Move second order utilities to dedicated sub-package.

This is part of a broader effort to restructure the current fully flat API surface, since optax has outgrown the flat structure due to broader scope and complexity.

PiperOrigin-RevId: 570042410

optax/__init__.py

@@ -16,6 +16,7 @@
 
 from optax import contrib
 from optax import experimental
+from optax import second_order
 from optax._src.alias import adabelief
 from optax._src.alias import adafactor
 from optax._src.alias import adagrad
@@ -115,9 +116,6 @@
 from optax._src.schedule import sgdr_schedule
 from optax._src.schedule import warmup_cosine_decay_schedule
 from optax._src.schedule import warmup_exponential_decay_schedule
-from optax._src.second_order import fisher_diag
-from optax._src.second_order import hessian_diag
-from optax._src.second_order import hvp
 from optax._src.state_utils import tree_map_params
 from optax._src.stochastic_gradient_estimators import measure_valued_jacobians
 from optax._src.stochastic_gradient_estimators import pathwise_jacobians
@@ -234,16 +232,13 @@
     "EmptyState",
     "exponential_decay",
     "FactoredState",
-    "fisher_diag",
     "flatten",
     "fromage",
     "global_norm",
     "GradientTransformation",
     "GradientTransformationExtraArgs",
     "hinge_loss",
-    "hessian_diag",
     "huber_loss",
-    "hvp",
     "identity",
     "incremental_update",
     "inject_hyperparams",

optax/second_order/__init__.py

@@ -0,0 +1,19 @@
+# Copyright 2019 DeepMind Technologies Limited. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Optax: composable gradient processing and optimization, in JAX."""
+
+from optax.second_order.fisher import fisher_diag
+from optax.second_order.hessian import hessian_diag
+from optax.second_order.hessian import hvp

optax/second_order/fisher.py

@@ -0,0 +1,55 @@
+# Copyright 2019 DeepMind Technologies Limited. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Functions for computing diagonals of the fisher information matrix.
+
+Computing the Fisher matrix for neural networks is typically intractible due to
+the quadratic memory requirements. Solving for the diagonal can be done cheaply,
+with sub-quadratic memory requirements.
+"""
+
+from typing import Any, Callable
+
+import jax
+import jax.numpy as jnp
+
+# TODO(b/160876114): use the pytypes defined in Chex.
+Array = jnp.ndarray
+
+
+def _ravel(p: Any) -> Array:
+  return jax.flatten_util.ravel_pytree(p)[0]
+
+
+def fisher_diag(
+    negative_log_likelihood: Callable[[Any, Array, Array], Array],
+    params: Any,
+    inputs: jnp.ndarray,
+    targets: jnp.ndarray,
+) -> jax.Array:
+  """Computes the diagonal of the (observed) Fisher information matrix.
+
+  Args:
+    negative_log_likelihood: the negative log likelihood function with
+      expected signature `loss = fn(params, inputs, targets)`.
+    params: model parameters.
+    inputs: inputs at which `negative_log_likelihood` is evaluated.
+    targets: targets at which `negative_log_likelihood` is evaluated.
+
+  Returns:
+    An Array corresponding to the product to the Hessian of
+    `negative_log_likelihood` evaluated at `(params, inputs, targets)`.
+  """
+  return jnp.square(
+      _ravel(jax.grad(negative_log_likelihood)(params, inputs, targets)))

optax/_src/second_order.py → optax/second_order/hessian.py

@@ -12,37 +12,28 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Functions for computing diagonals of Hessians & Fisher info of parameters.
-
-Computing the Hessian or Fisher information matrices for neural networks is
-typically intractible due to the quadratic memory requirements. Solving for the
-diagonals of these matrices is often a better solution.
-
-This module provides two functions for computing these diagonals, `hessian_diag`
-and `fisher_diag`., each with sub-quadratic memory requirements.
+"""Functions for computing diagonals of the Hessian wrt to a set of parameters.
 
+Computing the Hessian for neural networks is typically intractible due to the
+quadratic memory requirements. Solving for the diagonal can be done cheaply,
+with sub-quadratic memory requirements.
 """
 
 from typing import Any, Callable
 
 import jax
-from jax.flatten_util import ravel_pytree
 import jax.numpy as jnp
 
-
-# This covers both Jax and Numpy arrays.
 # TODO(b/160876114): use the pytypes defined in Chex.
 Array = jnp.ndarray
-# LossFun of type f(params, inputs, targets).
-LossFun = Callable[[Any, Array, Array], Array]
 
 
-def ravel(p: Any) -> Array:
-  return ravel_pytree(p)[0]
+def _ravel(p: Any) -> Array:
+  return jax.flatten_util.ravel_pytree(p)[0]
 
 
 def hvp(
-    loss: LossFun,
+    loss: Callable[[Any, Array, Array], Array],  # loss(params, inputs, targets)
     v: jax.Array,
     params: Any,
     inputs: jax.Array,
@@ -61,13 +52,13 @@ def hvp(
     An Array corresponding to the product of `v` and the Hessian of `loss`
     evaluated at `(params, inputs, targets)`.
   """
-  _, unravel_fn = ravel_pytree(params)
+  _, unravel_fn = jax.flatten_util.ravel_pytree(params)
   loss_fn = lambda p: loss(p, inputs, targets)
   return jax.jvp(jax.grad(loss_fn), [params], [unravel_fn(v)])[1]
 
 
 def hessian_diag(
-    loss: LossFun,
+    loss: Callable[[Any, Array, Array], Array],
     params: Any,
     inputs: jax.Array,
     targets: jax.Array,
@@ -84,28 +75,6 @@ def hessian_diag(
     A DeviceArray corresponding to the product to the Hessian of `loss`
     evaluated at `(params, inputs, targets)`.
   """
-  vs = jnp.eye(ravel(params).size)
-  comp = lambda v: jnp.vdot(v, ravel(hvp(loss, v, params, inputs, targets)))
+  vs = jnp.eye(_ravel(params).size)
+  comp = lambda v: jnp.vdot(v, _ravel(hvp(loss, v, params, inputs, targets)))
   return jax.vmap(comp)(vs)
-
-
-def fisher_diag(
-    negative_log_likelihood: LossFun,
-    params: Any,
-    inputs: jnp.ndarray,
-    targets: jnp.ndarray,
-) -> jax.Array:
-  """Computes the diagonal of the (observed) Fisher information matrix.
-
-  Args:
-    negative_log_likelihood: the negative log likelihood function.
-    params: model parameters.
-    inputs: inputs at which `negative_log_likelihood` is evaluated.
-    targets: targets at which `negative_log_likelihood` is evaluated.
-
-  Returns:
-    An Array corresponding to the product to the Hessian of
-    `negative_log_likelihood` evaluated at `(params, inputs, targets)`.
-  """
-  return jnp.square(
-      ravel(jax.grad(negative_log_likelihood)(params, inputs, targets)))

optax/second_order/hessian_test.py

@@ -0,0 +1,86 @@
+# Copyright 2019 DeepMind Technologies Limited. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for `hessian.py`."""
+
+import collections
+import functools
+import itertools
+
+from absl.testing import absltest
+
+import chex
+import haiku as hk
+import jax
+import jax.numpy as jnp
+import numpy as np
+
+from optax import second_order
+
+
+NUM_CLASSES = 2
+NUM_SAMPLES = 3
+NUM_FEATURES = 4
+
+
+class HessianTest(chex.TestCase):
+
+  def setUp(self):
+    super().setUp()
+
+    self.data = np.random.rand(NUM_SAMPLES, NUM_FEATURES)
+    self.labels = np.random.randint(NUM_CLASSES, size=NUM_SAMPLES)
+
+    def net_fn(z):
+      mlp = hk.Sequential(
+          [hk.Linear(10), jax.nn.relu, hk.Linear(NUM_CLASSES)], name='mlp')
+      return jax.nn.log_softmax(mlp(z))
+
+    net = hk.without_apply_rng(hk.transform(net_fn))
+    self.parameters = net.init(jax.random.PRNGKey(0), self.data)
+
+    def loss(params, inputs, targets):
+      log_probs = net.apply(params, inputs)
+      return -jnp.mean(hk.one_hot(targets, NUM_CLASSES) * log_probs)
+
+    self.loss_fn = loss
+
+    def jax_hessian_diag(loss_fun, params, inputs, targets):
+      """This is the 'ground-truth' obtained via the JAX library."""
+      hess = jax.hessian(loss_fun)(params, inputs, targets)
+
+      # Extracts the diagonal components.
+      hess_diag = collections.defaultdict(dict)
+      for k0, k1 in itertools.product(params.keys(), ['w', 'b']):
+        params_shape = params[k0][k1].shape
+        n_params = np.prod(params_shape)
+        hess_diag[k0][k1] = jnp.diag(hess[k0][k1][k0][k1].reshape(
+            n_params, n_params)).reshape(params_shape)
+      for k, v in hess_diag.items():
+        hess_diag[k] = v
+      return jax.flatten_util.ravel_pytree(hess_diag)[0]
+
+    self.hessian = jax_hessian_diag(
+        self.loss_fn, self.parameters, self.data, self.labels)
+
+  @chex.all_variants
+  def test_hessian_diag(self):
+    hessian_diag_fn = self.variant(
+        functools.partial(second_order.hessian_diag, self.loss_fn))
+    actual = hessian_diag_fn(self.parameters, self.data, self.labels)
+    np.testing.assert_array_almost_equal(self.hessian, actual, 5)
+
+
+if __name__ == '__main__':
+  absltest.main()