Add init_params argument to svi.init() and svi.run()

numpyro/infer/svi.py

@@ -169,26 +169,32 @@ def __init__(self, model, guide, optim, loss, **static_kwargs):
 
             self.optim = optax_to_numpyro(optim)
 
-    def init(self, rng_key, *args, **kwargs):
+    def init(self, rng_key, *args, init_params=None, **kwargs):
         """
         Gets the initial SVI state.
 
         :param jax.random.PRNGKey rng_key: random number generator seed.
         :param args: arguments to the model / guide (these can possibly vary during
             the course of fitting).
+        :param dict init_params: if not None, initialize :class:`numpyro.param` sites with values from
+            this dictionary instead of using ``init_value`` in :class:`numpyro.param` primitives.
         :param kwargs: keyword arguments to the model / guide (these can possibly vary
             during the course of fitting).
         :return: the initial :data:`SVIState`
         """
         rng_key, model_seed, guide_seed = random.split(rng_key, 3)
         model_init = seed(self.model, model_seed)
         guide_init = seed(self.guide, guide_seed)
+        if init_params is not None:
+            guide_init = substitute(guide_init, init_params)
         guide_trace = trace(guide_init).get_trace(*args, **kwargs, **self.static_kwargs)
         init_guide_params = {
             name: site["value"]
             for name, site in guide_trace.items()
             if site["type"] == "param"
         }
+        if init_params is not None:
+            init_guide_params.update(init_params)
         model_trace = trace(
             substitute(replay(model_init, guide_trace), init_guide_params)
         ).get_trace(*args, **kwargs, **self.static_kwargs)
@@ -305,6 +311,7 @@ def run(
         progress_bar=True,
         stable_update=False,
         init_state=None,
+        init_params=None,
         **kwargs,
     ):
         """
@@ -333,6 +340,8 @@ def run(
                 # continue from the end of the previous svi run rather than beginning again from iteration 0
                 svi_result = svi.run(random.PRNGKey(1), 2000, data, init_state=svi_result.state)
 
+        :param dict init_params: if not None, initialize :class:`numpyro.param` sites with values from
+            this dictionary instead of using ``init_value`` in :class:`numpyro.param` primitives.
         :param kwargs: keyword arguments to the model / guide
         :return: a namedtuple with fields `params` and `losses` where `params`
             holds the optimized values at :class:`numpyro.param` sites,
@@ -351,7 +360,7 @@ def body_fn(svi_state, _):
             return svi_state, loss
 
         if init_state is None:
-            svi_state = self.init(rng_key, *args, **kwargs)
+            svi_state = self.init(rng_key, *args, init_params=init_params, **kwargs)
         else:
             svi_state = init_state
         if progress_bar:

test/infer/test_svi.py

@@ -36,6 +36,10 @@
 from numpyro.util import fori_loop
 
 
+def assert_equal(a, b, prec=0):
+    return jax.tree_util.tree_map(lambda a, b: assert_allclose(a, b, atol=prec), a, b)
+
+
 @pytest.mark.parametrize("alpha", [0.0, 2.0])
 def test_renyi_elbo(alpha):
     def model(x):
@@ -224,6 +228,26 @@ def guide():
     assert_allclose(svi_result.params["shared"], target_value, atol=0.1)
 
 
+def test_init_params():
+    init_params = {"b": 1.0, "c": 2.0}
+
+    def model():
+        numpyro.param("a", 0.0)
+        # should receive initial value from init_params
+        numpyro.param("b")
+
+    def guide():
+        # should receive initial value from init_params
+        numpyro.param("c")
+
+    svi = SVI(model, guide, optim.Adam(0.01), Trace_ELBO())
+    svi_state = svi.init(random.PRNGKey(0), init_params=init_params)
+    params = svi.get_params(svi_state)
+    init_params["a"] = 0.0
+    # make sure init params ended up in the SVI state
+    assert_equal(params, init_params)
+
+
 def test_elbo_dynamic_support():
     x_prior = dist.TransformedDistribution(
         dist.Normal(),