Add Dali MNIST example (#3721)

* add MNIST DALI example, update README.md

* Fix PEP8 warnings

* reformatted using black

* add mnist_dali to test_examples.py

* Add documentation as docstrings

* add nvidia-pyindex and nvidia-dali-cuda100

* replace nvidia-pyindex with --extra-index-url

* mark mnist_dali test as Linux and GPU only

* adjust CUDA docker and examples.txt, fix import error in test_examples.py

* adjust the GPU check

* Exit when DALI is not available

* remove requirements-examples.txt and DALI pip install

* Refactored example, moved to new logging api, added runtime check for test and dali script

* Patch to reflect the mnist example module

* add req.

* Apply suggestions from code review

* Removed requirement as it breaks CPU install, added note in README to install DALI

* add DALI to Drone

* test examples

* Apply suggestions from code review

* imports

* ABC

* cuda

* cuda

* pip DALI

* Move build into init function

Co-authored-by: SeanNaren <sean@grid.ai>
Co-authored-by: Jirka Borovec <jirka@pytorchlightning.ai>
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: Sean Naren <sean.narenthiran@gmail.com>

.drone.yml

@@ -32,6 +32,8 @@ steps:
  - pip --version
  - nvidia-smi
  - pip install -r ./requirements/devel.txt --upgrade-strategy only-if-needed -v --no-cache-dir
+ # when Image has defined CUDa version we can switch to this package spec "nvidia-dali-cuda${CUDA_VERSION%%.*}0"
+ - pip install --extra-index-url https://developer.download.nvidia.com/compute/redist nvidia-dali-cuda100 --upgrade-strategy only-if-needed
  - pip list
  - coverage run --source pytorch_lightning -m pytest pytorch_lightning tests -v --color=yes --durations=25 # --flake8
  - python -m pytest benchmarks pl_examples -v --color=yes --maxfail=2 --durations=0 # --flake8

pl_examples/basic_examples/README.md

@@ -14,7 +14,15 @@ python mnist.py
 python mnist.py --gpus 2 --distributed_backend 'dp'
 ```
 
---- 
+---
+#### MNIST with DALI
+The MNIST example above using [NVIDIA DALI](https://developer.nvidia.com/DALI).
+Requires NVIDIA DALI to be installed based on your CUDA version, see [here](https://docs.nvidia.com/deeplearning/dali/user-guide/docs/installation.html).
+```bash
+python mnist_dali.py
+```
+
+---
 #### Image classifier
 Generic image classifier with an arbitrary backbone (ie: a simple system)
 ```bash

pl_examples/basic_examples/mnist_dali.py

@@ -0,0 +1,204 @@
+# 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.
+from abc import ABC
+from argparse import ArgumentParser
+from random import shuffle
+from warnings import warn
+
+import numpy as np
+import torch
+from torch.nn import functional as F
+from torch.utils.data import random_split
+
+import pytorch_lightning as pl
+
+try:
+ from torchvision.datasets.mnist import MNIST
+ from torchvision import transforms
+except Exception:
+ from tests.base.datasets import MNIST
+
+try:
+ import nvidia.dali.ops as ops
+ import nvidia.dali.types as types
+ from nvidia.dali.pipeline import Pipeline
+ from nvidia.dali.plugin.pytorch import DALIClassificationIterator
+except (ImportError, ModuleNotFoundError):
+ warn('NVIDIA DALI is not available')
+ ops, types, Pipeline, DALIClassificationIterator = ..., ..., ABC, ABC
+
+
+class ExternalMNISTInputIterator(object):
+ """
+ This iterator class wraps torchvision's MNIST dataset and returns the images and labels in batches
+ """
+
+ def __init__(self, mnist_ds, batch_size):
+ self.batch_size = batch_size
+ self.mnist_ds = mnist_ds
+ self.indices = list(range(len(self.mnist_ds)))
+ shuffle(self.indices)
+
+ def __iter__(self):
+ self.i = 0
+ self.n = len(self.mnist_ds)
+ return self
+
+ def __next__(self):
+ batch = []
+ labels = []
+ for _ in range(self.batch_size):
+ index = self.indices[self.i]
+ img, label = self.mnist_ds[index]
+ batch.append(img.numpy())
+ labels.append(np.array([label], dtype=np.uint8))
+ self.i = (self.i + 1) % self.n
+ return (batch, labels)
+
+
+class ExternalSourcePipeline(Pipeline):
+ """
+ This DALI pipeline class just contains the MNIST iterator
+ """
+
+ def __init__(self, batch_size, eii, num_threads, device_id):
+ super(ExternalSourcePipeline, self).__init__(batch_size, num_threads, device_id, seed=12)
+ self.source = ops.ExternalSource(source=eii, num_outputs=2)
+ self.build()
+
+ def define_graph(self):
+ images, labels = self.source()
+ return images, labels
+
+
+class DALIClassificationLoader(DALIClassificationIterator):
+ """
+ This class extends DALI's original DALIClassificationIterator with the __len__() function so that we can call len() on it
+ """
+
+ def __init__(
+ self,
+ pipelines,
+ size=-1,
+ reader_name=None,
+ auto_reset=False,
+ fill_last_batch=True,
+ dynamic_shape=False,
+ last_batch_padded=False,
+ ):
+ super().__init__(pipelines, size, reader_name, auto_reset, fill_last_batch, dynamic_shape, last_batch_padded)
+
+ def __len__(self):
+ batch_count = self._size // (self._num_gpus * self.batch_size)
+ last_batch = 1 if self._fill_last_batch else 0
+ return batch_count + last_batch
+
+
+class LitClassifier(pl.LightningModule):
+ def __init__(self, hidden_dim=128, learning_rate=1e-3):
+ super().__init__()
+ self.save_hyperparameters()
+
+ self.l1 = torch.nn.Linear(28 * 28, self.hparams.hidden_dim)
+ self.l2 = torch.nn.Linear(self.hparams.hidden_dim, 10)
+
+ def forward(self, x):
+ x = x.view(x.size(0), -1)
+ x = torch.relu(self.l1(x))
+ x = torch.relu(self.l2(x))
+ return x
+
+ def split_batch(self, batch):
+ return batch[0]["data"], batch[0]["label"].squeeze().long()
+
+ def training_step(self, batch, batch_idx):
+ x, y = self.split_batch(batch)
+ y_hat = self(x)
+ loss = F.cross_entropy(y_hat, y)
+ return loss
+
+ def validation_step(self, batch, batch_idx):
+ x, y = self.split_batch(batch)
+ y_hat = self(x)
+ loss = F.cross_entropy(y_hat, y)
+ self.log('valid_loss', loss)
+
+ def test_step(self, batch, batch_idx):
+ x, y = self.split_batch(batch)
+ y_hat = self(x)
+ loss = F.cross_entropy(y_hat, y)
+ self.log('test_loss', loss)
+
+ def configure_optimizers(self):
+ return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
+
+ @staticmethod
+ def add_model_specific_args(parent_parser):
+ parser = ArgumentParser(parents=[parent_parser], add_help=False)
+ parser.add_argument('--hidden_dim', type=int, default=128)
+ parser.add_argument('--learning_rate', type=float, default=0.0001)
+ return parser
+
+
+def cli_main():
+ pl.seed_everything(1234)
+
+ # ------------
+ # args
+ # ------------
+ parser = ArgumentParser()
+ parser.add_argument('--batch_size', default=32, type=int)
+ parser = pl.Trainer.add_argparse_args(parser)
+ parser = LitClassifier.add_model_specific_args(parser)
+ args = parser.parse_args()
+
+ # ------------
+ # data
+ # ------------
+ dataset = MNIST('', train=True, download=True, transform=transforms.ToTensor())
+ mnist_test = MNIST('', train=False, download=True, transform=transforms.ToTensor())
+ mnist_train, mnist_val = random_split(dataset, [55000, 5000])
+
+ eii_train = ExternalMNISTInputIterator(mnist_train, args.batch_size)
+ eii_val = ExternalMNISTInputIterator(mnist_val, args.batch_size)
+ eii_test = ExternalMNISTInputIterator(mnist_test, args.batch_size)
+
+ pipe_train = ExternalSourcePipeline(batch_size=args.batch_size, eii=eii_train, num_threads=2, device_id=0)
+ train_loader = DALIClassificationLoader(pipe_train, size=len(mnist_train), auto_reset=True, fill_last_batch=False)
+
+ pipe_val = ExternalSourcePipeline(batch_size=args.batch_size, eii=eii_val, num_threads=2, device_id=0)
+ val_loader = DALIClassificationLoader(pipe_val, size=len(mnist_val), auto_reset=True, fill_last_batch=False)
+
+ pipe_test = ExternalSourcePipeline(batch_size=args.batch_size, eii=eii_test, num_threads=2, device_id=0)
+ test_loader = DALIClassificationLoader(pipe_test, size=len(mnist_test), auto_reset=True, fill_last_batch=False)
+
+ # ------------
+ # model
+ # ------------
+ model = LitClassifier(args.hidden_dim, args.learning_rate)
+
+ # ------------
+ # training
+ # ------------
+ trainer = pl.Trainer.from_argparse_args(args)
+ trainer.fit(model, train_loader, val_loader)
+
+ # ------------
+ # testing
+ # ------------
+ trainer.test(test_dataloaders=test_loader)
+
+
+if __name__ == "__main__":
+ cli_main()

pl_examples/test_examples.py

@@ -1,6 +1,15 @@
+import platform
 from unittest import mock
-import torch
+
 import pytest
+import torch
+
+try:
+ from nvidia.dali import ops, types, pipeline, plugin
+except (ImportError, ModuleNotFoundError):
+ DALI_AVAILABLE = False
+else:
+ DALI_AVAILABLE = True
 
 dp_16_args = """
 --max_epochs 1 \
@@ -28,7 +37,7 @@
 --precision 16 \
 """
 
-
+# TODO
 # @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
 # @pytest.mark.parametrize('cli_args', [dp_16_args])
 # def test_examples_dp_mnist(cli_args):
@@ -38,15 +47,17 @@
 # cli_main()
 
 
+# TODO
 # @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
 # @pytest.mark.parametrize('cli_args', [dp_16_args])
 # def test_examples_dp_image_classifier(cli_args):
 # from pl_examples.basic_examples.image_classifier import cli_main
 #
 # with mock.patch("argparse._sys.argv", ["any.py"] + cli_args.strip().split()):
 # cli_main()
-#
-#
+
+
+# TODO
 # @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
 # @pytest.mark.parametrize('cli_args', [dp_16_args])
 # def test_examples_dp_autoencoder(cli_args):
@@ -56,24 +67,27 @@
 # cli_main()
 
 
+# TODO
 # @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
 # @pytest.mark.parametrize('cli_args', [ddp_args])
 # def test_examples_ddp_mnist(cli_args):
 # from pl_examples.basic_examples.mnist import cli_main
 #
 # with mock.patch("argparse._sys.argv", ["any.py"] + cli_args.strip().split()):
 # cli_main()
-#
-#
+
+
+# TODO
 # @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
 # @pytest.mark.parametrize('cli_args', [ddp_args])
 # def test_examples_ddp_image_classifier(cli_args):
 # from pl_examples.basic_examples.image_classifier import cli_main
 #
 # with mock.patch("argparse._sys.argv", ["any.py"] + cli_args.strip().split()):
 # cli_main()
-#
-#
+
+
+# TODO
 # @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
 # @pytest.mark.parametrize('cli_args', [ddp_args])
 # def test_examples_ddp_autoencoder(cli_args):
@@ -92,3 +106,14 @@ def test_examples_cpu(cli_args):
  for cli_cmd in [mnist_cli, ic_cli, ae_cli]:
  with mock.patch("argparse._sys.argv", ["any.py"] + cli_args.strip().split()):
  cli_cmd()
+
+
+@pytest.mark.skipif(not DALI_AVAILABLE, reason="Nvidia DALI required")
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
+@pytest.mark.skipif(platform.system() != 'Linux', reason='Only applies to Linux platform.')
+@pytest.mark.parametrize('cli_args', [cpu_args])
+def test_examples_mnist_dali(cli_args):
+ from pl_examples.basic_examples.mnist_dali import cli_main
+
+ with mock.patch("argparse._sys.argv", ["any.py"] + cli_args.strip().split()):
+ cli_main()

requirements/examples.txt

@@ -1,2 +1,2 @@
 torchvision>=0.4.1,<0.9.0
-gym>=0.17.0
+gym>=0.17.0