- Rename argument use_jax_gradients -> gradient_backend

- Rename function `laplace` -> `sample_laplace_posterior`

Author: jessegrabowski

pymc_experimental/inference/find_map.py

@@ -1,7 +1,7 @@
 import logging
 
 from collections.abc import Callable
-from typing import cast
+from typing import Literal, cast, get_args
 
 import jax
 import numpy as np
@@ -17,11 +17,15 @@
 from pymc.pytensorf import join_nonshared_inputs
 from pymc.util import get_default_varnames
 from pytensor.compile import Function
+from pytensor.compile.mode import Mode
 from pytensor.tensor import TensorVariable
 from scipy.optimize import OptimizeResult
 
 _log = logging.getLogger(__name__)
 
+GradientBackend = Literal["pytensor", "jax"]
+VALID_BACKENDS = get_args(GradientBackend)
+
 
 def set_optimizer_function_defaults(method, use_grad, use_hess, use_hessp):
     method_info = MINIMIZE_MODE_KWARGS[method].copy()
@@ -85,7 +89,11 @@ def _create_transformed_draws(H_inv, slices, out_shapes, posterior_draws, model,
 
         out.append(untransformed_X)
 
-    f_untransform = pytensor.function([X], out, mode="JAX")
+    f_untransform = pytensor.function(
+        inputs=[pytensor.In(X, borrow=True)],
+        outputs=pytensor.Out(out, borrow=True),
+        mode=Mode(linker="py", optimizer=None),
+    )
     return f_untransform(posterior_draws)
 
 
@@ -209,7 +217,7 @@ def scipy_optimize_funcs_from_loss(
     use_grad: bool,
     use_hess: bool,
     use_hessp: bool,
-    use_jax_gradients: bool = False,
+    gradient_backend: GradientBackend = "pytensor",
     compile_kwargs: dict | None = None,
 ) -> tuple[Callable, ...]:
     """
@@ -230,8 +238,8 @@ def scipy_optimize_funcs_from_loss(
         Whether to compile a function that computes the Hessian of the loss function.
     use_hessp: bool
         Whether to compile a function that computes the Hessian-vector product of the loss function.
-    use_jax_gradients: bool
-        If True, use JAX to compute gradients. This is only possible when ``compile_kwargs["mode"]`` is set to "JAX".
+    gradient_backend: str, one of "jax" or "pytensor"
+        Which backend to use to compute gradients.
     compile_kwargs:
         Additional keyword arguments to pass to the ``pm.compile_pymc`` function.
 
@@ -252,7 +260,12 @@ def scipy_optimize_funcs_from_loss(
             "Cannot compute hessian or hessian-vector product without also computing the gradient"
         )
 
-    use_jax_gradients = use_jax_gradients and use_grad
+    if gradient_backend not in VALID_BACKENDS:
+        raise ValueError(
+            f"Invalid gradient backend: {gradient_backend}. Must be one of {VALID_BACKENDS}"
+        )
+
+    use_jax_gradients = (gradient_backend == "jax") and use_grad
 
     mode = compile_kwargs.get("mode", None)
     if mode is None and use_jax_gradients:
@@ -307,7 +320,7 @@ def find_MAP(
     jitter_rvs: list[TensorVariable] | None = None,
     progressbar: bool = True,
     include_transformed: bool = True,
-    use_jax_gradients: bool = False,
+    gradient_backend: GradientBackend = "pytensor",
     compile_kwargs: dict | None = None,
     **optimizer_kwargs,
 ) -> dict[str, np.ndarray] | tuple[dict[str, np.ndarray], OptimizeResult]:
@@ -342,6 +355,10 @@ def find_MAP(
         Whether to display a progress bar during optimization. Defaults to True.
     include_transformed: bool, optional
         Whether to include transformed variable values in the returned dictionary. Defaults to True.
+    gradient_backend: str, default "pytensor"
+        Which backend to use to compute gradients. Must be one of "pytensor" or "jax".
+    compile_kwargs: dict, optional
+        Additional options to pass to the ``pytensor.function`` function when compiling loss functions.
     **optimizer_kwargs
         Additional keyword arguments to pass to the ``scipy.optimize.minimize`` function.
 
@@ -380,7 +397,7 @@ def find_MAP(
         use_grad=use_grad,
         use_hess=use_hess,
         use_hessp=use_hessp,
-        use_jax_gradients=use_jax_gradients,
+        gradient_backend=gradient_backend,
         compile_kwargs=compile_kwargs,
     )
 

pymc_experimental/inference/laplace.py

@@ -41,6 +41,7 @@
 from scipy import stats
 
 from pymc_experimental.inference.find_map import (
+    GradientBackend,
     _unconstrained_vector_to_constrained_rvs,
     find_MAP,
     get_nearest_psd,
@@ -235,7 +236,7 @@ def fit_mvn_to_MAP(
     model: pm.Model | None = None,
     on_bad_cov: Literal["warn", "error", "ignore"] = "ignore",
     transform_samples: bool = False,
-    use_jax_gradients: bool = False,
+    gradient_backend: GradientBackend = "pytensor",
     zero_tol: float = 1e-8,
     diag_jitter: float | None = 1e-8,
     compile_kwargs: dict | None = None,
@@ -256,12 +257,16 @@ def fit_mvn_to_MAP(
         If 'error', an error will be raised.
     transform_samples : bool
         Whether to transform the samples back to the original parameter space. Default is True.
+    gradient_backend: str, default "pytensor"
+        The backend to use for gradient computations. Must be one of "pytensor" or "jax".
     zero_tol: float
         Value below which an element of the Hessian matrix is counted as 0.
         This is used to stabilize the computation of the inverse Hessian matrix. Default is 1e-8.
     diag_jitter: float | None
         A small value added to the diagonal of the inverse Hessian matrix to ensure it is positive semi-definite.
         If None, no jitter is added. Default is 1e-8.
+    compile_kwargs: dict, optional
+        Additional keyword arguments to pass to pytensor.function when compiling loss functions
 
     Returns
     -------
@@ -294,7 +299,7 @@ def fit_mvn_to_MAP(
         use_grad=True,
         use_hess=True,
         use_hessp=False,
-        use_jax_gradients=use_jax_gradients,
+        gradient_backend=gradient_backend,
         compile_kwargs=compile_kwargs,
     )
 
@@ -323,7 +328,7 @@ def stabilize(x, jitter):
     return mu, H_inv
 
 
-def laplace(
+def sample_laplace_posterior(
     mu: RaveledVars,
     H_inv: np.ndarray,
     model: pm.Model | None = None,
@@ -416,7 +421,7 @@ def fit_laplace(
     jitter_rvs: list[pt.TensorVariable] | None = None,
     progressbar: bool = True,
     include_transformed: bool = True,
-    use_jax_gradients: bool = False,
+    gradient_backend: GradientBackend = "pytensor",
     chains: int = 2,
     draws: int = 500,
     on_bad_cov: Literal["warn", "error", "ignore"] = "ignore",
@@ -461,8 +466,8 @@ def fit_laplace(
         Whether to display a progress bar during optimization. Defaults to True.
     include_transformed: bool, optional
         Whether to include transformed variable values in the returned dictionary. Defaults to True.
-    use_jax_gradients: bool, optional
-        Whether to use JAX for gradient calculations. Defaults to False.
+    gradient_backend: str, default "pytensor"
+        The backend to use for gradient computations. Must be one of "pytensor" or "jax".
     chains: int, default: 2
         The number of sampling chains running in parallel.
     draws: int, default: 500
@@ -489,7 +494,7 @@ def fit_laplace(
 
     Examples
     --------
-    >>> from pymc_experimental.inference.laplace import fit_laplace
+    >>> from pymc_experimental.inference.sample_laplace_posterior import fit_laplace
     >>> import numpy as np
     >>> import pymc as pm
     >>> import arviz as az
@@ -526,7 +531,7 @@ def fit_laplace(
         jitter_rvs=jitter_rvs,
         progressbar=progressbar,
         include_transformed=include_transformed,
-        use_jax_gradients=use_jax_gradients,
+        gradient_backend=gradient_backend,
         compile_kwargs=compile_kwargs,
         **optimizer_kwargs,
     )
@@ -541,7 +546,7 @@ def fit_laplace(
         compile_kwargs=compile_kwargs,
     )
 
-    return laplace(
+    return sample_laplace_posterior(
         mu=mu,
         H_inv=H_inv,
         model=model,

tests/test_jax_find_map.py

@@ -4,6 +4,7 @@
 import pytest
 
 from pymc_experimental.inference.find_map import (
+    GradientBackend,
     find_MAP,
     scipy_optimize_funcs_from_loss,
 )
@@ -17,8 +18,8 @@ def rng():
     return np.random.default_rng(seed)
 
 
-@pytest.mark.parametrize("use_jax_gradients", [True, False], ids=["jax_grad", "pt_grad"])
-def test_jax_functions_from_graph(use_jax_gradients):
+@pytest.mark.parametrize("gradient_backend", ["jax", "pytensor"], ids=str)
+def test_jax_functions_from_graph(gradient_backend: GradientBackend):
     x = pt.tensor("x", shape=(2,))
 
     def compute_z(x):
@@ -34,7 +35,7 @@ def compute_z(x):
         use_grad=True,
         use_hess=True,
         use_hessp=True,
-        use_jax_gradients=use_jax_gradients,
+        gradient_backend=gradient_backend,
         compile_kwargs=dict(mode="JAX"),
     )
 
@@ -69,8 +70,8 @@ def compute_z(x):
         ("trust-constr", True, True),
     ],
 )
-@pytest.mark.parametrize("use_jax_gradients", [True, False], ids=["jax_grad", "pt_grad"])
-def test_JAX_map(method, use_grad, use_hess, use_jax_gradients, rng):
+@pytest.mark.parametrize("gradient_backend", ["jax", "pytensor"], ids=str)
+def test_JAX_map(method, use_grad, use_hess, gradient_backend: GradientBackend, rng):
     extra_kwargs = {}
     if method == "dogleg":
         # HACK -- dogleg requires that the hessian of the objective function is PSD, so we have to pick a point
@@ -88,7 +89,7 @@ def test_JAX_map(method, use_grad, use_hess, use_jax_gradients, rng):
             use_grad=use_grad,
             use_hess=use_hess,
             progressbar=False,
-            use_jax_gradients=use_jax_gradients,
+            gradient_backend=gradient_backend,
             compile_kwargs={"mode": "JAX"},
         )
     mu_hat, log_sigma_hat = optimized_point["mu"], optimized_point["sigma_log__"]

tests/test_laplace.py

@@ -20,7 +20,11 @@
 import pymc_experimental as pmx
 
 from pymc_experimental.inference.find_map import find_MAP
-from pymc_experimental.inference.laplace import fit_laplace, fit_mvn_to_MAP, laplace
+from pymc_experimental.inference.laplace import (
+    fit_laplace,
+    fit_mvn_to_MAP,
+    sample_laplace_posterior,
+)
 
 
 @pytest.fixture(scope="session")
@@ -86,7 +90,7 @@ def test_laplace_only_fit():
             method="laplace",
             optimize_method="BFGS",
             progressbar=True,
-            use_jax_gradients=True,
+            gradient_backend="jax",
             compile_kwargs={"mode": "JAX"},
             optimizer_kwargs=dict(maxiter=100_000, gtol=1e-100),
             random_seed=173300,
@@ -127,7 +131,7 @@ def test_fit_laplace_coords(rng, transform_samples, mode):
             use_hessp=True,
             progressbar=False,
             compile_kwargs=dict(mode=mode),
-            use_jax_gradients=mode == "JAX",
+            gradient_backend="jax" if mode == "JAX" else "pytensor",
         )
 
         for value in optimized_point.values():
@@ -139,7 +143,9 @@ def test_fit_laplace_coords(rng, transform_samples, mode):
             transform_samples=transform_samples,
         )
 
-        idata = laplace(mu=mu, H_inv=H_inv, model=model, transform_samples=transform_samples)
+        idata = sample_laplace_posterior(
+            mu=mu, H_inv=H_inv, model=model, transform_samples=transform_samples
+        )
 
     np.testing.assert_allclose(np.mean(idata.posterior.mu, axis=1), np.full((2, 3), 3), atol=0.5)
     np.testing.assert_allclose(
@@ -182,7 +188,7 @@ def test_fit_laplace_ragged_coords(rng):
             progressbar=False,
             use_grad=True,
             use_hessp=True,
-            use_jax_gradients=True,
+            gradient_backend="jax",
             compile_kwargs={"mode": "JAX"},
         )