Add nuts_sampler_kwargs to pm.sample

pymc/sampling/jax.py

@@ -317,6 +317,7 @@ def sample_blackjax_nuts(
     postprocessing_backend: Optional[str] = None,
     postprocessing_chunks: Optional[int] = None,
     idata_kwargs: Optional[Dict[str, Any]] = None,
+    **kwargs,
 ) -> az.InferenceData:
     """
     Draw samples from the posterior using the NUTS method from the ``blackjax`` library.
@@ -529,6 +530,7 @@ def sample_numpyro_nuts(
     postprocessing_chunks: Optional[int] = None,
     idata_kwargs: Optional[Dict] = None,
     nuts_kwargs: Optional[Dict] = None,
+    **kwargs,
 ) -> az.InferenceData:
     """
     Draw samples from the posterior using the NUTS method from the ``numpyro`` library.

pymc/sampling/mcmc.py

@@ -237,10 +237,14 @@ def _sample_external_nuts(
     model: Model,
     progressbar: bool,
     idata_kwargs: Optional[Dict],
+    nuts_sampler_kwargs: Optional[Dict],
     **kwargs,
 ):
     warnings.warn("Use of external NUTS sampler is still experimental", UserWarning)
 
+    if nuts_sampler_kwargs is None:
+        nuts_sampler_kwargs = {}
+
     if sampler == "nutpie":
         try:
             import nutpie
@@ -271,7 +275,7 @@ def _sample_external_nuts(
             target_accept=target_accept,
             seed=_get_seeds_per_chain(random_seed, 1)[0],
             progress_bar=progressbar,
-            **kwargs,
+            **nuts_sampler_kwargs,
         )
         return idata
 
@@ -288,7 +292,7 @@ def _sample_external_nuts(
             model=model,
             progressbar=progressbar,
             idata_kwargs=idata_kwargs,
-            **kwargs,
+            **nuts_sampler_kwargs,
         )
         return idata
 
@@ -304,7 +308,7 @@ def _sample_external_nuts(
             initvals=initvals,
             model=model,
             idata_kwargs=idata_kwargs,
-            **kwargs,
+            **nuts_sampler_kwargs,
         )
         return idata
 
@@ -334,6 +338,7 @@ def sample(
     keep_warning_stat: bool = False,
     return_inferencedata: bool = True,
     idata_kwargs: Optional[Dict[str, Any]] = None,
+    nuts_sampler_kwargs: Optional[Dict[str, Any]] = None,
     callback=None,
     mp_ctx=None,
     model: Optional[Model] = None,
@@ -410,6 +415,9 @@ def sample(
         `MultiTrace` (False). Defaults to `True`.
     idata_kwargs : dict, optional
         Keyword arguments for :func:`pymc.to_inference_data`
+    nuts_sampler_kwargs : dict, optional
+        Keyword arguments for the sampling library that implements nuts.
+        Only used when an external sampler is specified via the `nuts_sampler` kwarg. 
     callback : function, default=None
         A function which gets called for every sample from the trace of a chain. The function is
         called with the trace and the current draw and will contain all samples for a single trace.
@@ -493,6 +501,8 @@ def sample(
             stacklevel=2,
         )
         initvals = kwargs.pop("start")
+    if nuts_sampler_kwargs is None:
+        nuts_sampler_kwargs = {}
     if "target_accept" in kwargs:
         if "nuts" in kwargs and "target_accept" in kwargs["nuts"]:
             raise ValueError(
@@ -569,6 +579,7 @@ def sample(
             model=model,
             progressbar=progressbar,
             idata_kwargs=idata_kwargs,
+            nuts_sampler_kwargs=nuts_sampler_kwargs,
             **kwargs,
         )
 

tests/sampling/test_mcmc_external.py

@@ -13,13 +13,11 @@
 #   limitations under the License.
 
 import numpy as np
+import numpy.testing as npt
 import pytest
 
 from pymc import Model, Normal, sample
 
-# turns all warnings into errors for this module
-pytestmark = pytest.mark.filterwarnings("error")
-
 
 @pytest.mark.parametrize("nuts_sampler", ["pymc", "nutpie", "blackjax", "numpyro"])
 def test_external_nuts_sampler(recwarn, nuts_sampler):
@@ -63,3 +61,16 @@ def test_external_nuts_sampler(recwarn, nuts_sampler):
     assert idata1.posterior.chain.size == 2
     assert idata1.posterior.draw.size == 500
     np.testing.assert_array_equal(idata1.posterior.x, idata2.posterior.x)
+
+
+def test_step_args():
+    with Model() as model:
+        a = Normal("a")
+        idata = sample(
+            nuts_sampler="numpyro",
+            target_accept=0.5,
+            nuts={"max_treedepth": 10},
+            random_seed=1410,
+        )
+
+    npt.assert_almost_equal(idata.sample_stats.acceptance_rate.mean(), 0.5, decimal=1)