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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
"""
# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
import os
# set a common huggingface cache folder (used with datasets and transformers) and wandb cache folder (used with artifacts)
os.environ['HF_HOME'] = '/data/huggingface/' # required before importing transformers & datasets
os.environ['WANDB_CACHE_DIR'] = '/data/wandb/' # required before importing wandb
import logging as pylogging # To avoid collision with transformers.utils.logging
import sys
import time
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Optional
import datasets
import nltk # Here to have a nice missing dependency error message early on
import numpy as np
from datasets import Dataset, load_dataset, load_metric
from tqdm import tqdm
import jax
import jax.numpy as jnp
import optax
import transformers
from filelock import FileLock
from flax import jax_utils, traverse_util
import flax.linen as nn
from flax.jax_utils import unreplicate
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
from transformers import (
CONFIG_MAPPING,
FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
AutoConfig,
AutoTokenizer,
FlaxAutoModelForSeq2SeqLM,
FlaxBartForConditionalGeneration,
HfArgumentParser,
TrainingArguments,
)
from transformers.models.bart.modeling_flax_bart import *
from transformers.file_utils import is_offline_mode
import wandb
logger = pylogging.getLogger(__name__)
try:
nltk.data.find("tokenizers/punkt")
except (LookupError, OSError):
if is_offline_mode():
raise LookupError(
"Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files"
)
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
# Model hyperparameters, for convenience
OUTPUT_VOCAB_SIZE = 16384 + 1 # encoded image token space + 1 for bos
OUTPUT_LENGTH = 256 + 1 # number of encoded tokens + 1 for bos
BOS_TOKEN_ID = 16384
BASE_MODEL = 'facebook/bart-large-cnn'
@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=BASE_MODEL,
metadata={
"help": "The model checkpoint for weights initialization."
"Don't set if you want to train a model from scratch."
},
)
model_type: Optional[str] = field(
default=None,
metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
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.
"""
dataset_name: Optional[str] = field(
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
)
dataset_config_name: Optional[str] = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
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."},
)
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
validation_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
)
test_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input predict data file to do prediction on (a text file)."},
)
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_target_length: Optional[int] = field(
default=OUTPUT_LENGTH,
metadata={
"help": "The maximum total sequence length for target text after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
val_max_target_length: Optional[int] = field(
default=OUTPUT_LENGTH,
metadata={
"help": "The maximum total sequence length for validation target text after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`."
"This argument is also used to override the `max_length` param of `model.generate`, which is used "
"during evaluation."
},
)
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."
},
)
max_predict_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
},
)
preprocessing_num_workers: Optional[int] = field(
default=80, # ensure we have the same datasets cached data and avoid using too much space
metadata={"help": "The number of processes to use for the preprocessing."},
)
source_prefix: Optional[str] = field(
default=None, metadata={"help": "A prefix to add before every source text (useful for T5 models)."}
)
predict_with_generate: bool = field(
default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}
)
num_beams: Optional[int] = field(
default=None,
metadata={
"help": "Number of beams to use for evaluation. This argument will be passed to `model.generate`, "
"which is used during evaluation."
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
log_interval: Optional[int] = field(
default=40,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
},
)
eval_steps: Optional[int] = field(
default=400,
metadata={
"help": "Evaluation will be performed every eval_steps"
},
)
log_model: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
def __post_init__(self):
if self.dataset_name is None and self.train_file is None and self.validation_file is None:
raise ValueError("Need either a dataset name or a training/validation file.")
else:
if self.train_file is not None:
extension = self.train_file.split(".")[-1]
assert extension in ["tsv", "csv", "json"], "`train_file` should be a tsv, csv or json file."
if self.validation_file is not None:
extension = self.validation_file.split(".")[-1]
assert extension in ["tsv", "csv", "json"], "`validation_file` should be a tsv, csv or json file."
if self.val_max_target_length is None:
self.val_max_target_length = self.max_target_length
class TrainState(train_state.TrainState):
dropout_rng: jnp.ndarray
grad_accum: jnp.ndarray
optimizer_step: int
def replicate(self):
return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))
class CustomFlaxBartModule(FlaxBartModule):
def setup(self):
# we keep shared to easily load pre-trained weights
self.shared = nn.Embed(
self.config.vocab_size,
self.config.d_model,
embedding_init=jax.nn.initializers.normal(self.config.init_std, self.dtype),
dtype=self.dtype,
)
# a separate embedding is used for the decoder
self.decoder_embed = nn.Embed(
OUTPUT_VOCAB_SIZE,
self.config.d_model,
embedding_init=jax.nn.initializers.normal(self.config.init_std, self.dtype),
dtype=self.dtype,
)
self.encoder = FlaxBartEncoder(self.config, dtype=self.dtype, embed_tokens=self.shared)
# the decoder has a different config
decoder_config = BartConfig(self.config.to_dict())
decoder_config.max_position_embeddings = OUTPUT_LENGTH
decoder_config.min_length = OUTPUT_LENGTH
decoder_config.max_length = OUTPUT_LENGTH
decoder_config.vocab_size = OUTPUT_VOCAB_SIZE
self.decoder = FlaxBartDecoder(decoder_config, dtype=self.dtype, embed_tokens=self.decoder_embed)
class CustomFlaxBartForConditionalGenerationModule(FlaxBartForConditionalGenerationModule):
def setup(self):
self.model = CustomFlaxBartModule(config=self.config, dtype=self.dtype)
self.lm_head = nn.Dense(
OUTPUT_VOCAB_SIZE,
use_bias=False,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.init_std, self.dtype),
)
self.final_logits_bias = self.param("final_logits_bias", self.bias_init, (1, OUTPUT_VOCAB_SIZE))
class CustomFlaxBartForConditionalGeneration(FlaxBartForConditionalGeneration):
module_class = CustomFlaxBartForConditionalGenerationModule
def data_loader(rng: jax.random.PRNGKey, dataset: Dataset, batch_size: int, shuffle: bool = False):
"""
Returns batches of size `batch_size` from truncated `dataset`, sharded over all local devices.
Shuffle batches if `shuffle` is `True`.
"""
steps_per_epoch = len(dataset) // batch_size
if shuffle:
batch_idx = jax.random.permutation(rng, len(dataset))
else:
batch_idx = jnp.arange(len(dataset))
batch_idx = batch_idx[: steps_per_epoch * batch_size] # Skip incomplete batch.
batch_idx = batch_idx.reshape((steps_per_epoch, batch_size))
for idx in batch_idx:
batch = dataset[idx]
batch = {k: jnp.array(v) for k, v in batch.items()}
batch = shard(batch)
yield batch
def create_learning_rate_fn(
train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float
) -> Callable[[int], jnp.array]:
"""Returns a linear warmup, linear_decay learning rate function."""
steps_per_epoch = train_ds_size // train_batch_size
num_train_steps = steps_per_epoch * num_train_epochs
warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
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: jax.device_get(v) for k,v in metrics.items()}
if step is not None:
log_metrics = {**metrics, 'train/step': step}
wandb.log(log_metrics)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
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."
)
# Set up wandb run
wandb.init(
entity='wandb',
project='hf-flax-dalle-mini',
job_type='Seq2SeqVQGAN',
config=parser.parse_args()
)
# set default x-axis as 'train/step'
wandb.define_metric('train/step')
wandb.define_metric('*', step_metric='train/step')
# Make one log on every process with the configuration for debugging.
pylogging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=pylogging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
logger.setLevel(pylogging.INFO if jax.process_index() == 0 else pylogging.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}")
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
data_files = {}
logger.warning(f"Datasets path have been manually hardcoded") # TODO: remove it later, convenient for now
if data_args.train_file is not None:
data_files["train"] = ["/data/CC3M/training-encoded.tsv", "/data/CC12M/encoded-train.tsv"]
if data_args.validation_file is not None:
data_files["validation"] = ["/data/CC3M/validation-encoded.tsv"]
if data_args.test_file is not None:
data_files["test"] = data_args.test_file
dataset = load_dataset("csv", data_files=data_files, cache_dir=model_args.cache_dir, delimiter="\t")
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
base_model = FlaxAutoModelForSeq2SeqLM.from_pretrained(
model_args.model_name_or_path, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
)
# Set up our new model config
config = BartConfig.from_pretrained(model_args.model_name_or_path)
config.tie_word_embeddings = False
config.decoder_start_token_id = BOS_TOKEN_ID
config.bos_token_id = BOS_TOKEN_ID # should not be used
config.pos_token_id = BOS_TOKEN_ID # should not be needed (as we generate until max_length)
config.eos_token_id = BOS_TOKEN_ID + 1 # unreachable
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.min_length = data_args.max_target_length # Set only in decoder?
#config.max_length = data_args.max_target_length # Set only in decoder?
print(f"TPUs: {jax.device_count()}")
assert jax.device_count() == 8, "TPUs in use, please check running processes"
# Create a custom model and initialize it randomly
model = CustomFlaxBartForConditionalGeneration(config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype))
# Use pre-trained weights for encoder
model.params['model']['encoder'] = base_model.params['model']['encoder']
model.params['model']['shared'] = base_model.params['model']['shared']
del base_model
prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
# Preprocessing the datasets.
# We need to tokenize inputs and targets.
if training_args.do_train:
column_names = dataset["train"].column_names
elif training_args.do_eval:
column_names = dataset["validation"].column_names
elif training_args.do_predict:
column_names = dataset["test"].column_names
else:
logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
return
# Get the column names for input/target.
text_column = data_args.text_column
encoding_column = data_args.encoding_column
# Temporarily set max_target_length for training.
max_target_length = data_args.max_target_length
def shift_tokens_right(input_ids: np.array, decoder_start_token_id: int):
"""
Shift input ids one token to the right.
"""
shifted_input_ids = np.zeros(input_ids.shape)
shifted_input_ids[:, 1:] = input_ids[:, :-1]
shifted_input_ids[:, 0] = decoder_start_token_id
return shifted_input_ids
def preprocess_function(examples):
inputs = examples[text_column]
inputs = [prefix + inp for inp in inputs]
# Setting padding="max_length" as we need fixed length inputs for jitted functions
model_inputs = tokenizer(
inputs, max_length=data_args.max_source_length, padding="max_length", truncation=True, return_tensors="np"
)
# set up targets
# Note: labels correspond to our target indices
# decoder input ids are the same but shifted to the right with bos at the beginning (and without last token)
labels = [eval(indices) for indices in examples['encoding']]
labels = np.asarray(labels)
# We need the labels, in addition to the decoder_input_ids, for the compute_loss function
model_inputs["labels"] = labels
# In our case, this prepends the bos token and removes the last one
decoder_input_ids = shift_tokens_right(labels, config.decoder_start_token_id)
model_inputs["decoder_input_ids"] = decoder_input_ids
return model_inputs
if training_args.do_train:
if "train" not in dataset:
raise ValueError("--do_train requires a train dataset")
train_dataset = dataset["train"]
if data_args.max_train_samples is not None:
train_dataset = train_dataset.select(range(data_args.max_train_samples))
train_dataset = train_dataset.map(
preprocess_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on train dataset",
)
if training_args.do_eval:
max_target_length = data_args.val_max_target_length
if "validation" not in dataset:
raise ValueError("--do_eval requires a validation dataset")
eval_dataset = dataset["validation"]
if data_args.max_eval_samples is not None:
eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
eval_dataset = eval_dataset.map(
preprocess_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on validation dataset",
)
if training_args.do_predict:
max_target_length = data_args.val_max_target_length
if "test" not in dataset:
raise ValueError("--do_predict requires a test dataset")
predict_dataset = dataset["test"]
if data_args.max_predict_samples is not None:
predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
predict_dataset = predict_dataset.map(
preprocess_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on prediction dataset",
)
# Metric
#metric = load_metric("rouge")
def postprocess_text(preds, labels):
preds = [pred.strip() for pred in preds]
labels = [label.strip() for label in labels]
# rougeLSum expects newline after each sentence
preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]
return preds, labels
def compute_metrics(preds, labels):
decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
# Some simple post-processing
decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
# Extract a few results from ROUGE
result = {key: value.mid.fmeasure * 100 for key, value in result.items()}
prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
result["gen_len"] = np.mean(prediction_lens)
result = {k: round(v, 4) for k, v in result.items()}
return result
# Initialize our training
rng = jax.random.PRNGKey(training_args.seed)
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()
total_batch_size = int(train_batch_size) * training_args.gradient_accumulation_steps
eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()
steps_per_epoch = len(train_dataset) // train_batch_size
total_steps = steps_per_epoch * num_epochs
total_optimization_steps = (len(train_dataset) // total_batch_size) * num_epochs
# Create learning rate schedule
linear_decay_lr_schedule_fn = create_learning_rate_fn(
len(train_dataset),
total_batch_size,
training_args.num_train_epochs,
training_args.warmup_steps,
training_args.learning_rate,
)
# 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.
# For FlaxT5, one should correct the layer norm parameter naming
# accordingly - see `run_t5_mlm_flax.py` e.g.
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=linear_decay_lr_schedule_fn,
)
else:
optimizer = optax.adamw(
learning_rate=linear_decay_lr_schedule_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,
)
# Setup train state
state = TrainState.create(
apply_fn=model.__call__,
params=model.params,
tx=optimizer,
dropout_rng=dropout_rng,
grad_accum=jax.tree_map(jnp.zeros_like, model.params),
optimizer_step=0,
)
# 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):
dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)
def compute_loss(params):
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)
grad_accum = jax.tree_multimap(lambda x, y: x + y, grads, state.grad_accum)
def update_fn():
grads = jax.tree_map(lambda x: x / training_args.gradient_accumulation_steps, grad_accum)
grads = jax.lax.pmean(grads, "batch")
new_state = state.apply_gradients(
grads=grads, grad_accum=jax.tree_map(jnp.zeros_like, grads), optimizer_step=state.optimizer_step + 1
)
return new_state
new_state = jax.lax.cond(
(state.step + 1) % training_args.gradient_accumulation_steps == 0,
lambda _: update_fn(),
lambda _: state.replace(grad_accum=grad_accum, step=state.step + 1),
None,
)
metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.optimizer_step)}
metrics = jax.lax.pmean(metrics, axis_name="batch")
return new_state.replace(dropout_rng=new_dropout_rng), 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
# Define generation function
max_length = (
data_args.val_max_target_length if data_args.val_max_target_length is not None else model.config.max_length
)
num_beams = data_args.num_beams if data_args.num_beams is not None else model.config.num_beams
gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
def generate_step(params, batch):
model.params = params
output_ids = model.generate(batch["input_ids"], attention_mask=batch["attention_mask"], **gen_kwargs)
return output_ids.sequences
# 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")
p_generate_step = jax.pmap(generate_step, "batch")
# Replicate the train state on each device
state = state.replicate()
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) = {train_batch_size * training_args.gradient_accumulation_steps}"
)
logger.info(f" Total global steps = {total_steps}")
logger.info(f" Total optimization steps = {total_optimization_steps}")
train_time = 0
epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
global_step = 0
def run_evaluation():
# ======================== Evaluating ==============================
eval_metrics = []
if training_args.do_eval:
eval_preds = []
eval_labels = []
eval_loader = data_loader(input_rng, eval_dataset, eval_batch_size)
eval_steps = len(eval_dataset) // eval_batch_size
for _ in tqdm(range(eval_steps), desc="Evaluating...", position=2, leave=False):
# Model forward
batch = next(eval_loader)
labels = batch["labels"]
metrics = p_eval_step(state.params, batch)
eval_metrics.append(metrics)
# generation
if data_args.predict_with_generate:
generated_ids = p_generate_step(state.params, batch)
eval_preds.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"])))
eval_labels.extend(jax.device_get(labels.reshape(-1, labels.shape[-1])))
# 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=global_step, prefix='eval')
# compute ROUGE metrics
rouge_desc = ""
# if data_args.predict_with_generate:
# rouge_metrics = compute_metrics(eval_preds, eval_labels)
# eval_metrics.update(rouge_metrics)
# rouge_desc = " ".join([f"Eval {key}: {value} |" for key, value in rouge_metrics.items()])
# Print metrics and update progress bar
desc = f"Epoch... ({epoch + 1}/{num_epochs} | Eval Loss: {eval_metrics['loss']} | {rouge_desc})"
epochs.write(desc)
epochs.desc = desc
return eval_metrics
for epoch in epochs:
# ======================== Training ================================
train_start = time.time()
# Create sampling rng
rng, input_rng = jax.random.split(rng)
# Generate an epoch by shuffling sampling indices from the train dataset
train_loader = data_loader(input_rng, train_dataset, train_batch_size, shuffle=True)
steps_per_epoch = len(train_dataset) // train_batch_size
# train
for step in tqdm(range(steps_per_epoch), desc="Training...", position=1, leave=False):
global_step +=1
batch = next(train_loader)
state, train_metric = p_train_step(state, batch)
if global_step % data_args.log_interval == 0 and jax.process_index() == 0:
# log metrics
wandb_log(unreplicate(train_metric), step=global_step, prefix='tran')
if global_step % data_args.eval_steps == 0:
run_evaluation()
train_time += time.time() - train_start
train_metric = unreplicate(train_metric)
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 and push checkpoint to the hub
if jax.process_index() == 0:
params = jax.device_get(jax.tree_map(lambda x: x[0], state.params))
# save model locally
model.save_pretrained(
training_args.output_dir,
params=params,
)
# save to W&B
if data_args.log_model:
metadata = {'epoch': epoch+1, 'eval/loss': eval_metrics['loss']}
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'))
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 of epoch {epoch+1}",
temp_dir=True # avoid issues with being in a repository
)
# ======================== Prediction loop ==============================
if training_args.do_predict:
logger.info("*** Predict ***")
pred_metrics = []
pred_generations = []
pred_labels = []
pred_loader = data_loader(input_rng, predict_dataset, eval_batch_size)
pred_steps = len(predict_dataset) // eval_batch_size
for _ in tqdm(range(pred_steps), desc="Predicting...", position=2, leave=False):
# Model forward
batch = next(pred_loader)
labels = batch["labels"]
metrics = p_eval_step(state.params, batch)
pred_metrics.append(metrics)
# generation
if data_args.predict_with_generate:
generated_ids = p_generate_step(state.params, batch)
pred_generations.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"])))
pred_labels.extend(jax.device_get(labels.reshape(-1, labels.shape[-1])))
# normalize prediction metrics
pred_metrics = get_metrics(pred_metrics)
pred_metrics = jax.tree_map(jnp.mean, pred_metrics)
# compute ROUGE metrics
rouge_desc = ""
if data_args.predict_with_generate:
rouge_metrics = compute_metrics(pred_generations, pred_labels)
pred_metrics.update(rouge_metrics)
rouge_desc = " ".join([f"Predict {key}: {value} |" for key, value in rouge_metrics.items()])
# Print metrics
desc = f"Predict Loss: {pred_metrics['loss']} | {rouge_desc})"
logger.info(desc)
if __name__ == "__main__":
main()
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