dalle-mini / dev /seq2seq /run_seq2seq_flax.py
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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 os
import logging
import sys
import time
from dataclasses import dataclass, field
from pathlib import Path
from typing import Callable, Optional
import json
import datasets
from datasets import Dataset
from tqdm import tqdm
from dataclasses import asdict
import jax
import jax.numpy as jnp
import optax
import transformers
from flax import jax_utils, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.jax_utils import unreplicate
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard_prng_key
from transformers import (
AutoTokenizer,
HfArgumentParser,
)
from transformers.models.bart.modeling_flax_bart import BartConfig
import wandb
from dalle_mini.data import Dataset
from dalle_mini.model import CustomFlaxBartForConditionalGeneration
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"
},
)
image_vocab_size: Optional[int] = field(
default=None,
metadata={"help": "Vocab size of image encoder"},
)
image_length: Optional[int] = field(
default=None,
metadata={"help": "Number of tokens per image"},
)
tokenizer_name: Optional[str] = field(
default=None,
metadata={
"help": "Pretrained tokenizer name or path if not the same as model_name_or_path"
},
)
normalize_text: Optional[bool] = field(
default=None,
metadata={
"help": "Whether to normalize text or not. By default, we refer to base model or don't normalize for new models."
},
)
dtype: Optional[str] = field(
default="float32",
metadata={
"help": "Floating-point format in which the model weights should be initialized and trained. 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 acceptable)."},
)
validation_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input evaluation data file (glob acceptable)."},
)
dataset_type: str = field(
default="datasets",
metadata={"help": "Either 🤗 'dataset' (default) or 'webdataset'."},
)
# data loading should not be a bottleneck so we use "streaming" mode by default
streaming: bool = field(
default=True,
metadata={"help": "Whether to stream the dataset."},
)
use_auth_token: bool = field(
default=False,
metadata={
"help": "Whether to use the authentication token for private datasets."
},
)
max_source_length: Optional[int] = field(
default=128,
metadata={
"help": "The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
},
)
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 dev set."}
)
per_device_train_batch_size: int = field(
default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."}
)
per_device_eval_batch_size: int = field(
default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}
)
gradient_accumulation_steps: int = field(
default=1,
metadata={
"help": "Number of updates steps to accumulate before performing a backward/update pass."
},
)
learning_rate: float = field(
default=5e-5, metadata={"help": "The initial learning rate."}
)
adafactor: bool = field(
default=False,
metadata={"help": "Whether or not to replace AdamW by Adafactor."},
)
weight_decay: float = field(
default=None, metadata={"help": "Weight decay if we apply some."}
)
adam_beta1: float = field(
default=0.9, metadata={"help": "Beta1 for AdamW optimizer"}
)
adam_beta2: float = field(
default=0.999, metadata={"help": "Beta2 for AdamW optimizer"}
)
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."}
)
use_decay: bool = field(
default=False,
metadata={"help": "Whether to use decay in the learning rate scheduler."},
)
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."
},
)
push_to_hub: bool = field(
default=False,
metadata={
"help": "Whether or not to upload the trained model to the model hub after training."
},
)
resume_from_checkpoint: Optional[str] = field(
default=None,
metadata={"help": "Reference to a wandb artifact for resuming training."},
)
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"],
)
def create_learning_rate_fn(
num_warmup_steps: int,
learning_rate: float,
use_decay: bool,
num_train_steps: int = None, # used only with `use_decay`, typically train_size // batch_size * num_epochs
) -> Callable[[int], jnp.array]:
"""Returns a linear warmup, linear_decay learning rate function."""
if use_decay:
assert (
num_train_steps is not None
), "Learning rate with decay requires number of training steps"
warmup_fn = optax.linear_schedule(
init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps
)
if not use_decay:
return warmup_fn
decay_fn = optax.linear_schedule(
init_value=learning_rate,
end_value=0,
transition_steps=num_train_steps - num_warmup_steps,
)
schedule_fn = optax.join_schedules(
schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps]
)
return schedule_fn
def wandb_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,
)
# Set up wandb run
wandb.init(
entity="dalle-mini",
project="dalle-mini",
job_type="Seq2Seq",
config=parser.parse_args(),
)
if training_args.resume_from_checkpoint is not None:
artifact = wandb.run.use_artifact(training_args.resume_from_checkpoint)
artifact_dir = artifact.download()
# load model
model = CustomFlaxBartForConditionalGeneration.from_pretrained(artifact_dir)
# 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
# TODO: simplify with custom config class
if model_args.config_name:
config = BartConfig.from_pretrained(model_args.config_name)
else:
config = BartConfig.from_pretrained(model_args.model_name_or_path)
if model_args.image_vocab_size:
config.image_vocab_size = model_args.image_vocab_size
assert (
getattr(config, "image_vocab_size") is not None
), "image_vocab_size must be specified when not present in base model/config"
if model_args.image_length:
config.image_length = model_args.image_length
assert (
getattr(config, "image_length") is not None
), "image_length must be specified when not present in base model/config"
# we append decoder bos to image vocab
config.decoder_start_token_id = config.image_vocab_size
# ensure we don't generate bos (in addition to decoder start token)
config.force_bos_token_to_be_generated = False
config.forced_bos_token_id = None # we don't need this token
config.forced_eos_token_id = None # we don't need this token
config.tie_word_embeddings = False
config.min_length = config.image_length + 1
config.max_length = config.image_length + 1
# below tokens need to be set to avoid error during generation (converted to jnp.array)
# they are not expected to be used and are set to unreachable token id
config.bos_token_id = config.image_vocab_size + 1
config.pos_token_id = config.image_vocab_size + 1
config.eos_token_id = config.image_vocab_size + 1
# save whether we normalize the text
if model_args.normalize_text is not None:
config.normalize_text = model_args.normalize_text
else:
config.normalize_text = getattr(config, "normalize_text", False)
# Load or create new model
if model_args.model_name_or_path:
model = CustomFlaxBartForConditionalGeneration.from_pretrained(
model_args.model_name_or_path,
config=config,
seed=training_args.seed_model,
dtype=getattr(jnp, model_args.dtype),
)
# avoid OOM on TPU: see https://github.com/google/flax/issues/1658
print(model.params)
else:
model = CustomFlaxBartForConditionalGeneration(
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,
)
logger.info(f"TPUs: {jax.device_count()}")
assert jax.device_count() == 8, "TPUs in use, please check running processes"
# 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,
)
# 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)
train_batch_size = (
int(training_args.per_device_train_batch_size) * jax.device_count()
)
batch_size_per_update = train_batch_size * training_args.gradient_accumulation_steps
eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()
len_train_dataset, len_eval_dataset = dataset.length
steps_per_epoch = (
len_train_dataset // train_batch_size 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
)
# Create learning rate schedule
learning_rate_fn = create_learning_rate_fn(
training_args.warmup_steps,
training_args.learning_rate,
training_args.use_decay,
num_train_steps,
)
# 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.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,
)
else:
optimizer = optax.adamw(
learning_rate=learning_rate_fn,
b1=training_args.adam_beta1,
b2=training_args.adam_beta2,
eps=training_args.adam_epsilon,
weight_decay=training_args.weight_decay,
mask=decay_mask_fn,
)
# add gradient accumulation
if training_args.gradient_accumulation_steps > 1:
optimizer = optax.chain(
optax.apply_every(training_args.gradient_accumulation_steps), optimizer
)
# 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,
)
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" Instantaneous batch size per device = {training_args.per_device_train_batch_size}"
)
logger.info(
f" Total train batch size (w. parallel, distributed & gradient accumulation) = {batch_size_per_update}"
)
epochs = tqdm(
range(state.epoch, num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0
)
# set default x-axis as 'train/step'
wandb_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,
}
)
# 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
wandb_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,
)
# 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)
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)
# save to the hub
if training_args.push_to_hub:
model.save_pretrained(
training_args.output_dir,
params=params,
push_to_hub=training_args.push_to_hub,
commit_message=f"Saving weights and logs at step {unreplicate(state.step)+1}",
temp_dir=True, # avoid issues with being in a repository
)
# init variables
last_time = time.perf_counter()
train_metric = None
for epoch in epochs:
state.replace(epoch=jax_utils.replicate(epoch))
# ======================== Training ================================
wandb_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)
# 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_metric = 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:
# log metrics
wandb_log(unreplicate(train_metric), step=step, prefix="train")
# log state parameters
state_dict = {
k.split("_")[-1]: unreplicate(getattr(state, k))
for k in ["epoch", "train_time", "train_samples"]
}
wandb_log(state_dict, 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_metric is not None:
train_metric = unreplicate(train_metric)
wandb_log(train_metric, step=step, prefix="train")
epochs.write(
f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {train_metric['loss']}, Learning Rate: {train_metric['learning_rate']})"
)
# Final evaluation
eval_metrics = run_evaluation()
# save checkpoint after each epoch
run_save_model(state, eval_metrics)
if __name__ == "__main__":
main()