Fixes #3156 (#3157)

pyro/infer/mcmc/hmc.py

@@ -66,6 +66,8 @@ class HMC(MCMCKernel):
         step size, hence the sampling will be slower and more robust. Default to 0.8.
     :param callable init_strategy: A per-site initialization function.
         See :ref:`autoguide-initialization` section for available functions.
+    :param min_stepsize (float): Lower bound on stepsize in adaptation strategy.
+    :param max_stepsize (float): Upper bound on stepsize in adaptation strategy.
 
     .. note:: Internally, the mass matrix will be ordered according to the order
         of the names of latent variables, not the order of their appearance in
@@ -108,6 +110,9 @@ def __init__(
         ignore_jit_warnings=False,
         target_accept_prob=0.8,
         init_strategy=init_to_uniform,
+        *,
+        min_stepsize: float = 1e-10,
+        max_stepsize: float = 1e10,
     ):
         if not ((model is None) ^ (potential_fn is None)):
             raise ValueError("Only one of `model` or `potential_fn` must be specified.")
@@ -119,6 +124,8 @@ def __init__(
         self._jit_options = jit_options
         self._ignore_jit_warnings = ignore_jit_warnings
         self._init_strategy = init_strategy
+        self._min_stepsize = min_stepsize
+        self._max_stepsize = max_stepsize
 
         self.potential_fn = potential_fn
         if trajectory_length is not None:
@@ -188,9 +195,11 @@ def _find_reasonable_step_size(self, z):
         step_size_scale = 2**direction
         direction_new = direction
         # keep scale step_size until accept_prob crosses its target
-        # TODO: make thresholds for too small step_size or too large step_size
         t = 0
-        while direction_new == direction:
+        while (
+            direction_new == direction
+            and self._min_stepsize < step_size < self._max_stepsize
+        ):
             t += 1
             step_size = step_size_scale * step_size
             r, r_unscaled = self._sample_r(name="r_presample_{}".format(t))
@@ -206,6 +215,8 @@ def _find_reasonable_step_size(self, z):
             energy_new = self._kinetic_energy(r_new_unscaled) + potential_energy_new
             delta_energy = energy_new - energy_current
             direction_new = 1 if self._direction_threshold < -delta_energy else -1
+        step_size = max(step_size, self._min_stepsize)
+        step_size = min(step_size, self._max_stepsize)
         return step_size
 
     def _sample_r(self, name):