# Copyright 2022 Cerebras Systems.
#
# 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.
"""
Processor for PyTorch BERT fine tuning - Token classifier.
"""
import csv
import json
import os
import numpy as np
import torch
import cerebras.pytorch.distributed as dist
from cerebras.modelzoo.common.input_utils import get_streaming_batch_size
from cerebras.modelzoo.common.registry import registry
from cerebras.modelzoo.data.common.input_utils import (
check_sharding_sanity,
get_data_for_task,
task_id,
)
from cerebras.modelzoo.data.nlp.bert.bert_utils import (
build_vocab,
get_meta_data,
shard_and_shuffle_data,
)
[docs]@registry.register_datasetprocessor("BertTokenClassifierDataProcessor")
class BertTokenClassifierDataProcessor(torch.utils.data.IterableDataset):
"""
Reads csv file containing the input token ids, and label_ids.
Creates attention_masks and sedment_ids on the fly
:param <dict> params: dict containing input parameters for creating dataset.
Expects the following fields:
- "vocab_file" (str): Path to the vocab file.
- "label_vocab_file" (str): Path to json file with class name to class index.
- "data_dir" (str): Path to directory containing the CSV files.
- "batch_size" (int): Batch size.
- "max_sequence_length" (int): Maximum length of the sequence.
- "do_lower" (bool): Flag to lower case the texts.
- "shuffle" (bool): Flag to enable data shuffling.
- "shuffle_seed" (int): Shuffle seed.
- "shuffle_buffer" (int): Shuffle buffer size.
- "num_workers" (int): How many subprocesses to use for data loading.
- "drop_last" (bool): If True and the dataset size is not divisible
by the batch size, the last incomplete batch will be dropped.
- "prefetch_factor" (int): Number of samples loaded in advance by each worker.
- "persistent_workers" (bool): If True, the data loader will not shutdown
the worker processes after a dataset has been consumed once.
:pram model_params (dict): Model parameters for creating the dataset.
Expects the following to be defined:
- "include_padding_in_loss" (bool): If set to true then a loss mask will be
generated such that padding tokens will be included in the loss calculation.
"""
[docs] def __init__(self, params, model_params):
super(BertTokenClassifierDataProcessor, self).__init__()
# Input params.
self.meta_data = get_meta_data(params["data_dir"])
self.meta_data_values = list(self.meta_data.values())
self.meta_data_filenames = list(self.meta_data.keys())
# Please note the appending of [0]
self.meta_data_values_cum_sum = np.cumsum([0] + self.meta_data_values)
self.num_examples = sum(map(int, self.meta_data.values()))
self.batch_size = get_streaming_batch_size(params["batch_size"])
self.num_batches = self.num_examples // self.batch_size
assert (
self.num_batches > 0
), "Dataset does not contain enough samples for one batch. Please choose a smaller batch size"
self.num_tasks = dist.num_streamers() if dist.is_streamer() else 1
self.num_batch_per_task = self.num_batches // self.num_tasks
assert (
self.num_batch_per_task > 0
), "Dataset cannot be evenly distributed across the given tasks. Please choose fewer tasks to run with"
self.num_examples_per_task = self.num_batch_per_task * self.batch_size
self.files_in_task = get_data_for_task(
task_id(),
self.meta_data_values_cum_sum,
self.num_examples_per_task,
self.meta_data_values,
self.meta_data_filenames,
)
self.shuffle = params.get("shuffle", True)
self.shuffle_seed = params.get("shuffle_seed", None)
self.shuffle_buffer = params.get("shuffle_buffer", 10 * self.batch_size)
self.mask_whole_word = params.get("mask_whole_word", False)
self.do_lower = params.get("do_lower", False)
# Multi-processing params.
self.num_workers = params.get("num_workers", 0)
self.drop_last = params.get("drop_last", True)
self.prefetch_factor = params.get("prefetch_factor", 10)
self.persistent_workers = params.get("persistent_workers", True)
# Check that our sharding will produce at least one batch
check_sharding_sanity(
[num_examples for _, num_examples, _ in self.files_in_task],
self.batch_size,
self.num_workers,
self.drop_last,
)
self.special_tokens = {
"oov_token": "[UNK]",
"class_token": "[CLS]",
"pad_token": "[PAD]",
"document_separator_token": "[SEP]",
}
if self.do_lower:
self.special_tokens = {
key: value.lower() for key, value in self.special_tokens.items()
}
# Get vocab file and size.
self.vocab_file = params["vocab_file"]
self.vocab, self.vocab_size = build_vocab(
self.vocab_file, self.do_lower, self.special_tokens["oov_token"]
)
self.label_vocab_file = params["label_vocab_file"]
if not os.path.exists(self.label_vocab_file):
raise FileNotFoundError(f"{self.label_vocab_file} not found.")
with open(self.label_vocab_file, "r") as labelmap_fid:
self.label_map = json.load(labelmap_fid)
# Init tokenizer.
self.tokenize = self.vocab.forward
# Getting indices for special tokens.
self.special_tokens_indices = {
key: self.tokenize([value])[0]
for key, value in self.special_tokens.items()
}
# Padding indices.
# See https://huggingface.co/transformers/glossary.html#labels.
self.labels_pad_id = params.get(
"labels_pad_id", self.special_tokens_indices["pad_token"]
)
self.input_pad_id = params.get(
"input_pad_id", self.special_tokens_indices["pad_token"]
)
self.attn_mask_pad_id = params.get(
"attn_mask_pad_id", self.special_tokens_indices["pad_token"]
)
assert all(
pad >= 0
for pad in [
self.labels_pad_id,
self.input_pad_id,
self.attn_mask_pad_id,
]
), (
f"All padding must be non-negative, got"
f" `labels_pad_id` = {self.labels_pad_id}, `input_pad_id` = {self.input_pad_id},"
f" `attn_mask_pad_id` = {self.attn_mask_pad_id}."
)
self.max_sequence_length = params["max_sequence_length"]
self.include_padding_in_loss = model_params.get(
"include_padding_in_loss"
)
# Store params.
self.data_buffer = []
self.csv_files_per_task_per_worker = []
self.processed_buffers = 0
[docs] def create_dataloader(self):
"""
Classmethod to create the dataloader object.
"""
if self.num_workers:
dataloader = torch.utils.data.DataLoader(
self,
batch_size=self.batch_size,
num_workers=self.num_workers,
drop_last=self.drop_last,
prefetch_factor=self.prefetch_factor,
persistent_workers=self.persistent_workers,
)
else:
dataloader = torch.utils.data.DataLoader(
self,
batch_size=self.batch_size,
drop_last=self.drop_last,
)
return dataloader
[docs] def load_buffer(self):
"""
Generator to read the data in chunks of size of `data_buffer`.
:returns: Yields the data stored in the `data_buffer`.
"""
self.data_buffer = []
while self.processed_buffers < len(self.csv_files_per_task_per_worker):
(
current_file_path,
num_examples,
start_id,
) = self.csv_files_per_task_per_worker[self.processed_buffers]
with open(current_file_path, "r", newline="") as fid:
data_reader = csv.DictReader(
fid, delimiter=",", quoting=csv.QUOTE_MINIMAL
)
for row_id, row in enumerate(data_reader):
if start_id <= row_id < start_id + num_examples:
self.data_buffer.append(row)
else:
continue
if len(self.data_buffer) == self.shuffle_buffer:
if self.shuffle:
self.rng.shuffle(self.data_buffer)
for ind in range(len(self.data_buffer)):
yield self.data_buffer[ind]
self.data_buffer = []
self.processed_buffers += 1
if self.shuffle:
self.rng.shuffle(self.data_buffer)
for ind in range(len(self.data_buffer)):
yield self.data_buffer[ind]
self.data_buffer = []
def __len__(self):
"""
Returns the length of the dataset on task process.
"""
return self.num_examples_per_task
def __iter__(self):
"""
Iterator over the data to construct input features.
:return: A tuple with training features:
* np.array[int.32] input_ids: Numpy array with input token indices.
Shape: (`max_sequence_length`).
* np.array[int.32] labels: Numpy array with labels.
Shape: (`max_sequence_length`).
* np.array[int.32] attention_mask
Shape: (`max_sequence_length`).
* np.array[int.32] token_type_ids: Numpy array with segment indices.
Shape: (`max_sequence_length`).
"""
(
self.processed_buffers,
self.csv_files_per_task_per_worker,
self.shuffle_seed,
self.rng,
) = shard_and_shuffle_data(
self.files_in_task,
self.shuffle,
self.shuffle_seed,
)
# Iterate over the data rows to create input features.
for data_row in self.load_buffer():
# `data_row` is a dict with keys: ["tokens", "labels""].
tokens_list, labels_list = parse_ner_row(data_row, self.do_lower)
features = create_ner_features(
tokens_list,
labels_list,
self.label_map,
self.max_sequence_length,
self.input_pad_id,
self.attn_mask_pad_id,
self.labels_pad_id,
self.include_padding_in_loss,
self.tokenize,
)
yield features
[docs]def parse_ner_row(data_row, do_lower=False):
"""
Postprocessing of a row in the CSV file.
:param: dict data_row: dictionary with an input text tokens and labels.
:return: tuple: (list of parsed tokens, List of labels).
"""
tokens = data_row["tokens"].split()
tokens_list = (
list(map(lambda token: token.lower(), tokens)) if do_lower else tokens
)
labels_list = data_row["labels"].split()
return tokens_list, labels_list
[docs]def create_ner_features(
tokens_list,
labels_list,
label_map,
max_sequence_length,
input_pad_id,
attn_mask_pad_id,
labels_pad_id,
include_padding_in_loss,
tokenize,
):
"""
Creates the features dict for token classifier model.
:param list tokens_list: Tokens to process
:param list labels_list: Labels to process
:param dict label_map: Dictionary mapping label to int
:param int max_sequence_length: Maximum sequence length.
:param int input_pad_id: Input sequence padding id.
:param int attn_mask_pad_id: Attention mask padding id.
:param int labels_pad_id: Labels padding id.
:param bool include_padding_in_loss: Flag to generate loss mask.
:param callable tokenize: Method to tokenize the input sequence.
:returns: dict for features which includes keys:
* 'input_ids': Numpy array with input token indices.
shape: (`max_sequence_length`), dtype: int32.
* 'attention_mask': Numpy array with attention mask.
shape: (`max_sequence_length`), dtype: int32.
* 'loss_mask': Numpy array equal to attention mask if
`include_padding_in_loss` is False, else all ones.
shape: (`max_sequence_length`), dtype: int32.
* 'token_type_ids': Numpy array with segment ids.
shape: (`max_sequence_length`), dtype: int32.
* 'labels': Numpy array with labels.
shape: (`max_sequence_length`), dtype: int32.
"""
input_ids = np.ones((max_sequence_length,), dtype=np.int32) * input_pad_id
attention_mask = (
np.ones((max_sequence_length,), dtype=np.int32) * attn_mask_pad_id
)
loss_mask = np.ones((max_sequence_length,), dtype=np.int32)
# Convert tokens to integer ids.
token_ids = tokenize(tokens_list)
input_ids[0 : len(token_ids)] = token_ids
attention_mask[0 : len(token_ids)] = 1
label_ids = np.ones((max_sequence_length,), dtype=np.int32) * labels_pad_id
labels = [label_map[label] for label in labels_list]
label_ids[0 : len(token_ids)] = labels
# only one segment, so segment ids is all zeros
segment_ids = np.zeros((max_sequence_length,), dtype=np.int32)
# loss mask
if not include_padding_in_loss:
loss_mask = attention_mask.copy()
features = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"loss_mask": loss_mask,
"token_type_ids": segment_ids,
"labels": label_ids,
}
return features