Implement Resamper for interactive prior tuning

docs/source/conf.py

@@ -92,7 +92,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+language = "en"
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.

docs/source/infer.util.rst

@@ -11,3 +11,10 @@ Model inspection
 .. automodule:: pyro.infer.inspect
     :members:
     :member-order: bysource
+
+Interactive prior tuning
+------------------------
+
+.. automodule:: pyro.infer.resampler
+    :members:
+    :member-order: bysource

pyro/infer/resampler.py

@@ -0,0 +1,134 @@
+# Copyright Contributors to the Pyro project.
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Callable, Dict, Optional
+
+import torch
+
+import pyro
+import pyro.poutine as poutine
+from pyro.poutine.trace_struct import Trace
+from pyro.poutine.util import site_is_subsample
+
+
+class Resampler:
+    """Resampler for interactive tuning of generative models, typically
+    when preforming prior predictive checks as an early step of Bayesian
+    workflow.
+
+    This is intended as a computational cache to speed up the interactive
+    tuning of the parameters of prior distributions based on samples from a
+    downstream simulation. The idea is that the simulation can be expensive,
+    but that when one slightly tweaks parameters of the parameter distribution
+    then one can reuse most of the previous samples via importance resampling.
+
+    :param callable guide: A pyro model that takes no arguments. The guide
+        should be diffuse, covering more space than the subsequent ``model``
+        passed to :meth:`sample`.
+    :param callable simulator: An optional larger pyro model with a superset of
+        the guide's latent variables.
+    :param int num_guide_samples: Number of inital samples to draw from the
+        guide. This should be much larger than the ``num_samples`` requested in
+        subsequent calls to :meth:`sample`.
+    :param int max_plate_nesting: The maximum plate nesting in the model.
+        If absent this will be guessed by running the guide.
+    """
+
+    def __init__(
+        self,
+        guide: Callable,
+        simulator: Optional[Callable] = None,
+        *,
+        num_guide_samples: int,
+        max_plate_nesting: Optional[int] = None,
+    ):
+        super().__init__()
+        if max_plate_nesting is None:
+            max_plate_nesting = _guess_max_plate_nesting(
+                guide if simulator is None else simulator
+            )
+        self._particle_dim = -1 - max_plate_nesting
+        self._gumbels: Optional[torch.Tensor] = None
+
+        # Draw samples from the initial guide.
+        with pyro.plate("particles", num_guide_samples, dim=self._particle_dim):
+            trace = poutine.trace(guide).get_trace()
+            self._old_logp = _log_prob_sum(trace, num_guide_samples)
+
+            if simulator:
+                # Draw extended samples from the simulator.
+                trace = poutine.trace(poutine.replay(simulator, trace)).get_trace()
+        self._samples = {
+            name: site["value"]
+            for name, site in trace.nodes.items()
+            if site["type"] == "sample" and not site_is_subsample(site)
+        }
+
+    @torch.no_grad()
+    def sample(
+        self, model: Callable, num_samples: int, stable: bool = True
+    ) -> Dict[str, torch.Tensor]:
+        """Draws a set of at most ``num_samples`` many model samples,
+        optionally extended by the ``simulator``.
+
+        Internally this importance resamples the samples generated by the
+        ``guide`` in ``.__init__()``, and does not rerun the ``guide`` or
+        ``simulator``. If the original guide samples poorly cover the model
+        distribution, samples will show low diversity.
+
+        :param callable model: A model with the same latent variables as the
+            original ``guide``.
+        :param int num_samples: The number of samples to draw.
+        :param bool stable: Whether to use piecewise-constant multinomial
+            sampling. Set to True for visualization, False for Monte Carlo
+            integration. Defaults to True.
+        :returns: A dictionary of stacked samples.
+        :rtype: Dict[str, torch.Tensor]
+        """
+        # Importance sample: keep all weights >= 1; subsample weights < 1.
+        num_guide_samples = len(self._old_logp)
+        with pyro.plate("particles", num_guide_samples, dim=self._particle_dim):
+            trace = poutine.trace(poutine.condition(model, self._samples)).get_trace()
+        new_logp = _log_prob_sum(trace, num_guide_samples)
+        logits = new_logp - self._old_logp
+        i = self._categorical_sample(logits, num_samples, stable)
+        samples = {k: v[i] for k, v in self._samples.items()}
+        return samples
+
+    def _categorical_sample(
+        self, logits: torch.Tensor, num_samples: int, stable: bool
+    ) -> torch.Tensor:
+        if not stable:
+            return torch.multinomial(logits.exp(), num_samples, replacement=True)
+
+        # Implement stable categorical sampling via the Gumbel-max trick.
+        if self._gumbels is None or len(self._gumbels) < num_samples:
+            # gumbel ~ -log(-log(uniform(0,1)))
+            tiny = torch.finfo(logits.dtype).tiny
+            self._gumbels = logits.new_empty(num_samples, len(logits)).uniform_()
+            self._gumbels.clamp_(min=tiny).log_().neg_().clamp_(min=tiny).log_().neg_()
+        return self._gumbels[:num_samples].add(logits).max(-1).indices
+
+
+def _log_prob_sum(trace: Trace, batch_size: int) -> torch.Tensor:
+    """Computes vectorized log_prob_sum batched over the leftmost dimension."""
+    trace.compute_log_prob()
+    result = 0.0
+    for site in trace.nodes.values():
+        if site["type"] == "sample":
+            logp = site["log_prob"]
+            assert logp.shape[:1] == (batch_size,)
+            result += logp.reshape(batch_size, -1).sum(-1)
+    return result
+
+
+def _guess_max_plate_nesting(model: callable) -> int:
+    with torch.no_grad(), poutine.block(), poutine.mask(mask=False):
+        trace = poutine.trace(model).get_trace()
+    plate_nesting = {0}.union(
+        -f.dim
+        for site in trace.nodes.values()
+        for f in site.get("cond_indep_stack", [])
+        if f.vectorized
+    )
+    return max(plate_nesting)

tests/infer/test_resampler.py

@@ -0,0 +1,43 @@
+# Copyright Contributors to the Pyro project.
+# SPDX-License-Identifier: Apache-2.0
+
+import functools
+
+import pytest
+import torch
+
+import pyro
+import pyro.distributions as dist
+from pyro.infer.resampler import Resampler
+from tests.common import assert_close
+
+
+@pytest.mark.parametrize("stable", [False, True])
+def test_resampling_cache(stable):
+    def model_(a):
+        pyro.sample("alpha", dist.Dirichlet(a))
+
+    def simulator():
+        a = torch.tensor([2.0, 1.0, 1.0, 2.0])
+        alpha = pyro.sample("alpha", dist.Dirichlet(a))
+        pyro.sample("x", dist.Normal(alpha, 0.01).to_event(1))
+
+    # initialize
+    a = torch.tensor([1.0, 2.0, 1.0, 1.0])
+    guide = functools.partial(model_, a)
+    resampler = Resampler(guide, simulator, num_guide_samples=10000)
+
+    # resample
+    b = torch.tensor([1.0, 2.0, 3.0, 4.0])
+    model = functools.partial(model_, b)
+    samples = resampler.sample(model, 1000)
+    assert all(v.shape[:1] == (1000,) for v in samples.values())
+    num_unique = len(set(map(tuple, samples["alpha"].tolist())))
+    assert num_unique >= 500
+
+    # check moments
+    expected_mean = b / b.sum()
+    actual_mean = samples["alpha"].mean(0)
+    assert_close(actual_mean, expected_mean, atol=0.01)
+    actual_mean = samples["x"].mean(0)
+    assert_close(actual_mean, expected_mean, atol=0.01)

tutorial/source/conf.py

@@ -85,7 +85,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+language = "en"
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.

tutorial/source/index.rst

@@ -93,6 +93,7 @@ List of Tutorials
    tensor_shapes
    modules
    workflow
+   prior_predictive
    jit
    svi_horovod