Parity test

.drone.yml

@@ -22,7 +22,7 @@ steps:
     - pip install -r ./tests/requirements.txt --user
     - pip list
     - python -c "import torch ; print(' & '.join([torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())]) if torch.cuda.is_available() else 'only CPU')"
-    - coverage run --source pytorch_lightning -m py.test pytorch_lightning tests -v --doctest-modules #  --flake8
+    - coverage run --source pytorch_lightning -m py.test pytorch_lightning tests benchmarks -v --doctest-modules #  --flake8
     - coverage report
     - codecov --token $CODECOV_TOKEN  # --pr $DRONE_PULL_REQUEST --build $DRONE_BUILD_NUMBER --branch $DRONE_BRANCH --commit $DRONE_COMMIT --tag $DRONE_TAG
     - python tests/collect_env_details.py

CHANGELOG.md

@@ -8,6 +8,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
+- Added parity test between a  vanilla MNIST model  and  lightning model ([#1284](https://github.com/PyTorchLightning/pytorch-lightning/pull/1284))
 - Added Reinforcement Learning - Deep Q-network (DQN) lightning example ([#1232](https://github.com/PyTorchLightning/pytorch-lightning/pull/1232))
 - Added support for hierarchical `dict` ([#1152](https://github.com/PyTorchLightning/pytorch-lightning/pull/1152))
 - Added `TrainsLogger` class ([#1122](https://github.com/PyTorchLightning/pytorch-lightning/pull/1122))

benchmarks/test_trainer_parity.py

@@ -0,0 +1,151 @@
+import os
+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 DataLoader
+from torchvision import transforms
+from torchvision.datasets import MNIST
+
+from pytorch_lightning import Trainer, LightningModule
+
+
+class ParityMNIST(LightningModule):
+
+    def __init__(self):
+        super(ParityMNIST, self).__init__()
+        self.c_d1 = nn.Linear(in_features=28 * 28, out_features=128)
+        self.c_d1_bn = nn.BatchNorm1d(128)
+        self.c_d1_drop = nn.Dropout(0.3)
+        self.c_d2 = nn.Linear(in_features=128, out_features=10)
+
+    def forward(self, x):
+        x = x.view(x.size(0), -1)
+        x = self.c_d1(x)
+        x = torch.tanh(x)
+        x = self.c_d1_bn(x)
+        x = self.c_d1_drop(x)
+        x = self.c_d2(x)
+        return x
+
+    def training_step(self, batch, batch_nb):
+        x, y = batch
+        y_hat = self(x)
+        loss = F.cross_entropy(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(MNIST(os.getcwd(), train=True, download=True,
+                                transform=transforms.ToTensor()), batch_size=32)
+
+
+@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(ParityMNIST, num_rums, num_epochs)
+    manual_outs, pt_times = vanilla_loop(ParityMNIST, 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, 5)
+
+
+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
+        )
+        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