# 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, Union from warnings import warn import torch import torchtune.modules.common_utils as common_utils from omegaconf import DictConfig, ListConfig from torch import nn from torch.optim import Optimizer from torchdata.stateful_dataloader import StatefulDataLoader from torchdata.stateful_dataloader.sampler import StatefulDistributedSampler 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.modules.peft import ( get_adapter_params, get_adapter_state_dict, get_lora_module_names, get_merged_lora_ckpt, set_trainable_params, validate_missing_and_unexpected_for_lora, ) from torchtune.recipe_interfaces import FTRecipeInterface from torchtune.training import DummyProfiler, PROFILER_KEY from tqdm import tqdm log = utils.get_logger("DEBUG") class LoRAFinetuneRecipeSingleDevice(FTRecipeInterface): """ LoRA finetuning recipe for dense transformer-based LLMs such as Llama2. This recipe is optimized for single GPU training. Training on CPU is not supported. Features: - 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 * gradient accumulation steps. For example: with batch_size=1 and gradient_accumulation_steps=32 we get a total batch size of 32. 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. - Lower precision optimizers. This recipe supports lower-precision optimizers from the bitsandbytes library (https://huggingface.co/docs/bitsandbytes/main/en/index). We've tested the recipe with 8-bit AdamW and Paged AdamW. - 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. - 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 ``enable_activation_offloading`` is True and device is not CUDA. RuntimeError: If ``enable_activation_offloading`` is True and ``enable_activation_checkpointing`` is False. RuntimeError: If ``left_pad_sequence`` is set as the data collator """ def __init__(self, cfg: DictConfig) -> None: self._device = utils.get_device(device=cfg.device) # Reduced precision logic self._dtype = training.get_dtype(cfg.dtype, device=self._device) # fp16 precision is explicitly disabled as it is not supported in this # recipe (for example, no gradient scaling). if self._dtype == torch.float16: raise ValueError( "fp16 precision is not supported in this recipe. Please use fp32 or bf16." ) # 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 self._device.type == "cpu": log.info( "log_peak_memory_stats was set to True, however, training uses cpu. Setting log_peak_memory_stats=False." ) self._log_peak_memory_stats = False # 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 self._resume_from_checkpoint = cfg.resume_from_checkpoint self._save_adapter_weights_only = cfg.get("save_adapter_weights_only", False) self._gradient_accumulation_steps = cfg.gradient_accumulation_steps self._clip_grad_norm = cfg.get("clip_grad_norm", None) # 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 self._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.", ) 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, should_load_recipe_state=self._resume_from_checkpoint, ) checkpoint_dict = self._checkpointer.load_checkpoint() if self._resume_from_checkpoint: if training.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. """ 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 state. This includes recipe state (if resume_from_checkpoint is True), model, tokenizer, loss, optimizer, learning rate scheduler, sampler, and dataloader. """ self._metric_logger = config.instantiate(cfg.metric_logger) # log config with parameter override self._metric_logger.log_config(cfg) self._compile = cfg.compile if cfg.device == "npu" and cfg.compile: raise ValueError( "NPU does not support model compilation. Please set `compile: False` in the config." ) checkpoint_dict = self.load_checkpoint(cfg_checkpointer=cfg.checkpointer) # hack to toggle to the low cpu ram version of the reparametrize_as_dtype # hook based on the config. common_utils._use_low_cpu_ram = cfg.get("low_cpu_ram", False) # set up model self._model = self._setup_model( cfg_model=cfg.model, enable_activation_checkpointing=self._enable_activation_checkpointing, enable_activation_offloading=self._enable_activation_offloading, compile_model=cfg.compile, base_model_state_dict=checkpoint_dict[training.MODEL_KEY], lora_weights_state_dict=( checkpoint_dict[training.ADAPTER_KEY] if self._resume_from_checkpoint else None ), ) self._tokenizer = config.instantiate(cfg.tokenizer) log.info("Tokenizer is initialized from file.") self._optimizer = self._setup_optimizer( cfg_optimizer=cfg.optimizer, opt_state_dict=( checkpoint_dict[training.OPT_KEY] if self._resume_from_checkpoint else None ), ) # initialize loss self._loss_fn = config.instantiate(cfg.loss) if self._compile: self._loss_fn = training.compile_loss(self._loss_fn) 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) log.info("Loss is initialized.") # Dataloader depends on the tokenizer and loss_fn and should be # setup after all of these are setup collate_name = cfg.get("collate_fn", "torchtune.data.padded_collate_sft") self._dataloader = self._setup_data( cfg_dataset=cfg.dataset, shuffle=cfg.shuffle, batch_size=cfg.batch_size, collate_fn=collate_name, dataloader_state_dict=( checkpoint_dict[training.DATALOADER_KEY] if self._resume_from_checkpoint else None ), ) # 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.global_step = 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.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_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) log.info(f" Profiler config after instantiation: {profiler_cfg}") 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, compile_model: bool, base_model_state_dict: Dict[str, Any], lora_weights_state_dict: Optional[Dict[str, Any]] = None, ) -> nn.Module: with training.set_default_dtype(self._dtype), self._device: model = config.instantiate(cfg_model) self._lora_rank = cfg_model.lora_rank self._lora_alpha = cfg_model.lora_alpha self._lora_attn_modules = list(cfg_model.lora_attn_modules) self._apply_lora_to_mlp = cfg_model.apply_lora_to_mlp self._apply_lora_to_output = getattr(cfg_model, "apply_lora_to_output", False) self.adapter_params = get_adapter_params(model) self._is_dora = any(["magnitude" in k for k in self.adapter_params.keys()]) set_trainable_params(model, self.adapter_params) if compile_model: training.compile_model(model) if enable_activation_checkpointing: training.set_activation_checkpointing( model, auto_wrap_policy={modules.TransformerSelfAttentionLayer} ) base_missing, base_unexpected = model.load_state_dict( base_model_state_dict, strict=False ) # This is for any adapters that need to be initialized after base weights # have been loaded (e.g. DoRA). if self._is_dora: for m in model.modules(): if hasattr(m, "initialize_dora_magnitude"): m.initialize_dora_magnitude() if lora_weights_state_dict: lora_missing, lora_unexpected = model.load_state_dict( lora_weights_state_dict, strict=False ) else: lora_missing, lora_unexpected = None, None validate_missing_and_unexpected_for_lora( lora_attn_modules=self._lora_attn_modules, apply_lora_to_mlp=self._apply_lora_to_mlp, apply_lora_to_output=self._apply_lora_to_output, base_missing=base_missing, base_unexpected=base_unexpected, lora_missing=lora_missing, lora_unexpected=lora_unexpected, ) # Validate model adapter params were loaded in with the expected dtype # TODO (rohan-varma): Further validation to ensure the appropriate base params # are NF4 vs bf16 based on the quantization config. training.validate_expected_param_dtype( self.adapter_params.items(), dtype=self._dtype ) # activation offloading self.activations_handling_ctx = training.get_act_offloading_ctx_manager( model, enable_activation_offloading ) log.info(f"Model is initialized with precision {self._dtype}.") if self._device.type != "cpu": memory_stats = training.get_memory_stats(device=self._device) training.log_memory_stats(memory_stats) 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: optimizer.load_state_dict(opt_state_dict) 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, ) log.info("Learning rate scheduler is initialized.") return lr_scheduler def _setup_data( self, cfg_dataset: DictConfig, shuffle: bool, batch_size: int, collate_fn: str, dataloader_state_dict: Optional[Dict[str, Any]] = None, ) -> StatefulDataLoader: """ All data related setup happens here. This recipe currently supports only map-style datasets. If a state_dict is provided (meaning we are resuming a training run), it is loaded into the dataloader. """ 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 = StatefulDistributedSampler( ds, num_replicas=1, rank=0, shuffle=shuffle, seed=0, ) dataloader = StatefulDataLoader( dataset=ds, batch_size=batch_size, sampler=sampler, 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 ), # dropping last avoids shape issues with compile + flex attention drop_last=True, ) return 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 - If the `self._save_adapter_weights_only` option is True, the checkpointer will save only the adapter weights To correctly resume from training, the adapter weights and recipe state must be provided along with the base model weights. """ ckpt_dict = {} intermediate_checkpoint = epoch + 1 < self.total_epochs # if training is in-progress, checkpoint the optimizer state as well if intermediate_checkpoint: ckpt_dict.update( { training.OPT_KEY: self._optimizer.state_dict(), training.SEED_KEY: self.seed, training.EPOCHS_KEY: self.epochs_run, training.TOTAL_EPOCHS_KEY: self.total_epochs, training.MAX_STEPS_KEY: self.max_steps_per_epoch, training.DATALOADER_KEY: self._dataloader.state_dict(), } ) adapter_state_dict = get_adapter_state_dict(self._model.state_dict()) ckpt_dict.update({training.ADAPTER_KEY: adapter_state_dict}) if not self._save_adapter_weights_only: # Construct the full state dict with LoRA weights merged into base LLM weights # Move to CPU to avoid a copy on GPU state_dict = {k: v.cpu() for k, v in self._model.state_dict().items()} merged_state_dict = get_merged_lora_ckpt( state_dict, rank=self._lora_rank, alpha=self._lora_alpha, ) ckpt_dict.update({training.MODEL_KEY: merged_state_dict}) adapter_config = { "r": self._lora_rank, "lora_alpha": self._lora_alpha, "target_modules": get_lora_module_names( self._lora_attn_modules, self._apply_lora_to_mlp, self._apply_lora_to_output, ), "peft_type": "LORA", } ckpt_dict.update({training.ADAPTER_CONFIG: adapter_config}) self._checkpointer.save_checkpoint( ckpt_dict, epoch=epoch, intermediate_checkpoint=intermediate_checkpoint, adapter_only=self._save_adapter_weights_only, ) def _loss_step(self, batch: Dict[str, torch.Tensor]) -> torch.Tensor: # Shape [b, s], needed for the loss not the model labels = batch.pop("labels") # run model with self.activations_handling_ctx: logits = self._model(**batch) # Shift labels to compute loss # equivalent to doing labels[..., 1:] and logits[..., :-1, :] # But this way we dont need to slice the logits. We just add an ignore index to labels. labels = torch.hstack( (labels[..., 1:], self.ignore_labels_cache[: labels.shape[0]]) ) if not isinstance(logits, list): labels = labels.reshape(-1) logits = logits.reshape(-1, logits.size(-1)) loss = self._loss_fn(logits, labels) # free logits otherwise it peaks backward memory del logits return loss def train(self) -> None: """ The core training loop. """ if self._compile: log.info( "NOTE: torch.compile is enabled and model is compiled in first forward. Expect a relatively slow first iteration." ) # Initialize tokens count and running loss (for grad accumulation) t0 = time.perf_counter() running_loss = 0 num_tokens = 0 with self._profiler as prof: # 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): pbar = tqdm(total=self._steps_per_epoch) self._dataloader.sampler.set_epoch(curr_epoch) for idx, batch in enumerate(self._dataloader): # Start tracking CUDA memory for active steps for just the first epoch if ( curr_epoch == 0 and self.profiler_profile_memory and idx == self.profiler_wait_steps + self.profiler_warmup_steps and self._device.type == "cuda" ): torch.cuda.memory._record_memory_history() utils.batch_to_device(batch, self._device) # Calculate the number of unmasked tokens in the current batch # and increment the total number of tokens seen in the step current_num_tokens = ( batch["labels"] != self._loss_fn.ignore_index ).sum() num_tokens += current_num_tokens # Loss is normalized by default so we multiply by the number of tokens # This way we can normalize by the total number of tokens if we're accumulating gradients current_loss = self._loss_step(batch) * current_num_tokens running_loss += current_loss current_loss.backward() # Step with optimizer if (idx + 1) % self._gradient_accumulation_steps == 0: training.scale_grads(self._model, 1 / num_tokens) if self._clip_grad_norm is not None: grad_norm = torch.nn.utils.clip_grad_norm_( self._model.parameters(), max_norm=float(self._clip_grad_norm), ) 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.global_step += 1 loss_to_log = running_loss.item() / num_tokens pbar.update(1) pbar.set_description( f"{curr_epoch + 1}|{self.global_step}|Loss: {loss_to_log}" ) # Log per-step metrics if self.global_step % self._log_every_n_steps == 0: time_per_step = time.perf_counter() - t0 log_dict = { "loss": loss_to_log, "lr": self._optimizer.param_groups[0]["lr"], "tokens_per_second_per_gpu": num_tokens / time_per_step, } if ( self._device.type != "cpu" and self._log_peak_memory_stats ): log_dict.update( training.get_memory_stats(device=self._device) ) if self._clip_grad_norm is not None: log_dict.update({"grad_norm": grad_norm}) self._metric_logger.log_dict( log_dict, step=self.global_step, ) # Reset running stats for the next step running_loss = 0 num_tokens = 0 t0 = time.perf_counter() # Stop tracking CUDA memory now that active steps are complete if ( curr_epoch == 0 and self.profiler_profile_memory and idx == self.profiler_wait_steps + self.profiler_warmup_steps + self.profiler_active_steps and self._device.type == "cuda" ): torch.cuda.memory._record_memory_history(enabled=None) # Step the profiler # Note we are stepping each batch, which might not include optimizer step in the trace # if the schedule cycle doesn't align with gradient accumulation. prof.step() if ( (idx + 1) // self._gradient_accumulation_steps ) == self.max_steps_per_epoch: break self.epochs_run += 1 start_save_checkpoint = time.perf_counter() log.info("Starting checkpoint save...") self.save_checkpoint(epoch=curr_epoch) log.info( "Checkpoint saved in {:.2f} seconds.".format( time.perf_counter() - start_save_checkpoint ) ) def cleanup(self) -> None: self._metric_logger.close() @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 """ config.log_config(recipe_name="LoRAFinetuneRecipeSingleDevice", cfg=cfg) recipe = LoRAFinetuneRecipeSingleDevice(cfg=cfg) recipe.setup(cfg=cfg) recipe.train() recipe.cleanup() if __name__ == "__main__": sys.exit(recipe_main())