SteinVI: Recompute Score Function* For Each Particle Interaction in the Attractive Force

examples/hmcecs.py

@@ -75,9 +75,9 @@ def run_hmc(mcmc_key, args, data, obs, kernel):
 
 
 def main(args):
-    assert (
-        11_000_000 >= args.num_datapoints
-    ), "11,000,000 data points in the Higgs dataset"
+    assert 11_000_000 >= args.num_datapoints, (
+        "11,000,000 data points in the Higgs dataset"
+    )
     # full dataset takes hours for plain hmc!
     if args.dataset == "higgs":
         _, fetch = load_dataset(

examples/ssbvm_mixture.py

@@ -293,7 +293,7 @@ def main(args):
     parser.add_argument("--device", default="gpu", type=str, help='use "cpu" or "gpu".')
 
     args = parser.parse_args()
-    assert all(
-        aa in AMINO_ACIDS for aa in args.amino_acids
-    ), f"{list(filter(lambda aa: aa not in AMINO_ACIDS, args.amino_acids))} are not amino acids."
+    assert all(aa in AMINO_ACIDS for aa in args.amino_acids), (
+        f"{list(filter(lambda aa: aa not in AMINO_ACIDS, args.amino_acids))} are not amino acids."
+    )
     main(args)

examples/stein_bnn.py

@@ -25,7 +25,6 @@
 from numpyro.contrib.einstein import MixtureGuidePredictive, RBFKernel, SteinVI
 from numpyro.distributions import Gamma, Normal
 from numpyro.examples.datasets import BOSTON_HOUSING, load_dataset
-from numpyro.infer import init_to_uniform
 from numpyro.infer.autoguide import AutoNormal
 from numpyro.optim import Adagrad
 
@@ -121,12 +120,12 @@ def main(args):
     rng_key, inf_key = random.split(inf_key)
 
     # We find that SteinVI benefits from a small radius when inferring BNNs.
-    guide = AutoNormal(model, init_loc_fn=partial(init_to_uniform, radius=0.1))
+    guide = AutoNormal(model)
 
     stein = SteinVI(
         model,
         guide,
-        Adagrad(0.5),
+        Adagrad(1.0),
         RBFKernel(),
         repulsion_temperature=args.repulsion,
         num_stein_particles=args.num_stein_particles,

notebooks/source/lotka_volterra_multiple.ipynb

@@ -404,10 +404,10 @@
    "source": [
     "print(f\"The dataset has the shape {data.shape}, (n_datasets, n_points, n_observables)\")\n",
     "print(f\"The time matrix has the shape {ts.shape}, (n_datasets, n_timepoints)\")\n",
-    "print(f\"The time matrix has different spacing between timepoints: \\n {ts[:,:5]}\")\n",
-    "print(f\"The final timepoints are: {jnp.nanmax(ts,1)} years.\")\n",
+    "print(f\"The time matrix has different spacing between timepoints: \\n {ts[:, :5]}\")\n",
+    "print(f\"The final timepoints are: {jnp.nanmax(ts, 1)} years.\")\n",
     "print(\n",
-    "    f\"The dataset has {jnp.sum(jnp.isnan(data))/jnp.size(data):.0%} missing observations\"\n",
+    "    f\"The dataset has {jnp.sum(jnp.isnan(data)) / jnp.size(data):.0%} missing observations\"\n",
     ")\n",
     "print(f\"True params mean: {sample['theta'][0]}\")"
    ]
@@ -550,7 +550,7 @@
     "mcmc.print_summary()\n",
     "\n",
     "print(f\"True params mean: {sample['theta'][0]}\")\n",
-    "print(f\"Estimated params mean: {jnp.mean(mcmc.get_samples()['theta'], axis = 0)}\")"
+    "print(f\"Estimated params mean: {jnp.mean(mcmc.get_samples()['theta'], axis=0)}\")"
    ]
   },
   {
@@ -591,7 +591,7 @@
     "\n",
     "\n",
     "print(f\"True params mean: {sample['theta'][0]}\")\n",
-    "print(f\"Estimated params mean: {jnp.mean(mcmc.get_samples()['theta'], axis = 0)}\")"
+    "print(f\"Estimated params mean: {jnp.mean(mcmc.get_samples()['theta'], axis=0)}\")"
    ]
   },
   {

notebooks/source/ordinal_regression.ipynb

@@ -104,7 +104,7 @@
     "print(df.Y.value_counts())\n",
     "\n",
     "for i in range(nclasses):\n",
-    "    print(f\"mean(X) for Y == {i}: {X[np.where(Y==i)].mean():.3f}\")"
+    "    print(f\"mean(X) for Y == {i}: {X[np.where(Y == i)].mean():.3f}\")"
    ]
   },
   {

numpyro/contrib/control_flow/scan.py

@@ -82,7 +82,7 @@ def _subs_wrapper(subs_map, i, length, site):
                 )
         else:
             raise RuntimeError(
-                f"Something goes wrong. Expected ndim = {fn_ndim} or {fn_ndim+1},"
+                f"Something goes wrong. Expected ndim = {fn_ndim} or {fn_ndim + 1},"
                 f" but got {value_ndim}. This might happen when you use nested scan,"
                 " which is currently not supported. Please report the issue to us!"
             )

numpyro/contrib/ecs_proxies.py

@@ -12,13 +12,11 @@
 
 TaylorTwoProxyState = namedtuple(
     "TaylorProxyState",
-    "ref_subsample_log_liks,"
-    "ref_subsample_log_lik_grads,"
-    "ref_subsample_log_lik_hessians",
+    "ref_subsample_log_liks,ref_subsample_log_lik_grads,ref_subsample_log_lik_hessians",
 )
 
 TaylorOneProxyState = namedtuple(
-    "TaylorOneProxyState", "ref_subsample_log_liks," "ref_subsample_log_lik_grads,"
+    "TaylorOneProxyState", "ref_subsample_log_liks,ref_subsample_log_lik_grads,"
 )
 
 

numpyro/contrib/einstein/steinvi.py

@@ -10,6 +10,7 @@
 
 from jax import grad, numpy as jnp, random, tree, vmap
 from jax.flatten_util import ravel_pytree
+from jax.lax import scan
 
 from numpyro import handlers
 from numpyro.contrib.einstein.stein_loss import SteinLoss
@@ -33,7 +34,6 @@ def _numel(shape):
 class SteinVI:
     """Variational inference with Stein mixtures inference.
 
-
     **Example:**
 
     .. doctest::
@@ -138,9 +138,9 @@ def __init__(
             if isinstance(guide.init_loc_fn, partial):
                 init_fn_name = guide.init_loc_fn.func.__name__
                 if init_fn_name == "init_to_uniform":
-                    assert (
-                        guide.init_loc_fn.keywords.get("radius", None) != 0.0
-                    ), init_loc_error_message
+                    assert guide.init_loc_fn.keywords.get("radius", None) != 0.0, (
+                        init_loc_error_message
+                    )
             else:
                 init_fn_name = guide.init_loc_fn.__name__
             assert init_fn_name not in [
@@ -230,104 +230,84 @@ def local_trace(key):
         return vmap(local_trace)(random.split(particle_seed, self.num_stein_particles))
 
     def _svgd_loss_and_grads(self, rng_key, unconstr_params, *args, **kwargs):
-        # 0. Separate model and guide parameters, since only guide parameters are updated using Stein
-        non_mixture_uparams = {  # Includes any marked guide parameters and all model parameters
+        # Separate model and guide parameters, since only guide parameters are updated using Stein
+        # Split parameters into model and guide components - only unflagged guide parameters are
+        # optimized via Stein forces.
+        nonmix_uparams = {  # Includes any marked guide parameters and all model parameters
             p: v
             for p, v in unconstr_params.items()
             if p not in self.guide_sites or self.non_mixture_params_fn(p)
         }
+
         stein_uparams = {
-            p: v for p, v in unconstr_params.items() if p not in non_mixture_uparams
+            p: v for p, v in unconstr_params.items() if p not in nonmix_uparams
         }
 
-        # 1. Collect each guide parameter into monolithic particles that capture correlations
-        # between parameter values across each individual particle
+        # Collect guide parameters into a monolithic particle.
         stein_particles, unravel_pytree, unravel_pytree_batched = batch_ravel_pytree(
             stein_uparams, nbatch_dims=1
         )
+
+        # Kernel behavior varies based on particle site locations. The particle_info dictionary
+        # maps site names to their corresponding dimensional ranges as (start, end) tuples.
         particle_info, _ = self._calc_particle_info(
             stein_uparams, stein_particles.shape[0]
         )
-        attractive_key, classic_key = random.split(rng_key)
 
         def particle_transform_fn(particle):
             params = unravel_pytree(particle)
             ctparams = self.constrain_fn(self.particle_transform_fn(params))
             ctparticle, _ = ravel_pytree(ctparams)
             return ctparticle
 
-        # 2. Calculate gradients for each particle
-        def kernel_particles_loss_fn(rng_key, particles):
-            particle_keys = random.split(rng_key, self.stein_loss.stein_num_particles)
-            grads = vmap(
-                lambda i: grad(
-                    lambda particle: self.stein_loss.particle_loss(
-                        rng_key=particle_keys[i],
-                        model=handlers.scale(
-                            self._inference_model, self.loss_temperature
-                        ),
-                        guide=self.guide,
-                        selected_particle=self.constrain_fn(unravel_pytree(particle)),
-                        unravel_pytree=unravel_pytree,
-                        flat_particles=vmap(particle_transform_fn)(particles),
-                        select_index=i,
-                        model_args=args,
-                        model_kwargs=kwargs,
-                        param_map=self.constrain_fn(non_mixture_uparams),
-                    )
-                )(particles[i])
-            )(jnp.arange(self.stein_loss.stein_num_particles))
-
-            return grads
-
-        # 2.1 Compute particle gradients (for attractive force)
-        particle_ljp_grads = kernel_particles_loss_fn(attractive_key, stein_particles)
-
-        # 2.3 Lift particles to constraint space
-        ctstein_particles = vmap(particle_transform_fn)(stein_particles)
-
-        # 2.4 Compute non-mixture parameter gradients
-        non_mixture_param_grads = grad(
-            lambda cps: -self.stein_loss.loss(
-                classic_key,
-                self.constrain_fn(cps),
-                handlers.scale(self._inference_model, self.loss_temperature),
-                self.guide,
-                unravel_pytree_batched(ctstein_particles),
-                *args,
-                **kwargs,
-            )
-        )(non_mixture_uparams)
+        model = handlers.scale(self._inference_model, self.loss_temperature)
 
-        # 3. Calculate kernel of particles
-        def loss_fn(particle, i):
+        def stein_loss_fn(key, particle, particle_idx):
             return self.stein_loss.particle_loss(
-                rng_key=rng_key,
-                model=handlers.scale(self._inference_model, self.loss_temperature),
+                rng_key=key,
+                model=model,
                 guide=self.guide,
+                # Stein particles evolve in unconstrained space, but gradient computations must account
+                # for the transformation to constrained space
                 selected_particle=self.constrain_fn(unravel_pytree(particle)),
                 unravel_pytree=unravel_pytree,
-                flat_particles=ctstein_particles,
-                select_index=i,
+                flat_particles=vmap(particle_transform_fn)(stein_particles),
+                select_index=particle_idx,
                 model_args=args,
                 model_kwargs=kwargs,
-                param_map=self.constrain_fn(non_mixture_uparams),
+                param_map=self.constrain_fn(nonmix_uparams),
             )
 
         kernel = self.kernel_fn.compute(
-            rng_key, stein_particles, particle_info, loss_fn
+            rng_key, stein_particles, particle_info, stein_loss_fn
         )
+        attractive_key, classic_key = random.split(rng_key)
 
-        # 4. Calculate the attractive force and repulsive force on the particles
-        attractive_force = vmap(
-            lambda y: jnp.sum(
-                vmap(
-                    lambda x, x_ljp_grad: self._apply_kernel(kernel, x, y, x_ljp_grad)
-                )(stein_particles, particle_ljp_grads),
-                axis=0,
-            )
-        )(stein_particles)
+        def compute_attr_force(rng_key, particles):
+            # Second term of eq. 9 from https://arxiv.org/pdf/2410.22948.
+            def body(attr_force, state, y):
+                key, x, i = state
+                x_grad = grad(stein_loss_fn, argnums=1)(key, x, i)
+                attr_force = attr_force + self._apply_kernel(kernel, x, y, x_grad)
+                return attr_force, None
+
+            particle_keys = random.split(rng_key, self.stein_loss.stein_num_particles)
+            init = jnp.zeros_like(particles[0])
+            idxs = jnp.arange(self.num_stein_particles)
+
+            attr_force, _ = vmap(
+                lambda y, key: scan(
+                    partial(body, y=y),
+                    init,
+                    (random.split(key, self.num_stein_particles), particles, idxs),
+                )
+            )(particles, particle_keys)
+
+            return attr_force
 
+        attractive_force = compute_attr_force(attractive_key, stein_particles)
+
+        # Third term of eq. 9 from https://arxiv.org/pdf/2410.22948.
         repulsive_force = vmap(
             lambda y: jnp.mean(
                 vmap(
@@ -338,15 +318,27 @@ def loss_fn(particle, i):
             )
         )(stein_particles)
 
-        # 6. Compute the stein force
         particle_grads = attractive_force + repulsive_force
 
-        # 7. Decompose the monolithic particle forces back to concrete parameter values
-        stein_param_grads = unravel_pytree_batched(particle_grads)
+        # Compute non-mixture parameter gradients.
+        nonmix_uparam_grads = grad(
+            lambda cps: -self.stein_loss.loss(
+                classic_key,
+                self.constrain_fn(cps),
+                model,
+                self.guide,
+                unravel_pytree_batched(vmap(particle_transform_fn)(stein_particles)),
+                *args,
+                **kwargs,
+            )
+        )(nonmix_uparams)
+
+        # Decompose the monolithic particle forces back to concrete parameter values.
+        stein_uparam_grads = unravel_pytree_batched(particle_grads)
 
-        # 8. Return loss and gradients (based on parameter forces)
+        # Return loss and gradients (based on parameter forces).
         res_grads = tree.map(
-            lambda x: -x, {**non_mixture_param_grads, **stein_param_grads}
+            lambda x: -x, {**nonmix_uparam_grads, **stein_uparam_grads}
         )
         return jnp.linalg.norm(particle_grads), res_grads
 

numpyro/contrib/funsor/enum_messenger.py

@@ -436,9 +436,9 @@ class BaseEnumMessenger(NamedMessenger):
     """
 
     def __init__(self, fn=None, first_available_dim=None):
-        assert (
-            first_available_dim is None or first_available_dim < 0
-        ), first_available_dim
+        assert first_available_dim is None or first_available_dim < 0, (
+            first_available_dim
+        )
         self.first_available_dim = first_available_dim
         super().__init__(fn)
 

numpyro/contrib/tfp/mcmc.py

@@ -56,9 +56,9 @@ def log_prob_fn(x):
 class _TFPKernelMeta(ABCMeta):
     def __getitem__(cls, kernel_class):
         assert issubclass(kernel_class, tfp.mcmc.TransitionKernel)
-        assert (
-            "target_log_prob_fn" in inspect.getfullargspec(kernel_class).args
-        ), f"the first argument of {kernel_class} must be `target_log_prob_fn`"
+        assert "target_log_prob_fn" in inspect.getfullargspec(kernel_class).args, (
+            f"the first argument of {kernel_class} must be `target_log_prob_fn`"
+        )
 
         _PyroKernel = type(kernel_class.__name__, (TFPKernel,), {})
         _PyroKernel.kernel_class = kernel_class