Add parity test for simple RNN

benchmarks/test_rnn_parity.py

@@ -0,0 +1,155 @@
+import time
+
+import numpy as np
+import pytest
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import Dataset, DataLoader
+
+from pytorch_lightning import Trainer, LightningModule
+
+
+class AverageDataset(Dataset):
+    def __init__(self, dataset_len=300, sequence_len=100):
+        self.dataset_len = dataset_len
+        self.sequence_len = sequence_len
+        self.input_seq = torch.randn(dataset_len, sequence_len, 10)
+        top, bottom = self.input_seq.chunk(2, -1)
+        self.output_seq = top + bottom.roll(shifts=1, dims=-1)
+
+    def __len__(self):
+        return self.dataset_len
+
+    def __getitem__(self, item):
+        return self.input_seq[item], self.output_seq[item]
+
+
+class ParityRNN(LightningModule):
+    def __init__(self):
+        super(ParityRNN, self).__init__()
+        self.rnn = nn.LSTM(10, 20, batch_first=True)
+        self.linear_out = nn.Linear(in_features=20, out_features=5)
+
+    def forward(self, x):
+        seq, last = self.rnn(x)
+        return self.linear_out(seq)
+
+    def training_step(self, batch, batch_nb):
+        x, y = batch
+        y_hat = self(x)
+        loss = F.mse_loss(y_hat, y)
+        return {'loss': loss}
+
+    def configure_optimizers(self):
+        return torch.optim.Adam(self.parameters(), lr=0.02)
+
+    def train_dataloader(self):
+        return DataLoader(AverageDataset(), batch_size=30)
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
+def test_pytorch_parity(tmpdir):
+    """
+    Verify that the same  pytorch and lightning models achieve the same results
+    :param tmpdir:
+    :return:
+    """
+    num_epochs = 2
+    num_rums = 3
+
+    lightning_outs, pl_times = lightning_loop(ParityRNN, num_rums, num_epochs)
+    manual_outs, pt_times = vanilla_loop(ParityRNN, num_rums, num_epochs)
+    # make sure the losses match exactly  to 5 decimal places
+    for pl_out, pt_out in zip(lightning_outs, manual_outs):
+        np.testing.assert_almost_equal(pl_out, pt_out, 8)
+
+
+def set_seed(seed):
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(seed)
+
+
+def vanilla_loop(MODEL, num_runs=10, num_epochs=10):
+    """
+    Returns an array with the last loss from each epoch for each run
+    """
+    device = torch.device('cuda' if torch.cuda.is_available() else "cpu")
+    errors = []
+    times = []
+
+    for i in range(num_runs):
+        time_start = time.perf_counter()
+
+        # set seed
+        seed = i
+        set_seed(seed)
+
+        # init model parts
+        model = MODEL()
+        dl = model.train_dataloader()
+        optimizer = model.configure_optimizers()
+
+        # model to GPU
+        model = model.to(device)
+
+        epoch_losses = []
+        for epoch in range(num_epochs):
+
+            # run through full training set
+            for j, batch in enumerate(dl):
+                x, y = batch
+                x = x.cuda(0)
+                y = y.cuda(0)
+                batch = (x, y)
+
+                loss_dict = model.training_step(batch, j)
+                loss = loss_dict['loss']
+                loss.backward()
+                optimizer.step()
+                optimizer.zero_grad()
+
+            # track last epoch loss
+            epoch_losses.append(loss.item())
+
+        time_end = time.perf_counter()
+        times.append(time_end - time_start)
+
+        errors.append(epoch_losses[-1])
+
+    return errors, times
+
+
+def lightning_loop(MODEL, num_runs=10, num_epochs=10):
+    errors = []
+    times = []
+
+    for i in range(num_runs):
+        time_start = time.perf_counter()
+
+        # set seed
+        seed = i
+        set_seed(seed)
+
+        # init model parts
+        model = MODEL()
+        trainer = Trainer(
+            max_epochs=num_epochs,
+            show_progress_bar=False,
+            weights_summary=None,
+            gpus=1,
+            early_stop_callback=False,
+            checkpoint_callback=False,
+            distributed_backend='dp',
+        )
+        trainer.fit(model)
+
+        final_loss = trainer.running_loss.last().item()
+        errors.append(final_loss)
+
+        time_end = time.perf_counter()
+        times.append(time_end - time_start)
+
+    return errors, times