Implementation:Pyro ppl Pyro Autoname Mixture

Property	Value
Implementation Type	Pattern Doc
Source File	examples/contrib/autoname/mixture.py
Module	pyro.contrib.autoname
Pyro Features	pyro.contrib.autoname.named, named.Object, named.List, SVI, Trace_ELBO / JitTrace_ELBO
Pattern	Gaussian Mixture Model with named objects for modular model/guide design

Overview

This file demonstrates how to use pyro.contrib.autoname.named to build a Gaussian Mixture Model with clean, modular code. The named.Object API automatically generates unique site names for Pyro sample and param statements, making it easy to pass latent variable containers between model components.

The key pattern shown is passing named.Objects() from a global model to a local model implemented as a helper function. This allows:

• Global parameters (mixture weights, locations, scales) to be defined at the top level
• Local latent variables (component assignments, observations) to be defined in a reusable helper
• Automatic naming that avoids site name collisions

Code Reference

def model(data, k):
    latent = named.Object("latent")
    latent.probs.param_(torch.ones(k) / k, constraint=constraints.simplex)
    latent.locs.param_(torch.zeros(k))
    latent.scales.param_(torch.ones(k), constraint=constraints.positive)

    latent.local = named.List()
    for x in data:
        local_model(latent.local.add(), latent.probs, latent.locs, latent.scales, obs=x)

def local_model(latent, ps, locs, scales, obs=None):
    i = latent.id.sample_(dist.Categorical(ps))
    return latent.x.sample_(dist.Normal(locs[i], scales[i]), obs=obs)

def guide(data, k):
    latent = named.Object("latent")
    latent.local = named.List()
    for x in data:
        local_guide(latent.local.add(), k)

def local_guide(latent, k):
    latent.probs.param_(torch.ones(k) / k, constraint=constraints.positive)
    latent.id.sample_(dist.Categorical(latent.probs))

I/O Contract

Parameter	Type	Description
data	torch.Tensor	1D tensor of observed data points
k	int	Number of mixture components
-n / --num-epochs	int	Number of training epochs (default: 200)
--jit	flag	Use JIT-compiled ELBO

Named parameters (auto-generated):

• latent.probs: Mixture weights (simplex constraint)
• latent.locs: Component means
• latent.scales: Component standard deviations
• latent.local.{i}.id: Component assignment for data point i
• latent.local.{i}.x: Observation for data point i

Usage Examples

import pyro
from pyro.infer import SVI, Trace_ELBO
from pyro.optim import Adam

pyro.set_rng_seed(0)
optim = Adam({"lr": 0.1})
inference = SVI(model, guide, optim, loss=Trace_ELBO())
data = torch.tensor([0.0, 1.0, 2.0, 20.0, 30.0, 40.0])

for step in range(200):
    loss = inference.step(data, k=2)

Related Pages

• Pyro_ppl_Pyro_Autoname_Scoping - Mixture model using scope decorator
• Pyro_ppl_Pyro_Autoname_TreeData - Hierarchical model using named objects