[tab] slime/backends/megatron_utils/model.py (python)
import dataclasses
import gc
import logging
import math
import os
from argparse import Namespace
from collections.abc import Callable, Sequence
from functools import partial

import torch
from megatron.core import mpu
from megatron.core.distributed import DistributedDataParallel as DDP
from megatron.core.distributed import finalize_model_grads
from megatron.core.enums import ModelType
from megatron.core.models.gpt import GPTModel
from megatron.core.optimizer import OptimizerConfig, get_megatron_optimizer
from megatron.core.optimizer.optimizer import MegatronOptimizer
from megatron.core.optimizer_param_scheduler import OptimizerParamScheduler
from megatron.core.pipeline_parallel import get_forward_backward_func
from megatron.core.utils import get_model_config
from megatron.training.global_vars import get_args
from megatron.training.training import get_model

from slime.utils import tracking_utils
from slime.utils.memory_utils import clear_memory

from .checkpoint import load_checkpoint, save_checkpoint
from .cp_utils import slice_with_cp
from .data import DataIterator, get_batch
from .loss import loss_function
from .model_provider import get_model_provider_func

logger = logging.getLogger(__name__)


def get_optimizer_param_scheduler(args: Namespace, optimizer: MegatronOptimizer) -> OptimizerParamScheduler:
    """Create and configure the optimizer learning-rate/weight-decay scheduler.

    This configures iteration-based schedules derived from the global batch size
    and run-time arguments.

    Args:
        args (Namespace): Training/runtime arguments (argparse namespace).
        optimizer (MegatronOptimizer): Megatron optimizer bound to the model.

    Returns:
        OptimizerParamScheduler: Initialized scheduler bound to ``optimizer``.
    """
    # Iteration-based training.
    args.train_iters = args.num_rollout * args.rollout_batch_size * args.n_samples_per_prompt // args.global_batch_size
    if args.lr_decay_iters is None:
        args.lr_decay_iters = args.train_iters
    lr_decay_steps = args.lr_decay_iters * args.global_batch_size
    wd_incr_steps = args.train_iters * args.global_batch_size
    wsd_decay_steps = None
    if args.lr_wsd_decay_iters is not None:
        wsd_decay_steps = args.lr_wsd_decay_iters * args.global_batch_size
    if args.lr_warmup_fraction is not None:
        lr_warmup_steps = args.lr_warmup_fraction * lr_decay_steps
    else:
        lr_warmup_steps = args.lr_warmup_iters * args.global_batch_size

    opt_param_scheduler = OptimizerParamScheduler(
        optimizer,
        init_lr=args.lr_warmup_init,
        max_lr=args.lr,
        min_lr=args.min_lr,
        lr_warmup_steps=lr_warmup_steps,
        lr_decay_steps=lr_decay_steps,
        lr_decay_style=args.lr_decay_style,
        start_wd=args.start_weight_decay,
        end_wd=args.end_weight_decay,
        wd_incr_steps=wd_incr_steps,
        wd_incr_style=args.weight_decay_incr_style,
        use_checkpoint_opt_param_scheduler=args.use_checkpoint_opt_param_scheduler,
        override_opt_param_scheduler=args.override_opt_param_scheduler,
        wsd_decay_steps=wsd_decay_steps,
        lr_wsd_decay_style=args.lr_wsd_decay_style,
    )

    return opt_param_scheduler


def setup_model_and_optimizer(
    args: Namespace,
    role: str = "actor",
    no_wd_decay_cond: Callable[..., bool] | None = None,
    scale_lr_cond: Callable[..., bool] | None = None,
    lr_mult: float = 1.0,
) -> tuple[list[DDP], MegatronOptimizer, OptimizerParamScheduler]:
    """Build model(s), wrap with DDP, and construct optimizer and scheduler.

    Args:
        args (Namespace): Training/runtime arguments (argparse namespace).
        role (str): Logical role of the model (e.g., "actor", "critic").
        no_wd_decay_cond (Callable[..., bool] | None): Predicate to exclude
            parameters from weight decay.
        scale_lr_cond (Callable[..., bool] | None): Predicate to scale LR for
            selected parameter groups.
        lr_mult (float): Global learning-rate multiplier for the optimizer.

    Returns:
        tuple[list[DDP], MegatronOptimizer, OptimizerParamScheduler]:
            - List of model chunks wrapped by ``DDP``.
            - The constructed ``MegatronOptimizer`` instance.
            - The learning-rate/weight-decay scheduler tied to the optimizer.
    """
    assert not args.moe_use_upcycling
    assert args.load is not None or args.pretrained_checkpoint is not None

    model = get_model(get_model_provider_func(args, role), ModelType.encoder_or_decoder)

    # Optimizer
    kwargs = {}
    for f in dataclasses.fields(OptimizerConfig):
        if hasattr(args, f.name):
            kwargs[f.name] = getattr(args, f.name)
    config = OptimizerConfig(**kwargs)
    config.timers = None

    optimizer = get_megatron_optimizer(
        config,
        model,
        no_wd_decay_cond,
        scale_lr_cond,
        lr_mult,
        use_gloo_process_groups=args.enable_gloo_process_groups,
    )
    opt_param_scheduler = get_optimizer_param_scheduler(args, optimizer)
    return model, optimizer, opt_param_scheduler


def enable_forward_pre_hook(model_chunks: Sequence[DDP]) -> None:
    """Enable forward pre-hooks for provided DDP-wrapped model chunks.

    Args:
        model_chunks (Sequence[DDP]): Sequence of DDP modules to enable hooks on.
    """
    for model_chunk in model_chunks:
        assert isinstance(model_chunk, DDP)
        model_chunk.enable_forward_pre_hook()


def disable_forward_pre_hook(model_chunks: Sequence[DDP], param_sync: bool = True) -> None:
    """Disable forward pre-hooks for provided DDP-wrapped model chunks.

    Args:
        model_chunks (Sequence[DDP]): Sequence of DDP modules to disable hooks on.
        param_sync (bool): Whether to synchronize parameters when disabling.
    """
    for model_chunk in model_chunks:
        assert isinstance(model_chunk, DDP)
        model_chunk.disable_forward_pre_hook(param_sync=param_sync)


@torch.no_grad()
def forward_only(
    f: Callable[..., dict[str, list[torch.Tensor]]],
    args: Namespace,
    model: Sequence[DDP],
    data_iterator: Sequence[DataIterator],
    num_microbatches: Sequence[int],
    store_prefix: str = "",
) -> dict[str, list[torch.Tensor]]:
    """Run forward passes only and collect non-loss outputs (e.g., logprobs).

    The model is put into evaluation mode, a forward-only pipeline pass is
    executed, and relevant outputs are aggregated and returned.

    Args:
        f (Callable[..., dict[str, list[torch.Tensor]]]): Post-forward callback used to
            compute and package outputs to collect. This should accept a logits
            tensor as its first positional argument and additional keyword-only
            arguments; see ``get_log_probs_and_entropy``/``get_values`` in
            ``megatron_utils.loss`` for examples. It will be partially applied
            so that the callable returned from the internal forward step only
            requires the logits tensor.
        args (Namespace): Runtime arguments.
        model (Sequence[DDP]): Sequence of DDP-wrapped model chunks.
        data_iterator (Sequence[DataIterator]): Iterable(s) yielding batches for inference.
        num_microbatches (Sequence[int]): Number of microbatches per rollout step.
        store_prefix (str): Prefix to prepend to stored output keys.

    Returns:
        dict[str, list[torch.Tensor]]: Aggregated outputs keyed by ``store_prefix + key``.
    """

    # reset data iterator
    for iterator in data_iterator:
        iterator.reset()

    config = get_model_config(model[0])

    def forward_step(
        data_iterator: DataIterator, model: GPTModel, return_schedule_plan: bool = False
    ) -> tuple[torch.Tensor, Callable[[torch.Tensor], dict[str, list[torch.Tensor]]]]:
        """Forward step used by Megatron's pipeline engine.

        Args:
            data_iterator (DataIterator): Input data iterator.
            model (GPTModel): The GPT model chunk to execute.

        Returns:
            tuple[torch.Tensor, Callable[[torch.Tensor], dict[str, list[torch.Tensor]]]]:
            Output tensor(s) and a callable that computes and packages results
            to be collected by the engine.
        """

        assert not return_schedule_plan, "forward_only step should never return schedule plan"

        # Get the batch.
        batch = get_batch(
            data_iterator, ["tokens", "total_lengths", "response_lengths"], args.data_pad_size_multiplier
        )
        unconcat_tokens = batch["unconcat_tokens"]
        tokens = batch["tokens"]
        packed_seq_params = batch["packed_seq_params"]
        total_lengths = batch["total_lengths"]
        response_lengths = batch["response_lengths"]
        output_tensor = model(
            input_ids=tokens,
            position_ids=None,
            attention_mask=None,
            labels=None,
            packed_seq_params=packed_seq_params,
        )

        return output_tensor, partial(
            f,
            args=args,
            unconcat_tokens=unconcat_tokens,
            total_lengths=total_lengths,
            response_lengths=response_lengths,
            with_entropy=args.use_rollout_entropy,
        )

    # Turn on evaluation mode which disables dropout.
    for model_module in model:
        model_module.eval()

    if args.custom_megatron_before_log_prob_hook_path:
        from slime.utils.misc import load_function

        custom_before_log_prob_hook = load_function(args.custom_megatron_before_log_prob_hook_path)
        custom_before_log_prob_hook(args, model, store_prefix)

    forward_backward_func = get_forward_backward_func()
    # Don't care about timing during evaluation
    config.timers = None
    forward_data_store = []
    num_steps_per_rollout = len(num_microbatches)
    for step_id in range(num_steps_per_rollout):
        # collect_non_loss_data
        forward_data_store += forward_backward_func(
            forward_step_func=forward_step,
            data_iterator=data_iterator,
            model=model,
            num_microbatches=num_microbatches[step_id],
            seq_length=args.seq_length,
            micro_batch_size=args.micro_batch_size,
            forward_only=True,
            collect_non_loss_data=True,
        )

    # Move model back to the train mode.
    for model_module in model:
        model_module.train()

    rollout_data = {}
    # Store the results on the last stage
    if mpu.is_pipeline_last_stage():
        keys = forward_data_store[0].keys()
        for key in keys:
            values = []
            for value in forward_data_store:
                assert isinstance(value[key], list)
                values += value[key]

            if args.use_dynamic_batch_size:
                # TODO: This is ugly... Find a better way to make the data have the same order.
                # TODO: move this out of the loop.
                origin_values = [None] * len(values)
                origin_indices = sum(data_iterator[0].micro_batch_indices, [])
                for value, origin_index in zip(values, origin_indices, strict=False):
                    origin_values[origin_index] = value
                values = origin_values
            rollout_data[f"{store_prefix}{key}"] = values
    return rollout_data


def train_one_step(
    args: Namespace,
    rollout_id: int,
    step_id: int,
    data_iterator: Sequence[DataIterator],
    model: Sequence[DDP],
    optimizer: MegatronOptimizer,
    opt_param_scheduler: OptimizerParamScheduler,
    num_microbatches: int,
) -> tuple[dict[str, float], float]:
    """Execute a single pipeline-parallel training step.

    Runs forward/backward over ``num_microbatches``, applies optimizer step and
    one scheduler step when gradients are valid.

    Args:
        args (Namespace): Runtime arguments.
        rollout_id (int): Rollout identifier.
        step_id (int): Step index within the current rollout.
        data_iterator (Sequence[DataIterator]): Iterable(s) yielding training batches.
        model (Sequence[DDP]): Sequence of DDP-wrapped model chunks.
        optimizer (MegatronOptimizer): Optimizer instance.
        opt_param_scheduler (OptimizerParamScheduler): LR/WD scheduler.
        num_microbatches (int): Number of microbatches to process.

    Returns:
        tuple[dict[str, float], float]: Reduced loss dictionary (last stage only)
        and gradient norm for logging.
    """
    args = get_args()

    # Set grad to zero.
    for model_chunk in model:
        model_chunk.zero_grad_buffer()
    optimizer.zero_grad()

    if args.custom_megatron_before_train_step_hook_path:
        from slime.utils.misc import load_function

        custom_before_train_step_hook = load_function(args.custom_megatron_before_train_step_hook_path)
        custom_before_train_step_hook(args, rollout_id, step_id, model, optimizer, opt_param_scheduler)

    def forward_step(data_iterator: DataIterator, model: GPTModel, return_schedule_plan: bool = False) -> tuple[
        torch.Tensor,
        Callable[[torch.Tensor], tuple[torch.Tensor, int, dict[str, torch.Tensor | list[str]]]],
    ]:
        """Forward step used by Megatron's pipeline engine during training.

        Args:
            data_iterator (DataIterator): Input data iterator.
            model (GPTModel): The GPT model chunk to execute.

        Returns:
            tuple[torch.Tensor, Callable[[torch.Tensor], tuple[torch.Tensor, int, dict[str, torch.Tensor | list[str]]]]]:
            Output tensor(s) and the loss function, which returns
            (loss, num_elems, {"keys": list[str], "values": torch.Tensor}).
        """

        # Get the batch.
        batch = get_batch(
            data_iterator,
            [
                "tokens",
                "packed_seq_params",
                "total_lengths",
                "response_lengths",
                "loss_masks",
                "log_probs",
                "ref_log_probs",
                "values",
                "advantages",
                "returns",
                "rollout_log_probs",
            ],
            args.data_pad_size_multiplier,
        )

        if os.environ.get("ENABLE_ROUTING_REPLAY", "0") == "1":
            old_stage = os.environ["ROUTING_REPLAY_STAGE"]
            os.environ["ROUTING_REPLAY_STAGE"] = "replay_forward"

        def build_loss_mask_for_mtp(batch: dict[str, object]) -> torch.Tensor | None:
            tokens_tensor: torch.Tensor = batch["tokens"]

            mask_chunks: list[torch.Tensor] = []
            for total_len, response_len, resp_mask in zip(
                batch["total_lengths"], batch["response_lengths"], batch["loss_masks"], strict=False
            ):
                assert (
                    resp_mask.numel() == response_len
                ), f"Unexpected loss mask size {resp_mask.numel()} (expected {response_len} or {total_len})."
                prompt_len = total_len - response_len
                full_mask = resp_mask.new_zeros(total_len)
                full_mask[prompt_len:] = resp_mask

                mask_chunks.append(slice_with_cp(full_mask, 0.0))

            flattened_mask = torch.cat(mask_chunks, dim=0)
            seq_len = tokens_tensor.size(-1)
            assert (
                flattened_mask.numel() <= seq_len
            ), f"MTP loss mask ({flattened_mask.numel()}) exceeds token length ({seq_len})."

            # token tensor may be padded by 128, so pad loss mask to the same length
            loss_mask_tensor = flattened_mask.new_zeros(seq_len)
            loss_mask_tensor[: flattened_mask.numel()] = flattened_mask
            return loss_mask_tensor.unsqueeze(0)

        loss_mask = None
        mtp_kwargs = None

        if return_schedule_plan:
            assert not args.enable_mtp_training, "MTP training should not be enabled when using combined 1f1b"
            output_tensor = model.build_schedule_plan(
                input_ids=batch["tokens"],
                position_ids=None,
                attention_mask=None,
                labels=None,
                packed_seq_params=batch["packed_seq_params"],
            )
        else:
            # If enabling MTP training: trigger MTP loss inside Megatron while returning logits
            # for the target model's loss.
            if args.enable_mtp_training:
                loss_mask = build_loss_mask_for_mtp(batch)
                assert (
                    loss_mask.shape == batch["tokens"].shape
                ), f"loss_mask shape {loss_mask.shape} mismatches token shape {batch['tokens'].shape}"
                mtp_kwargs = {
                    # We have to set labels to tokens for MTP training, to point out samples to train.
                    "mtp_labels": batch["tokens"],
                }

            output_tensor = model(
                input_ids=batch["tokens"],
                position_ids=None,
                attention_mask=None,
                labels=None,
                packed_seq_params=batch["packed_seq_params"],
                loss_mask=loss_mask,
                **(dict(mtp_kwargs=mtp_kwargs) if mtp_kwargs is not None else {}),
            )

        if os.environ.get("ENABLE_ROUTING_REPLAY", "0") == "1":
            os.environ["ROUTING_REPLAY_STAGE"] = old_stage

        return output_tensor, partial(loss_function, args, batch, num_microbatches)

    # Forward pass.
    forward_backward_func = get_forward_backward_func()
    losses_reduced = forward_backward_func(
        forward_step_func=forward_step,
        data_iterator=data_iterator,
        model=model,
        num_microbatches=num_microbatches,
        seq_length=args.seq_length,
        micro_batch_size=args.micro_batch_size,
        decoder_seq_length=args.decoder_seq_length,
        forward_only=False,
    )

    valid_step = True
    if not getattr(args, "check_for_nan_in_loss_and_grad", True):
        found_inf_flag = optimizer.prepare_grads()
        if found_inf_flag:
            valid_step = False
        else:
            grad_norm = optimizer.get_grad_norm()
            if isinstance(grad_norm, torch.Tensor):
                valid_step = not (torch.isnan(grad_norm) or torch.isinf(grad_norm))
            else:
                valid_step = not (math.isnan(grad_norm) or math.isinf(grad_norm))

    if valid_step:
        # Update parameters.
        update_successful, grad_norm, num_zeros_in_grad = optimizer.step()

        # Update learning rate.
        assert update_successful
        opt_param_scheduler.step(increment=args.global_batch_size)

    # release grad
    for model_chunk in model:
        model_chunk.zero_grad_buffer()
    optimizer.zero_grad()

    if mpu.is_pipeline_last_stage(ignore_virtual=True):
        # Average loss across microbatches.
        keys = losses_reduced[0]["keys"]
        values = None
        for x in losses_reduced:
            if values is None:
                values = x["values"]
            else:
                values += x["values"]
        assert len(keys) + 1 == values.numel()
        torch.distributed.all_reduce(values, group=mpu.get_data_parallel_group(with_context_parallel=True))

        loss_reduced = {}
        values = values.tolist()
        num_samples_or_tokens = values[0]
        for key, value in zip(keys, values[1:], strict=False):
            loss_reduced[key] = value * mpu.get_context_parallel_world_size() / num_samples_or_tokens
        return loss_reduced, grad_norm
    return {}, grad_norm


def should_disable_forward_pre_hook(args: Namespace) -> bool:
    """Block forward pre-hook for certain configurations."""
    return args.use_distributed_optimizer and args.overlap_param_gather


def train(
    rollout_id: int,
    model: Sequence[DDP],
    optimizer: MegatronOptimizer,
    opt_param_scheduler: OptimizerParamScheduler,
    data_iterator: Sequence[DataIterator],
    num_microbatches: Sequence[int],
) -> None:
    """Run training over a rollout consisting of multiple steps.

    The model is switched to train mode, training hooks are configured, and
    ``train_one_step`` is invoked for each step in the rollout.

    Args:
        rollout_id (int): Rollout identifier.
        model (Sequence[DDP]): Sequence of DDP-wrapped model chunks.
        optimizer (MegatronOptimizer): Optimizer instance.
        opt_param_scheduler (OptimizerParamScheduler): LR/WD scheduler.
        data_iterator (Sequence[DataIterator]): Iterable(s) yielding training batches.
        num_microbatches (Sequence[int]): Microbatches per step in the rollout.
    """
    args = get_args()

    for iterator in data_iterator:
        iterator.reset()

    # Turn on training mode which enables dropout.
    for model_module in model:
        model_module.train()

    # Setup some training config params.
    config = get_model_config(model[0])
    config.grad_scale_func = optimizer.scale_loss
    config.timers = None
    if isinstance(model[0], DDP) and args.overlap_grad_reduce:
        assert config.no_sync_func is None, (
            "When overlap_grad_reduce is True, config.no_sync_func must be None; "
            "a custom no_sync_func is not supported when overlapping grad-reduce"
        )
        config.no_sync_func = [model_chunk.no_sync for model_chunk in model]
        if len(model) == 1:
            config.no_sync_func = config.no_sync_func[0]
        if args.align_grad_reduce:
            config.grad_sync_func = [model_chunk.start_grad_sync for model_chunk in model]
            if len(model) == 1:
                config.grad_sync_func = config.grad_sync_func[0]
    if args.overlap_param_gather and args.align_param_gather:
        config.param_sync_func = [model_chunk.start_param_sync for model_chunk in model]
        if len(model) == 1:
            config.param_sync_func = config.param_sync_func[0]
    config.finalize_model_grads_func = finalize_model_grads

    pre_hook_enabled = False

    if args.manual_gc:
        # Disable the default garbage collector and perform the collection manually.
        # This is to align the timing of garbage collection across ranks.
        assert args.manual_gc_interval >= 0, "Manual garbage collection interval should be larger than or equal to 0"
        gc.disable()
        gc.collect()

    # Disable forward pre-hook to start training to ensure that errors in checkpoint loading
    # or random initialization don't propagate to all ranks in first all-gather (which is a
    # no-op if things work correctly).
    if should_disable_forward_pre_hook(args):
        disable_forward_pre_hook(model, param_sync=False)
        # Also remove param_sync_func temporarily so that sync calls made in
        # `forward_backward_func` are no-ops.
        param_sync_func = config.param_sync_func
        config.param_sync_func = None
        pre_hook_enabled = False

    num_steps_per_rollout = len(num_microbatches)

    # Run training iterations till done.
    for step_id in range(num_steps_per_rollout):

        # Run training step.
        loss_dict, grad_norm = train_one_step(
            args,
            rollout_id,
            step_id,
            data_iterator,
            model,
            optimizer,
            opt_param_scheduler,
            num_microbatches[step_id],
        )

        if step_id == 0:
            # Enable forward pre-hook after training step has successfully run. All subsequent
            # forward passes will use the forward pre-hook / `param_sync_func` in
            # `forward_backward_func`.
            if should_disable_forward_pre_hook(args):
                enable_forward_pre_hook(model)
                config.param_sync_func = param_sync_func
                pre_hook_enabled = True

        if args.enable_mtp_training:
            from megatron.core.transformer.multi_token_prediction import MTPLossLoggingHelper

            mtp_loss_scale = 1 / num_microbatches[step_id]
            tracker = MTPLossLoggingHelper.tracker
            if "values" in tracker:
                values = tracker["values"]
                if tracker.get("reduce_group") is not None:
                    torch.distributed.all_reduce(values, group=tracker.get("reduce_group"))
                if tracker.get("avg_group") is not None:
                    torch.distributed.all_reduce(values, group=tracker["avg_group"], op=torch.distributed.ReduceOp.AVG)
                # here we assume only one mtp layer
                mtp_losses = (tracker["values"] * mtp_loss_scale).item()
                MTPLossLoggingHelper.clean_loss_in_tracker()

        # per train step log.
        if (
            mpu.get_data_parallel_rank(with_context_parallel=True) == 0
            and mpu.get_tensor_model_parallel_rank() == 0
            and mpu.get_pipeline_model_parallel_rank() == mpu.get_pipeline_model_parallel_world_size() - 1
        ):
            accumulated_step_id = rollout_id * num_steps_per_rollout + step_id
            role = getattr(model[0], "role", "actor")
            role_tag = "" if role == "actor" else f"{role}-"
            log_dict = {
                f"train/{role_tag}{key}": val.mean().item() if isinstance(val, torch.Tensor) else val
                for key, val in loss_dict.items()
            }
            log_dict[f"train/{role_tag}grad_norm"] = grad_norm
            if args.enable_mtp_training:
                log_dict[f"train/{role_tag}mtp_loss"] = mtp_losses

            for param_group_id, param_group in enumerate(optimizer.param_groups):
                log_dict[f"train/{role_tag}lr-pg_{param_group_id}"] = opt_param_scheduler.get_lr(param_group)

            log_dict["train/step"] = accumulated_step_id
            tracking_utils.log(args, log_dict, step_key="train/step")

            if args.ci_test and not args.ci_disable_kl_checker:
                if step_id == 0 and "train/ppo_kl" in log_dict and "train/pg_clipfrac" in log_dict:
                    if args.multi_latent_attention:
                        # TODO: mla currently have non-zero kl, need further investigation
                        assert log_dict["train/ppo_kl"] < 1e-8, f"{log_dict=}"
                    else:
                        assert log_dict["train/ppo_kl"] == 0.0 and log_dict["train/pg_clipfrac"] == 0.0, f"{log_dict=}"
                if accumulated_step_id == 0 and "train/kl_loss" in log_dict:
                    assert log_dict["train/kl_loss"] == 0.0, f"{log_dict=}"

            logger.info(f"{role_tag}step {accumulated_step_id}: {log_dict}")

            if args.ci_save_grad_norm is not None:
                ci_save_grad_norm_path = args.ci_save_grad_norm.format(
                    role=role,
                    rollout_id=rollout_id,
                    step_id=step_id,
                )
                torch.save(grad_norm, ci_save_grad_norm_path)
            elif args.ci_load_grad_norm is not None:
                ci_load_grad_norm_path = args.ci_load_grad_norm.format(
                    role=role,
                    rollout_id=rollout_id,
                    step_id=step_id,
                )
                expected_grad_norm = torch.load(ci_load_grad_norm_path)
                assert math.isclose(
                    grad_norm,
                    expected_grad_norm,
                    rel_tol=0.01,
                    abs_tol=0.01,
                ), f"grad norm mismatch: {grad_norm} != {expected_grad_norm}"
    # Close out pre-hooks if using distributed optimizer and overlapped param gather.
    if pre_hook_enabled:
        disable_forward_pre_hook(model)


def save(
    iteration: int, model: Sequence[DDP], optimizer: MegatronOptimizer, opt_param_scheduler: OptimizerParamScheduler
) -> None:
    """Persist a training checkpoint safely with forward hooks disabled.

    Args:
        iteration (int): Current global iteration number.
        model (Sequence[DDP]): Sequence of DDP-wrapped model chunks.
        optimizer (MegatronOptimizer): Optimizer instance.
        opt_param_scheduler (OptimizerParamScheduler): LR/WD scheduler.
    """
    args = get_args()
    if should_disable_forward_pre_hook(args):
        disable_forward_pre_hook(model)
    save_checkpoint(
        iteration,
        model,
        optimizer,
        opt_param_scheduler,
        num_floating_point_operations_so_far=0,
        checkpointing_context=None,
        train_data_iterator=None,
        preprocess_common_state_dict_fn=None,
    )
    if should_disable_forward_pre_hook(args):
        enable_forward_pre_hook(model)


def initialize_model_and_optimizer(
    args: Namespace, role: str = "actor"
) -> tuple[list[DDP], MegatronOptimizer, OptimizerParamScheduler, int]:
    """Initialize model(s), optimizer, scheduler, and load from checkpoint.

    Args:
        args (Namespace): Runtime arguments.
        role (str): Logical role of the model (e.g., "actor", "critic").

    Returns:
        tuple[list[DDP], MegatronOptimizer, OptimizerParamScheduler, int]:
            DDP-wrapped model chunks, optimizer, scheduler, and iteration index.
    """
    model, optimizer, opt_param_scheduler = setup_model_and_optimizer(args, role)
    model[0].role = role
    clear_memory()
    iteration, _ = load_checkpoint(
        model,
        optimizer,
        opt_param_scheduler,
        checkpointing_context={},
        skip_load_to_model_and_opt=False,
    )
    clear_memory()

    return model, optimizer, opt_param_scheduler, iteration