ENH: array-api support for minipcn

pyproject.toml

@@ -39,7 +39,8 @@ torch = [
     "tqdm",
 ]
 minipcn = [
-    "minipcn",
+    "minipcn[array-api]>=0.2.0a3",
+    "orng",
 ]
 emcee = [
     "emcee",

src/aspire/samplers/base.py

@@ -4,7 +4,7 @@
 from ..flows.base import Flow
 from ..samples import Samples
 from ..transforms import IdentityTransform
-from ..utils import track_calls
+from ..utils import asarray, track_calls
 
 logger = logging.getLogger(__name__)
 
@@ -56,7 +56,11 @@ def __init__(
 
     def fit_preconditioning_transform(self, x):
         """Fit the data transform to the data."""
-        x = self.preconditioning_transform.xp.asarray(x)
+        x = asarray(
+            x,
+            xp=self.preconditioning_transform.xp,
+            dtype=self.preconditioning_transform.dtype,
+        )
         return self.preconditioning_transform.fit(x)
 
     @track_calls

src/aspire/samplers/smc/minipcn.py

@@ -3,7 +3,11 @@
 import numpy as np
 
 from ...samples import SMCSamples
-from ...utils import to_numpy, track_calls
+from ...utils import (
+    asarray,
+    determine_backend_name,
+    track_calls,
+)
 from .base import NumpySMCSampler
 
 
@@ -13,7 +17,7 @@ class MiniPCNSMC(NumpySMCSampler):
     rng = None
 
     def log_prob(self, x, beta=None):
-        return to_numpy(super().log_prob(x, beta))
+        return super().log_prob(x, beta)
 
     @track_calls
     def sample(
@@ -29,11 +33,14 @@ def sample(
         sampler_kwargs: dict | None = None,
         rng: np.random.Generator | None = None,
     ):
+        from orng import ArrayRNG
+
         self.sampler_kwargs = sampler_kwargs or {}
         self.sampler_kwargs.setdefault("n_steps", 5 * self.dims)
         self.sampler_kwargs.setdefault("target_acceptance_rate", 0.234)
         self.sampler_kwargs.setdefault("step_fn", "tpcn")
-        self.rng = rng or np.random.default_rng()
+        self.backend_str = determine_backend_name(xp=self.xp)
+        self.rng = rng or ArrayRNG(backend=self.backend_str)
         return super().sample(
             n_samples,
             n_steps=n_steps,
@@ -58,9 +65,14 @@ def mutate(self, particles, beta, n_steps=None):
             target_acceptance_rate=self.sampler_kwargs[
                 "target_acceptance_rate"
             ],
+            xp=self.xp,
         )
         # Map to transformed dimension for sampling
-        z = to_numpy(self.fit_preconditioning_transform(particles.x))
+        z = asarray(
+            self.fit_preconditioning_transform(particles.x),
+            xp=self.xp,
+            dtype=self.dtype,
+        )
         chain, history = sampler.sample(
             z,
             n_steps=n_steps or self.sampler_kwargs["n_steps"],

src/aspire/samples.py

@@ -425,19 +425,23 @@ def __str__(self):
 
     def to_namespace(self, xp):
         return self.__class__(
-            x=asarray(self.x, xp),
+            x=asarray(self.x, xp, dtype=self.dtype),
             parameters=self.parameters,
-            log_likelihood=asarray(self.log_likelihood, xp)
+            log_likelihood=asarray(self.log_likelihood, xp, dtype=self.dtype)
             if self.log_likelihood is not None
             else None,
-            log_prior=asarray(self.log_prior, xp)
+            log_prior=asarray(self.log_prior, xp, dtype=self.dtype)
             if self.log_prior is not None
             else None,
-            log_q=asarray(self.log_q, xp) if self.log_q is not None else None,
-            log_evidence=asarray(self.log_evidence, xp)
+            log_q=asarray(self.log_q, xp, dtype=self.dtype)
+            if self.log_q is not None
+            else None,
+            log_evidence=asarray(self.log_evidence, xp, dtype=self.dtype)
             if self.log_evidence is not None
             else None,
-            log_evidence_error=asarray(self.log_evidence_error, xp)
+            log_evidence_error=asarray(
+                self.log_evidence_error, xp, dtype=self.dtype
+            )
             if self.log_evidence_error is not None
             else None,
         )

src/aspire/utils.py

@@ -12,7 +12,13 @@
 import wrapt
 from array_api_compat import (
     array_namespace,
+    is_cupy_namespace,
+    is_dask_namespace,
     is_jax_array,
+    is_jax_namespace,
+    is_ndonnx_namespace,
+    is_numpy_namespace,
+    is_pydata_sparse_namespace,
     is_torch_array,
     is_torch_namespace,
     to_device,
@@ -28,6 +34,17 @@
 logger = logging.getLogger(__name__)
 
 
+IS_NAMESPACE_FUNCTIONS = {
+    "numpy": is_numpy_namespace,
+    "torch": is_torch_namespace,
+    "jax": is_jax_namespace,
+    "cupy": is_cupy_namespace,
+    "dask": is_dask_namespace,
+    "pydata_sparse": is_pydata_sparse_namespace,
+    "ndonnx": is_ndonnx_namespace,
+}
+
+
 def configure_logger(
     log_level: str | int = "INFO",
     additional_loggers: list[str] = None,
@@ -234,7 +251,7 @@ def to_numpy(x: Array, **kwargs) -> np.ndarray:
         return np.asarray(x, **kwargs)
 
 
-def asarray(x, xp: Any = None, **kwargs) -> Array:
+def asarray(x, xp: Any = None, dtype: Any | None = None, **kwargs) -> Array:
     """Convert an array to the specified array API.
 
     Parameters
@@ -244,13 +261,51 @@ def asarray(x, xp: Any = None, **kwargs) -> Array:
     xp : Any
         The array API to use for the conversion. If None, the array API
         is inferred from the input array.
+    dtype : Any | str | None
+        The dtype to use for the conversion. If None, the dtype is not changed.
     kwargs : dict
         Additional keyword arguments to pass to xp.asarray.
     """
+    # Handle DLPack conversion from JAX to PyTorch to avoid shape issues when
+    # passing JAX arrays directly to torch.asarray.
     if is_jax_array(x) and is_torch_namespace(xp):
-        return xp.utils.dlpack.from_dlpack(x)
-    else:
-        return xp.asarray(x, **kwargs)
+        tensor = xp.utils.dlpack.from_dlpack(x)
+        if dtype is not None:
+            tensor = tensor.to(resolve_dtype(dtype, xp=xp))
+        return tensor
+
+    if dtype is not None:
+        kwargs["dtype"] = resolve_dtype(dtype, xp=xp)
+    return xp.asarray(x, **kwargs)
+
+
+def determine_backend_name(
+    x: Array | None = None, xp: Any | None = None
+) -> str:
+    """Determine the backend name from an array or array API module.
+
+    Parameters
+    ----------
+    x : Array or None
+        The array to infer the backend from. If None, xp must be provided.
+    xp : Any or None
+        The array API module to infer the backend from. If None, x must be provided.
+
+    Returns
+    -------
+    str
+        The name of the backend. If the backend cannot be determined, returns "unknown".
+    """
+    if x is not None:
+        xp = array_namespace(x)
+    if xp is None:
+        raise ValueError(
+            "Either x or xp must be provided to determine backend."
+        )
+    for name, is_namespace_fn in IS_NAMESPACE_FUNCTIONS.items():
+        if is_namespace_fn(xp):
+            return name
+    return "unknown"
 
 
 def resolve_dtype(dtype: Any | str | None, xp: Any) -> Any | None: