefer model-provided clip_grad_norm_ (now both FSDP1 and FSDP2 registers custom grad norm clipping)
	if hasattr(model, "clip_grad_norm_"):
	_gn = model.clip_grad_norm_(args.train.max_grad_norm)
	grad_norm = _gn.item() if hasattr(_gn, "item") else float(_gn)
	else:
	logger.info_rank0(
	"Can NOT find regitsered clip_grad_norm_ method in the model, using PyTorch default implementation.."
	)
	grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), args.train.max_grad_norm)

	optimizer.step()
	lr_scheduler.step()
	optimizer.zero_grad()
	if hasattr(grad_norm, "full_tensor"):
	grad_norm = grad_norm.full_tensor().item()

	# collect mean loss across data parallel group
	total_loss, grad_norm = all_reduce((total_loss, grad_norm), group=get_parallel_state().fsdp_group)
	synchronize()
	delta_time = time.time() - start_time
	lr = max(lr_scheduler.get_last_lr())
	train_metrics = environ_meter.step(delta_time, global_step=global_step)

	data_loader_tqdm.set_postfix_str(f"loss: {total_loss:.2f}, grad_norm: {grad_norm:.2f}, lr: {lr:.2e}")
	data_loader_tqdm.update()

	if args.train.global_rank == 0:
	if args.train.use_wandb:
	train_metrics.update(
	{"training/loss": total_loss, "training/grad_norm": grad_norm, "training/lr": lr}
	)
	wandb.log(train_metrics, step=global_step)

	if args.train.profile_this_rank and global_step <= args.train.profile_end_step:
	profiler.step()
	if global_step == args.train.profile_end_step:
	profiler.stop()

	if args.train.save_steps and global_step % args.train.save_steps == 0:
	helper.empty_cache()
	save_checkpoint_path = os.path.join(args.train.save_checkpoint_path, f"global_step_{global_step}")
	state = {
	"model": model,
	# "optimizer": optimizer,
	# "extra_state": {
	# "global_step": global_step,
	# "lr_scheduler": lr_scheduler.state_dict(),
	# "train_dataloader": train_dataloader.state_dict(),
	# "environ_meter": environ_meter.state_dict(),
	# "torch_rng_state": torch.get_rng_state(),
	# },
	}
	Checkpointer.save(args.train.save_checkpoint_path, state, global_steps=global_step)

	dist.barrier()
	logger.info_rank0(f"Distributed checkpoint saved at {save_checkpoint_path} successfully!")

	data_loader_tqdm.close()
	start_step = 0
	helper.print_device_mem_info(f"VRAM usage after epoch {epoch + 1}")
	if args.train.save_epochs and (epoch + 1) % args.train.save_epochs == 0:
	helper.empty_cache()
	save_checkpoint_path = os.path.join(args.train.save_checkpoint_path, f"global_step_{global_step}")
	state = {
	"model": model,
	# "optimizer": optimizer,
	# "extra_state": {
	# "global_step": global_step,
	# "lr_scheduler": lr_scheduler.state_dict(),
	# "train_dataloader": train_dataloader.state_dict(),
	# "environ_meter": environ_meter.state_dict(),
	# "torch_rng_state": torch.get_rng_state(),
	# },
	}
	Checkpointer.save(args.train.save_checkpoint_path, state, global_steps=global_step)
	dist.barrier()
	logger.info_rank0(f"Distributed checkpoint saved at {save_checkpoint_path} successfully!")

	synchronize()
	# release memory
	del optimizer, lr_scheduler
	helper.empty_cache()
	# save model in huggingface's format
	if args.train.global_rank == 0 and args.train.save_hf_weights and save_checkpoint_path is not None:
	hf_weights_path = os.path.join(save_checkpoint_path, "hf_ckpt")
	model_state_dict = ckpt_to_state_dict(
	save_checkpoint_path=save_checkpoint_path,
	output_dir=args.train.output_dir,
	ckpt_manager=args.train.ckpt_manager,
	)
	save_model_weights(hf_weights_path, model_state_dict, model_assets=model_assets)
	logger.info_rank0(f"Huggingface checkpoint saved at {hf_weights_path} successfully!")

	dist.barrier()
	dist.destroy_process_group()


	if __name__ == "__main__":
	main()