Skip to content

GitLab

Unverified Commit 75627148 authored by Funtowicz Morgan's avatar Funtowicz Morgan Committed by GitHub
Browse files

Flax Masked Language Modeling training example (#8728) 



* Remove "Model" suffix from Flax models to look more :hugs:
Signed-off-by: default avatarMorgan Funtowicz <morgan@huggingface.co>

* Initial working (forward + backward) for Flax MLM training example.
Signed-off-by: default avatarMorgan Funtowicz <morgan@huggingface.co>

* Simply code
Signed-off-by: default avatarMorgan Funtowicz <morgan@huggingface.co>

* Addressing comments, using module and moving to LM task.
Signed-off-by: default avatarMorgan Funtowicz <morgan@huggingface.co>

* Restore parameter name "module" wrongly renamed model.
Signed-off-by: default avatarMorgan Funtowicz <morgan@huggingface.co>

* Restore correct output ordering...
Signed-off-by: default avatarMorgan Funtowicz <morgan@huggingface.co>

* Actually commit the example 😅

Signed-off-by: default avatarMorgan Funtowicz <morgan@huggingface.co>

* Add FlaxBertModelForMaskedLM after rebasing.
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Make it possible to initialize the training from scratch
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Reuse flax linen example of cross entropy loss
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Added specific data collator for flax
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Remove todo for data collator
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Added evaluation step
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Added ability to provide dtype to support bfloat16 on TPU
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Enable flax tensorboard output
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Enable jax.pmap support.
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Ensure batches are correctly sized to be dispatched with jax.pmap
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Enable bfloat16 with --fp16 cmdline args
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Correctly export metrics to tensorboard
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Added dropout and ability to use it.
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Effectively enable & disable during training and evaluation steps.
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Oops.
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Enable specifying kernel initializer scale
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Style.
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Added warmup step to the learning rate scheduler.
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Fix typo.
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Print training loss
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Make style
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* fix linter issue (flake8)
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Fix model matching
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Fix dummies
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Fix non default dtype on Flax models
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Use the same create_position_ids_from_input_ids for FlaxRoberta
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Make Roberta attention as Bert
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* fix copy
Signed-off-by: default avatarMorgan Funtowicz <funtowiczmo@gmail.com>

* Wording.
Co-authored-by: default avatarMarc van Zee <marcvanzee@gmail.com>
Co-authored-by: default avatarMarc van Zee <marcvanzee@gmail.com>
parent df2af6d8
Hide whitespace changes
Inline Side-by-side
Showing with 1188 additions and 133 deletions
examples/language-modeling/run_mlm_flax.py 0 → 100644
View file @ 75627148
# coding=utf-8
# Copyright 2020 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 masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking on a
text file or a dataset.
Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
https://huggingface.co/models?filter=masked-lm
"""
import logging
import os
import sys
from dataclasses import dataclass, field
# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
import jax
import jax.numpy as jnp
from flax import jax_utils
from flax.optim import Adam
from flax.training import common_utils
from flax.training.common_utils import get_metrics
from jax.nn import log_softmax
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoTokenizer,
FlaxBertForMaskedLM,
HfArgumentParser,
PreTrainedTokenizerBase,
TensorType,
TrainingArguments,
is_tensorboard_available,
set_seed,
)
# Cache the result
has_tensorboard = is_tensorboard_available()
if has_tensorboard:
try:
from flax.metrics.tensorboard import SummaryWriter
except ImportError as ie:
has_tensorboard = False
print(f"Unable to display metrics through TensorBoard because some package are not installed: {ie}")
else:
print(
"Unable to display metrics through TensorBoard because the package is not installed: "
"Please run pip install tensorboard to enable."
)
MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@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."
},
)
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."},
)
@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)."}
)
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)."},
)
train_ref_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input train ref data file for whole word masking in Chinese."},
)
validation_ref_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
max_seq_length: Optional[int] = field(
default=None,
metadata={
"help": "The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated. Default to the max input length of the model."
},
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
mlm_probability: float = field(
default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}
)
pad_to_max_length: bool = field(
default=False,
metadata={
"help": "Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
},
)
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", "txt"], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
extension = self.validation_file.split(".")[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
# Adapted from transformers/data/data_collator.py
# Letting here for now, let's discuss where it should live
@dataclass
class FlaxDataCollatorForLanguageModeling:
"""
Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
are not all of the same length.
Args:
tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
The tokenizer used for encoding the data.
mlm (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to use masked language modeling. If set to :obj:`False`, the labels are the same as the
inputs with the padding tokens ignored (by setting them to -100). Otherwise, the labels are -100 for
non-masked tokens and the value to predict for the masked token.
mlm_probability (:obj:`float`, `optional`, defaults to 0.15):
The probability with which to (randomly) mask tokens in the input, when :obj:`mlm` is set to :obj:`True`.
.. note::
For best performance, this data collator should be used with a dataset having items that are dictionaries or
BatchEncoding, with the :obj:`"special_tokens_mask"` key, as returned by a
:class:`~transformers.PreTrainedTokenizer` or a :class:`~transformers.PreTrainedTokenizerFast` with the
argument :obj:`return_special_tokens_mask=True`.
"""
tokenizer: PreTrainedTokenizerBase
mlm: bool = True
mlm_probability: float = 0.15
def __post_init__(self):
if self.mlm and self.tokenizer.mask_token is None:
raise ValueError(
"This tokenizer does not have a mask token which is necessary for masked language modeling. "
"You should pass `mlm=False` to train on causal language modeling instead."
)
def __call__(self, examples: List[Dict[str, np.ndarray]], pad_to_multiple_of: int) -> Dict[str, np.ndarray]:
# Handle dict or lists with proper padding and conversion to tensor.
batch = self.tokenizer.pad(examples, pad_to_multiple_of=pad_to_multiple_of, return_tensors=TensorType.NUMPY)
# If special token mask has been preprocessed, pop it from the dict.
special_tokens_mask = batch.pop("special_tokens_mask", None)
if self.mlm:
batch["input_ids"], batch["labels"] = self.mask_tokens(
batch["input_ids"], special_tokens_mask=special_tokens_mask
)
else:
labels = batch["input_ids"].copy()
if self.tokenizer.pad_token_id is not None:
labels[labels == self.tokenizer.pad_token_id] = -100
batch["labels"] = labels
return batch
def mask_tokens(
self, inputs: np.ndarray, special_tokens_mask: Optional[np.ndarray]
) -> Tuple[jnp.ndarray, jnp.ndarray]:
"""
Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.
"""
labels = inputs.copy()
# We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`)
probability_matrix = np.full(labels.shape, self.mlm_probability)
special_tokens_mask = special_tokens_mask.astype("bool")
probability_matrix[special_tokens_mask] = 0.0
masked_indices = np.random.binomial(1, probability_matrix).astype("bool")
labels[~masked_indices] = -100 # We only compute loss on masked tokens
# 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
indices_replaced = np.random.binomial(1, np.full(labels.shape, 0.8)).astype("bool") & masked_indices
inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)
# 10% of the time, we replace masked input tokens with random word
indices_random = np.random.binomial(1, np.full(labels.shape, 0.5)).astype("bool")
indices_random &= masked_indices & ~indices_replaced
random_words = np.random.randint(self.tokenizer.vocab_size, size=labels.shape, dtype="i4")
inputs[indices_random] = random_words[indices_random]
# The rest of the time (10% of the time) we keep the masked input tokens unchanged
return inputs, labels
def create_learning_rate_scheduler(
factors="constant * linear_warmup * rsqrt_decay",
base_learning_rate=0.5,
warmup_steps=1000,
decay_factor=0.5,
steps_per_decay=20000,
steps_per_cycle=100000,
):
"""Creates learning rate schedule.
Interprets factors in the factors string which can consist of:
* constant: interpreted as the constant value,
* linear_warmup: interpreted as linear warmup until warmup_steps,
* rsqrt_decay: divide by square root of max(step, warmup_steps)
* rsqrt_normalized_decay: divide by square root of max(step/warmup_steps, 1)
* decay_every: Every k steps decay the learning rate by decay_factor.
* cosine_decay: Cyclic cosine decay, uses steps_per_cycle parameter.
Args:
factors: string, factors separated by "*" that defines the schedule.
base_learning_rate: float, the starting constant for the lr schedule.
warmup_steps: int, how many steps to warm up for in the warmup schedule.
decay_factor: float, the amount to decay the learning rate by.
steps_per_decay: int, how often to decay the learning rate.
steps_per_cycle: int, steps per cycle when using cosine decay.
Returns:
a function learning_rate(step): float -> {"learning_rate": float}, the
step-dependent lr.
"""
factors = [n.strip() for n in factors.split("*")]
def step_fn(step):
"""Step to learning rate function."""
ret = 1.0
for name in factors:
if name == "constant":
ret *= base_learning_rate
elif name == "linear_warmup":
ret *= jnp.minimum(1.0, step / warmup_steps)
elif name == "rsqrt_decay":
ret /= jnp.sqrt(jnp.maximum(step, warmup_steps))
elif name == "rsqrt_normalized_decay":
ret *= jnp.sqrt(warmup_steps)
ret /= jnp.sqrt(jnp.maximum(step, warmup_steps))
elif name == "decay_every":
ret *= decay_factor ** (step // steps_per_decay)
elif name == "cosine_decay":
progress = jnp.maximum(0.0, (step - warmup_steps) / float(steps_per_cycle))
ret *= jnp.maximum(0.0, 0.5 * (1.0 + jnp.cos(jnp.pi * (progress % 1.0))))
else:
raise ValueError("Unknown factor %s." % name)
return jnp.asarray(ret, dtype=jnp.float32)
return step_fn
def compute_metrics(logits, labels, weights, label_smoothing=0.0):
"""Compute summary metrics."""
loss, normalizer = cross_entropy(logits, labels, weights, label_smoothing)
acc, _ = accuracy(logits, labels, weights)
metrics = {"loss": loss, "accuracy": acc, "normalizer": normalizer}
metrics = jax.lax.psum(metrics, axis_name="batch")
return metrics
def accuracy(logits, targets, weights=None):
"""Compute weighted accuracy for log probs and targets.
Args:
logits: [batch, length, num_classes] float array.
targets: categorical targets [batch, length] int array.
weights: None or array of shape [batch, length]
Returns:
Tuple of scalar loss and batch normalizing factor.
"""
if logits.ndim != targets.ndim + 1:
raise ValueError(
"Incorrect shapes. Got shape %s logits and %s targets" % (str(logits.shape), str(targets.shape))
)
loss = jnp.equal(jnp.argmax(logits, axis=-1), targets)
loss *= weights
return loss.sum(), weights.sum()
def cross_entropy(logits, targets, weights=None, label_smoothing=0.0):
"""Compute cross entropy and entropy for log probs and targets.
Args:
logits: [batch, length, num_classes] float array.
targets: categorical targets [batch, length] int array.
weights: None or array of shape [batch, length]
label_smoothing: label smoothing constant, used to determine the on and off values.
Returns:
Tuple of scalar loss and batch normalizing factor.
"""
if logits.ndim != targets.ndim + 1:
raise ValueError(
"Incorrect shapes. Got shape %s logits and %s targets" % (str(logits.shape), str(targets.shape))
)
vocab_size = logits.shape[-1]
confidence = 1.0 - label_smoothing
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_targets = common_utils.onehot(targets, vocab_size, on_value=confidence, off_value=low_confidence)
loss = -jnp.sum(soft_targets * log_softmax(logits), axis=-1)
loss = loss - normalizing_constant
if weights is not None:
loss = loss * weights
normalizing_factor = weights.sum()
else:
normalizing_factor = np.prod(targets.shape)
return loss.sum(), normalizing_factor
def training_step(optimizer, batch, dropout_rng):
dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
def loss_fn(params):
targets = batch.pop("labels")
# Hide away tokens which doesn't participate in the optimization
token_mask = jnp.where(targets > 0, 1.0, 0.0)
pooled, logits = model(**batch, params=params, dropout_rng=dropout_rng, train=True)
loss, weight_sum = cross_entropy(logits, targets, token_mask)
return loss / weight_sum
step = optimizer.state.step
lr = lr_scheduler_fn(step)
grad_fn = jax.value_and_grad(loss_fn)
loss, grad = grad_fn(optimizer.target)
grad = jax.lax.pmean(grad, "batch")
optimizer = optimizer.apply_gradient(grad, learning_rate=lr)
return loss, optimizer, new_dropout_rng
def eval_step(params, batch):
"""
Calculate evaluation metrics on a batch.
"""
targets = batch.pop("labels")
# Hide away tokens which doesn't participate in the optimization
token_mask = jnp.where(targets > 0, 1.0, 0.0)
_, logits = model(**batch, params=params, train=False)
return compute_metrics(logits, targets, token_mask)
def generate_batch_splits(samples_idx: jnp.ndarray, batch_size: int) -> jnp.ndarray:
nb_samples = len(samples_idx)
samples_to_remove = nb_samples % batch_size
if samples_to_remove != 0:
samples_idx = samples_idx[:-samples_to_remove]
sections_split = nb_samples // batch_size
batch_idx = jnp.split(samples_idx, sections_split)
return batch_idx
if __name__ == "__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."
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
level="NOTSET",
datefmt="[%X]",
)
# Log on each process the small summary:
logger = logging.getLogger(__name__)
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
# Set the verbosity to info of the Transformers logger (on main process only):
logger.info("Training/evaluation parameters %s", training_args)
# Set seed before initializing model.
set_seed(training_args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT 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 column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
#
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
else:
data_files = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
if data_args.validation_file is not None:
data_files["validation"] = data_args.validation_file
extension = data_args.train_file.split(".")[-1]
if extension == "txt":
extension = "text"
datasets = load_dataset(extension, data_files=data_files)
# 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
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
if model_args.config_name:
config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
elif model_args.model_name_or_path:
config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
else:
config = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch.")
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:
tokenizer = AutoTokenizer.from_pretrained(
model_args.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."
)
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
column_names = datasets["train"].column_names
else:
column_names = datasets["validation"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
padding = "max_length" if data_args.pad_to_max_length else False
def tokenize_function(examples):
# Remove empty lines
examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()]
return tokenizer(
examples["text"],
return_special_tokens_mask=True,
padding=padding,
truncation=True,
max_length=data_args.max_seq_length,
)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=[text_column_name],
load_from_cache_file=not data_args.overwrite_cache,
)
# Enable tensorboard only on the master node
if has_tensorboard and jax.host_id() == 0:
summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir).joinpath("logs").as_posix())
# Data collator
# This one will take care of randomly masking the tokens.
data_collator = FlaxDataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)
# Initialize our training
rng = jax.random.PRNGKey(training_args.seed)
dropout_rngs = jax.random.split(rng, jax.local_device_count())
model = FlaxBertForMaskedLM.from_pretrained("bert-base-cased", dtype=jnp.float32, dropout_rate=0.1)
model.init(jax.random.PRNGKey(training_args.seed), (training_args.train_batch_size, model.config.max_length))
# Setup optimizer
optimizer = Adam(
learning_rate=training_args.learning_rate,
weight_decay=training_args.weight_decay,
beta1=training_args.adam_beta1,
beta2=training_args.adam_beta2,
).create(model.params)
# Create learning rate scheduler
lr_scheduler_fn = create_learning_rate_scheduler(
base_learning_rate=training_args.learning_rate, warmup_steps=training_args.warmup_steps
)
# Create parallel version of the training and evaluation steps
p_training_step = jax.pmap(training_step, "batch", donate_argnums=(0,))
p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,))
# Replicate the optimizer on each device
optimizer = jax_utils.replicate(optimizer)
# Store some constant
nb_epochs = int(training_args.num_train_epochs)
batch_size = int(training_args.train_batch_size)
eval_batch_size = int(training_args.eval_batch_size)
epochs = tqdm(range(nb_epochs), desc=f"Epoch ... (1/{nb_epochs})", position=0)
for epoch in epochs:
# ======================== Training ================================
# Create sampling rng
rng, training_rng, eval_rng = jax.random.split(rng, 3)
# Generate an epoch by shuffling sampling indices from the train dataset
nb_training_samples = len(tokenized_datasets["train"])
training_samples_idx = jax.random.permutation(training_rng, jnp.arange(nb_training_samples))
training_batch_idx = generate_batch_splits(training_samples_idx, batch_size)
# Gather the indexes for creating the batch and do a training step
for batch_idx in tqdm(training_batch_idx, desc="Training...", position=1):
samples = [tokenized_datasets["train"][int(idx)] for idx in batch_idx]
model_inputs = data_collator(samples, pad_to_multiple_of=16)
# Model forward
model_inputs = common_utils.shard(model_inputs.data)
loss, optimizer, dropout_rngs = p_training_step(optimizer, model_inputs, dropout_rngs)
epochs.write(f"Loss: {loss}")
# ======================== Evaluating ==============================
nb_eval_samples = len(tokenized_datasets["test"])
eval_samples_idx = jnp.arange(nb_eval_samples)
eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)
eval_metrics = []
for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
samples = [tokenized_datasets["test"][int(idx)] for idx in batch_idx]
model_inputs = data_collator(samples, pad_to_multiple_of=16)
# Model forward
model_inputs = common_utils.shard(model_inputs.data)
metrics = p_eval_step(optimizer.target, model_inputs)
eval_metrics.append(metrics)
eval_metrics_np = get_metrics(eval_metrics)
eval_metrics_np = jax.tree_map(jnp.sum, eval_metrics_np)
eval_normalizer = eval_metrics_np.pop("normalizer")
eval_summary = jax.tree_map(lambda x: x / eval_normalizer, eval_metrics_np)
# Update progress bar
epochs.desc = (
f"Epoch... ({epoch + 1}/{nb_epochs} | Loss: {eval_summary['loss']}, Acc: {eval_summary['accuracy']})"
)
# Save metrics
if has_tensorboard and jax.host_id() == 0:
for name, value in eval_summary.items():
summary_writer.scalar(name, value, epoch)
src/transformers/__init__.py
View file @ 75627148
......@@ -936,7 +936,7 @@ else:
if is_flax_available():
from .models.auto import FLAX_MODEL_MAPPING, FlaxAutoModel
from .models.bert import FlaxBertModel
from .models.bert import FlaxBertForMaskedLM, FlaxBertModel
from .models.roberta import FlaxRobertaModel
else:
# Import the same objects as dummies to get them in the namespace.
......
src/transformers/modeling_flax_utils.py
View file @ 75627148
......@@ -65,13 +65,12 @@ class FlaxPreTrainedModel(ABC):
base_model_prefix = ""
model_class = None
def __init__(self, config: PretrainedConfig, module: nn.Module, params: Dict, seed: int = 0):
def __init__(
self, config: PretrainedConfig, module: nn.Module, params: Dict, seed: int = 0, dtype: jnp.dtype = jnp.float32
):
if config is None:
raise ValueError("config cannot be None")
if module is None:
raise ValueError("module cannot be None")
if params is None:
raise ValueError("state cannot be None")
......@@ -82,19 +81,23 @@ class FlaxPreTrainedModel(ABC):
# Those are public as their type is generic to every derived classes.
self.key = PRNGKey(seed)
self.params = params
self.model = module
self.dtype = dtype
@property
def config(self) -> PretrainedConfig:
return self._config
@property
def module(self) -> nn.Module:
return self._module
@staticmethod
@abstractmethod
def convert_from_pytorch(pt_state: Dict, config: PretrainedConfig) -> Dict:
raise NotImplementedError()
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
def from_pretrained(cls, pretrained_model_name_or_path, dtype: jnp.dtype = jnp.float32, *model_args, **kwargs):
r"""
Instantiate a pretrained Flax model from a pre-trained model configuration.
"""
......@@ -127,6 +130,9 @@ class FlaxPreTrainedModel(ABC):
else:
model_kwargs = kwargs
# Add the dtype to model_kwargs
model_kwargs["dtype"] = dtype
# Load model
if pretrained_model_name_or_path is not None:
if os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
......
src/transformers/models/bert/__init__.py
View file @ 75627148
......@@ -59,4 +59,4 @@ if is_tf_available():
)
if is_flax_available():
from .modeling_flax_bert import FlaxBertModel
from .modeling_flax_bert import FlaxBertForMaskedLM, FlaxBertModel
src/transformers/models/bert/modeling_flax_bert.py
View file @ 75627148
......@@ -13,13 +13,14 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Callable, Dict
from typing import Callable, Dict, Tuple
import numpy as np
import flax.linen as nn
import jax
import jax.numpy as jnp
from jax.random import PRNGKey
from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_flax_utils import FlaxPreTrainedModel, gelu
......@@ -101,8 +102,8 @@ class FlaxBertLayerNorm(nn.Module):
scale: bool = True # If True, multiply by scale (gamma). When the next layer is linear
# (also e.g. nn.relu), this can be disabled since the scaling will be
# done by the next layer.
bias_init: jnp.ndarray = nn.initializers.zeros
scale_init: jnp.ndarray = nn.initializers.ones
scale_init: Callable[..., np.ndarray] = jax.nn.initializers.ones
bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros
@nn.compact
def __call__(self, x):
......@@ -122,11 +123,13 @@ class FlaxBertLayerNorm(nn.Module):
mean2 = jnp.mean(jax.lax.square(x), axis=-1, keepdims=True)
var = mean2 - jax.lax.square(mean)
mul = jax.lax.rsqrt(var + self.epsilon)
if self.scale:
mul = mul * jnp.asarray(self.param("gamma", self.scale_init, (features,)), self.dtype)
mul = mul * jnp.asarray(self.param("gamma", self.scale_init, (features,)))
y = (x - mean) * mul
if self.bias:
y = y + jnp.asarray(self.param("beta", self.bias_init, (features,)), self.dtype)
y = y + jnp.asarray(self.param("beta", self.bias_init, (features,)))
return y
......@@ -138,7 +141,9 @@ class FlaxBertEmbedding(nn.Module):
vocab_size: int
hidden_size: int
emb_init: Callable[..., np.ndarray] = nn.initializers.normal(stddev=0.1)
kernel_init_scale: float = 0.2
emb_init: Callable[..., np.ndarray] = jax.nn.initializers.normal(stddev=kernel_init_scale)
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
@nn.compact
def __call__(self, inputs):
......@@ -153,63 +158,105 @@ class FlaxBertEmbeddings(nn.Module):
hidden_size: int
type_vocab_size: int
max_length: int
kernel_init_scale: float = 0.2
dropout_rate: float = 0.0
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
@nn.compact
def __call__(self, input_ids, token_type_ids, position_ids, attention_mask):
def __call__(self, input_ids, token_type_ids, position_ids, attention_mask, deterministic: bool = True):
# Embed
w_emb = FlaxBertEmbedding(self.vocab_size, self.hidden_size, name="word_embeddings")(
jnp.atleast_2d(input_ids.astype("i4"))
)
p_emb = FlaxBertEmbedding(self.max_length, self.hidden_size, name="position_embeddings")(
jnp.atleast_2d(position_ids.astype("i4"))
)
t_emb = FlaxBertEmbedding(self.type_vocab_size, self.hidden_size, name="token_type_embeddings")(
jnp.atleast_2d(token_type_ids.astype("i4"))
)
w_emb = FlaxBertEmbedding(
self.vocab_size,
self.hidden_size,
kernel_init_scale=self.kernel_init_scale,
name="word_embeddings",
dtype=self.dtype,
)(jnp.atleast_2d(input_ids.astype("i4")))
p_emb = FlaxBertEmbedding(
self.max_length,
self.hidden_size,
kernel_init_scale=self.kernel_init_scale,
name="position_embeddings",
dtype=self.dtype,
)(jnp.atleast_2d(position_ids.astype("i4")))
t_emb = FlaxBertEmbedding(
self.type_vocab_size,
self.hidden_size,
kernel_init_scale=self.kernel_init_scale,
name="token_type_embeddings",
dtype=self.dtype,
)(jnp.atleast_2d(token_type_ids.astype("i4")))
# Sum all embeddings
summed_emb = w_emb + jnp.broadcast_to(p_emb, w_emb.shape) + t_emb
# Layer Norm
layer_norm = FlaxBertLayerNorm(name="layer_norm")(summed_emb)
return layer_norm
layer_norm = FlaxBertLayerNorm(name="layer_norm", dtype=self.dtype)(summed_emb)
embeddings = nn.Dropout(rate=self.dropout_rate)(layer_norm, deterministic=deterministic)
return embeddings
class FlaxBertAttention(nn.Module):
num_heads: int
head_size: int
dropout_rate: float = 0.0
kernel_init_scale: float = 0.2
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
@nn.compact
def __call__(self, hidden_state, attention_mask):
def __call__(self, hidden_state, attention_mask, deterministic: bool = True):
# Attention mask comes in as attention_mask.shape == (*batch_sizes, kv_length)
# FLAX expects: attention_mask.shape == (*batch_sizes, 1, 1, kv_length) such that it is broadcastable
# with attn_weights.shape == (*batch_sizes, num_heads, q_length, kv_length)
attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
self_att = nn.attention.SelfAttention(num_heads=self.num_heads, qkv_features=self.head_size, name="self")(
hidden_state, attention_mask
)
self_att = nn.attention.SelfAttention(
num_heads=self.num_heads,
qkv_features=self.head_size,
dropout_rate=self.dropout_rate,
deterministic=deterministic,
kernel_init=jax.nn.initializers.normal(self.kernel_init_scale, self.dtype),
bias_init=jax.nn.initializers.zeros,
name="self",
dtype=self.dtype,
)(hidden_state, attention_mask)
layer_norm = FlaxBertLayerNorm(name="layer_norm")(self_att + hidden_state)
layer_norm = FlaxBertLayerNorm(name="layer_norm", dtype=self.dtype)(self_att + hidden_state)
return layer_norm
class FlaxBertIntermediate(nn.Module):
output_size: int
kernel_init_scale: float = 0.2
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
@nn.compact
def __call__(self, hidden_state):
# TODO: Add ACT2FN reference to change activation function
dense = nn.Dense(features=self.output_size, name="dense")(hidden_state)
dense = nn.Dense(
features=self.output_size,
kernel_init=jax.nn.initializers.normal(self.kernel_init_scale, self.dtype),
name="dense",
dtype=self.dtype,
)(hidden_state)
return gelu(dense)
class FlaxBertOutput(nn.Module):
dropout_rate: float = 0.0
kernel_init_scale: float = 0.2
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
@nn.compact
def __call__(self, intermediate_output, attention_output):
hidden_state = nn.Dense(attention_output.shape[-1], name="dense")(intermediate_output)
hidden_state = FlaxBertLayerNorm(name="layer_norm")(hidden_state + attention_output)
def __call__(self, intermediate_output, attention_output, deterministic: bool = True):
hidden_state = nn.Dense(
attention_output.shape[-1],
kernel_init=jax.nn.initializers.normal(self.kernel_init_scale, self.dtype),
name="dense",
dtype=self.dtype,
)(intermediate_output)
hidden_state = nn.Dropout(rate=self.dropout_rate)(hidden_state, deterministic=deterministic)
hidden_state = FlaxBertLayerNorm(name="layer_norm", dtype=self.dtype)(hidden_state + attention_output)
return hidden_state
......@@ -217,12 +264,26 @@ class FlaxBertLayer(nn.Module):
num_heads: int
head_size: int
intermediate_size: int
dropout_rate: float = 0.0
kernel_init_scale: float = 0.2
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
@nn.compact
def __call__(self, hidden_state, attention_mask):
attention = FlaxBertAttention(self.num_heads, self.head_size, name="attention")(hidden_state, attention_mask)
intermediate = FlaxBertIntermediate(self.intermediate_size, name="intermediate")(attention)
output = FlaxBertOutput(name="output")(intermediate, attention)
def __call__(self, hidden_state, attention_mask, deterministic: bool = True):
attention = FlaxBertAttention(
self.num_heads,
self.head_size,
kernel_init_scale=self.kernel_init_scale,
dropout_rate=self.dropout_rate,
name="attention",
dtype=self.dtype,
)(hidden_state, attention_mask, deterministic=deterministic)
intermediate = FlaxBertIntermediate(
self.intermediate_size, kernel_init_scale=self.kernel_init_scale, name="intermediate", dtype=self.dtype
)(attention)
output = FlaxBertOutput(
kernel_init_scale=self.kernel_init_scale, dropout_rate=self.dropout_rate, name="output", dtype=self.dtype
)(intermediate, attention, deterministic=deterministic)
return output
......@@ -236,9 +297,12 @@ class FlaxBertLayerCollection(nn.Module):
num_heads: int
head_size: int
intermediate_size: int
dropout_rate: float = 0.0
kernel_init_scale: float = 0.2
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
@nn.compact
def __call__(self, inputs, attention_mask):
def __call__(self, inputs, attention_mask, deterministic: bool = True):
assert self.num_layers > 0, f"num_layers should be >= 1, got ({self.num_layers})"
# Initialize input / output
......@@ -246,8 +310,16 @@ class FlaxBertLayerCollection(nn.Module):
# Forward over all encoders
for i in range(self.num_layers):
layer = FlaxBertLayer(self.num_heads, self.head_size, self.intermediate_size, name=f"{i}")
input_i = layer(input_i, attention_mask)
layer = FlaxBertLayer(
self.num_heads,
self.head_size,
self.intermediate_size,
kernel_init_scale=self.kernel_init_scale,
dropout_rate=self.dropout_rate,
name=f"{i}",
dtype=self.dtype,
)
input_i = layer(input_i, attention_mask, deterministic=deterministic)
return input_i
......@@ -256,21 +328,39 @@ class FlaxBertEncoder(nn.Module):
num_heads: int
head_size: int
intermediate_size: int
dropout_rate: float = 0.0
kernel_init_scale: float = 0.2
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
@nn.compact
def __call__(self, hidden_state, attention_mask):
def __call__(self, hidden_state, attention_mask, deterministic: bool = True):
layer = FlaxBertLayerCollection(
self.num_layers, self.num_heads, self.head_size, self.intermediate_size, name="layer"
)(hidden_state, attention_mask)
self.num_layers,
self.num_heads,
self.head_size,
self.intermediate_size,
kernel_init_scale=self.kernel_init_scale,
dropout_rate=self.dropout_rate,
name="layer",
dtype=self.dtype,
)(hidden_state, attention_mask, deterministic=deterministic)
return layer
class FlaxBertPooler(nn.Module):
kernel_init_scale: float = 0.2
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
@nn.compact
def __call__(self, hidden_state):
cls_token = hidden_state[:, 0]
out = nn.Dense(hidden_state.shape[-1], name="dense")(cls_token)
return jax.lax.tanh(out)
out = nn.Dense(
hidden_state.shape[-1],
kernel_init=jax.nn.initializers.normal(self.kernel_init_scale, self.dtype),
name="dense",
dtype=self.dtype,
)(cls_token)
return nn.tanh(out)
class FlaxBertModule(nn.Module):
......@@ -282,24 +372,104 @@ class FlaxBertModule(nn.Module):
num_heads: int
head_size: int
intermediate_size: int
dropout_rate: float = 0.0
kernel_init_scale: float = 0.2
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
@nn.compact
def __call__(self, input_ids, attention_mask, token_type_ids, position_ids):
def __call__(self, input_ids, attention_mask, token_type_ids, position_ids, deterministic: bool = True):
# Embedding
embeddings = FlaxBertEmbeddings(
self.vocab_size, self.hidden_size, self.type_vocab_size, self.max_length, name="embeddings"
)(input_ids, token_type_ids, position_ids, attention_mask)
self.vocab_size,
self.hidden_size,
self.type_vocab_size,
self.max_length,
kernel_init_scale=self.kernel_init_scale,
dropout_rate=self.dropout_rate,
name="embeddings",
dtype=self.dtype,
)(input_ids, token_type_ids, position_ids, attention_mask, deterministic=deterministic)
# N stacked encoding layers
encoder = FlaxBertEncoder(
self.num_encoder_layers, self.num_heads, self.head_size, self.intermediate_size, name="encoder"
)(embeddings, attention_mask)
pooled = FlaxBertPooler(name="pooler")(encoder)
self.num_encoder_layers,
self.num_heads,
self.head_size,
self.intermediate_size,
kernel_init_scale=self.kernel_init_scale,
dropout_rate=self.dropout_rate,
name="encoder",
dtype=self.dtype,
)(embeddings, attention_mask, deterministic=deterministic)
pooled = FlaxBertPooler(kernel_init_scale=self.kernel_init_scale, name="pooler", dtype=self.dtype)(encoder)
return encoder, pooled
class FlaxBertPredictionHeadTransform(nn.Module):
dtype: jnp.dtype = jnp.float32
@nn.compact
def __call__(self, hidden_states):
hidden_states = nn.Dense(hidden_states.shape[-1], name="dense", dtype=self.dtype)(hidden_states)
hidden_states = nn.elu(hidden_states) # TODO: ACT2FN[config.hidden_act]
return FlaxBertLayerNorm(name="LayerNorm", dtype=self.dtype)(hidden_states)
class FlaxBertLMPredictionHead(nn.Module):
vocab_size: int
dtype: jnp.dtype = jnp.float32
@nn.compact
def __call__(self, hidden_states):
# TODO: The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
# Need a link between the two variables so that the bias is correctly
# resized with `resize_token_embeddings`
hidden_states = FlaxBertPredictionHeadTransform(name="transform", dtype=self.dtype)(hidden_states)
hidden_states = nn.Dense(self.vocab_size, name="decoder", dtype=self.dtype)(hidden_states)
return hidden_states
class FlaxBertOnlyMLMHead(nn.Module):
vocab_size: int
hidden_size: int
intermediate_size: int
head_size: int
num_heads: int
num_encoder_layers: int
type_vocab_size: int
max_length: int
dropout_rate: float = 0.0
dtype: jnp.dtype = jnp.float32
@nn.compact
def __call__(
self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, deterministic: bool = True
):
# Model
encoder, pooled = FlaxBertModule(
vocab_size=self.vocab_size,
type_vocab_size=self.type_vocab_size,
hidden_size=self.hidden_size,
intermediate_size=self.intermediate_size,
head_size=self.hidden_size,
num_heads=self.num_heads,
num_encoder_layers=self.num_encoder_layers,
max_length=self.max_length,
dropout_rate=self.dropout_rate,
dtype=self.dtype,
)(input_ids, attention_mask, token_type_ids, position_ids, deterministic=deterministic)
# Compute the prediction scores
encoder = nn.Dropout(rate=self.dropout_rate)(encoder, deterministic=deterministic)
logits = FlaxBertLMPredictionHead(vocab_size=self.vocab_size, name="predictions", dtype=self.dtype)(encoder)
return logits, pooled
@add_start_docstrings(
"The bare Bert Model transformer outputting raw hidden-states without any specific head on top.",
BERT_START_DOCSTRING,
......@@ -385,8 +555,8 @@ class FlaxBertModel(FlaxPreTrainedModel):
return jax_state
def __init__(self, config: BertConfig, state: dict, seed: int = 0, **kwargs):
model = FlaxBertModule(
def __init__(self, config: BertConfig, state: dict, seed: int = 0, dtype: jnp.dtype = jnp.float32):
module = FlaxBertModule(
vocab_size=config.vocab_size,
hidden_size=config.hidden_size,
type_vocab_size=config.type_vocab_size,
......@@ -395,16 +565,43 @@ class FlaxBertModel(FlaxPreTrainedModel):
num_heads=config.num_attention_heads,
head_size=config.hidden_size,
intermediate_size=config.intermediate_size,
dropout_rate=config.hidden_dropout_prob,
dtype=dtype,
)
super().__init__(config, model, state, seed)
@property
def module(self) -> nn.Module:
return self._module
super().__init__(config, module, state, seed)
@add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def __call__(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None):
def __call__(
self,
input_ids,
attention_mask=None,
token_type_ids=None,
position_ids=None,
params: dict = None,
dropout_rng: PRNGKey = None,
train: bool = False,
):
input_ids, attention_mask, token_type_ids, position_ids = self._check_inputs(
input_ids, attention_mask, token_type_ids, position_ids
)
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
return self.module.apply(
{"params": params or self.params},
jnp.array(input_ids, dtype="i4"),
jnp.array(attention_mask, dtype="i4"),
jnp.array(token_type_ids, dtype="i4"),
jnp.array(position_ids, dtype="i4"),
not train,
rngs=rngs,
)
def _check_inputs(self, input_ids, attention_mask, token_type_ids, position_ids):
if token_type_ids is None:
token_type_ids = jnp.ones_like(input_ids)
......@@ -414,10 +611,62 @@ class FlaxBertModel(FlaxPreTrainedModel):
if attention_mask is None:
attention_mask = jnp.ones_like(input_ids)
return self.model.apply(
{"params": self.params},
return input_ids, attention_mask, token_type_ids, position_ids
def init(self, rng: jax.random.PRNGKey, input_shape: Tuple):
input_ids, attention_mask, token_type_ids, position_ids = self._check_inputs(
jnp.zeros(input_shape, dtype="i4"), None, None, None
)
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
self.params = self.module.init(rngs, input_ids, attention_mask, token_type_ids, position_ids)["params"]
class FlaxBertForMaskedLM(FlaxBertModel):
def __init__(self, config: BertConfig, state: dict, seed: int = 0, dtype: jnp.dtype = jnp.float32, **kwargs):
super().__init__(config, state, seed, dtype)
self._module = FlaxBertOnlyMLMHead(
vocab_size=config.vocab_size,
type_vocab_size=config.type_vocab_size,
hidden_size=config.hidden_size,
intermediate_size=config.intermediate_size,
head_size=config.hidden_size,
num_heads=config.num_attention_heads,
num_encoder_layers=config.num_hidden_layers,
max_length=config.max_length,
**kwargs,
)
def __call__(
self,
input_ids,
attention_mask=None,
token_type_ids=None,
position_ids=None,
params: dict = None,
dropout_rng: PRNGKey = None,
train: bool = False,
):
input_ids, attention_mask, token_type_ids, position_ids = self._check_inputs(
input_ids, attention_mask, token_type_ids, position_ids
)
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
pooled, logits = self.module.apply(
{"params": params or self.params},
jnp.array(input_ids, dtype="i4"),
jnp.array(attention_mask, dtype="i4"),
jnp.array(token_type_ids, dtype="i4"),
jnp.array(position_ids, dtype="i4"),
not train,
rngs=rngs,
)
return logits, pooled
src/transformers/models/roberta/modeling_flax_roberta.py
View file @ 75627148
......@@ -12,13 +12,14 @@
# 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.
from typing import Callable, Dict
from typing import Callable, Dict, Tuple
import numpy as np
import flax.linen as nn
import jax
import jax.numpy as jnp
from jax.random import PRNGKey
from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_flax_utils import FlaxPreTrainedModel, gelu
......@@ -101,8 +102,8 @@ class FlaxRobertaLayerNorm(nn.Module):
scale: bool = True # If True, multiply by scale (gamma). When the next layer is linear
# (also e.g. nn.relu), this can be disabled since the scaling will be
# done by the next layer.
bias_init: jnp.ndarray = nn.initializers.zeros
scale_init: jnp.ndarray = nn.initializers.ones
scale_init: Callable[..., np.ndarray] = jax.nn.initializers.ones
bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros
@nn.compact
def __call__(self, x):
......@@ -122,11 +123,13 @@ class FlaxRobertaLayerNorm(nn.Module):
mean2 = jnp.mean(jax.lax.square(x), axis=-1, keepdims=True)
var = mean2 - jax.lax.square(mean)
mul = jax.lax.rsqrt(var + self.epsilon)
if self.scale:
mul = mul * jnp.asarray(self.param("gamma", self.scale_init, (features,)), self.dtype)
mul = mul * jnp.asarray(self.param("gamma", self.scale_init, (features,)))
y = (x - mean) * mul
if self.bias:
y = y + jnp.asarray(self.param("beta", self.bias_init, (features,)), self.dtype)
y = y + jnp.asarray(self.param("beta", self.bias_init, (features,)))
return y
......@@ -139,7 +142,9 @@ class FlaxRobertaEmbedding(nn.Module):
vocab_size: int
hidden_size: int
emb_init: Callable[..., np.ndarray] = nn.initializers.normal(stddev=0.1)
kernel_init_scale: float = 0.2
emb_init: Callable[..., np.ndarray] = jax.nn.initializers.normal(stddev=kernel_init_scale)
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
@nn.compact
def __call__(self, inputs):
......@@ -155,66 +160,108 @@ class FlaxRobertaEmbeddings(nn.Module):
hidden_size: int
type_vocab_size: int
max_length: int
kernel_init_scale: float = 0.2
dropout_rate: float = 0.0
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
@nn.compact
def __call__(self, input_ids, token_type_ids, position_ids, attention_mask):
def __call__(self, input_ids, token_type_ids, position_ids, attention_mask, deterministic: bool = True):
# Embed
w_emb = FlaxRobertaEmbedding(self.vocab_size, self.hidden_size, name="word_embeddings")(
jnp.atleast_2d(input_ids.astype("i4"))
)
p_emb = FlaxRobertaEmbedding(self.max_length, self.hidden_size, name="position_embeddings")(
jnp.atleast_2d(position_ids.astype("i4"))
)
t_emb = FlaxRobertaEmbedding(self.type_vocab_size, self.hidden_size, name="token_type_embeddings")(
jnp.atleast_2d(token_type_ids.astype("i4"))
)
w_emb = FlaxRobertaEmbedding(
self.vocab_size,
self.hidden_size,
kernel_init_scale=self.kernel_init_scale,
name="word_embeddings",
dtype=self.dtype,
)(jnp.atleast_2d(input_ids.astype("i4")))
p_emb = FlaxRobertaEmbedding(
self.max_length,
self.hidden_size,
kernel_init_scale=self.kernel_init_scale,
name="position_embeddings",
dtype=self.dtype,
)(jnp.atleast_2d(position_ids.astype("i4")))
t_emb = FlaxRobertaEmbedding(
self.type_vocab_size,
self.hidden_size,
kernel_init_scale=self.kernel_init_scale,
name="token_type_embeddings",
dtype=self.dtype,
)(jnp.atleast_2d(token_type_ids.astype("i4")))
# Sum all embeddings
summed_emb = w_emb + jnp.broadcast_to(p_emb, w_emb.shape) + t_emb
# Layer Norm
layer_norm = FlaxRobertaLayerNorm(name="layer_norm")(summed_emb)
return layer_norm
layer_norm = FlaxRobertaLayerNorm(name="layer_norm", dtype=self.dtype)(summed_emb)
embeddings = nn.Dropout(rate=self.dropout_rate)(layer_norm, deterministic=deterministic)
return embeddings
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertAttention with Bert->Roberta
class FlaxRobertaAttention(nn.Module):
num_heads: int
head_size: int
dropout_rate: float = 0.0
kernel_init_scale: float = 0.2
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
@nn.compact
def __call__(self, hidden_state, attention_mask):
def __call__(self, hidden_state, attention_mask, deterministic: bool = True):
# Attention mask comes in as attention_mask.shape == (*batch_sizes, kv_length)
# FLAX expects: attention_mask.shape == (*batch_sizes, 1, 1, kv_length) such that it is broadcastable
# with attn_weights.shape == (*batch_sizes, num_heads, q_length, kv_length)
attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
self_att = nn.attention.SelfAttention(num_heads=self.num_heads, qkv_features=self.head_size, name="self")(
hidden_state, attention_mask
)
self_att = nn.attention.SelfAttention(
num_heads=self.num_heads,
qkv_features=self.head_size,
dropout_rate=self.dropout_rate,
deterministic=deterministic,
kernel_init=jax.nn.initializers.normal(self.kernel_init_scale, self.dtype),
bias_init=jax.nn.initializers.zeros,
name="self",
dtype=self.dtype,
)(hidden_state, attention_mask)
layer_norm = FlaxRobertaLayerNorm(name="layer_norm")(self_att + hidden_state)
layer_norm = FlaxRobertaLayerNorm(name="layer_norm", dtype=self.dtype)(self_att + hidden_state)
return layer_norm
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertIntermediate with Bert->Roberta
class FlaxRobertaIntermediate(nn.Module):
output_size: int
kernel_init_scale: float = 0.2
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
@nn.compact
def __call__(self, hidden_state):
# TODO: Add ACT2FN reference to change activation function
dense = nn.Dense(features=self.output_size, name="dense")(hidden_state)
dense = nn.Dense(
features=self.output_size,
kernel_init=jax.nn.initializers.normal(self.kernel_init_scale, self.dtype),
name="dense",
dtype=self.dtype,
)(hidden_state)
return gelu(dense)
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOutput with Bert->Roberta
class FlaxRobertaOutput(nn.Module):
dropout_rate: float = 0.0
kernel_init_scale: float = 0.2
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
@nn.compact
def __call__(self, intermediate_output, attention_output):
hidden_state = nn.Dense(attention_output.shape[-1], name="dense")(intermediate_output)
hidden_state = FlaxRobertaLayerNorm(name="layer_norm")(hidden_state + attention_output)
def __call__(self, intermediate_output, attention_output, deterministic: bool = True):
hidden_state = nn.Dense(
attention_output.shape[-1],
kernel_init=jax.nn.initializers.normal(self.kernel_init_scale, self.dtype),
name="dense",
dtype=self.dtype,
)(intermediate_output)
hidden_state = nn.Dropout(rate=self.dropout_rate)(hidden_state, deterministic=deterministic)
hidden_state = FlaxRobertaLayerNorm(name="layer_norm", dtype=self.dtype)(hidden_state + attention_output)
return hidden_state
......@@ -222,14 +269,29 @@ class FlaxRobertaLayer(nn.Module):
num_heads: int
head_size: int
intermediate_size: int
dropout_rate: float = 0.0
kernel_init_scale: float = 0.2
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
@nn.compact
def __call__(self, hidden_state, attention_mask):
attention = FlaxRobertaAttention(self.num_heads, self.head_size, name="attention")(
hidden_state, attention_mask
)
intermediate = FlaxRobertaIntermediate(self.intermediate_size, name="intermediate")(attention)
output = FlaxRobertaOutput(name="output")(intermediate, attention)
def __call__(self, hidden_state, attention_mask, deterministic: bool = True):
attention = FlaxRobertaAttention(
self.num_heads,
self.head_size,
kernel_init_scale=self.kernel_init_scale,
dropout_rate=self.dropout_rate,
name="attention",
dtype=self.dtype,
)(hidden_state, attention_mask, deterministic=deterministic)
intermediate = FlaxRobertaIntermediate(
self.intermediate_size,
kernel_init_scale=self.kernel_init_scale,
name="intermediate",
dtype=self.dtype,
)(attention)
output = FlaxRobertaOutput(
kernel_init_scale=self.kernel_init_scale, dropout_rate=self.dropout_rate, name="output", dtype=self.dtype
)(intermediate, attention, deterministic=deterministic)
return output
......@@ -244,9 +306,12 @@ class FlaxRobertaLayerCollection(nn.Module):
num_heads: int
head_size: int
intermediate_size: int
dropout_rate: float = 0.0
kernel_init_scale: float = 0.2
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
@nn.compact
def __call__(self, inputs, attention_mask):
def __call__(self, inputs, attention_mask, deterministic: bool = True):
assert self.num_layers > 0, f"num_layers should be >= 1, got ({self.num_layers})"
# Initialize input / output
......@@ -254,8 +319,16 @@ class FlaxRobertaLayerCollection(nn.Module):
# Forward over all encoders
for i in range(self.num_layers):
layer = FlaxRobertaLayer(self.num_heads, self.head_size, self.intermediate_size, name=f"{i}")
input_i = layer(input_i, attention_mask)
layer = FlaxRobertaLayer(
self.num_heads,
self.head_size,
self.intermediate_size,
kernel_init_scale=self.kernel_init_scale,
dropout_rate=self.dropout_rate,
name=f"{i}",
dtype=self.dtype,
)
input_i = layer(input_i, attention_mask, deterministic=deterministic)
return input_i
......@@ -265,22 +338,40 @@ class FlaxRobertaEncoder(nn.Module):
num_heads: int
head_size: int
intermediate_size: int
dropout_rate: float = 0.0
kernel_init_scale: float = 0.2
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
@nn.compact
def __call__(self, hidden_state, attention_mask):
def __call__(self, hidden_state, attention_mask, deterministic: bool = True):
layer = FlaxRobertaLayerCollection(
self.num_layers, self.num_heads, self.head_size, self.intermediate_size, name="layer"
)(hidden_state, attention_mask)
self.num_layers,
self.num_heads,
self.head_size,
self.intermediate_size,
kernel_init_scale=self.kernel_init_scale,
dropout_rate=self.dropout_rate,
name="layer",
dtype=self.dtype,
)(hidden_state, attention_mask, deterministic=deterministic)
return layer
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPooler with Bert->Roberta
class FlaxRobertaPooler(nn.Module):
kernel_init_scale: float = 0.2
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
@nn.compact
def __call__(self, hidden_state):
cls_token = hidden_state[:, 0]
out = nn.Dense(hidden_state.shape[-1], name="dense")(cls_token)
return jax.lax.tanh(out)
out = nn.Dense(
hidden_state.shape[-1],
kernel_init=jax.nn.initializers.normal(self.kernel_init_scale, self.dtype),
name="dense",
dtype=self.dtype,
)(cls_token)
return nn.tanh(out)
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertModule with Bert->Roberta
......@@ -293,21 +384,38 @@ class FlaxRobertaModule(nn.Module):
num_heads: int
head_size: int
intermediate_size: int
dropout_rate: float = 0.0
kernel_init_scale: float = 0.2
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
@nn.compact
def __call__(self, input_ids, attention_mask, token_type_ids, position_ids):
def __call__(self, input_ids, attention_mask, token_type_ids, position_ids, deterministic: bool = True):
# Embedding
embeddings = FlaxRobertaEmbeddings(
self.vocab_size, self.hidden_size, self.type_vocab_size, self.max_length, name="embeddings"
)(input_ids, token_type_ids, position_ids, attention_mask)
self.vocab_size,
self.hidden_size,
self.type_vocab_size,
self.max_length,
kernel_init_scale=self.kernel_init_scale,
dropout_rate=self.dropout_rate,
name="embeddings",
dtype=self.dtype,
)(input_ids, token_type_ids, position_ids, attention_mask, deterministic=deterministic)
# N stacked encoding layers
encoder = FlaxRobertaEncoder(
self.num_encoder_layers, self.num_heads, self.head_size, self.intermediate_size, name="encoder"
)(embeddings, attention_mask)
pooled = FlaxRobertaPooler(name="pooler")(encoder)
self.num_encoder_layers,
self.num_heads,
self.head_size,
self.intermediate_size,
kernel_init_scale=self.kernel_init_scale,
dropout_rate=self.dropout_rate,
name="encoder",
dtype=self.dtype,
)(embeddings, attention_mask, deterministic=deterministic)
pooled = FlaxRobertaPooler(kernel_init_scale=self.kernel_init_scale, name="pooler", dtype=self.dtype)(encoder)
return encoder, pooled
......@@ -396,8 +504,8 @@ class FlaxRobertaModel(FlaxPreTrainedModel):
return jax_state
def __init__(self, config: RobertaConfig, state: dict, seed: int = 0, **kwargs):
model = FlaxRobertaModule(
def __init__(self, config: RobertaConfig, state: dict, seed: int = 0, dtype: jnp.dtype = jnp.float32):
module = FlaxRobertaModule(
vocab_size=config.vocab_size,
hidden_size=config.hidden_size,
type_vocab_size=config.type_vocab_size,
......@@ -406,31 +514,78 @@ class FlaxRobertaModel(FlaxPreTrainedModel):
num_heads=config.num_attention_heads,
head_size=config.hidden_size,
intermediate_size=config.intermediate_size,
dropout_rate=config.hidden_dropout_prob,
dtype=dtype,
)
super().__init__(config, model, state, seed)
@property
def module(self) -> nn.Module:
return self._module
super().__init__(config, module, state, seed)
@add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def __call__(self, input_ids, token_type_ids=None, attention_mask=None, position_ids=None):
def __call__(
self,
input_ids,
token_type_ids=None,
attention_mask=None,
position_ids=None,
params: dict = None,
dropout_rng: PRNGKey = None,
train: bool = False,
):
input_ids, attention_mask, token_type_ids, position_ids = self._check_inputs(
input_ids, attention_mask, token_type_ids, position_ids
)
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
return self.module.apply(
{"params": params or self.params},
jnp.array(input_ids, dtype="i4"),
jnp.array(attention_mask, dtype="i4"),
jnp.array(token_type_ids, dtype="i4"),
jnp.array(position_ids, dtype="i4"),
not train,
rngs=rngs,
)
def init(self, rng: jax.random.PRNGKey, input_shape: Tuple):
input_ids, attention_mask, token_type_ids, position_ids = self._check_inputs(
jnp.zeros(input_shape, dtype="i4"), None, None, None
)
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
self.params = self.module.init(rngs, input_ids, attention_mask, token_type_ids, position_ids)["params"]
def _check_inputs(self, input_ids, attention_mask, token_type_ids, position_ids):
if token_type_ids is None:
token_type_ids = jnp.ones_like(input_ids)
if position_ids is None:
position_ids = jnp.arange(
self.config.pad_token_id + 1, jnp.atleast_2d(input_ids).shape[-1] + self.config.pad_token_id + 1
)
position_ids = create_position_ids_from_input_ids(input_ids, self.config.pad_token_id)
if attention_mask is None:
attention_mask = jnp.ones_like(input_ids)
return self.model.apply(
{"params": self.params},
jnp.array(input_ids, dtype="i4"),
jnp.array(attention_mask, dtype="i4"),
jnp.array(token_type_ids, dtype="i4"),
jnp.array(position_ids, dtype="i4"),
)
return input_ids, attention_mask, token_type_ids, position_ids
def create_position_ids_from_input_ids(input_ids, padding_idx):
"""
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
are ignored. This is modified from fairseq's `utils.make_positions`.
Args:
input_ids: jnp.ndarray
padding_idx: int
Returns: jnp.ndarray
"""
# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
mask = (input_ids != padding_idx).astype("i4")
incremental_indices = jnp.cumsum(mask, axis=1).astype("i4") * mask
return incremental_indices.astype("i4") + padding_idx
src/transformers/utils/dummy_flax_objects.py
View file @ 75627148
......@@ -14,6 +14,15 @@ class FlaxAutoModel:
requires_flax(self)
class FlaxBertForMaskedLM:
def __init__(self, *args, **kwargs):
requires_flax(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_flax(self)
class FlaxBertModel:
def __init__(self, *args, **kwargs):
requires_flax(self)
......
tests/test_modeling_flax_roberta.py
View file @ 75627148
......@@ -57,7 +57,7 @@ class FlaxRobertaModelTest(unittest.TestCase):
self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
for fx_output, pt_output in zip(fx_outputs, pt_outputs.to_tuple()):
self.assert_almost_equals(fx_output, pt_output.numpy(), 6e-4)
self.assert_almost_equals(fx_output, pt_output.numpy(), 5e-3)
def test_multiple_sequences(self):
tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment