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#!/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
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."
},
)
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: int = field(
default=8, metadata={"help": "Batch size per GPU/TPU/CPU for evaluation."}
)
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)."
},
)
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."
},
)
num_train_epochs: float = field(
default=3.0, metadata={"help": "Total number of training epochs to perform."}
)
warmup_steps: int = field(
default=0, metadata={"help": "Linear warmup over warmup_steps."}
)
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."},
)
def __post_init__(self):
assert self.optim in [
"distributed_shampoo",
"adam",
"adafactor",
], f"Selected optimizer not supported: {self.optim}"
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()
if (
os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir)
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty."
"Use --overwrite_output_dir to overcome."
)
# 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()}")
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="dalle-mini",
project="dalle-mini",
job_type="Seq2Seq",
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 = AutoTokenizer.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 = DalleBartConfig.from_pretrained(model_args.model_name_or_path)
# 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 = AutoTokenizer.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 = int(training_args.num_train_epochs)
# batch size per node
train_batch_size = (
int(training_args.per_device_train_batch_size) * jax.local_device_count()
)
batch_size_per_update = (
train_batch_size
* training_args.gradient_accumulation_steps
* jax.process_count()
)
eval_batch_size = (
int(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 // (train_batch_size * jax.process_count())
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
if training_args.weight_decay is not None
else 0.0,
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
if training_args.weight_decay is not None
else 0.0,
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,
)
# add gradient accumulation
if training_args.gradient_accumulation_steps > 1:
optimizer = optax.MultiSteps(
optimizer, training_args.gradient_accumulation_steps
)
# 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, batch):
labels = batch.pop("labels")
logits = state.apply_fn(
**batch, params=params, dropout_rng=dropout_rng, train=True
)[0]
loss = loss_fn(logits, labels)
return loss
grad_fn = jax.value_and_grad(compute_loss)
loss, grads = grad_fn(state.params, batch)
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 + train_batch_size * jax.process_count(),
)
metrics = {
"loss": loss,
"learning_rate": learning_rate_fn(
state.step // training_args.gradient_accumulation_steps
),
}
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" Instantaneous batch size per device = {training_args.per_device_train_batch_size}"
)
logger.info(f" Number of devices = {jax.device_count()}")
logger.info(
f" Total train batch size (w. parallel, distributed & gradient accumulation) = {batch_size_per_update}"
)
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_update": batch_size_per_update,
"num_params": num_params,
}
)
# replicate state on each device
state = state.replicate()
def run_evaluation():
# ======================== Evaluating ==============================
eval_metrics = []
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
)
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", train_batch_size, 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()
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