Support forward mode differentiation for SVI

numpyro/infer/hmc.py

@@ -265,6 +265,13 @@ def init_kernel(
             `d2` is the max tree depth during post warmup phase.
         :param bool find_heuristic_step_size: whether to a heuristic function to adjust the
             step size at the beginning of each adaptation window. Defaults to False.
+        :param bool forward_mode_differentiation: whether to use forward-mode differentiation
+            or reverse-mode differentiation. By default, we use reverse mode but the forward
+            mode can be useful in some cases to improve the performance. In addition, some
+            control flow utility on JAX such as `jax.lax.while_loop` or `jax.lax.fori_loop`
+            only supports forward-mode differentiation. See
+            `JAX's The Autodiff Cookbook <https://jax.readthedocs.io/en/latest/notebooks/autodiff_cookbook.html>`_
+            for more information.
         :param bool regularize_mass_matrix: whether or not to regularize the estimated mass
             matrix for numerical stability during warmup phase. Defaults to True. This flag
             does not take effect if ``adapt_mass_matrix == False``.

numpyro/infer/hmc_util.py

@@ -241,9 +241,13 @@ def final_fn(state, regularize=False):
 
 def _value_and_grad(f, x, forward_mode_differentiation=False):
     if forward_mode_differentiation:
-        return f(x), jacfwd(f)(x)
+        def _wrapper(x):
+            out = f(x)
+            return out, out
+        grads, out = jacfwd(_wrapper, has_aux=True)(x)
+        return out, grads
     else:
-        return value_and_grad(f)(x)
+        return value_and_grad(f, has_aux=False)(x)
 
 
 def _kinetic_grad(kinetic_fn, inverse_mass_matrix, r):

numpyro/infer/svi.py

@@ -256,14 +256,16 @@ def get_params(self, svi_state):
         params = self.constrain_fn(self.optim.get_params(svi_state.optim_state))
         return params
 
-    def update(self, svi_state, *args, **kwargs):
+    def update(self, svi_state, *args, forward_mode_differentiation=False, **kwargs):
         """
         Take a single step of SVI (possibly on a batch / minibatch of data),
         using the optimizer.
 
         :param svi_state: current state of SVI.
         :param args: arguments to the model / guide (these can possibly vary during
             the course of fitting).
+        :param forward_mode_differentiation: boolean flag indicating whether to use forward mode differentiation.
+            Defaults to False.
         :param kwargs: keyword arguments to the model / guide (these can possibly vary
             during the course of fitting).
         :return: tuple of `(svi_state, loss)`.
@@ -281,18 +283,20 @@ def update(self, svi_state, *args, **kwargs):
             mutable_state=svi_state.mutable_state,
         )
         (loss_val, mutable_state), optim_state = self.optim.eval_and_update(
-            loss_fn, svi_state.optim_state
+            loss_fn, svi_state.optim_state, forward_mode_differentiation=forward_mode_differentiation
         )
         return SVIState(optim_state, mutable_state, rng_key), loss_val
 
-    def stable_update(self, svi_state, *args, **kwargs):
+    def stable_update(self, svi_state, *args, forward_mode_differentiation=False, **kwargs):
         """
         Similar to :meth:`update` but returns the current state if the
         the loss or the new state contains invalid values.
 
         :param svi_state: current state of SVI.
         :param args: arguments to the model / guide (these can possibly vary during
             the course of fitting).
+        :param forward_mode_differentiation: boolean flag indicating whether to use forward mode differentiation.
+            Defaults to False.
         :param kwargs: keyword arguments to the model / guide (these can possibly vary
             during the course of fitting).
         :return: tuple of `(svi_state, loss)`.
@@ -310,7 +314,7 @@ def stable_update(self, svi_state, *args, **kwargs):
             mutable_state=svi_state.mutable_state,
         )
         (loss_val, mutable_state), optim_state = self.optim.eval_and_stable_update(
-            loss_fn, svi_state.optim_state
+            loss_fn, svi_state.optim_state, forward_mode_differentiation=forward_mode_differentiation
         )
         return SVIState(optim_state, mutable_state, rng_key), loss_val
 
@@ -321,6 +325,7 @@ def run(
         *args,
         progress_bar=True,
         stable_update=False,
+        forward_mode_differentiation=False,
         init_state=None,
         init_params=None,
         **kwargs,
@@ -342,6 +347,13 @@ def run(
             ``True``.
         :param bool stable_update: whether to use :meth:`stable_update` to update
             the state. Defaults to False.
+        :param bool forward_mode_differentiation: whether to use forward-mode differentiation
+            or reverse-mode differentiation. By default, we use reverse mode but the forward
+            mode can be useful in some cases to improve the performance. In addition, some
+            control flow utility on JAX such as `jax.lax.while_loop` or `jax.lax.fori_loop`
+            only supports forward-mode differentiation. See
+            `JAX's The Autodiff Cookbook <https://jax.readthedocs.io/en/latest/notebooks/autodiff_cookbook.html>`_
+            for more information.
         :param SVIState init_state: if not None, begin SVI from the
             final state of previous SVI run. Usage::
 
@@ -365,9 +377,13 @@ def run(
 
         def body_fn(svi_state, _):
             if stable_update:
-                svi_state, loss = self.stable_update(svi_state, *args, **kwargs)
+                svi_state, loss = self.stable_update(
+                    svi_state, *args, forward_mode_differentiation=forward_mode_differentiation, **kwargs
+                )
             else:
-                svi_state, loss = self.update(svi_state, *args, **kwargs)
+                svi_state, loss = self.update(
+                    svi_state, *args, forward_mode_differentiation=forward_mode_differentiation, **kwargs
+                )
             return svi_state, loss
 
         if init_state is None:

numpyro/optim.py

@@ -11,7 +11,7 @@
 from collections.abc import Callable
 from typing import Any, TypeVar
 
-from jax import lax, value_and_grad
+from jax import jacfwd, lax, value_and_grad
 from jax.example_libraries import optimizers
 from jax.flatten_util import ravel_pytree
 import jax.numpy as jnp
@@ -34,6 +34,15 @@
 _OptState = TypeVar("_OptState")
 _IterOptState = tuple[int, _OptState]
 
+def _value_and_grad(f, x, forward_mode_differentiation=False):
+    if forward_mode_differentiation:
+        def _wrapper(x):
+            out, aux = f(x)
+            return out, (out, aux)
+        grads, (out, aux) = jacfwd(_wrapper, has_aux=True)(x)
+        return (out, aux), grads
+    else:
+        return value_and_grad(f, has_aux=True)(x)
 
 class _NumPyroOptim(object):
     def __init__(self, optim_fn: Callable, *args, **kwargs) -> None:
@@ -61,7 +70,9 @@ def update(self, g: _Params, state: _IterOptState) -> _IterOptState:
         opt_state = self.update_fn(i, g, opt_state)
         return i + 1, opt_state
 
-    def eval_and_update(self, fn: Callable[[Any], tuple], state: _IterOptState):
+    def eval_and_update(
+            self, fn: Callable[[Any], tuple], state: _IterOptState, forward_mode_differentiation: bool = False
+    ):
         """
         Performs an optimization step for the objective function `fn`.
         For most optimizers, the update is performed based on the gradient
@@ -74,24 +85,32 @@ def eval_and_update(self, fn: Callable[[Any], tuple], state: _IterOptState):
             is a scalar loss function to be differentiated and the second item
             is an auxiliary output.
         :param state: current optimizer state.
+        :param forward_mode_differentiation: boolean flag indicating whether to use forward mode differentiation.
         :return: a pair of the output of objective function and the new optimizer state.
         """
         params = self.get_params(state)
-        (out, aux), grads = value_and_grad(fn, has_aux=True)(params)
+        (out, aux), grads = _value_and_grad(
+            fn, x=params, forward_mode_differentiation=forward_mode_differentiation
+        )
         return (out, aux), self.update(grads, state)
 
-    def eval_and_stable_update(self, fn: Callable[[Any], tuple], state: _IterOptState):
+    def eval_and_stable_update(
+            self, fn: Callable[[Any], tuple], state: _IterOptState, forward_mode_differentiation: bool = False
+        ):
         """
         Like :meth:`eval_and_update` but when the value of the objective function
         or the gradients are not finite, we will not update the input `state`
         and will set the objective output to `nan`.
 
         :param fn: objective function.
         :param state: current optimizer state.
+        :param forward_mode_differentiation: boolean flag indicating whether to use forward mode differentiation.
         :return: a pair of the output of objective function and the new optimizer state.
         """
         params = self.get_params(state)
-        (out, aux), grads = value_and_grad(fn, has_aux=True)(params)
+        (out, aux), grads = _value_and_grad(
+            fn, x=params, forward_mode_differentiation=forward_mode_differentiation
+        )
         out, state = lax.cond(
             jnp.isfinite(out) & jnp.isfinite(ravel_pytree(grads)[0]).all(),
             lambda _: (out, self.update(grads, state)),
@@ -266,7 +285,9 @@ def __init__(self, method="BFGS", **kwargs):
         self._method = method
         self._kwargs = kwargs
 
-    def eval_and_update(self, fn: Callable[[Any], tuple], state: _IterOptState):
+    def eval_and_update(
+            self, fn: Callable[[Any], tuple], state: _IterOptState, forward_mode_differentiation=False
+        ):
         i, (flat_params, unravel_fn) = state
 
         def loss_fn(x):

test/infer/test_svi.py

@@ -8,7 +8,7 @@
 import pytest
 
 import jax
-from jax import jit, random, value_and_grad
+from jax import jit, lax, random, value_and_grad
 from jax.example_libraries import optimizers
 import jax.numpy as jnp
 from jax.tree_util import tree_all, tree_map
@@ -757,3 +757,20 @@ def guide():
     params = svi_results.params
     assert_allclose(params["loc"], actual_loc, rtol=0.1)
     assert_allclose(params["scale"], actual_scale, rtol=0.1)
+
+
+def test_forward_mode_differentiation():
+    def model():
+        x = numpyro.sample("x", dist.Normal(0, 1))
+        y = lax.while_loop(lambda x: x < 10, lambda x: x + 1, x)
+        numpyro.sample("obs", dist.Normal(y, 1), obs=1.0)
+
+    def guide():
+        loc = numpyro.param("loc", 0.)
+        scale = numpyro.param("scale", 1., constraint=dist.constraints.positive)
+        numpyro.sample("x", dist.Normal(loc, scale))
+
+    # this fails in reverse mode
+    optimizer = numpyro.optim.Adam(step_size=0.01)
+    svi = SVI(model, guide, optimizer, loss=Trace_ELBO())
+    svi.run(random.PRNGKey(0), 1000, forward_mode_differentiation=True)