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from lightning.pytorch.demos.lstm import LightningLSTM, SequenceSampler, SimpleLSTM # noqa: F401 | ||
from lightning.pytorch.demos.transformer import LightningTransformer, Transformer, WikiText2 # noqa: F401 |
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"""Demo of a simple LSTM language model. | ||
Code is adapted from the PyTorch examples at | ||
https://github.com/pytorch/examples/blob/main/word_language_model | ||
""" | ||
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from typing import Iterator, List, Optional, Sized, Tuple | ||
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import torch | ||
import torch.nn as nn | ||
import torch.nn.functional as F | ||
from torch import Tensor | ||
from torch.optim import Optimizer | ||
from torch.utils.data import DataLoader, Sampler | ||
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from lightning.pytorch.core import LightningModule | ||
from lightning.pytorch.demos.transformer import WikiText2 | ||
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class SimpleLSTM(nn.Module): | ||
def __init__( | ||
self, vocab_size: int = 33278, ninp: int = 512, nhid: int = 512, nlayers: int = 4, dropout: float = 0.2 | ||
): | ||
super().__init__() | ||
self.vocab_size = vocab_size | ||
self.drop = nn.Dropout(dropout) | ||
self.encoder = nn.Embedding(vocab_size, ninp) | ||
self.rnn = nn.LSTM(ninp, nhid, nlayers, dropout=dropout, batch_first=True) | ||
self.decoder = nn.Linear(nhid, vocab_size) | ||
self.nlayers = nlayers | ||
self.nhid = nhid | ||
self.init_weights() | ||
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def init_weights(self) -> None: | ||
nn.init.uniform_(self.encoder.weight, -0.1, 0.1) | ||
nn.init.zeros_(self.decoder.bias) | ||
nn.init.uniform_(self.decoder.weight, -0.1, 0.1) | ||
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def forward(self, input: Tensor, hidden: Tuple[Tensor, Tensor]) -> Tuple[Tensor, Tensor]: | ||
emb = self.drop(self.encoder(input)) | ||
output, hidden = self.rnn(emb, hidden) | ||
output = self.drop(output) | ||
decoded = self.decoder(output).view(-1, self.vocab_size) | ||
return F.log_softmax(decoded, dim=1), hidden | ||
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def init_hidden(self, batch_size: int) -> Tuple[Tensor, Tensor]: | ||
weight = next(self.parameters()) | ||
return ( | ||
weight.new_zeros(self.nlayers, batch_size, self.nhid), | ||
weight.new_zeros(self.nlayers, batch_size, self.nhid), | ||
) | ||
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class SequenceSampler(Sampler[List[int]]): | ||
def __init__(self, dataset: Sized, batch_size: int) -> None: | ||
super().__init__() | ||
self.dataset = dataset | ||
self.batch_size = batch_size | ||
self.chunk_size = len(self.dataset) // self.batch_size | ||
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def __iter__(self) -> Iterator[List[int]]: | ||
n = len(self.dataset) | ||
for i in range(self.chunk_size): | ||
yield list(range(i, n - (n % self.batch_size), self.chunk_size)) | ||
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def __len__(self) -> int: | ||
return self.chunk_size | ||
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class LightningLSTM(LightningModule): | ||
def __init__(self, vocab_size: int = 33278): | ||
super().__init__() | ||
self.model = SimpleLSTM(vocab_size=vocab_size) | ||
self.hidden: Optional[Tuple[Tensor, Tensor]] = None | ||
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def on_train_epoch_end(self) -> None: | ||
self.hidden = None | ||
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def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor: | ||
input, target = batch | ||
if self.hidden is None: | ||
self.hidden = self.model.init_hidden(input.size(0)) | ||
self.hidden = (self.hidden[0].detach(), self.hidden[1].detach()) | ||
output, self.hidden = self.model(input, self.hidden) | ||
loss = F.nll_loss(output, target.view(-1)) | ||
self.log("train_loss", loss, prog_bar=True) | ||
return loss | ||
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def prepare_data(self) -> None: | ||
WikiText2(download=True) | ||
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def train_dataloader(self) -> DataLoader: | ||
dataset = WikiText2() | ||
return DataLoader(dataset, batch_sampler=SequenceSampler(dataset, batch_size=20)) | ||
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def configure_optimizers(self) -> Optimizer: | ||
return torch.optim.SGD(self.parameters(), lr=20.0) |
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from lightning.pytorch.demos.lstm import SequenceSampler | ||
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def test_sequence_sampler(): | ||
dataset = list(range(103)) | ||
sampler = SequenceSampler(dataset, batch_size=4) | ||
assert len(sampler) == 25 | ||
batches = list(sampler) | ||
assert batches[0] == [0, 25, 50, 75] | ||
assert batches[1] == [1, 26, 51, 76] | ||
assert batches[24] == [24, 49, 74, 99] |