# 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.
"""Pytorch Generic Iterable Dataloader"""
import numpy as np
import torch
from cerebras.modelzoo.data.common.GenericDataProcessor import (
GenericDataProcessor,
GenericDataProcessorConfig,
)
[docs]class DummyIterableDataset(torch.utils.data.IterableDataset):
"""
A Dummy iterable torch.utils.data.IterableDataset.
"""
def __init__(self):
self.length = 10000
self.max_seq_len = 128
self.vocab_size = 32000
np.random.seed(seed=0)
self.data = dict()
input_mask = np.zeros((self.length, self.max_seq_len), dtype=np.int32)
seq_mid_idx = np.cast["int32"](self.max_seq_len / 2)
for i in range(self.length):
start_idx = np.random.randint(seq_mid_idx, self.max_seq_len + 1)
input_mask[i, start_idx : self.max_seq_len] = 1
self.data["attention_mask"] = 1 - input_mask
self.data["input_ids"] = np.random.randint(
low=0,
high=self.vocab_size,
size=(self.length, self.max_seq_len),
dtype=np.int32,
) * (1 - input_mask)
super(DummyIterableDataset, self).__init__()
def __iter__(self):
for idx in range(self.length):
feature = {
"input_ids": self.data["input_ids"][idx],
"attention_mask": self.data["attention_mask"][idx],
"labels": self.data["input_ids"][idx],
}
yield feature
[docs]class DummyIterableDataProcessor(GenericDataProcessor):
def __init__(self, config: GenericDataProcessorConfig):
super().__init__(config, DummyIterableDataset())
[docs]class DummyTinyIterableDataset(torch.utils.data.IterableDataset):
"""
A Dummy iterable torch.utils.data.IterableDataset.
"""
def __init__(self):
self.length = 9
self.max_seq_len = 128
self.vocab_size = 32000
np.random.seed(seed=0)
self.data = dict()
input_mask = np.zeros((self.length, self.max_seq_len), dtype=np.int32)
seq_mid_idx = np.cast["int32"](self.max_seq_len / 2)
for i in range(self.length):
start_idx = np.random.randint(seq_mid_idx, self.max_seq_len + 1)
input_mask[i, start_idx : self.max_seq_len] = 1
self.data["attention_mask"] = 1 - input_mask
self.data["input_ids"] = np.random.randint(
low=0,
high=self.length,
size=(self.length, self.max_seq_len),
dtype=np.int32,
) * (1 - input_mask)
super(DummyTinyIterableDataset, self).__init__()
def __iter__(self):
for idx in range(self.length):
feature = {
"input_ids": self.data["input_ids"][idx],
"attention_mask": self.data["attention_mask"][idx],
"labels": self.data["input_ids"][idx],
}
yield feature
[docs]class DummyTinyIterableDataProcessor(GenericDataProcessor):
"""
A Generic PyTorch Data Processor.
:param dict params: dict containing training
input parameters for creating dataset.
Expects the following fields:
- "batch_size" (int): Batch size.
- "shuffle" (bool): Flag to enable data shuffling.
- "shuffle_seed" (int): Shuffle seed.
- "shuffle_buffer" (int): Size of shuffle buffer in samples.
- "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 batches 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.
"""
def __init__(self, params):
self.dataset = DummyTinyIterableDataset()
super().__init__(params)