Support scan for Trace_ELBO

numpyro/contrib/control_flow/scan.py

@@ -15,6 +15,24 @@
 from numpyro.util import not_jax_tracer
 
 
+def _replay_wrapper(replay_trace, trace, i, length):
+    def get_ith_value(site):
+        value_shape = jnp.shape(site["value"])
+        site_len = value_shape[0] if value_shape else 0
+        if (
+            site["name"] not in trace
+            or site_len != length
+            or site["type"] not in ("sample", "deterministic")
+        ):
+            return site
+
+        site = site.copy()
+        site["value"] = site["value"][i]
+        return site
+
+    return {k: get_ith_value(v) for k, v in replay_trace.items()}
+
+
 def _subs_wrapper(subs_map, i, length, site):
     if site["type"] != "sample":
         return
@@ -264,10 +282,10 @@ def scan_wrapper(
     first_available_dim=None,
 ):
     if length is None:
-        length = tree_flatten(xs)[0][0].shape[0]
+        length = jnp.shape(tree_flatten(xs)[0][0])[0]
 
     if enum and history > 0:
-        return scan_enum(
+        return scan_enum(  # TODO: replay for enum
             f,
             init,
             xs,
@@ -289,14 +307,17 @@ def body_fn(wrapped_carry, x):
             fn = handlers.infer_config(
                 f, config_fn=lambda msg: {"_scan_current_index": i}
             )
-
             seeded_fn = handlers.seed(fn, subkey) if subkey is not None else fn
             for subs_type, subs_map in substitute_stack:
                 subs_fn = partial(_subs_wrapper, subs_map, i, length)
                 if subs_type == "condition":
                     seeded_fn = handlers.condition(seeded_fn, condition_fn=subs_fn)
                 elif subs_type == "substitute":
                     seeded_fn = handlers.substitute(seeded_fn, substitute_fn=subs_fn)
+                elif subs_type == "replay":
+                    trace = handlers.trace(seeded_fn).get_trace(carry, x)
+                    replay_trace_i = _replay_wrapper(subs_map, trace, i, length)
+                    seeded_fn = handlers.replay(seeded_fn, trace=replay_trace_i)
 
             with handlers.trace() as trace:
                 carry, y = seeded_fn(carry, x)

numpyro/handlers.py

@@ -222,6 +222,8 @@ def process_message(self, msg):
                 raise RuntimeError(f"Site {name} must be sampled in trace.")
             msg["value"] = guide_msg["value"]
             msg["infer"] = guide_msg["infer"].copy()
+        if msg["type"] == "control_flow":
+            msg["kwargs"]["substitute_stack"].append(("replay", self.trace))
 
 
 class block(Messenger):

test/contrib/test_control_flow.py

@@ -12,7 +12,9 @@
 import numpyro.distributions as dist
 from numpyro.handlers import mask, seed, substitute, trace
 from numpyro.infer import MCMC, NUTS, SVI, Predictive, Trace_ELBO
+from numpyro.infer.autoguide import AutoNormal
 from numpyro.infer.util import log_density, potential_energy
+from numpyro.optim import Adam
 
 
 def test_scan():
@@ -241,3 +243,32 @@ def transition(carry, y_curr):
         assert model_density
         assert model_trace["x"]["fn"].batch_shape == (12, 10)
         assert model_trace["x"]["fn"].event_shape == (3,)
+
+
+def test_scan_svi():
+    T = 3
+    N = 5
+
+    def gaussian_hmm(y=None, T=T, N=N):
+        def transition(x_prev, y_curr):
+            with numpyro.plate("data", N):
+                x_curr = numpyro.sample("x", dist.Normal(x_prev, 1.5))
+                y_curr = numpyro.sample("y", dist.Normal(x_curr, 0.1), obs=y_curr)
+            return x_curr, (x_curr, y_curr)
+
+        with numpyro.plate("data", N):
+            x0 = numpyro.sample("x_0", dist.Normal(jnp.zeros(N), 5.0))
+        _, (x, y) = scan(transition, x0, y, length=T)
+        return (x, y)
+
+    with numpyro.handlers.seed(rng_seed=0):
+        x, y = gaussian_hmm()
+    with numpyro.handlers.seed(rng_seed=0):
+        tr = numpyro.handlers.trace(gaussian_hmm).get_trace(y=y, T=T, N=N)
+
+    guide = AutoNormal(gaussian_hmm)
+    svi = SVI(gaussian_hmm, guide, Adam(0.1), Trace_ELBO(), y=y, T=T, N=N)
+    results = svi.run(random.PRNGKey(0), 10**3)
+
+    xhat = results.params["x_auto_loc"]
+    assert_allclose(xhat, tr["x"]["value"], rtol=0.1)