Add waste-free SMC test

blackjax/smc/base.py

@@ -120,10 +120,11 @@ def step(
 
     if num_resample is None:
         num_resample = num_particles
+
     resampling_idx = resample_fn(resampling_key, state.weights, num_resample)
     particles = jax.tree_map(lambda x: x[resampling_idx], state.particles)
 
-    keys = jax.random.split(updating_key, num_particles)
+    keys = jax.random.split(updating_key, num_resample)
     particles, update_info = update_fn(keys, particles)
 
     log_weights = weigh_fn(particles)

blackjax/smc/tempered.py

@@ -134,8 +134,8 @@ def body_fn(state, rng_key):
                 return new_state, info
 
             keys = jax.random.split(rng_key, num_mcmc_steps)
-            state, info = jax.lax.scan(body_fn, state, keys)
-            return state.position, info
+            last_state, info = jax.lax.scan(body_fn, state, keys)
+            return last_state.position, info
 
         smc_state, info = smc.base.step(
             rng_key,

tests/test_smc.py

@@ -1,14 +1,124 @@
 """Test the generic SMC sampler"""
 import chex
 import jax
+import jax.numpy as jnp
+import jax.scipy.stats as stats
+import numpy as np
 from absl.testing import absltest
 
+import blackjax
+import blackjax.smc.resampling as resampling
+from blackjax.smc.base import init, step
+
+
+def logdensity_fn(position):
+    return jnp.sum(stats.norm.logpdf(position))
+
+
+def _weighted_avg_and_std(values, weights):
+    average = jnp.average(values, weights=weights)
+    variance = jnp.average((values - average) ** 2, weights=weights)
+    return average, jnp.sqrt(variance)
+
 
 class SMCTest(chex.TestCase):
     def setUp(self):
         super().setUp()
         self.key = jax.random.PRNGKey(42)
 
+    @chex.variants(with_jit=True)
+    def test_smc(self):
+
+        num_mcmc_steps = 20
+        num_particles = 1000
+
+        hmc = blackjax.hmc(
+            logdensity_fn,
+            step_size=1e-2,
+            inverse_mass_matrix=jnp.eye(1),
+            num_integration_steps=50,
+        )
+
+        def update_fn(rng_key, position):
+            state = hmc.init(position)
+
+            def body_fn(state, rng_key):
+                new_state, info = hmc.step(rng_key, state)
+                return new_state, info
+
+            keys = jax.random.split(rng_key, num_mcmc_steps)
+            last_state, info = jax.lax.scan(body_fn, state, keys)
+            return last_state.position, info
+
+        init_key, sample_key = jax.random.split(self.key)
+
+        # Initialize the state of the SMC sampler
+        init_particles = 0.25 + jax.random.normal(init_key, shape=(num_particles,))
+        state = init(init_particles)
+
+        # Run the SMC sampler once
+        new_state, info = self.variant(step, static_argnums=(2, 3, 4))(
+            sample_key,
+            state,
+            jax.vmap(update_fn),
+            jax.vmap(logdensity_fn),
+            resampling.systematic,
+        )
+
+        mean, std = _weighted_avg_and_std(new_state.particles, state.weights)
+        np.testing.assert_allclose(0.0, mean, atol=1e-1)
+        np.testing.assert_allclose(1.0, std, atol=1e-1)
+
+    @chex.variants(with_jit=True)
+    def test_smc_waste_free(self):
+
+        num_mcmc_steps = 10
+        num_particles = 1000
+        num_resample = num_particles // num_mcmc_steps
+
+        hmc = blackjax.hmc(
+            logdensity_fn,
+            step_size=1e-2,
+            inverse_mass_matrix=jnp.eye(1),
+            num_integration_steps=100,
+        )
+
+        def waste_free_update_fn(keys, particles):
+            def one_particle_fn(rng_key, position):
+                state = hmc.init(position)
+
+                def body_fn(state, rng_key):
+                    new_state, info = hmc.step(rng_key, state)
+                    return new_state, (state, info)
+
+                keys = jax.random.split(rng_key, num_mcmc_steps)
+                _, (states, info) = jax.lax.scan(body_fn, state, keys)
+                return states.position, info
+
+            particles, info = jax.vmap(one_particle_fn)(keys, particles)
+            particles = particles.reshape((num_particles,))
+            return particles, info
+
+        init_key, sample_key = jax.random.split(self.key)
+
+        # Initialize the state of the SMC sampler
+        init_particles = 0.25 + jax.random.normal(init_key, shape=(num_particles,))
+        state = init(init_particles)
+
+        # Run the SMC sampler once
+        new_state, info = self.variant(step, static_argnums=(2, 3, 4, 5))(
+            sample_key,
+            state,
+            waste_free_update_fn,
+            jax.vmap(logdensity_fn),
+            resampling.systematic,
+            num_resample,
+        )
+
+        mean, std = _weighted_avg_and_std(new_state.particles, state.weights)
+        np.testing.assert_allclose(0.0, mean, atol=1e-1)
+        np.testing.assert_allclose(1.0, std, atol=1e-1)
+
 
 if __name__ == "__main__":
     absltest.main()