record blackjax sample stats

pymc/sampling/jax.py

@@ -142,6 +142,40 @@ def _sample_stats_to_xarray(posterior):
     return data
 
 
+def _blackjax_stats_to_dict(sample_stats, potential_energy) -> Dict:
+    """Extract compatible stats from blackjax NUTS sampler
+    with PyMC/Arviz naming conventions.
+
+    Parameters
+    ----------
+    sample_stats: NUTSInfo
+        Blackjax NUTSInfo object containing sampler statistics
+    potential_energy: ArrayLike
+        Potential energy values of sampled positions.
+
+    Returns
+    -------
+    Dict[str, ArrayLike]
+        Dictionary of sampler statistics.
+    """
+    rename_key = {
+        "is_divergent": "diverging",
+        "energy": "energy",
+        "num_trajectory_expansions": "tree_depth",
+        "num_integration_steps": "n_steps",
+        "acceptance_rate": "acceptance_rate",  # naming here is
+        "acceptance_probability": "acceptance_rate",  # depending on blackjax version
+    }
+    converted_stats = {}
+    converted_stats["lp"] = potential_energy
+    for old_name, new_name in rename_key.items():
+        value = getattr(sample_stats, old_name, None)
+        if value is None:
+            continue
+        converted_stats[new_name] = value
+    return converted_stats
+
+
 def _get_log_likelihood(model: Model, samples, backend=None) -> Dict:
     """Compute log-likelihood for all observations"""
     elemwise_logp = model.logp(model.observed_RVs, sum=False)
@@ -360,9 +394,9 @@ def sample_blackjax_nuts(
             "Only supporting the following methods to draw chains:" ' "parallel" or "vectorized"'
         )
 
-    states, _ = map_fn(get_posterior_samples)(keys, init_params)
+    states, stats = map_fn(get_posterior_samples)(keys, init_params)
     raw_mcmc_samples = states.position
-
+    potential_energy = states.potential_energy
     tic3 = datetime.now()
     print("Sampling time = ", tic3 - tic2, file=sys.stdout)
 
@@ -372,7 +406,7 @@ def sample_blackjax_nuts(
         *jax.device_put(raw_mcmc_samples, jax.devices(postprocessing_backend)[0])
     )
     mcmc_samples = {v.name: r for v, r in zip(vars_to_sample, result)}
-
+    mcmc_stats = _blackjax_stats_to_dict(stats, potential_energy)
     tic4 = datetime.now()
     print("Transformation time = ", tic4 - tic3, file=sys.stdout)
 
@@ -410,7 +444,7 @@ def sample_blackjax_nuts(
         dims=dims,
         attrs=make_attrs(attrs, library=blackjax),
     )
-    az_trace = to_trace(posterior=posterior, **idata_kwargs)
+    az_trace = to_trace(posterior=posterior, sample_stats=mcmc_stats, **idata_kwargs)
 
     return az_trace