# 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 Dataloader"""
import numpy as np
import torch
from cerebras.modelzoo.data.common.GenericDataProcessor import (
GenericDataProcessor,
GenericDataProcessorConfig,
)
[docs]class DummyDataset(torch.utils.data.Dataset):
"""
A Dummy map-style torch.utils.data.Dataset.
"""
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(DummyDataset, self).__init__()
def __getitem__(self, index):
feature = {
"input_ids": self.data["input_ids"][index],
"attention_mask": self.data["attention_mask"][index],
"labels": self.data["input_ids"][index],
}
return feature
def __len__(self):
return self.length
[docs]class DummyDataProcessor(GenericDataProcessor):
def __init__(self, config: GenericDataProcessorConfig):
super().__init__(config, DummyDataset())