Refactor Pathfinder VI: Default to PSIS, Add Concurrency, and Improved Computational Performance

- Significantly computational efficiency by combining 3 computational graphs into 1 larger compile. Removed non-shared inputs and used  with  for significant performance gains.
- Set default importance sampling method to 'psis' for more stable posterior results, avoiding local peaks seen with 'psir'.
- Introduce concurrency options ('thread' and 'process') for multithreading and multiprocessing. Defaults to No concurrency as there haven't been any/or much reduction to the compute time.
- Adjusted default  from 8 to 4 and  from 1.0 to 2.0 and maxcor to max(3*log(N), 5). This default setting lessens computational time and and the degree by which the posterior variance is being underestimated.

Author: aphc14

pymc_experimental/inference/pathfinder/importance_sampling.py

@@ -1,6 +1,8 @@
 import logging
 import warnings
 
+from typing import Literal
+
 import arviz as az
 import numpy as np
 import pytensor.tensor as pt
@@ -31,12 +33,13 @@ def perform(self, node: Apply, inputs, outputs) -> None:
             outputs[1][0] = pareto_k
 
 
-def psir(
+def importance_sampling(
     samples: TensorVariable,
     # logP: TensorVariable,
     # logQ: TensorVariable,
     logiw: TensorVariable,
-    num_draws: int = 1000,
+    num_draws: int,
+    method: Literal["psis", "psir", "identity", "none"],
     random_seed: int | None = None,
 ) -> np.ndarray:
     """Pareto Smoothed Importance Resampling (PSIR)
@@ -52,6 +55,8 @@ def psir(
         log probability of proposal distribution
     num_draws : int
         number of draws to return where num_draws <= samples.shape[0]
+    method : str, optional
+        importance sampling method to use. Options are "psis" (default), "psir", "identity", "none. Pareto Smoothed Importance Sampling (psis) is recommended in many cases for more stable results than Pareto Smoothed Importance Resampling (psir). identity applies the log importance weights directly without resampling. none applies no importance sampling weights and returns the samples as is of size num_draws_per_path * num_paths.
     random_seed : int | None
 
     Returns
@@ -74,30 +79,50 @@ def psir(
     Zhang, L., Carpenter, B., Gelman, A., & Vehtari, A. (2022). Pathfinder: Parallel quasi-Newton variational inference. Journal of Machine Learning Research, 23(306), 1-49.
     """
 
-    psislw, pareto_k = PSIS()(logiw)
-    pareto_k = pareto_k.eval()
-    if pareto_k < 0.5:
-        pass
-    elif 0.5 <= pareto_k < 0.70:
-        logger.warning(
-            f"Pareto k value ({pareto_k:.2f}) is between 0.5 and 0.7 which indicates an imperfect approximation however still useful."
-        )
-        logger.info("Consider increasing ftol, gtol, maxcor or num_paths.")
-    elif pareto_k >= 0.7:
+    if method == "psis":
+        replace = False
+        logiw, pareto_k = PSIS()(logiw)
+    elif method == "psir":
+        replace = True
+        logiw, pareto_k = PSIS()(logiw)
+    elif method == "identity":
+        replace = False
+        logiw = logiw
+        pareto_k = None
+    elif method == "none":
         logger.warning(
-            f"Pareto k value ({pareto_k:.2f}) exceeds 0.7 which indicates a bad approximation."
-        )
-        logger.info(
-            "Consider increasing ftol, gtol, maxcor, num_paths or reparametrising the model."
-        )
-    else:
-        logger.warning(
-            f"Received an invalid Pareto k value of {pareto_k:.2f} which indicates the model is seriously flawed."
-        )
-        logger.info(
-            "Consider reparametrising the model all together or ensure the input data are correct."
+            "importance sampling is disabled. The samples are returned as is which may include samples from failed paths with non-finite logP or logQ values. It is recommended to use importance_sampling='psis' for better stability."
         )
+        return samples
+
+    # NOTE: Pareto k is normally bad for Pathfinder even when the posterior is close to the NUTS posterior or closer to NUTS than ADVI.
+    # Pareto k may not be a good diagnostic for Pathfinder.
+    if pareto_k is not None:
+        pareto_k = pareto_k.eval()
+        if pareto_k < 0.5:
+            pass
+        elif 0.5 <= pareto_k < 0.70:
+            logger.info(
+                f"Pareto k value ({pareto_k:.2f}) is between 0.5 and 0.7 which indicates an imperfect approximation however still useful."
+            )
+            logger.info("Consider increasing ftol, gtol, maxcor or num_paths.")
+        elif pareto_k >= 0.7:
+            logger.info(
+                f"Pareto k value ({pareto_k:.2f}) exceeds 0.7 which indicates a bad approximation."
+            )
+            logger.info(
+                "Consider increasing ftol, gtol, maxcor, num_paths or reparametrising the model."
+            )
+        else:
+            logger.info(
+                f"Received an invalid Pareto k value of {pareto_k:.2f} which indicates the model is seriously flawed."
+            )
+            logger.info(
+                "Consider reparametrising the model all together or ensure the input data are correct."
+            )
+
+    logger.warning(f"Pareto k value: {pareto_k:.2f}")
 
-    p = pt.exp(psislw - pt.logsumexp(psislw)).eval()
+    p = pt.exp(logiw - pt.logsumexp(logiw)).eval()
     rng = np.random.default_rng(random_seed)
-    return rng.choice(samples, size=num_draws, replace=True, p=p, shuffle=False, axis=0)
+    return rng.choice(samples, size=num_draws, replace=replace, p=p, shuffle=False, axis=0)

pymc_experimental/inference/pathfinder/lbfgs.py

@@ -69,6 +69,8 @@ def __init__(self, message=None):
 
 
 class LBFGSOp(Op):
+    __props__ = ("fn", "grad_fn", "maxcor", "maxiter", "ftol", "gtol", "maxls")
+
     def __init__(self, fn, grad_fn, maxcor, maxiter=1000, ftol=1e-5, gtol=1e-8, maxls=1000):
         self.fn = fn
         self.grad_fn = grad_fn