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| Original file line number | Diff line number | Diff line change |
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| @@ -1 +1,2 @@ | ||
| 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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| @@ -0,0 +1,98 @@ | ||
| """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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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,11 @@ | ||
| 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] |