#!/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 Any, Callable, NamedTuple, Optional import datasets import jax import jax.numpy as jnp import numpy as np import optax import transformers import wandb from datasets import Dataset from distributed_shampoo import GraftingType, distributed_shampoo from flax.core.frozen_dict import FrozenDict, freeze from flax.serialization import from_bytes, to_bytes from flax.training import train_state from flax.training.common_utils import onehot from jax.experimental import PartitionSpec, maps from jax.experimental.pjit import pjit, with_sharding_constraint from tqdm import tqdm from transformers import HfArgumentParser from dalle_mini.data import Dataset from dalle_mini.model import ( DalleBart, DalleBartConfig, DalleBartTokenizer, set_partitions, ) 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"' }, ) 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."}, ) start_preconditioning_step: int = field( default=100, metadata={"help": "Number of steps before starting to update preconditioner."}, ) 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."}, ) mp_devices: Optional[int] = field( default=1, metadata={ "help": "Number of devices required for model parallelism. The other dimension of available devices is used for data parallelism." }, ) 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 ( 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." ) assert ( jax.device_count() % self.mp_devices == 0 ), f"Number of available devices ({jax.device_count()} must be divisible by number of devices used for model parallelism ({self.mp_devices})." self.dp_devices = jax.device_count() // self.mp_devices 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 class MetricsLogger: def __init__(self, state): self.step = int(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 = train_metrics # get state parameters state_dict = { k.split("_")[-1]: getattr(state, k) for k in ["epoch", "train_time", "train_samples"] } # timing metrics new_step = int(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, load_on_cpu=True, ) # 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, load_on_cpu=True, ) else: model = DalleBart( config, seed=training_args.seed_model, dtype=getattr(jnp, model_args.dtype), load_on_cpu=True, ) # Load tokenizer if model_args.tokenizer_name is not None: tokenizer = DalleBartTokenizer.from_pretrained( model_args.tokenizer_name, use_fast=True ) else: tokenizer = DalleBartTokenizer.from_pretrained( model_args.model_name_or_path, use_fast=True, ) # get PartitionSpec for model params (required to be a dict) param_spec = set_partitions(model.params) # convert params to frozen dict model._params = freeze(model.params) # 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 minibatch_size = ( training_args.per_device_train_batch_size * training_args.dp_devices ) batch_size_per_node = minibatch_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 * training_args.dp_devices ) 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() # create adam optimizer if training_args.optim == "distributed_shampoo": # parameters from https://github.com/tensorflow/lingvo/blob/03ee9d7cd50764b0424c7c863733c91fc0b053ec/lingvo/jax/optimizers.py#L729 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, start_preconditioning_step=training_args.start_preconditioning_step, 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, statistics_partition_spec=PartitionSpec(None, "batch", None), preconditioner_partition_spec=PartitionSpec("batch", None, None), num_devices_for_pjit=training_args.dp_devices, shard_optimizer_states=True, 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, ) # get the real optimizer and helper functions update_fn = optimizer.update optimizer = optimizer.init(model.params) opt_fn = NamedTuple("opt_fn", pspec_fn=Any, shape_and_dtype_fn=Any)( optimizer.pspec_fn, optimizer.shape_and_dtype_fn ) optimizer = optax.GradientTransformation(optimizer.init_fn, update_fn) 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, ) 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, clipping_threshold=training_args.max_grad_norm, ) # get PartitionSpec for optimizer state def get_opt_state_spec_and_shape(param_spec): if training_args.optim in ["adam", "adafactor"]: # get opt_state shape without actual init opt_state_shape = jax.eval_shape(optimizer.init, model.params) if training_args.optim == "adam": def _opt_state_spec_per_leaf(x): if isinstance(x, FrozenDict): # variables with same structure as params return param_spec else: # other variables such as count return None opt_state_spec = jax.tree_map( _opt_state_spec_per_leaf, opt_state_shape, # return None spec for empty elements is_leaf=lambda x: isinstance(x, (FrozenDict, optax.EmptyState)), ) elif training_args.optim == "adafactor": # factorized state must be replicated (rank different than params) opt_state_spec = None elif training_args.optim == "distributed_shampoo": opt_state_spec = opt_fn.pspec_fn( params=model.params, params_partition_spec=param_spec, partition_spec_for_statistics=PartitionSpec(None, "batch", None), ) opt_state_shape = opt_fn.shape_and_dtype_fn(model.params) else: raise NotImplementedError return opt_state_spec, opt_state_shape opt_state_spec, opt_state_shape = get_opt_state_spec_and_shape(param_spec) # create a mesh mesh_shape = (training_args.dp_devices, training_args.mp_devices) devices = np.asarray(jax.devices()).reshape(*mesh_shape) mesh = maps.Mesh(devices, ("batch", "mp")) # Create state spec state_spec = TrainState( params=param_spec, opt_state=opt_state_spec, dropout_rng=None, step=None, epoch=None, train_time=None, train_samples=None, apply_fn=model.__call__, tx=optimizer, ) # create training state with maps.mesh(mesh.devices, mesh.axis_names): if training_args.resume_from_checkpoint is None: def init_state(params): return TrainState.create( apply_fn=model.__call__, tx=optimizer, params=params, dropout_rng=dropout_rng, ) state = pjit( init_state, in_axis_resources=(param_spec,), out_axis_resources=state_spec, donate_argnums=(0,), )(model.params) else: # restore opt_state with (Path(artifact_dir) / "opt_state.msgpack").open("rb") as f: opt_state = from_bytes(opt_state_shape, f.read()) # restore other attributes with (Path(artifact_dir) / "training_state.json").open("r") as f: attr_state = json.load(f) def restore_state(params, opt_state): return TrainState( apply_fn=model.__call__, tx=optimizer, params=params, opt_state=opt_state, dropout_rng=dropout_rng, **attr_state, ) state = pjit( restore_state, in_axis_resources=(param_spec, opt_state_spec), out_axis_resources=state_spec, donate_argnums=(0, 1), )(model.params, opt_state) # remove opt_state from CPU del opt_state # free memory del model._params # define batch specs keys = ["attention_mask", "decoder_input_ids", "input_ids", "labels"] batch_spec = freeze({k: PartitionSpec("batch") for k in keys}) grad_batch_spec = freeze({k: PartitionSpec(None, "batch") for k in keys}) # 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): # batch is (gradient_accumulation_steps, minibatch_size, ...) # check correct batch shape during compilation assert batch["labels"].shape[0:3] == ( training_args.gradient_accumulation_steps, training_args.dp_devices, training_args.per_device_train_batch_size, ), f"Expected label batch of shape dp_devices x gradient_acculumation x batch_per_device and got {batch['labels'].shape}" # get a minibatch (one gradient accumulation slice) def get_minibatch(batch, grad_idx): return jax.tree_map( lambda x: jax.lax.dynamic_index_in_dim(x, grad_idx, keepdims=False), batch, ) def compute_loss(params, minibatch, dropout_rng): # minibatch has dim (batch_size, ...) 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) def loss_and_grad(grad_idx, dropout_rng): # minibatch at grad_idx, shape (dp_devices, per_device_train_batch_size, ...) minibatch = get_minibatch(batch, grad_idx) # ensure batch is sharded over devices minibatch = jax.tree_map( lambda x: with_sharding_constraint(x, PartitionSpec("batch")), minibatch ) # calculate loss and grads independently per dp_device loss_grads = jax.vmap(grad_fn, in_axes=(None, 0, None), out_axes=(0, 0))( state.params, minibatch, dropout_rng ) # ensure they are sharded over devices loss_grads = jax.tree_map( lambda x: with_sharding_constraint(x, PartitionSpec("batch")), loss_grads, ) # average across all devices loss_grads = jax.tree_map(lambda x: jnp.mean(x, axis=0), loss_grads) # return loss and grads return loss_grads # create a new rng dropout_rng, _ = jax.random.split(state.dropout_rng) # use a different rng per node dropout_rng = jax.random.fold_in(dropout_rng, jax.process_index()) if training_args.gradient_accumulation_steps == 1: def batch_step(dropout_rng): dropout_rng, new_dropout_rng = jax.random.split(dropout_rng) loss_grad = loss_and_grad(0, dropout_rng) return loss_grad, new_dropout_rng loss_grad, dropout_rng = batch_step(dropout_rng) else: # create initial state for per_minibatch_step loop init_cumul_loss_grad = ( 0.0, jax.tree_map(jnp.zeros_like, state.params), ) init_minibatch_step = (init_cumul_loss_grad, dropout_rng) # accumulate gradients def cumul_minibatch_step(grad_idx, cumul_loss_grad_dropout): cumul_loss_grad, dropout_rng = cumul_loss_grad_dropout dropout_rng, new_dropout_rng = jax.random.split(dropout_rng) loss_grad = loss_and_grad(grad_idx, dropout_rng) cumul_loss_grad = jax.tree_map(jnp.add, cumul_loss_grad, loss_grad) return cumul_loss_grad, new_dropout_rng # loop over gradients loss_grad, dropout_rng = jax.lax.fori_loop( 0, training_args.gradient_accumulation_steps, cumul_minibatch_step, init_minibatch_step, ) # sum -> mean loss_grad = jax.tree_map( lambda x: x / training_args.gradient_accumulation_steps, loss_grad ) # update state loss, grads = loss_grad state = state.apply_gradients( grads=grads, dropout_rng=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), } return state, metrics # Define eval fn def eval_step(params, batch): batch, labels = batch.pop("labels") logits = model(**batch, params=params, train=False)[0] loss = loss_fn(logits, labels) return loss # Create parallel version of the train and eval step p_train_step = pjit( train_step, in_axis_resources=(state_spec, grad_batch_spec, None), out_axis_resources=(state_spec, None), donate_argnums=(0,), ) p_eval_step = pjit( eval_step, in_axis_resources=(param_spec, batch_spec), out_axis_resources=None, ) 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(), } ) def run_evaluation(): # ======================== Evaluating ============================== if training_args.do_eval: eval_loader = dataset.dataloader("eval", eval_batch_size) eval_steps = ( len_eval_dataset // eval_batch_size if len_eval_dataset is not None else None ) eval_loss = [] for batch in tqdm( eval_loader, desc="Evaluating...", position=2, leave=False, total=eval_steps, ): # freeze batch to pass safely to JAX transforms batch = freeze(batch) # accumulate losses async eval_loss.append(p_eval_step(state.params, batch)) # get the mean of the loss eval_loss = jnp.stack(eval_loss) eval_loss = jnp.mean(eval_loss) eval_metrics = {"loss": eval_loss} # log metrics metrics_logger.log(eval_metrics, step=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(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 = jax.device_get(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(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 step = int(state.step) with maps.mesh(mesh.devices, mesh.axis_names): for epoch in epochs: state.replace(epoch=epoch) # ======================== Training ================================ metrics_logger.log({"train/epoch": epoch}, step=state.step) # Generate an epoch by shuffling sampling indices from the train dataset train_loader = dataset.dataloader( "train", batch_size_per_node, 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 # reshape data into (gradient_accumulation_steps, dp_devices, batch_per_dp, ...) batch = jax.tree_map( lambda x: x.reshape( ( training_args.gradient_accumulation_steps, training_args.dp_devices, training_args.per_device_train_batch_size, ) + x.shape[1:] ), batch, ) # freeze batch to pass safely to jax transforms batch = freeze(batch) # train step state, train_metrics = p_train_step(state, batch, delta_time) step += 1 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 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()