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	# 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, Optional, Tuple
	from warnings import warn

	import torch
	from omegaconf import DictConfig

	from torch import nn
	from torch.distributed import destroy_process_group, init_process_group
	from torch.distributed.fsdp import (
	FullOptimStateDictConfig,
	FullStateDictConfig,
	FullyShardedDataParallel as FSDP,
	StateDictType,
	)
	from torch.optim import Optimizer
	from torch.utils.data import DataLoader, DistributedSampler
	from torchtune import config, modules, utils
	from torchtune.modules.peft.peft_utils import (
	get_adapter_params,
	get_merged_lora_ckpt,
	set_trainable_params,
	validate_state_dict_for_lora,
	)
	from torchtune.recipe_interfaces import FTRecipeInterface

	from tqdm import tqdm

	log = utils.get_logger("DEBUG")


	class LoRAFinetuneRecipeDistributed(FTRecipeInterface):
	"""
	Distributed LoRA 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. DDP is currently not supported. Traning on CPU is not
	supported.

	- Activation Checkpointing. This can be controlled using the ``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.

	- 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. Currently we checkpoint both the adapter weights (trainable params only) and the
	complete merged weights (adapter weights added back to the base model). For more details
	please take a look at our LoRA tutorial
	(https://pytorch.org/torchtune/main/tutorials/lora_finetune.html).

	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/tutorials/checkpointer.html).

	- Logging. Terminal, Disk, WandB and TensorBoard are all supported.

	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.
	ValueError: If world_size is 1
	RuntimeError: If ``dtype`` is set to bf16 and the hardware does not support bf16.
	"""

	def __init__(self, cfg: DictConfig) -> None:
	self._device = utils.get_device(device=cfg.device)
	self._dtype = utils.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."
	)

	_, rank = utils.get_world_size_and_rank()

	# _is_rank_zero is used primarily for logging. In the future, the logger
	# should directly take care of this
	self._is_rank_zero = rank == 0

	# logging attributes
	self._output_dir = cfg.output_dir
	self._log_every_n_steps = cfg.log_every_n_steps if cfg.log_every_n_steps else 1
	self._log_peak_memory_every_n_steps = 100

	# training attributes
	self._enable_activation_checkpointing = cfg.enable_activation_checkpointing

	# These attributes constitute the recipe state and are updated by ``load_checkpoint``
	# when ``resume_from_checkpoint`` is ``True``
	self.seed = utils.set_seed(seed=cfg.seed)
	self.epochs_run = 0
	self.total_epochs = cfg.epochs
	self.max_steps_per_epoch = cfg.max_steps_per_epoch
	self.total_training_steps = 0

	self._resume_from_checkpoint = cfg.resume_from_checkpoint
	self._gradient_accumulation_steps = cfg.gradient_accumulation_steps

	def load_checkpoint(self, cfg_checkpointer: DictConfig) -> Dict[str, Any]:
	"""
	Extract the checkpoint state from file and validate. This includes the
	base model weights. If resume_from_checkpoint is True, this also includes
	the adapter weights and recipe state
	"""
	self._checkpointer = config.instantiate(
	cfg_checkpointer,
	resume_from_checkpoint=self._resume_from_checkpoint,
	)
	checkpoint_dict = self._checkpointer.load_checkpoint()

	# When resuming from checkpoint for LoRA, the recipe expects the adapter weights
	# and recipe state to be present. The keys should match up with what ``save_checkpoint``
	# used to create these intermediate checkpoints
	if self._resume_from_checkpoint:
	if utils.ADAPTER_KEY not in checkpoint_dict:
	raise ValueError(
	"Adapter weights not found. Please ensure a valid adapter checkpoint is provided."
	)
	# _update_recipe_state will throw an exception if the recipe state is not corrctly loaded
	# no need to check here
	self._update_recipe_state(checkpoint_dict)
	return checkpoint_dict

	def _update_recipe_state(self, ckpt_dict: Dict[str, Any]) -> None:
	"""
	Updates the recipe state from checkpoint.
	"""
	if not (
	utils.SEED_KEY in ckpt_dict
	and utils.TOTAL_EPOCHS_KEY in ckpt_dict
	and utils.MAX_STEPS_KEY in ckpt_dict
	):
	raise KeyError(
	"Checkpoint does not contain the required keys needed for updating recipe state."
	"Are you sure you passed in the right recipe checkpoint?"
	)
	# If seed, total_epoch or max_steps_per_epoch don't match,
	# warn the user and overwrite
	if (
	self.seed != ckpt_dict[utils.SEED_KEY]
	or self.total_epochs != ckpt_dict[utils.TOTAL_EPOCHS_KEY]
	or self.max_steps_per_epoch != ckpt_dict[utils.MAX_STEPS_KEY]
	):
	warn(
	message="""Configured value for seed, epochs or max_steps_per_epoch
	does not match the value stored in checkpoint."""
	)
	self.seed = utils.set_seed(seed=ckpt_dict[utils.SEED_KEY])
	self.epochs_run = ckpt_dict[utils.EPOCHS_KEY]
	self.total_epochs = ckpt_dict[utils.TOTAL_EPOCHS_KEY]
	self.max_steps_per_epoch = ckpt_dict[utils.MAX_STEPS_KEY]

	def setup(self, cfg: DictConfig) -> None:
	"""
	Setup the recipe state. This includes recipe state (if resume_from_checkpoint is True),
	model, tokenizer, loss, optimizer, learning rate scheduler, sampler, and dataloader.
	"""
	if self._is_rank_zero:
	self._metric_logger = config.instantiate(cfg.metric_logger)

	# log config with parameter override
	self._metric_logger.log_config(cfg)

	checkpoint_dict = self.load_checkpoint(cfg_checkpointer=cfg.checkpointer)

	self._model = self._setup_model(
	cfg_model=cfg.model,
	enable_activation_checkpointing=cfg.enable_activation_checkpointing,
	base_model_state_dict=checkpoint_dict[utils.MODEL_KEY],
	lora_weights_state_dict=(
	checkpoint_dict[utils.ADAPTER_KEY]
	if self._resume_from_checkpoint
	else None
	),
	)
	self._tokenizer = config.instantiate(cfg.tokenizer)

	self._optimizer = self._setup_optimizer(
	cfg_optimizer=cfg.optimizer,
	opt_state_dict=checkpoint_dict[utils.OPT_KEY]
	if self._resume_from_checkpoint
	else None,
	)

	self._loss_fn = config.instantiate(cfg.loss)

	# sampler and dataloader depend on the tokenizer and loss_fn and should be
	# setup after all of these are setup
	self._sampler, self._dataloader = self._setup_data(
	cfg_dataset=cfg.dataset,
	shuffle=cfg.shuffle,
	batch_size=cfg.batch_size,
	)

	# 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 and the max_steps_per_epoch param set by the user and is used
	# for logging and tracking training state. This should be computed 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.total_training_steps = self.epochs_run * self._steps_per_epoch

	# Learning rate scheduler can only be set up after number of steps
	# has been computed
	self._lr_scheduler = self._setup_lr_scheduler(
	cfg_lr_scheduler=cfg.lr_scheduler,
	num_training_steps=self.total_epochs * self._steps_per_epoch,
	last_epoch=self.total_training_steps - 1,
	)

	def _setup_model(
	self,
	cfg_model: DictConfig,
	enable_activation_checkpointing: bool,
	base_model_state_dict: Dict[str, Any],
	lora_weights_state_dict: Optional[Dict[str, Any]] = None,
	) -> nn.Module:
	"""
	Model initialization has some important considerations:
	a. To minimize GPU peak memory, we load the model on CPU with the right
	dtype. To ensure that we don't instantiate ``world_size`` number of models,
	we initialize on meta_device for all ranks other than rank 0.
	b. Rank 0 is also responsible for calling ``load_state_dict`` and loading the
	model weights from checkpoint.
	c. While wrapping the model with FSDP, we set ``sync_module_states``
	to TRUE and broadcast module params and buffers from rank 0.
	d. The ``device_id`` param ensures that the FSDP initialization happens on
	the correct device.
	"""

	if self._is_rank_zero:
	log.info("FSDP is enabled. Instantiating Model on CPU for Rank 0 ...")
	init_start = time.perf_counter()

	with utils.set_default_dtype(self._dtype):
	model = config.instantiate(cfg_model)

	log.info(
	f"Model instantiation took {time.perf_counter() - init_start:.2f} secs"
	)

	# The model contains LoRA params which won't have any matching keys in
	# the state dict. As a result, we need to load with strict=False.
	# Before loading the state dict, ensure the state dict keys for the base
	# model and adapters (if available) match the keys in the full LoRA model
	# This is a good sanity check to prevent silent errors
	validate_state_dict_for_lora(
	lora_attn_modules=cfg_model.lora_attn_modules,
	apply_lora_to_mlp=cfg_model.apply_lora_to_mlp,
	apply_lora_to_output=cfg_model.apply_lora_to_output,
	full_model_state_dict_keys=model.state_dict().keys(),
	lora_state_dict_keys=(
	lora_weights_state_dict.keys()
	if lora_weights_state_dict is not None
	else None
	),
	base_model_state_dict_keys=base_model_state_dict.keys(),
	)

	# Load both the base model weights and (if available) the adapter weights. Both
	# of this should happen only on Rank 0
	model.load_state_dict(base_model_state_dict, strict=False)
	if lora_weights_state_dict:
	model.load_state_dict(lora_weights_state_dict, strict=False)

	else:
	# For non-zero ranks, load the model on meta device
	with utils.set_default_dtype(self._dtype), torch.device("meta"):
	model = config.instantiate(cfg_model)

	if self._dtype == torch.bfloat16:
	model = model.to(torch.bfloat16)

	# LoRA hyper-params needed for merging weights while saving checkpoints
	self._lora_rank = cfg_model.lora_rank
	self._lora_alpha = cfg_model.lora_alpha

	# Note: this needs to be set before wrapping with FSDP
	self.adapter_params = get_adapter_params(model)
	set_trainable_params(model, self.adapter_params)

	model = FSDP(
	module=model,
	auto_wrap_policy=utils.lora_fsdp_wrap_policy(
	modules_to_wrap={modules.TransformerDecoderLayer}
	),
	sharding_strategy=torch.distributed.fsdp.ShardingStrategy.FULL_SHARD,
	device_id=self._device,
	# this recipe does not currently support mixed precision training
	mixed_precision=None,
	# Ensure we broadcast params and buffers from rank 0
	sync_module_states=True,
	# Initialize empty modules on all non-zero ranks
	param_init_fn=(
	lambda module: module.to_empty(
	device=torch.device("cuda"), recurse=False
	)
	if not self._is_rank_zero
	else None
	),
	)

	# Ensure no params and buffers are on meta device
	utils.validate_no_params_on_meta_device(model)

	if enable_activation_checkpointing:
	utils.set_activation_checkpointing(
	model, auto_wrap_policy={modules.TransformerDecoderLayer}
	)
	if self._is_rank_zero:
	memory_stats = utils.memory_stats_log(device=self._device)
	log.info(f"Memory Stats after model init:\n{memory_stats}")

	# synchronize before training begins
	torch.distributed.barrier()

	return model

	def _setup_optimizer(
	self, cfg_optimizer: DictConfig, opt_state_dict: Optional[Dict[str, Any]] = None
	) -> Optimizer:
	optimizer = config.instantiate(cfg_optimizer, self._model.parameters())
	if opt_state_dict:
	# Note: technically we should check _contains_fsdp for
	# just the state dict of the adapter cfg, but should be equivalent
	opt_state_dict = utils.transform_opt_state_dict(
	opt_state_dict, self._model, optimizer
	)
	optimizer.load_state_dict(opt_state_dict)

	if self._is_rank_zero:
	log.info("Optimizer and loss are initialized.")
	return optimizer

	def _setup_lr_scheduler(
	self,
	cfg_lr_scheduler: DictConfig,
	num_training_steps: int,
	last_epoch: int,
	) -> Optimizer:
	lr_scheduler = config.instantiate(
	cfg_lr_scheduler,
	self._optimizer,
	num_training_steps=num_training_steps,
	last_epoch=last_epoch,
	)
	if self._is_rank_zero:
	log.info("Learning rate scheduler is initialized.")
	return lr_scheduler

	def _setup_data(
	self,
	cfg_dataset: DictConfig,
	shuffle: bool,
	batch_size: int,
	) -> 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.
	"""
	world_size, rank = utils.get_world_size_and_rank()
	ds = config.instantiate(cfg_dataset, tokenizer=self._tokenizer)
	sampler = DistributedSampler(
	ds, num_replicas=world_size, rank=rank, shuffle=shuffle, seed=0
	)

	dataloader = DataLoader(
	dataset=ds,
	batch_size=batch_size,
	sampler=sampler,
	collate_fn=partial(
	utils.padded_collate,
	padding_idx=self._tokenizer.pad_id,
	ignore_idx=self._loss_fn.ignore_index,
	),
	)

	if self._is_rank_zero:
	log.info("Dataset and Sampler are initialized.")

	return sampler, dataloader

	def save_checkpoint(
	self,
	epoch: int,
	) -> None:
	"""
	Checkpoint the state of the recipe. The constructed checkpoint state dict
	contains the following information:
	- Merged weights with key MODEL_KEY
	- Adapter weights with key ADAPTER_KEY
	- Relevant recipe state if training is not complete

	Checkpointer will save the merged weights, adapter weights and recipe state in
	different checkpoint files. To correctly resume from training, the adapter weights
	and recipe state must be provided along with the base model weights.
	"""
	# final dict passed onto the checkpointer
	checkpoint_dict = {}

	intermediate_checkpoint = epoch + 1 < self.total_epochs
	# To prevent GPU memory from spiking during checkpoint save,
	# we consolidate the full model and optim state dicts on CPU for rank 0
	with FSDP.state_dict_type(
	self._model,
	StateDictType.FULL_STATE_DICT,
	FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
	FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=True),
	):
	cpu_state_dict = self._model.state_dict()
	if intermediate_checkpoint:
	opt_state_dict = FSDP.optim_state_dict(self._model, self._optimizer)
	else:
	opt_state_dict = None

	# Now that we have the model and opt state dict, create the actual checkpoint dict
	# to be sent to the checkpointer and ultimately written to file
	if self._is_rank_zero:

	# Filter out the adapter keys and weights from the model state dict. These will
	# be saved separately
	adapter_key_filter = lambda x: x in self.adapter_params
	adapter_state_dict = {
	k: v for k, v in cpu_state_dict.items() if adapter_key_filter(k)
	}
	checkpoint_dict.update({utils.ADAPTER_KEY: adapter_state_dict})

	# merge the adapter weights and base weights to create the model checkpoint
	merged_state_dict = get_merged_lora_ckpt(
	cpu_state_dict,
	rank=self._lora_rank,
	alpha=self._lora_alpha,
	)
	checkpoint_dict.update({utils.MODEL_KEY: merged_state_dict})

	# if training is in-progress, checkpoint the optimizer state and recipe state
	# as well.
	if intermediate_checkpoint:
	checkpoint_dict.update(
	{
	utils.OPT_KEY: opt_state_dict,
	utils.SEED_KEY: self.seed,
	utils.EPOCHS_KEY: self.epochs_run,
	utils.TOTAL_EPOCHS_KEY: self.total_epochs,
	utils.MAX_STEPS_KEY: self.max_steps_per_epoch,
	}
	)

	self._checkpointer.save_checkpoint(
	checkpoint_dict,
	epoch=epoch,
	intermediate_checkpoint=intermediate_checkpoint,
	)

	def train(self) -> None:
	"""
	The core training loop.
	"""
	# clean up before training begins
	utils.cleanup_before_training()

	_, rank = utils.get_world_size_and_rank()

	# zero out the gradients before starting training
	self._optimizer.zero_grad()

	# self.epochs_run should be non-zero when we're resuming from a checkpoint
	for curr_epoch in range(self.epochs_run, self.total_epochs):

	# Update the sampler to ensure data is correctly shuffled across epochs
	# in case shuffle is True
	self._sampler.set_epoch(curr_epoch)

	for idx, batch in enumerate(
	pbar := tqdm(self._dataloader, disable=not (rank == 0))
	):
	if (
	self.max_steps_per_epoch is not None
	and (idx // self._gradient_accumulation_steps)
	== self.max_steps_per_epoch
	):
	break

	input_ids, labels = batch
	input_ids = input_ids.to(self._device)
	labels = labels.to(self._device)

	logits = self._model(input_ids)
	# Shift so that tokens < n predict n
	logits = logits[..., :-1, :].contiguous()
	labels = labels[..., 1:].contiguous()
	logits = logits.transpose(1, 2)
	# Compute loss
	loss = self._loss_fn(logits, labels)

	if (
	self.total_training_steps % self._log_every_n_steps == 0
	and self._is_rank_zero
	):
	pbar.set_description(f"{curr_epoch+1}\|{idx+1}\|Loss: {loss.item()}")
	self._metric_logger.log_dict(
	{
	"loss": loss.item(),
	"lr": self._optimizer.param_groups[0]["lr"],
	"gpu_resources": torch.cuda.memory_allocated(),
	},
	step=self.total_training_steps, # Each step is unique, not limited to each epoch
	)

	loss = loss / self._gradient_accumulation_steps
	loss.backward()

	if (idx + 1) % self._gradient_accumulation_steps == 0:
	self._optimizer.step()
	self._optimizer.zero_grad(set_to_none=True)
	self._lr_scheduler.step()

	# Update the number of steps when the weights are updated
	self.total_training_steps += 1

	if (
	self.total_training_steps % self._log_peak_memory_every_n_steps == 0
	and self._is_rank_zero
	):
	# Log peak memory for iteration
	memory_stats = utils.memory_stats_log(device=self._device)
	self._metric_logger.log_dict(
	memory_stats, step=self.total_training_steps
	)

	self.epochs_run += 1
	self.save_checkpoint(epoch=curr_epoch)

	def cleanup(self) -> None:
	if self._is_rank_zero:
	self._metric_logger.close()
	destroy_process_group()


	@config.parse
	def recipe_main(cfg: DictConfig) -> None:
	"""
	Entry point for the recipe.

	Configurable parameters are read in the following order:
	- Parameters specified in config (see available configs through ``tune ls``)
	- Overwritten by arguments from the command-line
	"""
	if not utils.is_distributed():
	raise RuntimeError(
	"Distributed finetune recipe should be run via a distributed launcher."
	"If using tune CLI, please specify --nnodes 1 and --nproc_per_node [num_gpus]"
	)

	init_process_group(backend="gloo" if cfg.device == "cpu" else "nccl")

	config.log_config(recipe_name="LoRAFinetuneRecipeDistributed", cfg=cfg)

	recipe = LoRAFinetuneRecipeDistributed(cfg=cfg)
	recipe.setup(cfg=cfg)
	recipe.train()
	recipe.cleanup()


	if __name__ == "__main__":
	sys.exit(recipe_main())