#!/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 json 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 from flax.serialization import from_bytes, to_bytes 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' # we currently have issues with bart-large @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]`." }, ) from_checkpoint: Optional[str] = field( default=None, metadata={ "help": "Loads a pretrained wandb checkpoint. Use artifact reference." }, ) @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." }, ) no_decay: bool = field( default=False, metadata={"help": "Whether to use decay in the learning rate scheduler."} ) 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." }, ) log_model: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) save_model_steps: Optional[int] = field( default=3000, # about once every hour in our experiments metadata={ "help": "For logging the model more frequently. Used only when `log_model` is set." }, ) 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): # check config is valid, otherwise set default values self.config.vocab_size_output = getattr(self.config, 'vocab_size_output', OUTPUT_VOCAB_SIZE) self.config.max_position_embeddings_decoder = getattr(self.config, 'max_position_embeddings_decoder', OUTPUT_LENGTH) # 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( self.config.vocab_size_output, 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 = self.config.max_position_embeddings_decoder decoder_config.vocab_size = self.config.vocab_size_output self.decoder = FlaxBartDecoder(decoder_config, dtype=self.dtype, embed_tokens=self.decoder_embed) class CustomFlaxBartForConditionalGenerationModule(FlaxBartForConditionalGenerationModule): def setup(self): # check config is valid, otherwise set default values self.config.vocab_size_output = getattr(self.config, 'vocab_size_output', OUTPUT_VOCAB_SIZE) self.model = CustomFlaxBartModule(config=self.config, dtype=self.dtype) self.lm_head = nn.Dense( self.config.vocab_size_output, 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, self.config.vocab_size_output)) 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, no_decay: bool ) -> 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) if no_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: jax.device_get(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 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() logger.warning(f"WARNING: eval_steps has been manually hardcoded") # TODO: remove it later, convenient for now training_args.eval_steps = 400 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"WARNING: 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", "/data/YFCC/metadata_encoded.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. # Set up items to load or create tokenizer = None artifact_dir = None def restore_state(state, artifact_dir): # restore optimizer state if (Path(artifact_dir) / 'opt_state.msgpack').exists(): with (Path(artifact_dir) / 'opt_state.msgpack').open('rb') as f: opt_state = from_bytes(state.opt_state, f.read()) # restore steps if (Path(artifact_dir) / 'training_state.json').exists(): with (Path(artifact_dir) / 'opt_state.msgpack').open('r') as f: training_state = json.load(f) step = training_state['step'] optimizer_step = step // training_args.gradient_accumulation_steps state.replace(step=step, optimizer_step=optimizer_step) if model_args.from_checkpoint is not None: artifact = wandb.run.use_artifact(model_args.from_checkpoint) artifact_dir = artifact.download() model = CustomFlaxBartForConditionalGeneration.from_pretrained(artifact_dir) # some models will try to change bos (because of force_bos_token_to_be_generated) # we ensure bos and eos are not forced model.config.force_bos_token_to_be_generated = False model.config.forced_bos_token_id = None model.config.forced_eos_token_id = None # used in the preprocessing function config = model.config # load tokenizer if present if (Path(artifact_dir) / 'tokenizer_config.json').exists(): tokenizer = AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer ) else: base_model = FlaxAutoModelForSeq2SeqLM.from_pretrained( model_args.model_name_or_path, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype) ) # 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 # for first token config.bos_token_id = BOS_TOKEN_ID # should not be used (due to forced_bos_token_id) 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.force_bos_token_to_be_generated = False # otherwise it sets bos_token_id at loading config.min_length = data_args.max_target_length config.max_length = data_args.max_target_length # 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 # Load tokenizer if it has not been set if tokenizer is None: tokenizer = AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer ) print(f"TPUs: {jax.device_count()}") assert jax.device_count() == 8, "TPUs in use, please check running processes" 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, data_args.no_decay ) # 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, ) if model_args.from_checkpoint is not None: # restore optimizer state, step and optimizer_step restore_state(state) # 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 def run_save_model(state, step, epoch, eval_metrics=None): 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 tokenizer tokenizer.save_pretrained(training_args.output_dir) # save state state = unreplicate(state) with (Path(training_args.output_dir) / 'opt_state.msgpack').open('wb') as f: f.write(to_bytes(state.opt_state)) with (Path(training_args.output_dir) / 'training_state.json').open('w') as f: json.dump({'step': state.step.item()}, f) # save to W&B if data_args.log_model: metadata = {'step': step, 'epoch': epoch} if eval_metrics is not None: metadata['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) / 'tokenizer_config.json')) artifact.add_file(str(Path(training_args.output_dir) / 'special_tokens_map.json')) artifact.add_file(str(Path(training_args.output_dir) / 'vocab.json')) artifact.add_file(str(Path(training_args.output_dir) / 'added_tokens.json')) artifact.add_file(str(Path(training_args.output_dir) / 'merges.txt')) artifact.add_file(str(Path(training_args.output_dir) / 'config.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 of epoch {epoch+1}", temp_dir=True # avoid issues with being in a repository ) 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='train') if global_step % training_args.eval_steps == 0: run_evaluation() if global_step % data_args.save_model_steps == 0: run_save_model(state, global_step, epoch) # log final train metrics wandb_log(unreplicate(train_metric), step=global_step, prefix='train') 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 run_save_model(state, global_step, epoch, eval_metrics) # ======================== 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()