fixed haiku for ecs (#1750)

numpyro/contrib/ecs_proxies.py

@@ -116,7 +116,11 @@ def construct_proxy_fn(
         num_blocks=1,
     ):
         ref_params = {
-            name: biject_to(prototype_trace[name]["fn"].support).inv(value)
+            name: (
+                biject_to(prototype_trace[name]["fn"].support).inv(value)
+                if prototype_trace[name]["type"] == "sample"
+                else value
+            )
             for name, value in reference_params.items()
         }
 
@@ -131,7 +135,11 @@ def log_likelihood(params_flat, subsample_indices=None):
             with warnings.catch_warnings():
                 warnings.simplefilter("ignore")
                 params = {
-                    name: biject_to(prototype_trace[name]["fn"].support)(value)
+                    name: (
+                        biject_to(prototype_trace[name]["fn"].support)(value)
+                        if prototype_trace[name]["type"] == "sample"
+                        else value
+                    )
                     for name, value in params.items()
                 }
                 with (
@@ -167,9 +175,7 @@ def log_likelihood_sum(params_flat, subsample_indices=None):
         elif 1:
             TPState = TaylorOneProxyState
         else:
-            raise ValueError(
-                "Taylor proxy only defined for first and second degree."
-            )
+            raise ValueError("Taylor proxy only defined for first and second degree.")
 
         # those stats are dict keyed by subsample names
         ref_sum_log_lik = log_likelihood_sum(ref_params_flat)

test/contrib/test_esc_proxies.py

@@ -5,7 +5,14 @@
 
 from jax import numpy as jnp, random
 
+from numpyro import plate, prng_key, sample
 from numpyro.contrib.ecs_proxies import block_update
+from numpyro.contrib.module import random_haiku_module
+from numpyro.distributions import Cauchy, Normal
+from numpyro.handlers import seed
+from numpyro.infer import HMC, HMCECS, MCMC, SVI, Trace_ELBO
+from numpyro.infer.autoguide import AutoDelta
+from numpyro.optim import Adam
 
 
 @pytest.mark.parametrize("num_blocks", [1, 2, 50, 100])
@@ -26,3 +33,42 @@ def test_block_update_partitioning(num_blocks):
         )
 
     assert gibbs_state == new_gibbs_state
+
+
+def test_haiku_compatiable():
+    try:
+        import haiku as hk  # noqa: F401
+
+        data_points = 6
+        x_dim = 4
+
+        def model(x, y):
+            net = random_haiku_module(
+                "net",
+                hk.transform(lambda x: hk.Linear(1)(x)),
+                prior={"linear.b": Cauchy(), "linear.w": Normal()},
+                input_shape=(1, x_dim),
+            )
+
+            with plate("data", data_points, subsample_size=2) as idx:
+                yb = y[idx]
+                xb = x[idx]
+                sample("y", Normal(net(xb).squeeze()), obs=yb)
+
+        x = jnp.ones((data_points, x_dim))
+        y = jnp.array((data_points, 0))
+
+        with seed(rng_seed=0):
+            svi = SVI(model, AutoDelta(model), Adam(step_size=1e-3), Trace_ELBO())
+            svi_result = svi.run(prng_key(), 1, x, y)
+            ref_params = {
+                k.removesuffix("_auto_loc"): v for k, v in svi_result.params.items()
+            }
+
+            proxy = HMCECS.taylor_proxy(ref_params, degree=2)
+            kernel = HMCECS(HMC(model), num_blocks=2, proxy=proxy)
+
+            mcmc = MCMC(kernel, num_warmup=2, num_samples=2)
+            mcmc.run(prng_key(), x, y)
+    except ImportError:
+        pass