Add some models for generic testing of MCMC

docs/source/poutine.rst

@@ -36,6 +36,8 @@ Handlers
 
 .. autofunction:: pyro.poutine.scale
 
+.. autofunction:: pyro.poutine.seed
+
 .. autofunction:: pyro.poutine.trace
 
 .. autofunction:: pyro.infer.enum.config_enumerate

docs/source/primitives.rst

@@ -18,3 +18,5 @@ Primitives
 .. autofunction:: pyro.enable_validation
 
 .. autofunction:: pyro.ops.jit.trace
+
+.. autofunction:: pyro.set_rng_seed

docs/source/pyro.generic.rst

@@ -4,7 +4,7 @@ Generic Interface
 The ``pyro.generic`` module provides an interface to dynamically dispatch Pyro code
 to custom backends.
 
-.. automodule:: pyro.generic
+.. automodule:: pyro.generic.dispatch
     :members:
     :undoc-members:
     :show-inheritance:

pyro/generic/__init__.py

@@ -0,0 +1,11 @@
+from pyro.generic.dispatch import distributions, handlers, infer, ops, optim, pyro, pyro_backend
+
+__all__ = [
+    'distributions',
+    'handlers',
+    'infer',
+    'ops',
+    'optim',
+    'pyro',
+    'pyro_backend',
+]

pyro/generic.py → pyro/generic/dispatch.py

@@ -1,5 +1,4 @@
 import importlib
-
 from contextlib import contextmanager
 
 
@@ -54,37 +53,43 @@ def pyro_backend(*aliases, **new_backends):
 
 _ALIASES = {
     'pyro': {
-        'pyro': 'pyro',
         'distributions': 'pyro.distributions',
+        'handlers': 'pyro.poutine',
         'infer': 'pyro.infer',
-        'optim': 'pyro.optim',
         'ops': 'torch',
+        'optim': 'pyro.optim',
+        'pyro': 'pyro',
     },
     'minipyro': {
-        'pyro': 'pyro.contrib.minipyro',
+        'distributions': 'pyro.distributions',
+        'handlers': 'pyro.poutine',
         'infer': 'pyro.contrib.minipyro',
-        'optim': 'pyro.contrib.minipyro',
         'ops': 'torch',
+        'optim': 'pyro.contrib.minipyro',
+        'pyro': 'pyro.contrib.minipyro',
     },
     'funsor': {
-        'pyro': 'funsor.minipyro',
-        'infer': 'funsor.minipyro',
-        'optim': 'funsor.minipyro',
         'distributions': 'funsor.distributions',
+        'handlers': 'pyro.poutine',
+        'infer': 'funsor.minipyro',
         'ops': 'funsor.ops',
+        'optim': 'funsor.minipyro',
+        'pyro': 'funsor.minipyro',
     },
     'numpy': {
-        'pyro': 'numpyro.compat.pyro',
         'distributions': 'numpyro.compat.distributions',
+        'handlers': 'numpyro.compat.handlers',
         'infer': 'numpyro.compat.infer',
+        'ops': 'numpyro.compat.ops',
         'optim': 'numpyro.compat.optim',
-        'ops': 'numpy',
+        'pyro': 'numpyro.compat.pyro',
     },
 }
 
 # These modules can be overridden.
 pyro = GenericModule('pyro', 'pyro')
 distributions = GenericModule('distributions', 'pyro.distributions')
+handlers = GenericModule('handlers', 'pyro.poutine')
 infer = GenericModule('infer', 'pyro.infer')
 optim = GenericModule('optim', 'pyro.optim')
 ops = GenericModule('ops', 'torch')

pyro/generic/testing.py

@@ -0,0 +1,111 @@
+"""
+Models for testing the generic interface.
+
+For specifying the arguments to model functions, the convention followed is
+that positional arguments are inputs to the model and keyword arguments denote
+observed data.
+"""
+
+import argparse
+from collections import OrderedDict
+
+from pyro.generic import distributions as dist, handlers, ops, pyro, pyro_backend
+
+MODELS = OrderedDict()
+
+
+def register(rng_seed=None):
+    def _register_fn(fn):
+        MODELS[fn.__name__] = handlers.seed(fn, rng_seed)
+
+    return _register_fn
+
+
+@register(rng_seed=1)
+def logistic_regression():
+    N, dim = 3000, 3
+    # generic way to sample from distributions
+    data = pyro.sample('data', dist.Normal(0., 1.), sample_shape=(N, dim))
+    true_coefs = ops.arange(1., dim + 1.)
+    logits = ops.sum(true_coefs * data, axis=-1)
+    labels = pyro.sample('labels', dist.Bernoulli(logits=logits))
+
+    def model(x, y=None):
+        coefs = pyro.sample('coefs', dist.Normal(ops.zeros(dim), ops.ones(dim)))
+        intercept = pyro.sample('intercept', dist.Normal(0., 1.))
+        logits = ops.sum(coefs * x, axis=-1) + intercept
+        return pyro.sample('obs', dist.Bernoulli(logits=logits), obs=y)
+
+    return {'model': model, 'model_args': (data,), 'model_kwargs': {'y': labels}}
+
+
+@register(rng_seed=1)
+def neals_funnel():
+    def model(dim):
+        y = pyro.sample('y', dist.Normal(0, 3))
+        pyro.sample('x', dist.TransformedDistribution(
+            dist.Normal(ops.zeros(dim - 1), 1), dist.transforms.AffineTransform(0, ops.exp(y / 2))))
+
+    return {'model': model, 'model_args': (10,)}
+
+
+@register(rng_seed=1)
+def eight_schools():
+    J = 8
+    y = ops.tensor([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0])
+    sigma = ops.tensor([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0])
+
+    def model(J, sigma, y=None):
+        mu = pyro.sample('mu', dist.Normal(0, 5))
+        tau = pyro.sample('tau', dist.HalfCauchy(5))
+        with pyro.plate('J', J):
+            theta = pyro.sample('theta', dist.Normal(mu, tau))
+            pyro.sample('obs', dist.Normal(theta, sigma), obs=y)
+
+    return {'model': model, 'model_args': (J, sigma), 'model_kwargs': {'y': y}}
+
+
+@register(rng_seed=1)
+def beta_binomial():
+    N, D1, D2 = 10, 2, 2
+    true_probs = ops.tensor([[0.7, 0.4], [0.6, 0.4]])
+    total_count = ops.tensor([[1000, 600], [400, 800]])
+
+    data = pyro.sample('data', dist.Binomial(total_count=total_count, probs=true_probs),
+                       sample_shape=(N,))
+
+    def model(N, D1, D2, data=None):
+        with pyro.plate("plate_0", D1):
+            alpha = pyro.sample("alpha", dist.HalfCauchy(1.))
+            beta = pyro.sample("beta", dist.HalfCauchy(1.))
+            with pyro.plate("plate_1", D2):
+                probs = pyro.sample("probs", dist.Beta(alpha, beta))
+                with pyro.plate("data", N):
+                    pyro.sample("binomial", dist.Binomial(probs=probs, total_count=total_count), obs=data)
+
+    return {'model': model, 'model_args': (N, D1, D2), 'model_kwargs': {'data': data}}
+
+
+def check_model(backend, name):
+    get_model = MODELS[name]
+    print('Running model "{}" on backend "{}".'.format(name, args.backend))
+    with pyro_backend(backend), handlers.seed(rng_seed=2):
+        f = get_model()
+        model, model_args, model_kwargs = f['model'], f.get('model_args', ()), f.get('model_kwargs', {})
+        print('Sample from prior...')
+        model(*model_args)
+        print('Trace model...')
+        handlers.trace(model).get_trace(*model_args, **model_kwargs)
+
+
+def main(args):
+    for name in MODELS:
+        check_model(args.backend, name)
+
+
+if __name__ == '__main__':
+    assert pyro.__version__.startswith('0.4.1')
+    parser = argparse.ArgumentParser(description="Mini Pyro demo")
+    parser.add_argument("-b", "--backend", default="pyro")
+    args = parser.parse_args()
+    main(args)

pyro/infer/__init__.py

@@ -4,6 +4,9 @@
 from pyro.infer.elbo import ELBO
 from pyro.infer.enum import config_enumerate
 from pyro.infer.importance import Importance
+from pyro.infer.mcmc.api import MCMC
+from pyro.infer.mcmc.hmc import HMC
+from pyro.infer.mcmc.nuts import NUTS
 from pyro.infer.renyi_elbo import RenyiELBO
 from pyro.infer.smcfilter import SMCFilter
 from pyro.infer.svi import SVI
@@ -23,13 +26,16 @@
     "is_validation_enabled",
     "ELBO",
     "EmpiricalMarginal",
+    "HMC",
     "Importance",
     "IMQSteinKernel",
     "infer_discrete",
     "JitTraceEnum_ELBO",
     "JitTraceGraph_ELBO",
     "JitTraceMeanField_ELBO",
     "JitTrace_ELBO",
+    "MCMC",
+    "NUTS",
     "RBFSteinKernel",
     "RenyiELBO",
     "SMCFilter",

pyro/poutine/__init__.py

@@ -1,5 +1,5 @@
 from .handlers import (block, broadcast, condition, do, enum, escape, infer_config, lift, markov, mask, queue, replay,
-                       scale, trace, uncondition)
+                       scale, seed, trace, uncondition)
 from .runtime import NonlocalExit
 from .trace_struct import Trace
 from .util import enable_validation, is_validation_enabled
@@ -21,6 +21,7 @@
     "replay",
     "queue",
     "scale",
+    "seed",
     "trace",
     "Trace",
     "uncondition",

pyro/poutine/handlers.py

@@ -49,7 +49,8 @@
 import functools
 
 from pyro.poutine import util
-
+from pyro.poutine.messenger import Messenger
+from pyro.util import set_rng_seed
 from .block_messenger import BlockMessenger
 from .broadcast_messenger import BroadcastMessenger
 from .condition_messenger import ConditionMessenger
@@ -66,6 +67,7 @@
 from .trace_messenger import TraceMessenger
 from .uncondition_messenger import UnconditionMessenger
 
+
 ############################################
 # Begin primitive operations
 ############################################
@@ -497,3 +499,29 @@ def markov(fn=None, history=1, keep=False):
         return MarkovMessenger(history=history, keep=keep).generator(iterable=fn)
     # Used as a decorator with bound args
     return MarkovMessenger(history=history, keep=keep)(fn)
+
+
+class _SeedMessenger(Messenger):
+    def __init__(self, rng_seed):
+        assert isinstance(rng_seed, int)
+        self.rng_seed = rng_seed
+        super(_SeedMessenger, self).__init__()
+
+    def __enter__(self):
+        set_rng_seed(self.rng_seed)
+        super(_SeedMessenger, self).__enter__()
+
+
+def seed(fn=None, rng_seed=None):
+    """
+    Handler to set the random number generator to a pre-defined state by setting its
+    seed. This is the same as calling :func:`pyro.set_rng_seed` before the
+    call to `fn`. This handler has no additional effect on primitive statements on the
+    standard Pyro backend, but it might intercept ``pyro.sample`` calls in other
+    backends. e.g. the NumPy backend.
+
+    :param fn: a stochastic function (callable containing Pyro primitive calls).
+    :param int rng_seed: rng seed.
+    """
+    msngr = _SeedMessenger(rng_seed)
+    return msngr(fn) if fn is not None else msngr

pyro/util.py

@@ -15,7 +15,8 @@
 
 def set_rng_seed(rng_seed):
     """
-    Sets seeds of torch and torch.cuda (if available).
+    Sets seeds of `torch` and `torch.cuda` (if available).
+
     :param int rng_seed: The seed value.
     """
     torch.manual_seed(rng_seed)

tests/test_generic.py

@@ -0,0 +1,19 @@
+import pytest
+
+from pyro.generic import infer, pyro_backend, handlers
+from pyro.generic.testing import MODELS
+
+
+pytestmark = pytest.mark.stage('unit')
+
+
+@pytest.mark.parametrize('model', MODELS)
+@pytest.mark.parametrize('backend', ['pyro'])
+def test_mcmc_interface(model, backend):
+    with pyro_backend(backend), handlers.seed(rng_seed=20):
+        f = MODELS[model]()
+        model, args, kwargs = f['model'], f.get('model_args', ()), f.get('model_kwargs', {})
+        nuts_kernel = infer.NUTS(model=model)
+        mcmc = infer.MCMC(nuts_kernel, num_samples=10, warmup_steps=10)
+        mcmc.run(*args, **kwargs)
+        mcmc.summary()