#!/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 vision-encoder-decoder models for image captioning. """ import json import logging import os import sys import time from dataclasses import asdict, dataclass, field from enum import Enum 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 from PIL import Image import jax import jax.numpy as jnp import optax import transformers from filelock import FileLock from flax import jax_utils, traverse_util 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 huggingface_hub import Repository from transformers import ( CONFIG_MAPPING, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING, AutoConfig, AutoFeatureExtractor, AutoTokenizer, HfArgumentParser, is_tensorboard_available, FlaxAutoModelForVision2Seq, ) from transformers.file_utils import get_full_repo_name, is_offline_mode logger = logging.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_VISION_2_SEQ_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) DECODER_MODEL_TYPES = tuple(conf.model_type for conf in list(FLAX_MODEL_FOR_CAUSAL_LM_MAPPING.keys())) # Copied from transformers.models.bart.modeling_flax_bart.shift_tokens_right def shift_tokens_right(input_ids: np.ndarray, pad_token_id: int, decoder_start_token_id: int) -> np.ndarray: """ Shift input ids one token to the right. """ shifted_input_ids = np.zeros_like(input_ids) shifted_input_ids[:, 1:] = input_ids[:, :-1] shifted_input_ids[:, 0] = decoder_start_token_id shifted_input_ids = np.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids) return shifted_input_ids @dataclass class TrainingArguments: 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."}) do_predict: bool = field(default=False, metadata={"help": "Whether to run predictions on the test 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."} ) _block_size_doc = \ """ The default value `0` will preprocess (tokenization + feature extraction) the whole dataset before training and cache the results. This uses more disk space, but avoids (repeated) processing time during training. This is a good option if your disk space is large enough to store the whole processed dataset. If a positive value is given, the captions in the dataset will be tokenized before training and the results are cached. During training, it iterates the dataset in chunks of size `block_size`. On each block, images are transformed by the feature extractor with the results being kept in memory (no cache), and batches of size `batch_size` are yielded before processing the next block. This could avoid the heavy disk usage when the dataset is large. """ block_size: int = field( default=0, metadata={"help": _block_size_doc} ) learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."}) weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW 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."}) label_smoothing_factor: float = field( default=0.0, metadata={"help": "The label smoothing epsilon to apply (zero means no label smoothing)."} ) 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=500, metadata={"help": "Log every X updates steps."}) eval_steps: int = field(default=None, metadata={"help": "Run an evaluation every X steps."}) seed: int = field(default=42, metadata={"help": "Random seed 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."} ) hub_model_id: str = field( default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."} ) hub_token: str = field(default=None, metadata={"help": "The token to use to push to the Model Hub."}) def __post_init__(self): if self.output_dir is not None: self.output_dir = os.path.expanduser(self.output_dir) def to_dict(self): """ Serializes this instance while replace `Enum` by their values (for JSON serialization support). It obfuscates the token values by removing their value. """ d = asdict(self) for k, v in d.items(): if isinstance(v, Enum): d[k] = v.value if isinstance(v, list) and len(v) > 0 and isinstance(v[0], Enum): d[k] = [x.value for x in v] if k.endswith("_token"): d[k] = f"<{k.upper()}>" return d @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." }, ) encoder_model_name_or_path: Optional[str] = field( default=None, metadata={ "help": "The encoder model checkpoint for weights initialization." "Don't set if you want to train an encoder model from scratch." }, ) decoder_model_name_or_path: Optional[str] = field( default=None, metadata={ "help": "The decoder model checkpoint for weights initialization." "Don't set if you want to train a decoder model from scratch." }, ) model_type: Optional[str] = field( default='vision-encoder-decoder', metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)} ) encoder_model_type: Optional[str] = field( default=None, metadata={"help": "If training from scratch, pass a vision encoder model type from the library. For example, 'vit'"} ) decoder_model_type: Optional[str] = field( default=None, metadata={"help": "If training from scratch, pass a decoder model type from the list: " + ", ".join(DECODER_MODEL_TYPES)} ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) encoder_config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"} ) decoder_config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"} ) feature_extractor_name: Optional[str] = field( default=None, metadata={"help": "Pretrained encoder feature extractor_name or path if not the same as encoder_model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained decoder tokenizer name or path if not the same as decoder_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)."} ) data_dir: Optional[str] = field( default=None, metadata={"help": "The data directory of the dataset to use (via the datasets library)."} ) image_column: Optional[str] = field( default=None, metadata={"help": "The name of the column in the datasets containing the full image file paths."}, ) caption_column: Optional[str] = field( default=None, metadata={"help": "The name of the column in the datasets containing the image captions."}, ) 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_target_length: Optional[int] = field( default=128, 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=None, 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=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) 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"} ) 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 ["csv", "json"], "`train_file` should be a csv or a json file." if self.validation_file is not None: extension = self.validation_file.split(".")[-1] assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." if self.val_max_target_length is None: self.val_max_target_length = self.max_target_length image_captioning_name_mapping = { "image_caption_dataset.py": ("image_path", "caption"), } class TrainState(train_state.TrainState): dropout_rng: jnp.ndarray def replicate(self): return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng)) 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 = len(dataset) // batch_size # Skip incomplete batch. if shuffle: batch_idx = jax.random.permutation(rng, len(dataset)) batch_idx = np.asarray(batch_idx) else: batch_idx = np.arange(len(dataset)) for idx in range(steps): start_idx = batch_size * idx end_idx = batch_size * (idx + 1) selected_indices = batch_idx[start_idx:end_idx] batch = dataset[selected_indices] batch = shard(batch) yield batch def write_metric(summary_writer, metrics, train_time, step, metric_key_prefix="train"): if train_time: summary_writer.scalar("train_time", train_time, step) metrics = get_metrics(metrics) for key, vals in metrics.items(): tag = f"{metric_key_prefix}_{key}" for i, val in enumerate(vals): summary_writer.scalar(tag, val, step - len(vals) + i + 1) else: for metric_name, value in metrics.items(): summary_writer.scalar(f"{metric_key_prefix}_{metric_name}", value, step) 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 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." ) # 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}") # Handle the repository creation if training_args.push_to_hub: if training_args.hub_model_id is None: repo_name = get_full_repo_name( Path(training_args.output_dir).absolute().name, token=training_args.hub_token ) else: repo_name = training_args.hub_model_id repo = Repository(training_args.output_dir, clone_from=repo_name) # 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). # # For CSV/JSON files this script will use the first column for the full image path and the second column for the # captions (unless you specify column names for this with the `image_column` and `caption_column` arguments). # if data_args.dataset_name is not None: # Downloading and loading a dataset from the hub. dataset = load_dataset( data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, keep_in_memory=False, data_dir=data_args.data_dir, ) else: data_files = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file extension = data_args.train_file.split(".")[-1] if data_args.validation_file is not None: data_files["validation"] = data_args.validation_file extension = data_args.validation_file.split(".")[-1] if data_args.test_file is not None: data_files["test"] = data_args.test_file extension = data_args.test_file.split(".")[-1] dataset = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir) # 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 encoder_cache_dir, decoder_cache_dir = None, None if model_args.cache_dir: encoder_cache_dir = os.path.join(model_args.cache_dir, "encoder") decoder_cache_dir = os.path.join(model_args.cache_dir, "decoder") # Use explicit specified config if model_args.config_name: config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir) # Use pretrained model's config elif model_args.model_name_or_path: config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir) # Use specified `model_type` (default to `vision-encoder-decoder`) else: if not model_args.model_type in MODEL_TYPES: raise ValueError( f"Unrecognized model identifier: {model_args.model_type}. Should contain one of {', '.join(MODEL_TYPES)}." ) config_class = CONFIG_MAPPING[model_args.model_type] # Deal with encoder-decoder models that require specifying encoder/decoder if hasattr(config_class, "from_encoder_decoder_configs"): # Use explicit specified encoder config if model_args.encoder_config_name: encoder_config = AutoConfig.from_pretrained(model_args.encoder_config_name, cache_dir=encoder_cache_dir) # Use pretrained encoder model's config elif model_args.encoder_model_name_or_path: encoder_config = AutoConfig.from_pretrained(model_args.encoder_model_name_or_path, cache_dir=encoder_cache_dir) # Use specified encoder model type elif model_args.encoder_model_type: encoder_config = AutoConfig.for_model(model_args.encoder_model_type) logger.warning("You are instantiating a new config instance from scratch for the encoder.") else: raise ValueError("Encoder Config: if pretrained config or model location is not provided, `encoder_model_type` is required.") # Use explicit specified decoder config if model_args.decoder_config_name: decoder_config = AutoConfig.from_pretrained(model_args.decoder_config_name, cache_dir=decoder_cache_dir) # Use pretrained decoder model's config elif model_args.decoder_model_name_or_path: decoder_config = AutoConfig.from_pretrained(model_args.decoder_model_name_or_path, cache_dir=decoder_cache_dir) # Use specified decoder model type elif model_args.decoder_model_type: decoder_config = AutoConfig.for_model(model_args.decoder_model_type) logger.warning("You are instantiating a new config instance from scratch for the decoder.") else: raise ValueError("Decoder Config: if pretrained config or model location is not provided, `decoder_model_type` is required.") logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config") decoder_config.is_decoder = True decoder_config.add_cross_attention = True config = config_class.from_encoder_decoder_configs(encoder_config, decoder_config) # For self-contained model else: config = config_class() logger.warning("You are instantiating a new config instance from scratch.") decoder_start_token_id = getattr(config, "decoder_start_token_id", None) if not decoder_start_token_id and getattr(config, "decoder", None): decoder_start_token_id = getattr(config.decoder, "decoder_start_token_id", None) bos_token_id = getattr(config, "bos_token_id", None) if not bos_token_id and getattr(config, "decoder", None): bos_token_id = getattr(config.decoder, "bos_token_id", None) eos_token_id = getattr(config, "eos_token_id", None) if not eos_token_id and getattr(config, "decoder", None): eos_token_id = getattr(config.decoder, "eos_token_id", None) pad_token_id = getattr(config, "pad_token_id", None) if not pad_token_id and getattr(config, "decoder", None): pad_token_id = getattr(config.decoder, "pad_token_id", None) if decoder_start_token_id is None: decoder_start_token_id = bos_token_id if pad_token_id is None: pad_token_id = eos_token_id if getattr(config, "decoder", None): config.decoder.decoder_start_token_id = decoder_start_token_id config.decoder.bos_token_id = bos_token_id config.decoder.eos_token_id = eos_token_id config.decoder.pad_token_id = pad_token_id # Set `encoder-decoder` (top-level) specific config (not always necessary, but can avoid generate() error sometimes) config.decoder_start_token_id = decoder_start_token_id config.bos_token_id = bos_token_id config.eos_token_id = eos_token_id config.pad_token_id = pad_token_id if model_args.model_name_or_path: model = FlaxAutoModelForVision2Seq.from_pretrained( model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype) ) else: # model_class = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING[config.__class__] model = FlaxAutoModelForVision2Seq.from_config(config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)) model_class = model.__class__ # encoder_class = FlaxAutoModel # decoder_class = FlaxAutoModelForCausalLM module = model.module.bind(model.params) encoder_class_name = type(module.encoder).__name__.replace("Module", "Model") decoder_class_name = type(module.decoder).__name__.replace("Module", "Model") encoder_class = getattr(transformers, encoder_class_name, None) decoder_class = getattr(transformers, decoder_class_name, None) if hasattr(model_class, "from_encoder_decoder_pretrained"): if model_args.encoder_model_name_or_path: encoder = encoder_class.from_pretrained( model_args.encoder_model_name_or_path, config=config.encoder, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype) ) else: encoder = encoder_class(config=config.encoder, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)) logger.warning("You are instantiating a new model instance from scratch for the encoder.") if model_args.decoder_model_name_or_path: decoder = decoder_class.from_pretrained( model_args.decoder_model_name_or_path, config=config.decoder, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype) ) else: decoder = decoder_class(config=config.decoder, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)) logger.warning("You are instantiating a new model instance from scratch for the decoder.") model = model_class.from_encoder_decoder_pretrained( model_args.encoder_model_name_or_path, model_args.decoder_model_name_or_path, encoder_model=encoder, decoder_model=decoder, encoder_config=config.encoder, decoder_config=config.decoder, encoder_seed=training_args.seed, decoder_seed=training_args.seed, encoder_dtype=getattr(jnp, model_args.dtype), decoder_dtype=getattr(jnp, model_args.dtype), ) # Set `encoder-decoder` (top-level) specific config (not always necessary, but can avoid generate() error sometimes) model.config.decoder_start_token_id = decoder_start_token_id model.config.bos_token_id = bos_token_id model.config.eos_token_id = eos_token_id model.config.pad_token_id = pad_token_id else: logger.warning("You are instantiating a new model instance from scratch.") feature_extractor = None if model_args.feature_extractor_name: feature_extractor = AutoFeatureExtractor.from_pretrained( model_args.feature_extractor_name, cache_dir=model_args.cache_dir, ) elif model_args.model_name_or_path: try: feature_extractor = AutoFeatureExtractor.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir ) except ValueError as e: logger.warning(e) # Check encoder if not feature_extractor: if model_args.encoder_model_name_or_path: feature_extractor = AutoFeatureExtractor.from_pretrained( model_args.encoder_model_name_or_path, cache_dir=model_args.cache_dir ) else: raise ValueError( "You are instantiating a new feature extractor from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --feature_extractor_name." ) tokenizer = None if model_args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained( model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer ) elif model_args.model_name_or_path: try: tokenizer = AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer ) except ValueError as e: logger.warning(e) # Check decoder if not tokenizer: if model_args.decoder_model_name_or_path: tokenizer = AutoTokenizer.from_pretrained( model_args.decoder_model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer ) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) tokenizer.pad_token = tokenizer.convert_ids_to_tokens(config.pad_token_id) # 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. dataset_columns = image_captioning_name_mapping.get(data_args.dataset_name, None) if data_args.image_column is None: assert dataset_columns is not None image_column = dataset_columns[0] else: image_column = data_args.image_column if image_column not in column_names: raise ValueError( f"--image_column' value '{data_args.image_column}' needs to be one of: {', '.join(column_names)}" ) if data_args.caption_column is None: assert dataset_columns is not None caption_column = dataset_columns[1] else: caption_column = data_args.caption_column if caption_column not in column_names: raise ValueError( f"--caption_column' value '{data_args.caption_column}' needs to be one of: {', '.join(column_names)}" ) # In Flax, for seq2seq models we need to pass `decoder_input_ids` # as the Flax models don't accept `labels`, we need to prepare the decoder_input_ids here # for that dynamically import the `shift_tokens_right` function from the model file model_module = __import__(model.__module__, fromlist=["shift_tokens_right"]) shift_tokens_right_fn = getattr(model_module, "shift_tokens_right", shift_tokens_right) def filter_fn(examples): """remove problematic images""" bools = [] for image_file in examples[image_column]: try: image = Image.open(image_file) encoder_inputs = feature_extractor(images=image, return_tensors="np") bools.append(True) except: bools.append(False) return bools # Setting padding="max_length" as we need fixed length inputs for jitted functions def tokenization_fn(examples, max_target_length): """Run tokenization on captions.""" captions = [] for caption in examples[caption_column]: captions.append(caption.lower() + ' ' + tokenizer.eos_token) targets = captions model_inputs = {} # Setup the tokenizer for targets with tokenizer.as_target_tokenizer(): labels = tokenizer( targets, max_length=max_target_length, padding="max_length", truncation=True, return_tensors="np" ) model_inputs["labels"] = labels["input_ids"] decoder_input_ids = shift_tokens_right_fn( labels["input_ids"], config.pad_token_id, config.decoder_start_token_id ) model_inputs["decoder_input_ids"] = np.asarray(decoder_input_ids) # We need decoder_attention_mask so we can ignore pad tokens from loss model_inputs["decoder_attention_mask"] = labels["attention_mask"] model_inputs[image_column] = examples[image_column] return model_inputs def feature_extraction_fn(examples, check_image=True): """ Run feature extraction on images If `check_image` is `True`, the examples that fails during `Image.open()` will be caught and discarded. Otherwise, an exception will be thrown. """ model_inputs = {} if check_image: images = [] to_keep = [] for image_file in examples[image_column]: try: img = Image.open(image_file) images.append(img) to_keep.append(True) except: to_keep.append(False) for k, v in examples.items(): if k != image_column: model_inputs[k] = v[to_keep] else: images = [Image.open(image_file) for image_file in examples[image_column]] encoder_inputs = feature_extractor(images=images, return_tensors="np") model_inputs["pixel_values"] = encoder_inputs.pixel_values return model_inputs def preprocess_fn(examples, max_target_length, check_image=True): """Run tokenization + image feature extraction""" model_inputs = {} # This contains image path column model_inputs.update(tokenization_fn(examples, max_target_length)) model_inputs.update(feature_extraction_fn(model_inputs, check_image=check_image)) # Remove image path column model_inputs.pop(image_column) return model_inputs features = datasets.Features( { "pixel_values": datasets.Array3D( shape=( getattr(config.encoder, "num_channels", 3), config.encoder.image_size, config.encoder.image_size, ), dtype="float32", ), "labels": datasets.Sequence(feature=datasets.Value(dtype='int32', id=None), length=-1, id=None), "decoder_input_ids": datasets.Sequence(feature=datasets.Value(dtype='int32', id=None), length=-1, id=None), "decoder_attention_mask": datasets.Sequence(feature=datasets.Value(dtype='int32', id=None), length=-1, id=None), } ) # If `block_size` is `0`, tokenization & image feature extraction is done at the beginning run_feat_ext_at_beginning = training_args.block_size == 0 # Used in .map() below function_kwarg = preprocess_fn if run_feat_ext_at_beginning else tokenization_fn # `features` is used only for the final preprocessed dataset (for the performance purpose). features_kwarg = features if run_feat_ext_at_beginning else None # Keep `image_column` if the feature extraction is done during training remove_columns_kwarg = [x for x in column_names if x != image_column or run_feat_ext_at_beginning] processor_names = "tokenizer and feature extractor" if run_feat_ext_at_beginning else "tokenizer" 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)) # remove problematic examples # (if feature extraction is performed at the beginning, the filtering is done during preprocessing below # instead here.) if not run_feat_ext_at_beginning: train_dataset = train_dataset.filter(filter_fn, batched=True, num_proc=data_args.preprocessing_num_workers) train_dataset = train_dataset.map( function=function_kwarg, batched=True, num_proc=data_args.preprocessing_num_workers, # kept image paths remove_columns=remove_columns_kwarg, load_from_cache_file=not data_args.overwrite_cache, desc=f"Running {processor_names} on train dataset", fn_kwargs={"max_target_length": data_args.max_target_length}, features=features_kwarg, ) if run_feat_ext_at_beginning: # set format (for performance) since the dataset is ready to be used train_dataset = train_dataset.with_format("numpy") if training_args.do_eval: 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)) # remove problematic examples # (if feature extraction is performed at the beginning, the filtering is done during preprocessing below # instead here.) if not run_feat_ext_at_beginning: eval_dataset = eval_dataset.filter(filter_fn, batched=True, num_proc=data_args.preprocessing_num_workers) eval_dataset = eval_dataset.map( function=function_kwarg, batched=True, num_proc=data_args.preprocessing_num_workers, # kept image paths remove_columns=remove_columns_kwarg, load_from_cache_file=not data_args.overwrite_cache, desc=f"Running {processor_names} on validation dataset", fn_kwargs={"max_target_length": data_args.val_max_target_length}, features=features_kwarg, ) if run_feat_ext_at_beginning: # set format (for performance) since the dataset is ready to be used eval_dataset = eval_dataset.with_format("numpy") if training_args.do_predict: 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)) # remove problematic examples # (if feature extraction is performed at the beginning, the filtering is done during preprocessing below # instead here.) if not run_feat_ext_at_beginning: predict_dataset = predict_dataset.filter(filter_fn, batched=True, num_proc=data_args.preprocessing_num_workers) predict_dataset = predict_dataset.map( function=function_kwarg, batched=True, num_proc=data_args.preprocessing_num_workers, # kept image paths remove_columns=remove_columns_kwarg, load_from_cache_file=not data_args.overwrite_cache, desc=f"Running {processor_names} on prediction dataset", fn_kwargs={"max_target_length": data_args.val_max_target_length}, features=features_kwarg, ) if run_feat_ext_at_beginning: # set format (for performance) since the dataset is ready to be used predict_dataset = predict_dataset.with_format("numpy") # Store some constant train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count() if training_args.block_size % train_batch_size > 0: raise ValueError(f"`training_args.block_size` needs to be a multiple of the global batch size. Got {training_args.block_size} and {train_batch_size} instead.") if training_args.do_train: steps_per_epoch = len(train_dataset) // train_batch_size num_train_examples_per_epoch = steps_per_epoch * train_batch_size num_epochs = int(training_args.num_train_epochs) total_train_steps = steps_per_epoch * num_epochs else: num_train_examples_per_epoch = 0 eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count() if training_args.do_eval: num_eval_examples = len(eval_dataset) eval_steps = num_eval_examples // eval_batch_size if training_args.do_predict: num_test_examples = len(predict_dataset) test_steps = num_test_examples // eval_batch_size def blockwise_data_loader( rng: jax.random.PRNGKey, ds: Dataset, block_size: int, batch_size: int, shuffle: bool = False, keep_in_memory: bool = False, split: str = "" ): """ Wrap the simple `data_loader` in a block-wise way if `block_size` > 0, else it's the same as `data_loader`. If `block_size` > 0, it requires `ds` to have a column that gives image paths in order to perform image feature extraction (with the column name being specified by `image_column`). The tokenization should be done before training in this case. """ if shuffle: indices = jax.random.permutation(rng, len(ds)) indices = np.asarray(indices) else: indices = np.arange(len(ds)) _block_size = len(ds) if not block_size else block_size steps_per_block = _block_size // batch_size num_examples = len(ds) steps = num_examples // batch_size num_splits = steps // steps_per_block + int(steps % steps_per_block > 0) for idx in range(num_splits): if not block_size: _ds = ds else: start_idx = block_size * idx end_idx = block_size * (idx + 1) selected_indices = indices[start_idx:end_idx] _ds = ds.select(selected_indices) _ds = _ds.map( feature_extraction_fn, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=[image_column], load_from_cache_file=not data_args.overwrite_cache, features=features, keep_in_memory=keep_in_memory, # The images are already checked either in `.filter()` or in `preprocess_fn()` fn_kwargs={"check_image": False}, desc=f"Running feature extraction on {split} dataset".replace(" ", " "), ) _ds = _ds.with_format("numpy") # No need to shuffle here loader = data_loader(rng, _ds, batch_size=batch_size, shuffle=False) for batch in loader: yield batch # 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, 6) for k, v in result.items()} return result, decoded_preds, decoded_labels # Enable tensorboard only on the master node has_tensorboard = is_tensorboard_available() if has_tensorboard and jax.process_index() == 0: try: from flax.metrics.tensorboard import SummaryWriter summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir)) except ImportError as ie: has_tensorboard = False logger.warning( f"Unable to display metrics through TensorBoard because some package are not installed: {ie}" ) else: logger.warning( "Unable to display metrics through TensorBoard because the package is not installed: " "Please run pip install tensorboard to enable." ) # Initialize our training rng = jax.random.PRNGKey(training_args.seed) rng, dropout_rng = jax.random.split(rng) # Create learning rate schedule linear_decay_lr_schedule_fn = create_learning_rate_fn( num_train_examples_per_epoch, train_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 adamw = 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=adamw, dropout_rng=dropout_rng) # label smoothed cross entropy def loss_fn(logits, labels, padding_mask, label_smoothing_factor=0.0): """ The label smoothing implementation is adapted from Flax's official example: https://github.com/google/flax/blob/87a211135c6a377c8f29048a1cac3840e38b9da4/examples/wmt/train.py#L104 """ vocab_size = logits.shape[-1] confidence = 1.0 - label_smoothing_factor low_confidence = (1.0 - confidence) / (vocab_size - 1) normalizing_constant = -( confidence * jnp.log(confidence) + (vocab_size - 1) * low_confidence * jnp.log(low_confidence + 1e-20) ) soft_labels = onehot(labels, vocab_size, on_value=confidence, off_value=low_confidence) loss = optax.softmax_cross_entropy(logits, soft_labels) loss = loss - normalizing_constant # ignore padded tokens from loss loss = loss * padding_mask loss = loss.sum() / padding_mask.sum() return loss # Define gradient update step fn def train_step(state, batch, label_smoothing_factor=0.0): 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, batch["decoder_attention_mask"], label_smoothing_factor) return loss grad_fn = jax.value_and_grad(compute_loss) loss, grad = grad_fn(state.params) grad = jax.lax.pmean(grad, "batch") new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng) metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)} metrics = jax.lax.pmean(metrics, axis_name="batch") return new_state, metrics # Define eval fn def eval_step(params, batch, label_smoothing_factor=0.0): labels = batch.pop("labels") logits = model(**batch, params=params, train=False)[0] loss = loss_fn(logits, labels, batch["decoder_attention_mask"], label_smoothing_factor) # 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['pixel_values'], **gen_kwargs) return output_ids.sequences # Create parallel version of the train and eval step p_train_step = jax.pmap( partial(train_step, label_smoothing_factor=training_args.label_smoothing_factor), "batch", donate_argnums=(0,) ) p_eval_step = jax.pmap(partial(eval_step, label_smoothing_factor=training_args.label_smoothing_factor), "batch") p_generate_step = jax.pmap(generate_step, "batch") # Replicate the train state on each device state = state.replicate() if training_args.do_train: logger.info("***** Running training *****") logger.info(f" Num train examples = {num_train_examples_per_epoch}") logger.info(f" Num Epochs = {num_epochs}") logger.info(f" Instantaneous train batch size per device = {training_args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel & distributed) = {train_batch_size}") logger.info(f" Optimization steps per epoch = {steps_per_epoch}") logger.info(f" Total optimization steps = {total_train_steps}") if training_args.do_eval: logger.info(f" Num evaluation examples = {num_eval_examples}") logger.info(f" Instantaneous evaluation batch size per device = {training_args.per_device_eval_batch_size}") logger.info(f" Total evaluation batch size (w. parallel & distributed) = {eval_batch_size}") logger.info(f" Evaluation steps = {eval_steps}") if training_args.do_predict: logger.info(f" Num test examples = {num_test_examples}") logger.info(f" Instantaneous test batch size per device = {training_args.per_device_eval_batch_size}") logger.info(f" Total test batch size (w. parallel & distributed) = {eval_batch_size}") logger.info(f" Test steps = {test_steps}") # create output directory if not os.path.isdir(os.path.join(training_args.output_dir)): os.makedirs(os.path.join(training_args.output_dir), exist_ok=True) def save_ckpt(ckpt_dir: str, commit_msg: str = ""): """save checkpoints and push to Hugging Face Hub if specified""" # 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)) model.save_pretrained(os.path.join(training_args.output_dir, ckpt_dir), params=params) tokenizer.save_pretrained(os.path.join(training_args.output_dir, ckpt_dir)) if training_args.push_to_hub: repo.push_to_hub(commit_message=commit_msg, blocking=False) def evaluation_loop(rng: jax.random.PRNGKey, dataset: Dataset, metric_key_prefix: str = "eval", ckpt_dir: str = "", is_prediction=False): logger.info(f"*** {'Predict' if is_prediction else 'Evaluate'} ***") metrics = [] preds = [] labels = [] batches = blockwise_data_loader( rng, dataset, block_size=training_args.block_size, batch_size=eval_batch_size, keep_in_memory=False, shuffle=False, split="prediction" if is_prediction else "validation", ) steps = len(dataset) // eval_batch_size for _ in tqdm(range(steps), desc=f"{'Predicting' if is_prediction else 'Evaluating'}...", position=2, leave=False): # Model forward batch = next(batches) _labels = batch.get("labels", None) if not is_prediction and _labels is None: raise ValueError("Evaluation requires the validation dataset to have `labels`") if _labels is not None: _metrics = p_eval_step(state.params, batch) metrics.append(_metrics) # generation if data_args.predict_with_generate: generated_ids = p_generate_step(state.params, batch) preds.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"]))) if _labels is not None: labels.extend(jax.device_get(_labels.reshape(-1, _labels.shape[-1]))) if metrics: # normalize metrics metrics = get_metrics(metrics) metrics = jax.tree_map(jnp.mean, metrics) # compute ROUGE metrics generations = [] rouge_desc = "" if data_args.predict_with_generate: if labels: rouge_metrics, decoded_preds, decoded_labels = compute_metrics(preds, labels) metrics.update(rouge_metrics) rouge_desc = " ".join([f"{'Predict' if is_prediction else 'Eval'} {key}: {value} |" for key, value in rouge_metrics.items()]) for pred, label in zip(decoded_preds, decoded_labels): pred = pred.replace("\n", " ") label = label.replace("\n", " ") generations.append({"label": label, "pred": pred}) else: decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True) # Some simple post-processing decoded_preds = [pred.strip() for pred in decoded_preds] # rougeLSum expects newline after each sentence decoded_preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in decoded_preds] for pred in decoded_preds: pred = pred.replace("\n", " ") generations.append({"pred": pred}) if metrics: # Print metrics and update progress bar desc = f"{'Predict' if is_prediction else 'Eval'} Loss: {metrics['loss']} | {rouge_desc})" if training_args.do_train and not is_prediction: desc = f"Epoch... ({epoch + 1}/{num_epochs} | Step: {cur_step} | " + desc epochs.write(desc) epochs.desc = desc logger.info(desc) if jax.process_index() == 0: if not os.path.isdir(os.path.join(training_args.output_dir, ckpt_dir)): os.makedirs(os.path.join(training_args.output_dir, ckpt_dir), exist_ok=True) if metrics: # Save metrics (only for the evaluation/prediction being done along with training) if has_tensorboard and training_args.do_train: write_metric(summary_writer, metrics, train_time=None, step=cur_step, metric_key_prefix=metric_key_prefix) # save final metrics in json metrics = {f"{metric_key_prefix}_{metric_name}": round(value.item(), 6) for metric_name, value in metrics.items()} _path = os.path.join(training_args.output_dir, ckpt_dir, f"{metric_key_prefix}_results.json") with open(_path, "w") as f: json.dump(metrics, f, indent=4, sort_keys=True) # Update report with open(os.path.join(training_args.output_dir, 'log'), 'a', encoding='UTF-8') as fp: fp.write(desc + '\n') # Save generations if generations: with open(os.path.join(training_args.output_dir, ckpt_dir, f'{metric_key_prefix}_generation.json'), 'w', encoding='UTF-8') as fp: json.dump(generations, fp, ensure_ascii=False, indent=4) def evaluate(rng: jax.random.PRNGKey, dataset: Dataset, ckpt_dir: str = ""): evaluation_loop(rng, dataset, metric_key_prefix='eval', ckpt_dir=ckpt_dir) def predict(rng: jax.random.PRNGKey, dataset: Dataset): evaluation_loop(rng, dataset, metric_key_prefix='test', is_prediction=True) input_rng = None if training_args.do_train: cur_step = 0 train_time = 0 epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0) for epoch in epochs: # ======================== Training ================================ # Create sampling rng rng, input_rng = jax.random.split(rng) train_metrics = [] train_batches = blockwise_data_loader( input_rng, train_dataset, block_size=training_args.block_size, batch_size=train_batch_size, keep_in_memory=True, shuffle=True, split="train" ) # train for (batch_idx, _) in enumerate(tqdm(range(steps_per_epoch), desc="Training...", position=1, leave=False)): cur_step += 1 batch = next(train_batches) batch_start = time.time() state, train_metric = p_train_step(state, batch) train_metrics.append(train_metric) train_time += time.time() - batch_start time_per_step = train_time / cur_step _train_metric = unreplicate(train_metric) desc = f"Epoch... ({epoch + 1}/{num_epochs} | Step: {cur_step} | Loss: {_train_metric['loss']} | Learning Rate: {_train_metric['learning_rate']} | Time per step: {time_per_step})" epochs.desc = desc epochs.write(desc) # log and save info if training_args.logging_steps > 0 and cur_step % training_args.logging_steps == 0: logger.info(desc) with open(os.path.join(training_args.output_dir, 'log'), 'a', encoding='UTF-8') as fp: fp.write(desc + '\n') # Save metrics if has_tensorboard and jax.process_index() == 0: write_metric(summary_writer, train_metrics, train_time=train_time, step=cur_step, metric_key_prefix="train") # ======================== Evaluating (inside an epoch) ============================== if training_args.do_eval and (training_args.eval_steps is not None and training_args.eval_steps > 0) and cur_step % training_args.eval_steps == 0: ckpt_dir = f"ckpt_epoch_{epoch + 1}_step_{cur_step}" commit_msg = f"Saving weights and logs of epoch {epoch + 1} - step {cur_step}" evaluate(input_rng, eval_dataset, ckpt_dir) save_ckpt(ckpt_dir=ckpt_dir, commit_msg=commit_msg) # ======================== Epoch End ============================== # log and save info if training_args.logging_steps <= 0: logger.info(desc) with open(os.path.join(training_args.output_dir, 'log'), 'a', encoding='UTF-8') as fp: fp.write(desc + '\n') # Save metrics if has_tensorboard and jax.process_index() == 0: write_metric(summary_writer, train_metrics, train_time=train_time, step=cur_step, metric_key_prefix="train") # ======================== Evaluating (after each epoch) ============================== if training_args.do_eval and (training_args.eval_steps is None or training_args.eval_steps <= 0): ckpt_dir = f"ckpt_epoch_{epoch + 1}_step_{cur_step}" commit_msg = f"Saving weights and logs of epoch {epoch + 1} - step {cur_step}" evaluate(input_rng, eval_dataset, ckpt_dir) save_ckpt(ckpt_dir=ckpt_dir, commit_msg=commit_msg) # ======================== Evaluating | Predicting ============================== # Create sampling rng if input_rng is None: rng, input_rng = jax.random.split(rng) # run evaluation without training if training_args.do_eval and not training_args.do_train: evaluate(input_rng, eval_dataset) # run prediction after (or without) training if training_args.do_predict: predict(input_rng, predict_dataset) if __name__ == "__main__": main()