Add a simple PyTorch training example

examples/svi_torch.py

@@ -0,0 +1,111 @@
+# Copyright Contributors to the Pyro project.
+# SPDX-License-Identifier: Apache-2.0
+
+# Using vanilla PyTorch to perform optimization in SVI.
+#
+# This tutorial demonstrates how to use standard PyTorch optimizers, dataloaders and training loops
+# to perform optimization in SVI. This is useful when you want to use custom optimizers,
+# learning rate schedules, dataloaders, or other advanced training techniques,
+# or just to simplify integration with other elements of the PyTorch ecosystem.
+
+import argparse
+from typing import Callable
+
+import torch
+
+import pyro
+import pyro.distributions as dist
+from pyro.infer import Trace_ELBO
+from pyro.infer.autoguide import AutoNormal
+from pyro.nn import PyroModule
+
+
+# We define a model as usual. This model is data parallel and supports subsampling.
+class Model(PyroModule):
+    def __init__(self, size):
+        super().__init__()
+        self.size = size
+        # We register a buffer for a constant scalar tensor to represent zero.
+        # This is useful for making priors that do not depend on inputs
+        # or learnable parameters compatible with the Module.to() method
+        # for setting the device or dtype of a module and its parameters.
+        self.register_buffer("zero", torch.tensor(0.0))
+
+    def forward(self, covariates, data=None):
+        # Sample parameters from priors that make use of the zero buffer trick
+        coeff = pyro.sample("coeff", dist.Normal(self.zero, 1))
+        bias = pyro.sample("bias", dist.Normal(self.zero, 1))
+        scale = pyro.sample("scale", dist.LogNormal(self.zero, 1))
+
+        # Since we'll use a PyTorch dataloader during training, we need to
+        # manually pass minibatches of (covariates,data) that are smaller than
+        # the full self.size, rather than relying on pyro.plate to automatically subsample.
+        with pyro.plate("data", self.size, len(covariates)):
+            loc = bias + coeff * covariates
+            return pyro.sample("obs", dist.Normal(loc, scale), obs=data)
+
+
+def main(args):
+    # Make PyroModule parameters local (like ordinary torch.nn.Parameters),
+    # rather than shared by name through Pyro's global parameter store.
+    # This is highly recommended whenever models can be written without pyro.param().
+    pyro.settings.set(module_local_params=True)
+
+    # set seed for reproducibility
+    pyro.set_rng_seed(args.seed)
+
+    # Create a synthetic dataset from a randomly initialized model.
+    with torch.no_grad():
+        covariates = torch.randn(args.size)
+        data = Model(args.size)(covariates)
+        covariates = covariates.to(device=torch.device("cuda" if args.cuda else "cpu"))
+        data = data.to(device=torch.device("cuda" if args.cuda else "cpu"))
+
+    # Create a model and a guide, both as (Pyro)Modules.
+    model: torch.nn.Module = Model(args.size)
+    guide: torch.nn.Module = AutoNormal(model)
+
+    # Create a loss function as a Module that includes model and guide parameters.
+    # All Pyro ELBO estimators can be __call__()ed with a model and guide pair as arguments
+    # to return a loss function Module that takes the same arguments as the model and guide
+    # and exposes all of their torch.nn.Parameters and pyro.nn.PyroParam parameters.
+    elbo: Callable[[torch.nn.Module, torch.nn.Module], torch.nn.Module] = Trace_ELBO()
+    loss_fn: torch.nn.Module = elbo(model, guide)
+    loss_fn.to(device=torch.device("cuda" if args.cuda else "cpu"))
+
+    # Create a dataloader.
+    dataset = torch.utils.data.TensorDataset(covariates, data)
+    dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size)
+
+    # All relevant parameters need to be initialized before an optimizer can be created.
+    # Since we used AutoNormal guide our parameters have not be initialized yet.
+    # Therefore we initialize the model and guide by running one mini-batch through the loss.
+    mini_batch = dataset[: args.batch_size]
+    loss_fn(*mini_batch)
+
+    # Create a PyTorch optimizer for the parameters of the model and guide in loss_fn.
+    optimizer = torch.optim.Adam(loss_fn.parameters(), lr=args.learning_rate)
+
+    # Run stochastic variational inference using PyTorch optimizers from torch.optim
+    for epoch in range(args.num_epochs):
+        for batch in dataloader:
+            optimizer.zero_grad()
+            loss = loss_fn(*batch)
+            loss.backward()
+            optimizer.step()
+        print(f"epoch {epoch} loss = {loss}")
+
+
+if __name__ == "__main__":
+    assert pyro.__version__.startswith("1.8.6")
+    parser = argparse.ArgumentParser(
+        description="Using vanilla PyTorch to perform optimization in SVI"
+    )
+    parser.add_argument("--size", default=10000, type=int)
+    parser.add_argument("--batch-size", default=100, type=int)
+    parser.add_argument("--learning-rate", default=0.01, type=float)
+    parser.add_argument("--seed", default=20200723, type=int)
+    parser.add_argument("--num-epochs", default=10, type=int)
+    parser.add_argument("--cuda", action="store_true", default=False)
+    args = parser.parse_args()
+    main(args)

tests/test_examples.py

@@ -110,6 +110,7 @@
     "sparse_gamma_def.py --num-epochs=2 --eval-particles=2 --eval-frequency=1 --guide custom",
     "sparse_gamma_def.py --num-epochs=2 --eval-particles=2 --eval-frequency=1 --guide auto",
     "sparse_gamma_def.py --num-epochs=2 --eval-particles=2 --eval-frequency=1 --guide easy",
+    "svi_torch.py --num-epochs=2 --size=400",
     "svi_horovod.py --num-epochs=2 --size=400 --no-horovod",
     pytest.param(
         "svi_lightning.py --max_epochs=2 --size=400 --accelerator cpu --devices 1",
@@ -181,6 +182,7 @@
     "sir_hmc.py -t=2 -w=2 -n=4 -d=2 -m=1 --enum --cuda",
     "sir_hmc.py -t=2 -w=2 -n=4 -d=2 -p=10000 --sequential --cuda",
     "sir_hmc.py -t=2 -w=2 -n=4 -d=100 -p=10000 --cuda",
+    "svi_torch.py --num-epochs=2 --size=400 --cuda",
     "svi_horovod.py --num-epochs=2 --size=400 --cuda --no-horovod",
     pytest.param(
         "svi_lightning.py --max_epochs=2 --size=400 --accelerator gpu --devices 1",

tutorial/source/index.rst

@@ -22,7 +22,8 @@ and look carefully through the series :ref:`practical-pyro-and-pytorch`,
 especially the :doc:`first Bayesian regression tutorial <bayesian_regression>`.
 This tutorial goes step-by-step through solving a simple Bayesian machine learning problem with Pyro,
 grounding the concepts from the introductory tutorials in runnable code.
-Industry users interested in serving predictions from a trained model in C++ should also read :doc:`the PyroModule tutorial <modules>`.
+Users interested in integrating with existing PyTorch training and serving infrastructure should also read :doc:`the PyroModule tutorial <modules>`
+and look at the :doc:`SVI with PyTorch <svi_torch>` and :doc:`SVI with Lightning <svi_lightning>` examples.
 
 Most users who reach this point will also find our :doc:`guide to tensor shapes in Pyro <tensor_shapes>` essential reading.
 Pyro makes extensive use of the behavior of `"array broadcasting" <https://numpy.org/doc/stable/user/basics.broadcasting.html>`_
@@ -95,6 +96,7 @@ List of Tutorials
    workflow
    prior_predictive
    jit
+   svi_torch
    svi_horovod
    svi_lightning
    svi_flow_guide

tutorial/source/svi_torch.rst

@@ -0,0 +1,15 @@
+Example: using vanilla PyTorch to perform optimization in SVI
+=============================================================
+
+This script uses argparse arguments to construct PyTorch optimizer and dataloader, for example::
+
+    $ python examples/svi_torch.py --size 10000 --batch-size 100 --num-epochs 100
+
+`View svi_torch.py on github`__
+
+.. _github: https://github.com/pyro-ppl/pyro/blob/dev/examples/svi_torch.py
+
+__ github_
+
+.. literalinclude:: ../../examples/svi_torch.py
+    :language: python