#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Team All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning the library models for seq2seq, text to image. Script adapted from run_summarization_flax.py """ import json import logging import os import sys import time from dataclasses import asdict, dataclass, field from pathlib import Path from typing import Callable, Optional import datasets import jax import jax.numpy as jnp import optax import transformers import wandb from datasets import Dataset from distributed_shampoo import GraftingType, distributed_shampoo from flax import jax_utils, traverse_util from flax.jax_utils import unreplicate from flax.serialization import from_bytes, to_bytes from flax.training import train_state from flax.training.common_utils import get_metrics, onehot, shard_prng_key from tqdm import tqdm from transformers import AutoTokenizer, HfArgumentParser from dalle_mini.data import Dataset from dalle_mini.model import DalleBart, DalleBartConfig, DalleBartTokenizer logger = logging.getLogger(__name__) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. """ model_name_or_path: Optional[str] = field( default=None, metadata={ "help": "The model checkpoint for weights initialization. " "Don't set if you want to train a model from scratch. " "W&B artifact references are supported in addition to the sources supported by `PreTrainedModel`." }, ) config_name: Optional[str] = field( default=None, metadata={ "help": "Pretrained config name or path if not the same as model_name_or_path" }, ) tokenizer_name: Optional[str] = field( default=None, metadata={ "help": "Pretrained tokenizer name or path if not the same as model_name_or_path" }, ) dtype: Optional[str] = field( default="float32", metadata={ "help": "Floating-point format in which the computations will be performed (not the model weights). Choose one of `[float32, float16, bfloat16]`." }, ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ text_column: Optional[str] = field( default="caption", metadata={ "help": "The name of the column in the datasets containing the full texts (for summarization)." }, ) encoding_column: Optional[str] = field( default="encoding", metadata={ "help": "The name of the column in the datasets containing the image encodings." }, ) dataset_repo_or_path: str = field( default=None, metadata={"help": "The dataset repository containing encoded files."}, ) train_file: Optional[str] = field( default=None, metadata={ "help": "The input training data file (glob & braceexpand acceptable)." }, ) validation_file: Optional[str] = field( default=None, metadata={ "help": "An optional input evaluation data file (glob & braceexpand acceptable)." }, ) # data loading should not be a bottleneck so we use "streaming" mode by default streaming: Optional[bool] = field( default=True, metadata={"help": "Whether to stream the dataset."}, ) use_auth_token: Optional[bool] = field( default=False, metadata={ "help": "Whether to use the authentication token for private datasets." }, ) shard_by_host: Optional[bool] = field( default=False, metadata={ "help": "Whether to shard data files by host in multi-host environments." }, ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": "For debugging purposes or quicker training, truncate the number of training examples." }, ) max_eval_samples: Optional[int] = field( default=None, metadata={ "help": "For debugging purposes or quicker training, truncate the number of evaluation examples." }, ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={ "help": "The number of processes to use for the preprocessing. Not used in streaming mode." }, ) overwrite_cache: bool = field( default=False, metadata={ "help": "Overwrite the cached training and evaluation sets. Not used in streaming mode." }, ) # default seed of None ensures we don't repeat the same items if script was interrupted during an epoch seed_dataset: int = field( default=None, metadata={ "help": "Random seed for the dataset that will be set at the beginning of training." }, ) def __post_init__(self): if self.dataset_repo_or_path is None: raise ValueError("Need a dataset repository or path.") @dataclass class TrainingArguments: """ Arguments pertaining to training parameters. """ output_dir: str = field( metadata={ "help": "The output directory where the model predictions and checkpoints will be written." }, ) overwrite_output_dir: bool = field( default=False, metadata={ "help": ( "Overwrite the content of the output directory. " "Use this to continue training if output_dir points to a checkpoint directory." ) }, ) do_train: bool = field(default=False, metadata={"help": "Whether to run training."}) do_eval: bool = field( default=False, metadata={"help": "Whether to run eval on the validation set."} ) per_device_train_batch_size: int = field( default=8, metadata={"help": "Batch size per GPU/TPU/CPU for training."} ) per_device_eval_batch_size: Optional[int] = field( default=None, metadata={ "help": "Batch size per GPU/TPU/CPU for evaluation. Same as training batch size if not set." }, ) gradient_accumulation_steps: int = field( default=1, metadata={ "help": "Number of updates steps to accumulate before performing an update pass." }, ) learning_rate: float = field( default=5e-5, metadata={"help": "The initial learning rate."} ) optim: str = field( default="distributed_shampoo", metadata={ "help": 'The optimizer to use. Can be "distributed_shampoo" (default), "adam" or "adafactor"' }, ) weight_decay: float = field(default=None, metadata={"help": "Weight decay."}) beta1: float = field( default=0.9, metadata={"help": "Beta1 for Adam & Distributed Shampoo."}, ) beta2: float = field( default=0.999, metadata={"help": "Beta2 for for Adam & Distributed Shampoo."}, ) adam_epsilon: float = field( default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."} ) max_grad_norm: float = field( default=1.0, metadata={"help": "Max gradient norm for Adafactor."} ) block_size: int = field( default=1024, metadata={"help": "Chunked size for large layers with Distributed Shampoo."}, ) preconditioning_compute_steps: int = field( default=10, metadata={"help": "Number of steps to update preconditioner."} ) skip_preconditioning_dim_size_gt: int = field( default=4096, metadata={"help": "Max size for preconditioning with Distributed Shampoo."}, ) optim_quantized: bool = field( default=False, metadata={ "help": "Whether to quantize optimizer (only supported with Distributed Shampoo)." }, ) num_train_epochs: int = field( default=3, metadata={"help": "Total number of training epochs to perform."} ) warmup_steps: int = field( default=0, metadata={"help": "Linear warmup over warmup_steps."} ) lr_decay: str = field( default=None, metadata={ "help": "Decay to be used in the learning rate scheduler. Can be None (default), linear or exponential." }, ) lr_transition_steps: int = field( default=None, metadata={ "help": "Number of transition steps associated with learning rate decay when using exponential decay." }, ) lr_decay_rate: float = field( default=None, metadata={ "help": "Decay rate associated with learning rate when using exponential decay." }, ) lr_staircase: bool = field( default=False, metadata={ "help": "Whether to use staircase or continuous learning rate when using exponential decay." }, ) logging_steps: int = field( default=40, metadata={"help": "Log every X updates steps."} ) eval_steps: int = field( default=400, metadata={"help": "Run an evaluation every X steps."} ) save_steps: int = field( default=4000, metadata={"help": "Save checkpoint every X updates steps."} ) log_model: bool = field( default=False, metadata={"help": "Log model to wandb at `save_steps` frequency."}, ) seed_model: int = field( default=42, metadata={ "help": "Random seed for the model that will be set at the beginning of training." }, ) resume_from_checkpoint: Optional[str] = field( default=None, metadata={"help": "Reference to a wandb artifact for resuming training."}, ) wandb_entity: Optional[str] = field( default=None, metadata={"help": "The wandb entity to use (for teams)."}, ) wandb_project: str = field( default="dalle-mini", metadata={"help": "The name of the wandb project."}, ) wandb_job_type: str = field( default="Seq2Seq", metadata={"help": "The name of the wandb job type."}, ) assert_TPU_available: bool = field( default=False, metadata={"help": "Verify that TPU is not in use."}, ) def __post_init__(self): assert self.optim in [ "distributed_shampoo", "adam", "adafactor", ], f"Selected optimizer not supported: {self.optim}" if self.per_device_eval_batch_size is None: self.per_device_eval_batch_size = self.per_device_train_batch_size if self.weight_decay is None: if self.optim in ["distributed_shampoo", "adam"]: self.weight_decay = 0.0 if ( os.path.exists(self.output_dir) and os.listdir(self.output_dir) and self.do_train and not self.overwrite_output_dir ): raise ValueError( f"Output directory ({self.output_dir}) already exists and is not empty." "Use --overwrite_output_dir to overcome." ) class TrainState(train_state.TrainState): dropout_rng: jnp.ndarray = None epoch: int = 0 train_time: float = 0.0 # total time the model trained train_samples: int = 0 # number of samples seen def replicate(self): return jax_utils.replicate(self).replace( dropout_rng=shard_prng_key(self.dropout_rng) ) def restore_state(self, artifact_dir): # restore optimizer state with (Path(artifact_dir) / "opt_state.msgpack").open("rb") as f: new_opt_state = from_bytes(self.opt_state, f.read()) # restore other parameters with (Path(artifact_dir) / "training_state.json").open("r") as f: training_state = json.load(f) # replace state return self.replace( opt_state=new_opt_state, step=training_state["step"], train_time=training_state["train_time"], train_samples=training_state["train_samples"], ) class MetricsLogger: def __init__(self, state): self.step = state.step self.time = time.perf_counter() def get_all_train_metrics(self, train_metrics, state): """Make a dict of training metrics to be logged""" metrics = unreplicate(train_metrics) # get state parameters state_dict = { k.split("_")[-1]: unreplicate(getattr(state, k)) for k in ["epoch", "train_time", "train_samples"] } # timing metrics new_step = int(unreplicate(state.step)) new_time = time.perf_counter() if new_step > self.step: time_per_step = (new_time - self.time) / (new_step - self.step) self.step = new_step self.time = new_time state_dict["time_per_step"] = time_per_step return {**metrics, **state_dict} @staticmethod def log(metrics, step=None, prefix=None): if jax.process_index() == 0: log_metrics = { f"{prefix}/{k}" if prefix is not None else k: v for k, v in metrics.items() } if step is not None: log_metrics["train/step"] = step wandb.log(log_metrics) def main(): # See all possible arguments by passing the --help flag to this script. parser = HfArgumentParser( (ModelArguments, DataTrainingArguments, TrainingArguments) ) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file( json_file=os.path.abspath(sys.argv[1]) ) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) # Setup logging, we only want one process per machine to log things on the screen. logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR) if jax.process_index() == 0: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # Set the verbosity to info of the Transformers logger (on main process only): logger.info(f"Training/evaluation parameters {training_args}") # Load dataset dataset = Dataset( **asdict(data_args), do_train=training_args.do_train, do_eval=training_args.do_eval, ) logger.info(f"Local TPUs: {jax.local_device_count()}") logger.info(f"Global TPUs: {jax.device_count()}") if training_args.assert_TPU_available: assert ( jax.local_device_count() == 8 ), "TPUs in use, please check running processes" # Set up wandb run if jax.process_index() == 0: wandb.init( entity=training_args.wandb_entity, project=training_args.wandb_project, job_type=training_args.wandb_job_type, config=parser.parse_args(), ) if training_args.resume_from_checkpoint is not None: if jax.process_index() == 0: artifact = wandb.run.use_artifact(training_args.resume_from_checkpoint) else: artifact = wandb.Api().artifact(training_args.resume_from_checkpoint) artifact_dir = artifact.download() # load model model = DalleBart.from_pretrained( artifact_dir, dtype=getattr(jnp, model_args.dtype), abstract_init=True, ) # avoid OOM on TPU: see https://github.com/google/flax/issues/1658 print(model.params) # load tokenizer tokenizer = DalleBartTokenizer.from_pretrained( artifact_dir, use_fast=True, ) else: # Set up our new model config if model_args.config_name: config = DalleBartConfig.from_pretrained(model_args.config_name) else: config = None # Load or create new model if model_args.model_name_or_path: model = DalleBart.from_pretrained( model_args.model_name_or_path, config=config, seed=training_args.seed_model, dtype=getattr(jnp, model_args.dtype), abstract_init=True, ) # avoid OOM on TPU: see https://github.com/google/flax/issues/1658 print(model.params) else: model = DalleBart( config, seed=training_args.seed_model, dtype=getattr(jnp, model_args.dtype), ) # Load tokenizer if model_args.tokenizer_name is not None: tokenizer = AutoTokenizer.from_pretrained( model_args.tokenizer_name, use_fast=True ) else: tokenizer = DalleBartTokenizer.from_pretrained( model_args.model_name_or_path, use_fast=True, ) # Preprocessing the datasets. # We need to normalize and tokenize inputs and targets. dataset.preprocess( tokenizer=tokenizer, decoder_start_token_id=model.config.decoder_start_token_id, normalize_text=model.config.normalize_text, max_length=model.config.max_text_length, ) # Initialize our training rng = jax.random.PRNGKey(training_args.seed_model) rng, dropout_rng = jax.random.split(rng) # Store some constant num_epochs = training_args.num_train_epochs # batch size per node train_batch_size = ( training_args.per_device_train_batch_size * jax.local_device_count() ) batch_size_per_node = train_batch_size * training_args.gradient_accumulation_steps batch_size_per_step = batch_size_per_node * jax.process_count() eval_batch_size = ( training_args.per_device_eval_batch_size * jax.local_device_count() ) len_train_dataset, len_eval_dataset = dataset.length steps_per_epoch = ( len_train_dataset // batch_size_per_node if len_train_dataset is not None else None ) num_train_steps = ( steps_per_epoch * num_epochs if steps_per_epoch is not None else None ) num_params = model.num_params # Create learning rate schedule def create_learning_rate_fn() -> Callable[[int], jnp.array]: """Create the learning rate function.""" warmup_fn = optax.linear_schedule( init_value=0.0, end_value=training_args.learning_rate, transition_steps=training_args.warmup_steps, ) if training_args.lr_decay is None: return warmup_fn elif training_args.lr_decay == "linear": assert ( num_train_steps is not None ), "linear decay requires knowing the dataset length" decay_fn = optax.linear_schedule( init_value=training_args.learning_rate, end_value=0, transition_steps=num_train_steps - training_args.warmup_steps, ) elif training_args.lr_decay == "exponential": decay_fn = optax.exponential_decay( init_value=training_args.learning_rate, transition_steps=training_args.lr_transition_steps, decay_rate=training_args.lr_decay_rate, staircase=training_args.lr_staircase, ) schedule_fn = optax.join_schedules( schedules=[warmup_fn, decay_fn], boundaries=[training_args.warmup_steps] ) return schedule_fn learning_rate_fn = create_learning_rate_fn() # We use Optax's "masking" functionality to not apply weight decay # to bias and LayerNorm scale parameters. decay_mask_fn returns a # mask boolean with the same structure as the parameters. # The mask is True for parameters that should be decayed. # Note that this mask is specifically adapted for FlaxBart. def decay_mask_fn(params): flat_params = traverse_util.flatten_dict(params) layer_norm_params = [ (name, "scale") for name in [ "self_attn_layer_norm", "layernorm_embedding", "final_layer_norm", ] ] flat_mask = { path: (path[-1] != "bias" and path[-2:] not in layer_norm_params) for path in flat_params } return traverse_util.unflatten_dict(flat_mask) # create adam optimizer if training_args.optim == "distributed_shampoo": # parameters from https://github.com/tensorflow/lingvo/blob/03ee9d7cd50764b0424c7c863733c91fc0b053ec/lingvo/jax/optimizers.py#L729 # Notes: # - mask for weight decay is not implemented optimizer = distributed_shampoo( learning_rate_fn, block_size=training_args.block_size, beta1=training_args.beta1, beta2=training_args.beta2, diagonal_epsilon=1e-10, matrix_epsilon=1e-8, weight_decay=training_args.weight_decay, start_preconditioning_step=training_args.warmup_steps, preconditioning_compute_steps=training_args.preconditioning_compute_steps, statistics_compute_steps=1, best_effort_shape_interpretation=True, graft_type=GraftingType.RMSPROP_NORMALIZED, nesterov=False, exponent_override=0, batch_axis_name="batch", inverse_failure_threshold=0.1, moving_average_for_momentum=True, skip_preconditioning_dim_size_gt=training_args.skip_preconditioning_dim_size_gt, clip_by_scaled_gradient_norm=None, precision=jax.lax.Precision.HIGHEST, best_effort_memory_usage_reduction=training_args.optim_quantized, ) elif training_args.optim == "adam": optimizer = optax.adamw( learning_rate=learning_rate_fn, b1=training_args.beta1, b2=training_args.beta2, eps=training_args.adam_epsilon, weight_decay=training_args.weight_decay, mask=decay_mask_fn, ) elif training_args.optim == "adafactor": # We use the default parameters here to initialize adafactor, # For more details about the parameters please check https://github.com/deepmind/optax/blob/ed02befef9bf81cbbf236be3d2b0e032e9ed4a40/optax/_src/alias.py#L74 optimizer = optax.adafactor( learning_rate=learning_rate_fn, weight_decay_rate=training_args.weight_decay, weight_decay_mask=decay_mask_fn, clipping_threshold=training_args.max_grad_norm, ) # Setup train state state = TrainState.create( apply_fn=model.__call__, params=model.params, tx=optimizer, dropout_rng=dropout_rng, ) if training_args.resume_from_checkpoint is not None: # restore optimizer state and other parameters # we currently ignore partial epoch training: see https://github.com/borisdayma/dalle-mini/issues/105 state = state.restore_state(artifact_dir) # label smoothed cross entropy def loss_fn(logits, labels): loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) loss = loss.mean() return loss # Define gradient update step fn def train_step(state, batch, delta_time): dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng) def compute_loss(params, minibatch): labels = minibatch.pop("labels") logits = state.apply_fn( **minibatch, params=params, dropout_rng=dropout_rng, train=True )[0] return loss_fn(logits, labels) grad_fn = jax.value_and_grad(compute_loss) if training_args.gradient_accumulation_steps == 1: minibatch = jax.tree_map(lambda x: x[0], batch) loss, grads = grad_fn(state.params, minibatch) else: def _cumul_loss_grads(i, cumul_loss_grads): minibatch = jax.tree_map(lambda x: x[i], batch) return jax.tree_map( lambda x, y: x + y, cumul_loss_grads, grad_fn(state.params, minibatch), ) init_loss_grads = ( 0.0, jax.tree_map(jnp.zeros_like, state.params), ) loss, grads = jax.tree_map( lambda x: x / training_args.gradient_accumulation_steps, jax.lax.fori_loop( 0, training_args.gradient_accumulation_steps, _cumul_loss_grads, init_loss_grads, ), ) grads = jax.lax.pmean(grads, "batch") state = state.apply_gradients( grads=grads, dropout_rng=new_dropout_rng, train_time=state.train_time + delta_time, train_samples=state.train_samples + batch_size_per_step, ) metrics = { "loss": loss, "learning_rate": learning_rate_fn(state.step), } metrics = jax.lax.pmean(metrics, axis_name="batch") return state, metrics # Define eval fn def eval_step(params, batch): labels = batch.pop("labels") logits = model(**batch, params=params, train=False)[0] loss = loss_fn(logits, labels) # summarize metrics metrics = {"loss": loss} metrics = jax.lax.pmean(metrics, axis_name="batch") return metrics # Create parallel version of the train and eval step p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,)) p_eval_step = jax.pmap(eval_step, "batch") logger.info("***** Running training *****") logger.info(f" Num examples = {len_train_dataset}") logger.info(f" Num Epochs = {num_epochs}") logger.info( f" Batch size per device = {training_args.per_device_train_batch_size}" ) logger.info(f" Number of devices = {jax.device_count()}") logger.info( f" Gradient accumulation steps = {training_args.gradient_accumulation_steps}" ) logger.info(f" Batch size per update = {batch_size_per_step}") logger.info(f" Model parameters = {num_params:,}") epochs = tqdm( range(state.epoch, num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0 ) metrics_logger = MetricsLogger(state) if jax.process_index() == 0: # set default x-axis as 'train/step' metrics_logger.log({}, step=state.step) wandb.define_metric("*", step_metric="train/step") # add interesting config parameters wandb.config.update( { "len_train_dataset": len_train_dataset, "len_eval_dataset": len_eval_dataset, "batch_size_per_step": batch_size_per_step, "num_params": num_params, "num_devices": jax.device_count(), } ) # replicate state on each device state = state.replicate() def run_evaluation(): # ======================== Evaluating ============================== eval_metrics = [] if training_args.do_eval: eval_loader = dataset.dataloader( "eval", training_args.per_device_eval_batch_size ) eval_steps = ( len_eval_dataset // eval_batch_size if len_eval_dataset is not None else None ) for batch in tqdm( eval_loader, desc="Evaluating...", position=2, leave=False, total=eval_steps, ): # Model forward metrics = p_eval_step(state.params, batch) eval_metrics.append(metrics) # normalize eval metrics eval_metrics = get_metrics(eval_metrics) eval_metrics = jax.tree_map(jnp.mean, eval_metrics) # log metrics metrics_logger.log( eval_metrics, step=unreplicate(state.step), prefix="eval" ) # Print metrics and update progress bar desc = f"Epoch... ({epoch + 1}/{num_epochs} | Eval Loss: {eval_metrics['loss']})" epochs.write(desc) epochs.desc = desc return eval_metrics def run_save_model(state, eval_metrics=None): if jax.process_index() == 0: params = jax.device_get(unreplicate(state.params)) # save model locally model.save_pretrained( training_args.output_dir, params=params, ) # save tokenizer tokenizer.save_pretrained(training_args.output_dir) # save state opt_state = unreplicate(state.opt_state) with (Path(training_args.output_dir) / "opt_state.msgpack").open("wb") as f: f.write(to_bytes(opt_state)) state_dict = { k: jax.device_get(unreplicate(getattr(state, k))).item() for k in ["step", "epoch", "train_time", "train_samples"] } with (Path(training_args.output_dir) / "training_state.json").open( "w" ) as f: json.dump( state_dict, f, ) if jax.process_index() == 0: # save to W&B if training_args.log_model: # save some space c = wandb.wandb_sdk.wandb_artifacts.get_artifacts_cache() c.cleanup(wandb.util.from_human_size("10GB")) metadata = dict(state_dict) metadata["num_params"] = num_params if eval_metrics is not None: metadata["eval"] = eval_metrics artifact = wandb.Artifact( name=f"model-{wandb.run.id}", type="bart_model", metadata=metadata, ) artifact.add_file( str(Path(training_args.output_dir) / "flax_model.msgpack") ) artifact.add_file( str(Path(training_args.output_dir) / "config.json") ) artifact.add_file( str(Path(training_args.output_dir) / "tokenizer.json") ) artifact.add_file( str(Path(training_args.output_dir) / "tokenizer_config.json") ) artifact.add_file( str(Path(training_args.output_dir) / "vocab.json") ) artifact.add_file( str(Path(training_args.output_dir) / "merges.txt") ) artifact.add_file( str(Path(training_args.output_dir) / "special_tokens_map.json") ) artifact.add_file( str(Path(training_args.output_dir) / "opt_state.msgpack") ) artifact.add_file( str(Path(training_args.output_dir) / "training_state.json") ) wandb.run.log_artifact(artifact) # init variables last_time = time.perf_counter() train_metrics = None for epoch in epochs: state.replace(epoch=jax_utils.replicate(epoch)) # ======================== Training ================================ metrics_logger.log({"train/epoch": epoch}, step=unreplicate(state.step)) # Generate an epoch by shuffling sampling indices from the train dataset train_loader = dataset.dataloader( "train", training_args.per_device_train_batch_size, training_args.gradient_accumulation_steps, epoch, ) # train for batch in tqdm( train_loader, desc="Training...", position=1, leave=False, total=steps_per_epoch, ): # calculate delta time (we have a lag of one step but it's ok) new_time = time.perf_counter() delta_time = new_time - last_time last_time = new_time # train step state, train_metrics = p_train_step( state, batch, jax_utils.replicate(delta_time) ) step = unreplicate(state.step) if step % training_args.logging_steps == 0 and jax.process_index() == 0: all_metrics = metrics_logger.get_all_train_metrics(train_metrics, state) metrics_logger.log(all_metrics, step=step, prefix="train") eval_metrics = None if training_args.eval_steps and step % training_args.eval_steps == 0: eval_metrics = run_evaluation() if step % training_args.save_steps == 0: run_save_model(state, eval_metrics) # log final train metrics if train_metrics is not None: all_metrics = metrics_logger.get_all_train_metrics(train_metrics, state) metrics_logger.log(all_metrics, step=step, prefix="train") epochs.write( f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {train_metrics['loss']}, Learning Rate: {train_metrics['learning_rate']})" ) # Final evaluation eval_metrics = run_evaluation() # save checkpoint after each epoch run_save_model(state, eval_metrics) if __name__ == "__main__": main()