content: # Copyright (c) Meta Platforms, Inc. and affiliates.
  # All rights reserved.
  #
  # This source code is licensed under the BSD-style license found in the
  # LICENSE file in the root directory of this source tree.
  
  import sys
  import time
  
  from functools import partial
  from typing import Any, Dict, List, Optional, Tuple, Union
  from warnings import warn
  
  import torch
  from omegaconf import DictConfig, ListConfig
  
  from torch import nn
  from torch.distributed import (
      destroy_process_group,
      init_device_mesh,
      init_process_group,
  )
  from torch.distributed._tensor import DTensor
  from torch.distributed.tensor.parallel import parallelize_module
  from torch.optim import Optimizer
  from torch.utils.data import DataLoader, DistributedSampler
  from torchtune import config, modules, training, utils
  from torchtune.config._utils import _get_component_from_path
  from torchtune.data import padded_collate_packed
  from torchtune.datasets import ConcatDataset
  from torchtune.recipe_interfaces import FTRecipeInterface
  from torchtune.training import DummyProfiler, PROFILER_KEY
  from torchtune.training.activations import apply_selective_activation_checkpointing
  from torchtune.training.checkpointing._checkpoint_client import (
      CheckpointClient,
      TrainingProgress,
  )
  from torchtune.training.lr_schedulers import get_lr
  
  from tqdm import tqdm
  
  log = utils.get_logger("DEBUG")
  
  
  class FullFinetuneRecipeDistributed(FTRecipeInterface):
      """
      Full finetuning recipe for dense transformer-based LLMs such as Llama2. This recipe supports
      distributed training and can be run on a single node (1 to 8 GPUs).
  
      Features:
          - FSDP. Supported using PyTorch's FSDP APIs. CPU offload of parameters, gradients, and optimizer states
              is supported via ``fsdp_cpu_offload``. Resharding of parameters after the forward pass is
              done by default (corresponding to FULL_SHARD sharding strategy), but can be disabled by setting the config
              ``fsdp_reshard_after_forward`` to False (this corresponds to SHARD_GRAD_OP sharding strategy).
              DDP is currently not supported. Training on CPU is not supported.
  
          - Activation Checkpointing. This can be controlled using the ``enable_activation_checkpointing``
              flag. Activation checkpointing helps reduce the memory footprint since we no longer keep
              activations in memory and instead recompute them during the backward pass. This is especially
              helpful for larger batch sizes when you're memory constrained. But these savings in memory
              come at the cost of training performance. In most cases training can slow-down quite a bit as
              a result of this activation recomputation.
  
          - Activation Offloading. This can be controlled using the ``enable_activation_offloading``
              flag. Activation offloading is a technique similar to activations checkpointing that helps
              reduce the memory footprint to prevent OOMs on CUDA and enable bigger batches. Where activations
              checkpointing drops the activation in the forward to recompute it later in the backward,
              activations offloading will drop the activation in the forward to the CPU and bring it
              back during the backward pass. As always, there is a tradeoff--these savings in memory can
              come at the cost of training performance and CPU resources. To recover some runtime cost,
              we've added an option to enable offloading on a different stream to permit overlapping with
              the computation. This option is currently only available on PyTorch 2.5 or later and will
              be enabled by default if an acceptable torch version is found. Activation offloading can be
              used in conjunction with activation checkpointing.
  
          - Precision. Full fp32 and bf16 training are supported. Precision is controlled using the ``dtype``
              flag. When ``dtype=bf16``, all activations, gradients and optimizer states are in bfloat16. In
              most cases this should halve the memory footprint of full precision (fp32) training, without
              loss in model quality (will depend on the model, training data and other settings). For
              GPUs which do not support bfloat16, we fall back to fp32. Mixed precision training and fp16
              precision are currently not supported.
  
          - Gradient Accumulation. You can simulate larger batch sizes by accumulating gradients. This is
              controlled using the ``gradient_accumulation_steps`` flag.
  
                  Total Batch Size = batch_size * number of GPUs * gradient accumulation steps.
  
              For example: with batch_size=1, nproc_per_node=2 and gradient_accumulation_steps=32 we get a
              total batch size of 64.
  
              Gradient accumulation is especially useful when you are memory constrained. In this case,
              accumulating gradients might give you better training speed than enabling activation
              checkpointing.
  
          - Checkpointing. Model weights are checkpointed both at the end of each epoch and at the end of
              training. Optimizer state and recipe state (seed, total_epochs, number of epochs run etc) are
              only saved at the end of a given epoch and used in case of resuming training.
  
              Resuming training is controlled by the ``resume_from_checkpoint`` flag. Mid-epoch checkpointing is
              currently not supported.
  
              For more details on the checkpointer, please take a look at
              our checkpointer deepdive (https://pytorch.org/torchtune/main/deep_dives/checkpointer.html).
  
          - Logging. Terminal, Disk, WandB and TensorBoard are all supported.
  
          - Gradient Clipping. Gradient clipping is supported using the ``clip_grad_norm`` flag. By default,
              ``clip_grad_norm`` is set to ``None``. If you only want to log the grad norm, you can set
              ``clip_grad_norm='inf'``.
  
      For a full list of example configs for this recipe, run ``tune ls`` on the command line. Each config
      has example commands for how to kick-off training.
  
      Args:
          cfg (DictConfig): OmegaConf object parsed from yaml file
  
      Raises:
          ValueError: If ``dtype`` is set to fp16.
          RuntimeError: If ``dtype`` is set to bf16 and the hardware does not support bf16.
          RuntimeError: If ``left_pad_sequence`` is set as the data collator.
          RuntimeError: If ``enable_activation_offloading`` is True and device is not CUDA.
          RuntimeError: If ``enable_activation_offloading`` is True and ``enable_activation_checkpointing`` is False.
      """
  
      def __init__(self, cfg: DictConfig) -> None:
          device_type = cfg.device
          self._device = utils.get_device(device=device_type)
          self._dtype = training.get_dtype(cfg.dtype, device=self._device)
  
          if self._dtype == torch.float16:
              raise ValueError(
                  "full fp16 training is not supported with this recipe. Please use bf16 or fp32 instead."
              )
  
          # Set up the backend for distributed training (NCCL, GLOO, etc.)
          self._enable_async_checkpointing = cfg.get("enable_async_checkpointing", False)
          self.fsdp_cpu_offload = cfg.get("fsdp_cpu_offload", False)
          self.distributed_backend = training.get_distributed_backend(
              device_type,
              offload_ops_to_cpu=self.fsdp_cpu_offload
              or self._enable_async_checkpointing,
          )
          init_process_group(self.distributed_backend)
  
          # Initialize distributed variables
          self.world_size, self.rank = utils.get_world_size_and_rank()
          self._is_rank_zero = self.rank == 0
          self.tensor_parallel_plan = config.instantiate(
              cfg.get("tensor_parallel_plan", None)
          )
          self.tensor_parallel_dim = cfg.get("tensor_parallel_dim", 1)
          if self.tensor_parallel_dim > 1 and self.tensor_parallel_plan is None:
              raise ValueError(
                  "Tensor Parallel plan needs to be provided when tensor parallel is enabled."
              )
          if self.world_size % self.tensor_parallel_dim != 0:
              raise ValueError(
                  f"world_size {self.world_size} must be divisible by tensor_parallel_dim {self.tensor_parallel_dim}"
              )
          self.data_parallel_dim = self.world_size // self.tensor_parallel_dim
  
          # Logging attributes
          self._output_dir = cfg.output_dir
          self._log_every_n_steps = cfg.get("log_every_n_steps", 1)
          self._log_peak_memory_stats = cfg.get("log_peak_memory_stats", False)
          if self._log_peak_memory_stats and device_type != "cuda":
              log.info(
                  "log_peak_memory_stats was set to True, however, training does not use cuda. Setting log_peak_memory_stats=False."
              )
              self._log_peak_memory_stats = False
  
          # Training cfg
          self._resume_from_checkpoint = cfg.resume_from_checkpoint
          self._gradient_accumulation_steps = cfg.gradient_accumulation_steps
          self._optimizer_in_bwd = cfg.get("optimizer_in_bwd", False)
          self._clip_grad_norm = cfg.get("clip_grad_norm", None)
          self._checkpoint_client = CheckpointClient(cfg)
          self.save_every_epochs = cfg.get("save_every_epochs", 1)
  
          # Optimizer in backward is not compatible with gradient accumulation or gradient clipping
          if self._optimizer_in_bwd:
              if self._clip_grad_norm is not None:
                  raise RuntimeError(
                      "Gradient clipping is not supported with optimizer in bwd."
                      "Please set clip_grad_norm=None, or optimizer_in_bwd=False."
                  )
              if self._gradient_accumulation_steps > 1:
                  raise RuntimeError(
                      "Gradient accumulation is not supported with optimizer in bwd."
                      "Please set gradient_accumulation_steps=1, or optimizer_in_bwd=False."
                  )
  
          # activation checkpointing/offloading
          self._enable_activation_checkpointing = cfg.get(
              "enable_activation_checkpointing", False
          )
          self._enable_activation_offloading = cfg.get(
              "enable_activation_offloading", False
          )
          if self._enable_activation_offloading:
              if device_type != "cuda":
                  raise RuntimeError(
                      "enable_activation_offloading should only be True when training on CUDA"
                  )
              if not self._enable_activation_checkpointing:
                  raise RuntimeError(
                      "enable_activation_offloading should only be True when enable_activation_checkpointing is True"
                  )
          elif (
              self._enable_activation_checkpointing
              and cfg.checkpointer.model_type != "LLAMA3_VISION"
          ):
              utils.log_rank_zero(
                  log,
                  "Hint: enable_activation_checkpointing is True, but enable_activation_offloading isn't. "
                  "Enabling activation offloading should reduce memory further.",
              )
  
          # These are public properties which are updated by the checkpoint loader
          # when ``resume_from_checkpoint`` is `True` or validated in tests
          self.seed = training.set_seed(
              seed=cfg.seed, debug_mode=cfg.get("cudnn_deterministic_mode", None)
          )
          self.epochs_run = 0
          self.total_epochs = cfg.epochs
          self.max_steps_per_epoch = cfg.max_steps_per_epoch
          self.global_step = 0
  
      def _update_recipe_state(self, ckpt_dict: Dict[str, Any]) -> None:
          """
          Updates the recipe state from checkpoint.
          """
          try:
              self.epochs_run = ckpt_dict[training.EPOCHS_KEY]
  
              # on mismatch, warn the user and prevent the override
              if self.seed != ckpt_dict[training.SEED_KEY]:
                  warn(
                      message=(
                          "Config value for seed does not match the checkpoint value, "
                          f"using the checkpoint value: {ckpt_dict[training.SEED_KEY]}"
                      )
                  )
                  self.seed = ckpt_dict[training.SEED_KEY]
              if self.max_steps_per_epoch != ckpt_dict[training.MAX_STEPS_KEY]:
                  warn(
                      message=(
                          "Config value for max_steps_per_epoch does not match the checkpoint value, "
                          f"using the checkpoint value: {ckpt_dict[training.MAX_STEPS_KEY]}"
                      )
                  )
                  self.max_steps_per_epoch = ckpt_dict[training.MAX_STEPS_KEY]
  
              # on mismatch, warn the user but allow the override
              if self.total_epochs != ckpt_dict[training.TOTAL_EPOCHS_KEY]:
                  warn(
                      message=(
                          "Config value for total_epochs does not match the checkpoint value, "
                          f"using the config value: {self.total_epochs}"
                      )
                  )
  
          except KeyError as e:
              raise KeyError(
                  "Checkpoint does not contain the required keys needed for updating recipe state. "
                  "Are you sure you passed in the right recipe checkpoint?"
              ) from e
  
      def setup(self, cfg: DictConfig) -> None:
          """
          Setup the recipe. This includes training state (if resume_from_checkpoint is True),
          model, tokenizer, loss, optimizer, lr scheduler, sampler, and dataloader.
          """
          if self.fsdp_cpu_offload:
              # Utilize all available CPU cores for intra-op parallelism. This provides ~2x
              # speed up when benchmarking fused AdamW on CPU
              training.set_torch_num_threads()
  
          if self._is_rank_zero:
              self._metric_logger = config.instantiate(cfg.metric_logger)
              # log config with parameter override
              self._metric_logger.log_config(cfg)
  
          # Load the base model
          checkpoint_dict = self._checkpoint_client.load_base_checkpoint()
  
          self._compile = cfg.get("compile", False)
          self._model = self._setup_model(
              cfg_model=cfg.model,
              enable_activation_checkpointing=self._enable_activation_checkpointing,
              enable_activation_offloading=self._enable_activation_offloading,
              custom_sharded_layers=cfg.get("custom_sharded_layers", None),
              fsdp_cpu_offload=self.fsdp_cpu_offload,
              reshard_after_forward=cfg.get("fsdp_reshard_after_forward", True),
              model_state_dict=checkpoint_dict[training.MODEL_KEY],
              ac_mode=cfg.get("ac_mode", None),
              ac_option=cfg.get("ac_option", None),
          )
          self._tokenizer = config.instantiate(cfg.tokenizer)
  
          self._optimizer = self._setup_optimizer(
              cfg_optimizer=cfg.optimizer,
              optimizer_in_bwd=self._optimizer_in_bwd,
              opt_state_dict=(
                  checkpoint_dict[training.OPT_KEY]
                  if training.OPT_KEY in checkpoint_dict
                  else None
              ),
          )
  
          if self._resume_from_checkpoint:
              # If async checkpointing is enabled, intermediate checkpoints are saved asynchronously
              # using the DistributedCheckpointer.
              # Therefore the recipe needs to load the distributed checkpoint to restore the training
              # progress.
              if self._enable_async_checkpointing:
                  try:
                      checkpoint_dict = (
                          self._checkpoint_client.load_distributed_checkpoint(
                              self._model,
                              (
                                  self._optim_ckpt_wrapper
                                  if self._optimizer_in_bwd
                                  else self._optimizer
                              ),
                          )
                      )
                  except Exception as e:
                      log.warning(
                          f"Failed to load distributed checkpoint: {e}. Training will start from the base checkpoint."
                      )
  
              # Update the recipe state from the checkpoint state dict.
              self._update_recipe_state(checkpoint_dict)
  
          # initialize loss
          self._loss_fn = config.instantiate(cfg.loss)
  
          if self._compile:
              training.compile_loss(self._loss_fn, verbose=self._is_rank_zero)
  
          if self._loss_fn.__class__.__name__ == "CEWithChunkedOutputLoss":
              # set num_output_chunks for model
              self._model.set_num_output_chunks(self._loss_fn.num_output_chunks)
  
          utils.log_rank_zero(log, "Loss is initialized.")
  
          # sampler and dataloader depend on the tokenizer and loss_fn and should be
          # setup after both of these are initialized
          collate_name = cfg.get("collate_fn", "torchtune.data.padded_collate_sft")
          self._sampler, self._dataloader = self._setup_data(
              cfg_dataset=cfg.dataset,
              shuffle=cfg.shuffle,
              batch_size=cfg.batch_size,
              collate_fn=collate_name,
          )
  
          # Finally update the recipe state which can only be correctly set after all of the
          # other components have been initialized and updated.
          #
          # Number of training steps in each epoch depends on the number of batches produced
          # by the dataloader, the max_steps_per_epoch param set by the user and the
          # gradient_accumulation_steps param. This value is used for logging and tracking
          # training state. The computation should happen after the dataloader has been setup
          self._steps_per_epoch = (
              len(self._dataloader) // self._gradient_accumulation_steps
          )
          if (
              self.max_steps_per_epoch is not None
              and self.max_steps_per_epoch < self._steps_per_epoch
          ):
              self._steps_per_epoch = self.max_steps_per_epoch
          self.global_step = self.epochs_run * self._steps_per_epoch
  
          # Setup lr scheduler
          self._lr_scheduler = self._setup_lr_scheduler(
              cfg_lr_scheduler=cfg.get("lr_scheduler", None),
              num_training_steps=self.total_epochs * self._steps_per_epoch,
              last_epoch=self.global_step - 1,
          )
  
          # Set up profiler, returns DummyProfiler (nullcontext object with no-op `step` method)
          # if cfg is missing profiler key or if `cfg.profiler.enabled = False`
          self._profiler = self._setup_profiler(cfg.get(PROFILER_KEY, None))
  
          # Used to ignore labels for loss computation
          self.ignore_labels_cache = torch.full(
              (cfg.batch_size, 1), self._loss_fn.ignore_index, device=self._device
          )
  
      def _setup_lr_scheduler(
          self,
          cfg_lr_scheduler: Optional[DictConfig],
          num_training_steps: int,
          last_epoch: int,
      ) -> Optional[Optimizer]:
          """
          Set up the learning rate scheduler based on the provided configuration.
          It supports both standard optimization and optimizer-in-backward cases.
  
          Args:
              cfg_lr_scheduler (Optional[DictConfig]): The learning rate scheduler configuration.
              num_training_steps (int): The total number of training steps.
              last_epoch (int): The index of the last epoch.
  
          Returns:
              lr_scheduler (Optional[Optimizer]): The learning rate scheduler.
          """
          if cfg_lr_scheduler is None:
              if self._is_rank_zero:
                  log.info(
                      "No learning rate scheduler configured. Using constant learning rate."
                  )
              return None
  
          if self._optimizer_in_bwd:
              # Use the first optimizer from the wrapper to represent the learning rate
              optimizer = next(iter(self._optim_ckpt_wrapper.optim_map.values()))
          else:
              # Standard case: use the single optimizer
              optimizer = self._optimizer
  
          # Instantiate the learning rate scheduler
          lr_scheduler = config.instantiate(
              cfg_lr_scheduler,
              optimizer,
              num_training_steps=num_training_steps,
              last_epoch=last_epoch,
          )
  
          if self._optimizer_in_bwd:
              # Modify the scheduler for optimizer_in_bwd case
              self._optim_ckpt_wrapper.set_lr_scheduler(lr_scheduler)
  
          if self._is_rank_zero:
              log.info("Learning rate scheduler is initialized.")
  
          return lr_scheduler
  
      def _setup_profiler(
          self, cfg_profiler: Optional[DictConfig] = None
      ) -> Union[torch.profiler.profile, DummyProfiler]:
          """
          Parses the `profiler` section of top-level `cfg` and sets up profiler
  
          Args:
              cfg_profiler (Optional[DictConfig]): ``profiler`` section of the top-level ``cfg`` (the main config passed to
                  `recipe.main`). Default None.
  
          Returns:
              profiler: Union[torch.profiler.profile, DummyProfiler] - DummyProfiler is a nullcontext with no-op methods
              for `start`, `stop`, and `step` that can be used in place of `torch.profiler.profile` if profiler is not enabled such
              that the instrumented training loop does not need to be changed profiling is disabled.
  
          The profiler config can be provided in configs under the `profiler` key with the following layout:
  
          .. code-block:: yaml
              profiler:
                  enabled: bool
  
                  #Output directory of trace artifacts
                  output_dir: str
  
              #`torch.profiler.ProfilerActivity` types to trace
              cpu: bool
              cuda: bool
  
                  #Trace options
                  profile_memory: bool
                  with_stack: bool
                  record_shapes: bool
                  with_flops: bool
  
              # `torch.profiler.schedule` options:
              # wait_steps -> wait, warmup_steps -> warmup, active_steps -> active, num_cycles -> repeat
              wait_steps: int
              warmup_steps: int
              active_steps: int
              num_cycles: int
          """
          # Missing profiler section in config, assume disabled
          if cfg_profiler is None:
              cfg_profiler = DictConfig({"enabled": False})
  
          # Check that component is included and set correctly
          if cfg_profiler.get("_component_", None) is None:
              cfg_profiler["_component_"] = "torchtune.training.setup_torch_profiler"
          else:
              assert (
                  cfg_profiler.get("_component_")
                  == "torchtune.training.setup_torch_profiler"
              ), "Only torch profiler supported currently: component must be `torchtune.training.setup_torch_profiler`"
  
          profiler, profiler_cfg = config.instantiate(cfg_profiler)
  
          utils.log_rank_zero(
              log, f" Profiler config after instantiation: {profiler_cfg}"
          )
          if self._is_rank_zero:
              self.profiler_profile_memory = profiler_cfg.get("profile_memory", False)
              if profiler_cfg["enabled"]:
                  self.profiler_wait_steps = profiler_cfg["wait_steps"]
                  self.profiler_warmup_steps = profiler_cfg["warmup_steps"]
                  self.profiler_active_steps = profiler_cfg["active_steps"]
  
          return profiler
  
      def _setup_model(
          self,
          cfg_model: DictConfig,
          enable_activation_checkpointing: bool,
          enable_activation_offloading: bool,
          fsdp_cpu_offload: bool,
          reshard_after_forward: bool,
          model_state_dict: Dict[str, Any],
          custom_sharded_layers: Optional[List[str]] = None,
          ac_mode: Optional[str] = None,
          ac_option: Optional[int] = None,
      ) -> nn.Module:
          """
          Model initialization has some important considerations:
             a. To minimize GPU peak memory, we initialize the model on meta device with
                the right dtype
             b. All ranks calls ``load_state_dict`` without peaking CPU RAMs since
                full state dicts are loaded with ``torch.load(mmap=True)``
          """
  
          utils.log_rank_zero(
              log,
              "Distributed training is enabled. Instantiating model and loading checkpoint on Rank 0 ...",
          )
          init_start = time.perf_counter()
  
          with training.set_default_dtype(self._dtype), torch.device("meta"):
              model = config.instantiate(cfg_model)
  
          if self._compile:
              training.compile_model(model, verbose=self._is_rank_zero)
  
          device_mesh = init_device_mesh(
              self._device.type,
              mesh_shape=(self.data_parallel_dim, self.tensor_parallel_dim),
              mesh_dim_names=("dp", "tp"),
          )
          self.dp_size = device_mesh["dp"].size()
          self.dp_rank = device_mesh["dp"].get_local_rank()
  
          # Apply tensor parallelism to the model
          if self.tensor_parallel_dim > 1:
              # Use the local number (num_heads, num_kv_heads, embed_dim) to account for tensor parallel
              model = training.prepare_mha_for_tp(model, device_mesh["tp"])
              parallelize_module(
                  model,
                  device_mesh["tp"],
                  parallelize_plan=self.tensor_parallel_plan,
              )
  
          # We currently have two versions of activation checkpointing in this recipe
          # for testing and BC purposes. ``enable_activation_checkpointing`` controls
          # the older version of AC and this behavior is unchanged
          # ac_mode and ac_option together control selective AC. This is only enabled
          # when these are set AND ``enable_activation_checkpointing`` is set to False
          # We'll clean this up as soon as testing of AC is complete
          if (not enable_activation_checkpointing) and (ac_mode is not None):
              apply_selective_activation_checkpointing(
                  model,
                  ac_mode,
                  ac_option,
              )
  
          # original activation checkpointing (full) - flip the condition above
          if enable_activation_checkpointing and ac_mode is None:
              training.set_activation_checkpointing(
                  model, auto_wrap_policy={modules.TransformerSelfAttentionLayer}
              )
  
          # Apply Fully Sharded Data Parallelism to the model
          if self.data_parallel_dim > 1:
              fsdp_shard_conditions = [
                  partial(
                      training.get_shard_conditions,
                      names_to_match=custom_sharded_layers,
                  )
              ]
              training.shard_model(
                  model=model,
                  shard_conditions=fsdp_shard_conditions,
                  cpu_offload=fsdp_cpu_offload,
                  reshard_after_forward=reshard_after_forward,
                  dp_mesh=device_mesh["dp"],
              )
  
          with training.set_default_dtype(self._dtype), self._device:
              for m in model.modules():
                  # RoPE is not covered in state dict
                  if hasattr(m, "rope_init"):
                      m.rope_init()
  
          # This method will convert the full model state dict into a sharded state
          # dict and load into the model
          training.load_from_full_model_state_dict(
              model,
              model_state_dict,
              self._device,
              strict=True,
              cpu_offload=fsdp_cpu_offload,
          )
  
          # activation offloading
          self.activations_handling_ctx = training.get_act_offloading_ctx_manager(
              model, enable_activation_offloading
          )
  
          # Ensure no params and buffers are on meta device
          training.validate_no_params_on_meta_device(model)
  
          utils.log_rank_zero(
              log,
              f"Instantiating model and loading checkpoint took {time.perf_counter() - init_start:.2f} secs",
          )
  
          if self._is_rank_zero:
              memory_stats = training.get_memory_stats(device=self._device)
              training.log_memory_stats(memory_stats)
  
          # synchronize before training begins
          torch.distributed.barrier()
  
          return model
  
      def _setup_optimizer(
          self,
          cfg_optimizer: DictConfig,
          optimizer_in_bwd: bool = False,
          opt_state_dict: Optional[Dict[str, Any]] = None,
      ) -> Optional[Optimizer]:
          if optimizer_in_bwd:
              # Maintain a dict of optims for every parameter.
              optim_dict = {
                  param: config.instantiate(cfg_optimizer, [param])
                  for param in self._model.parameters()
              }
  
              # Register optimizer step hooks on the model to run optimizer in backward.
              training.register_optim_in_bwd_hooks(
                  model=self._model, optim_dict=optim_dict
              )
              # Create a wrapper for checkpoint save/load of optimizer states when running in backward.
              self._optim_ckpt_wrapper = training.create_optim_in_bwd_wrapper(
                  model=self._model, optim_dict=optim_dict
              )
              # Load optimizer states for each param. If optimizer states are being restored in an optimizer in
              # backward run, these need to have been saved with the same setting. Cannot restore from runs that
              # did not use optimizer in backward.
              if opt_state_dict is not None:
                  for param in opt_state_dict.keys():
                      try:
                          training.load_from_full_optimizer_state_dict(
                              self._model,
                              self._optim_ckpt_wrapper.optim_map[param],
                              opt_state_dict[param],
                              self._device,
                          )
                      except BaseException as e:
                          raise RuntimeError(
                              "Failed loading in-backward optimizer checkpoints."
                              "Please make sure run being restored from was using in-backward optimizer."
                          ) from e
              utils.log_rank_zero(log, "In-backward optimizers are set up.")
              return None
          else:
              optimizer = config.instantiate(cfg_optimizer, self._model.parameters())
              if opt_state_dict:
                  training.load_from_full_optimizer_state_dict(
                      self._model,
                      optimizer,
                      opt_state_dict,
                      self._device,
                  )
  
              utils.log_rank_zero(log, "Optimizer is initialized.")
              return optimizer
  
      def _setup_data(
          self,
          cfg_dataset: DictConfig,
          shuffle: bool,
          batch_size: int,
          collate_fn: str,
      ) -> Tuple[DistributedSampler, DataLoader]:
          """
          All data related setup happens here. Currently this recipe only supports the
          DistributedSamplers with Map-style Datasets which fit into memory. Other samplers,
          iterable datasets and streaming datasets are not supported.
          """
          if isinstance(cfg_dataset, ListConfig):
              datasets = [
                  config.instantiate(single_cfg_dataset, self._tokenizer)
                  for single_cfg_dataset in cfg_dataset
              ]
              ds = ConcatDataset(datasets=datasets)
              packed = getattr(ds, "packed", False)
          else:
              ds = config.instantiate(cfg_dataset, self._tokenizer)
              packed = cfg_dataset.get("packed", False)
  
          # Instantiate collate_fn
          if "left_pad_sequence" in collate_fn:
              raise RuntimeError("left_pad_sequence collator is only for inference.")
          collate_fn = _get_component_from_path(collate_fn)
  
          sampler = DistributedSampler(
              ds, num_replicas=self.dp_size, rank=self.dp_rank, shuffle=shuffle, seed=0
          )
          dataloader = DataLoader(
              dataset=ds,
              batch_size=batch_size,
              sampler=sampler,
              # dropping last avoids shape issues with compile + flex attention
              drop_last=True,
              collate_fn=(
                  partial(
                      collate_fn,
                      padding_idx=self._tokenizer.pad_id,
                      ignore_idx=self._loss_fn.ignore_index,
                  )
                  if not packed
                  else padded_collate_packed
              ),
          )
  
          utils.log_rank_zero(log, "Dataset and Sampler are initialized.")
  
          return sampler, dataloader
  
      def train(self) -> None:
          """
          The core training loop.
          """
          # clean up before training begins
          training.cleanup_before_training()
  
  