Document speed comparison (#2072)

* docs

* script

* dump

* desc

* import

* import

* if

* norm

* t

* finished

* isort

* typing

Co-authored-by: Nicki Skafte <[email protected]>

* xlabel

* pandas

* time

Co-authored-by: Nicki Skafte <[email protected]>

Author: Borda

benchmarks/__init__.py

@@ -0,0 +1,17 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+
+BENCHMARK_ROOT = os.path.dirname(__file__)
+PROJECT_ROOT = os.path.dirname(BENCHMARK_ROOT)

benchmarks/generate_comparison.py

@@ -0,0 +1,60 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+
+import matplotlib.pylab as plt
+import pandas as pd
+
+from benchmarks.test_basic_parity import lightning_loop, vanilla_loop
+from tests.base.models import ParityModuleMNIST, ParityModuleRNN
+
+NUM_EPOCHS = 20
+NUM_RUNS = 50
+MODEL_CLASSES = (ParityModuleRNN, ParityModuleMNIST)
+PATH_HERE = os.path.dirname(__file__)
+FIGURE_EXTENSION = '.png'
+
+
+def _main():
+    fig, axarr = plt.subplots(nrows=len(MODEL_CLASSES))
+
+    for i, cls_model in enumerate(MODEL_CLASSES):
+        path_csv = os.path.join(PATH_HERE, f'dump-times_{cls_model.__name__}.csv')
+        if os.path.isfile(path_csv):
+            df_time = pd.read_csv(path_csv, index_col=0)
+        else:
+            vanilla = vanilla_loop(cls_model, num_epochs=NUM_EPOCHS, num_runs=NUM_RUNS)
+            lightning = lightning_loop(cls_model, num_epochs=NUM_EPOCHS, num_runs=NUM_RUNS)
+
+            df_time = pd.DataFrame({'vanilla PT': vanilla['durations'][1:], 'PT Lightning': lightning['durations'][1:]})
+            df_time /= NUM_RUNS
+            df_time.to_csv(os.path.join(PATH_HERE, f'dump-times_{cls_model.__name__}.csv'))
+        # todo: add also relative X-axis ticks to see both: relative and absolute time differences
+        df_time.plot.hist(
+            ax=axarr[i],
+            bins=20,
+            alpha=0.5,
+            title=cls_model.__name__,
+            legend=True,
+            grid=True,
+        )
+        axarr[i].set(xlabel='time [seconds]')
+
+    path_fig = os.path.join(PATH_HERE, f'figure-parity-times{FIGURE_EXTENSION}')
+    fig.tight_layout()
+    fig.savefig(path_fig)
+
+
+if __name__ == '__main__':
+    _main()

benchmarks/test_parity.py renamed to benchmarks/test_basic_parity.py

@@ -1,8 +1,23 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import time
 
 import numpy as np
 import pytest
 import torch
+from tqdm import tqdm
 
 from pytorch_lightning import seed_everything, Trainer
 import tests.base.develop_utils as tutils
@@ -15,34 +30,33 @@
     (ParityModuleMNIST, 0.25),  # todo: lower this thr
 ])
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
-def test_pytorch_parity(tmpdir, cls_model, max_diff):
+def test_pytorch_parity(tmpdir, cls_model, max_diff: float, num_epochs: int = 4, num_runs: int = 3):
     """
     Verify that the same  pytorch and lightning models achieve the same results
     """
-    num_epochs = 4
-    num_rums = 3
-    lightning_outs, pl_times = lightning_loop(cls_model, num_rums, num_epochs)
-    manual_outs, pt_times = vanilla_loop(cls_model, num_rums, num_epochs)
+    lightning = lightning_loop(cls_model, num_runs, num_epochs)
+    vanilla = vanilla_loop(cls_model, num_runs, num_epochs)
 
     # make sure the losses match exactly  to 5 decimal places
-    for pl_out, pt_out in zip(lightning_outs, manual_outs):
+    for pl_out, pt_out in zip(lightning['losses'], vanilla['losses']):
         np.testing.assert_almost_equal(pl_out, pt_out, 5)
 
     # the fist run initialize dataset (download & filter)
-    tutils.assert_speed_parity_absolute(pl_times[1:], pt_times[1:],
-                                        nb_epochs=num_epochs, max_diff=max_diff)
+    tutils.assert_speed_parity_absolute(
+        lightning['durations'][1:], vanilla['durations'][1:], nb_epochs=num_epochs, max_diff=max_diff
+    )
 
 
 def vanilla_loop(cls_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 = []
+    hist_losses = []
+    hist_durations = []
 
+    device = torch.device('cuda' if torch.cuda.is_available() else "cpu")
     torch.backends.cudnn.deterministic = True
-    for i in range(num_runs):
+    for i in tqdm(range(num_runs), desc=f'Vanilla PT with {cls_model.__name__}'):
         time_start = time.perf_counter()
 
         # set seed
@@ -74,18 +88,21 @@ def vanilla_loop(cls_model, num_runs=10, num_epochs=10):
             epoch_losses.append(loss.item())
 
         time_end = time.perf_counter()
-        times.append(time_end - time_start)
+        hist_durations.append(time_end - time_start)
 
-        errors.append(epoch_losses[-1])
+        hist_losses.append(epoch_losses[-1])
 
-    return errors, times
+    return {
+        'losses': hist_losses,
+        'durations': hist_durations,
+    }
 
 
 def lightning_loop(cls_model, num_runs=10, num_epochs=10):
-    errors = []
-    times = []
+    hist_losses = []
+    hist_durations = []
 
-    for i in range(num_runs):
+    for i in tqdm(range(num_runs), desc=f'PT Lightning with {cls_model.__name__}'):
         time_start = time.perf_counter()
 
         # set seed
@@ -108,9 +125,12 @@ def lightning_loop(cls_model, num_runs=10, num_epochs=10):
         trainer.fit(model)
 
         final_loss = trainer.train_loop.running_loss.last().item()
-        errors.append(final_loss)
+        hist_losses.append(final_loss)
 
         time_end = time.perf_counter()
-        times.append(time_end - time_start)
+        hist_durations.append(time_end - time_start)
 
-    return errors, times
+    return {
+        'losses': hist_losses,
+        'durations': hist_durations,
+    }

benchmarks/test_sharded_parity.py

@@ -1,3 +1,17 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import os
 import platform
 import time

docs/source/_images/benchmarks/figure-parity-times.png

@@ -0,0 +1,14 @@
+Benchmark with vanilla PyTorch
+==============================
+
+In this section we set grounds for comparison between vanilla PyTorch and PT Lightning for most common scenarios.
+
+Time comparison
+---------------
+
+We have set regular benchmarking against PyTorch vanilla training loop on with RNN and simple MNIST classifier as per of out CI.
+In average for simple MNIST CNN classifier we are only about 0.06s slower per epoch, see detail chart bellow.
+
+.. figure:: _images/benchmarks/figure-parity-times.png
+   :alt: Speed parity to vanilla PT, created on 2020-12-16
+   :width: 500

docs/source/benchmarking.rst

@@ -24,6 +24,7 @@ PyTorch Lightning Documentation
    style_guide
    performance
    Lightning project template<https://github.com/PyTorchLightning/pytorch-lightning-conference-seed>
+   benchmarking
 
 
 .. toctree::

docs/source/index.rst

@@ -17,3 +17,4 @@ pre-commit>=1.0
 
 cloudpickle>=1.3
 nltk>=3.3
+pandas  # needed in benchmarks

requirements/test.txt

@@ -63,8 +63,13 @@ class MNIST(Dataset):
     TEST_FILE_NAME = 'test.pt'
     cache_folder_name = 'complete'
 
-    def __init__(self, root: str = PATH_DATASETS, train: bool = True,
-                 normalize: tuple = (0.5, 1.0), download: bool = True):
+    def __init__(
+            self,
+            root: str = PATH_DATASETS,
+            train: bool = True,
+            normalize: tuple = (0.5, 1.0),
+            download: bool = True,
+    ):
         super().__init__()
         self.root = root
         self.train = train  # training set or test set