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import torch
from torch.utils.data import Dataset
def collate_sentences_lm(samples):
if len(samples) == 0:
return {}
id = torch.LongTensor([s["id"] for s in samples])
src_tokens = torch.stack([s["source"] for s in samples], 0)
tgt_tokens = torch.stack([s["target"] for s in samples], 0)
ntokens = len(samples) * len(samples[0]["target"])
src_lengths = torch.LongTensor([len(samples[0]["source"])] * len(samples))
batch = {
"id": id,
"nsentences": len(samples),
"ntokens": ntokens,
"input": src_tokens,
"target": tgt_tokens,
}
return batch
class BenchmarkLMDataset(Dataset):
"""
Dataset to benchmark a translation like seq2seq task.
Args:
vocab_size (int, optional): size of the vocabulary (default 10000).
max_source_positions (int, optional): max number of tokens in the
source sentence (default: 1024).
total_samples (int, optional): the total number of rows in the
dataset (default: 10000).
"""
def __init__(
self, vocab_size=10000, max_source_positions=1024, total_samples=10000,
):
self.vocab_size = vocab_size
self.max_source_positions = max_source_positions
self.total_samples = total_samples
self.sizes = [self.max_source_positions] * self.total_samples
def __getitem__(self, index):
length = self.sizes[index]
source = torch.randint(1, self.vocab_size, (length,))
target = source.clone()
return {
"id": index,
"source": source,
"target": target,
}
def __len__(self):
return self.total_samples
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