Changing accountant from log_mgf language to RDP. Adding more tests.

research/differential_privacy/privacy_accountant/python/rdp_accountant.py

@@ -12,43 +12,49 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""A standalone utility for tracking the RDP accountant.
+"""RDP analysis of the Gaussian-with-sampling mechanism.
 
-The utility for computing Renyi differential privacy. Its public interface
-consists of two methods:
+Functionality for computing Renyi differential privacy of an additive Gaussian
+mechanism with sampling. Its public interface consists of two methods:
     compute_rdp(q, sigma, T, order) computes RDP with sampling probability q,
-                                    noise sigma, T steps at order.
+                                    noise sigma, T steps at a given order.
     get_privacy_spent computes delta (or eps) given RDP and eps (or delta).
 
 Example use:
 
 Suppose that we have run an algorithm with parameters, an array of
-(q1, sigma1, T1) ... (qk, sigmak, Tk), and we wish to compute eps for a given
+(q1, sigma1, T1) ... (qk, sigma_k, Tk), and we wish to compute eps for a given
 delta. The example code would be:
 
-  max_order = 32
-  orders = range(1, max_order + 1)
-  rdp_list = []
-  for order in orders:
-    rdp = 0
-    for q, sigma, T in parameters:
-      rdp += compute_rdp(q, sigma, T, order)
-    rdp_list.append((order, rdp))
-  eps, delta, opt_order = get_privacy_spent(rdp_list, target_delta=delta)
+max_order = 32
+orders = range(2, max_order + 1)
+rdp = np.zeros_like(orders, dtype=float)
+for q, sigma, T in parameters:
+  rdp += rdp_accountant.compute_rdp(q, sigma, T, orders)
+eps, _, opt_order = rdp_accountant.get_privacy_spent(rdp, target_delta=delta)
 """
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from absl import app
+from absl import flags
+
 import math
+import numpy as np
 import sys
 
-import numpy as np
 from scipy import special
 
-######################
-# FLOAT64 ARITHMETIC #
-######################
+FLAGS = flags.FLAGS
+flags.DEFINE_boolean("rdp_verbose", False,
+                     "Output intermediate results for RDP computation.")
+FLAGS(sys.argv)  # Load the flags (including on import)
+
+
+########################
+# LOG-SPACE ARITHMETIC #
+########################
 
 
 def _log_add(logx, logy):
@@ -56,8 +62,8 @@ def _log_add(logx, logy):
   a, b = min(logx, logy), max(logx, logy)
   if a == -np.inf:  # adding 0
     return b
-  # Apply exp(a) + exp(b) = (exp(a - b) + 1.) * exp(b)
-  return np.log(np.exp(a - b) + 1.) + b
+  # Use exp(a) + exp(b) = (exp(a - b) + 1) * exp(b)
+  return math.log1p(math.exp(a - b)) + b  # log1p(x) = log(x + 1)
 
 
 def _log_sub(logx, logy):
@@ -65,78 +71,81 @@ def _log_sub(logx, logy):
   if logy == -np.inf:  # subtracting 0
     return logx
   assert logx > logy
-  with np.errstate(over="raise"):
+
   try:
-      return np.log(np.exp(logx - logy) - 1.) + logy
-    except FloatingPointError:
+    # Use exp(x) - exp(y) = (exp(x - y) - 1) * exp(y).
+    return math.log(math.expm1(logx - logy)) + logy  # expm1(x) = exp(x) - 1
+  except OverflowError:
     return logx
 
 
 def _log_print(logx):
   """Pretty print."""
-  if logx < np.log(sys.float_info.max):
-    return "{}".format(np.exp(logx))
+  if logx < math.log(sys.float_info.max):
+    return "{}".format(math.exp(logx))
   else:
     return "exp({})".format(logx)
 
 
-def _compute_log_a_int(q, sigma, alpha, verbose=False):
+def _compute_log_a_int(q, sigma, alpha):
   """Compute log(A_alpha) for integer alpha."""
   assert isinstance(alpha, (int, long))
-  log_a1, log_a2 = -np.inf, -np.inf  # log of the first and second terms of A_alpha
+
+  # The first and second terms of A_alpha in the log space:
+  log_a1, log_a2 = -np.inf, -np.inf
 
   for i in range(alpha + 1):
-    # Do computation in the log space. Extra care needed for q = 0 or 1.
-    log_coef_i = np.log(special.binom(alpha, i))
+    # Compute in the log space. Extra care needed for q = 0 or 1.
+    log_coef_i = math.log(special.binom(alpha, i))
     if q > 0:
-      log_coef_i += i * np.log(q)
+      log_coef_i += i * math.log(q)
     elif i > 0:
-      continue  # the term is 0, skip the rest
+      continue  # The term is 0, skip the rest.
 
     if q < 1.0:
-      log_coef_i += (alpha - i) * np.log(1 - q)
+      log_coef_i += (alpha - i) * math.log(1 - q)
     elif i < alpha:
-      continue  # the term is 0, skip the rest
+      continue  # The term is 0, skip the rest.
 
-    s1 = log_coef_i + (i * i - i) / (2.0 * (sigma**2))
-    s2 = log_coef_i + (i * i + i) / (2.0 * (sigma**2))
+    s1 = log_coef_i + (i * i - i) / (2.0 * (sigma ** 2))
+    s2 = log_coef_i + (i * i + i) / (2.0 * (sigma ** 2))
     log_a1 = _log_add(log_a1, s1)
     log_a2 = _log_add(log_a2, s2)
 
-  log_a = _log_add(np.log(1.0 - q) + log_a1, np.log(q) + log_a2)
-  if verbose:
+  log_a = _log_add(math.log(1 - q) + log_a1, math.log(q) + log_a2)
+  if FLAGS.rdp_verbose:
     print("A: by binomial expansion    {} = {} + {}".format(
         _log_print(log_a),
-        _log_print(np.log(1.0 - q) + log_a1), _log_print(np.log(q) + log_a2)))
-  return np.float64(log_a)
+        _log_print(math.log(1 - q) + log_a1), _log_print(math.log(q) + log_a2)))
+  return float(log_a)
 
 
-def _compute_log_a_frac(q, sigma, alpha, verbose=False):
+def _compute_log_a_frac(q, sigma, alpha):
   """Compute log(A_alpha) for fractional alpha."""
-  # The four parts of A_alpha:
+  # The four parts of A_alpha in the log space:
   log_a11, log_a12 = -np.inf, -np.inf
   log_a21, log_a22 = -np.inf, -np.inf
   i = 0
 
   z0, _ = _compute_zs(sigma, q)
 
-  while i == 0 or max(log_s11, log_s21, log_s21, log_s22) > -30:
+  while True:  # do ... until loop
     coef = special.binom(alpha, i)
-    log_coef = np.log(abs(coef))
+    log_coef = math.log(abs(coef))
     j = alpha - i
 
-    log_t1 = log_coef + i * np.log(q) + j * np.log(1 - q)
-    log_t2 = log_coef + j * np.log(q) + i * np.log(1 - q)
+    log_t1 = log_coef + i * math.log(q) + j * math.log(1 - q)
+    log_t2 = log_coef + j * math.log(q) + i * math.log(1 - q)
 
-    log_e11 = np.log(.5) + _log_erfc((i - z0) / (2**.5 * sigma))
-    log_e12 = np.log(.5) + _log_erfc((z0 - j) / (2**.5 * sigma))
-    log_e21 = np.log(.5) + _log_erfc((i - (z0 - 1)) / (2**.5 * sigma))
-    log_e22 = np.log(.5) + _log_erfc((z0 - 1 - j) / (2**.5 * sigma))
+    log_e11 = math.log(.5) + _log_erfc((i - z0) / (math.sqrt(2) * sigma))
+    log_e12 = math.log(.5) + _log_erfc((z0 - j) / (math.sqrt(2) * sigma))
+    log_e21 = math.log(.5) + _log_erfc((i - (z0 - 1)) / (math.sqrt(2) * sigma))
+    log_e22 = math.log(.5) + _log_erfc((z0 - 1 - j) / (math.sqrt(2) * sigma))
 
-    log_s11 = log_t1 + (i * i - i) / (2.0 * (sigma**2)) + log_e11
-    log_s12 = log_t2 + (j * j - j) / (2.0 * (sigma**2)) + log_e12
-    log_s21 = log_t1 + (i * i + i) / (2.0 * (sigma**2)) + log_e21
-    log_s22 = log_t2 + (j * j + j) / (2.0 * (sigma**2)) + log_e22
+    log_s11 = log_t1 + (i * i - i) / (2 * (sigma ** 2)) + log_e11
+    log_s12 = log_t2 + (j * j - j) / (2 * (sigma ** 2)) + log_e12
+    log_s21 = log_t1 + (i * i + i) / (2 * (sigma ** 2)) + log_e21
+    log_s22 = log_t2 + (j * j + j) / (2 * (sigma ** 2)) + log_e22
 
     if coef > 0:
       log_a11 = _log_add(log_a11, log_s11)
@@ -150,18 +159,20 @@ def _compute_log_a_frac(q, sigma, alpha, verbose=False):
       log_a22 = _log_sub(log_a22, log_s22)
 
     i += 1
+    if max(log_s11, log_s21, log_s21, log_s22) < -30:
+      break
 
   log_a = _log_add(
-      np.log(1. - q) + _log_add(log_a11, log_a12),
-      np.log(q) + _log_add(log_a21, log_a22))
-  return np.float64(log_a)
+      math.log(1. - q) + _log_add(log_a11, log_a12),
+      math.log(q) + _log_add(log_a21, log_a22))
+  return log_a
 
 
-def compute_log_a(q, sigma, alpha, verbose=False):
+def _compute_log_a(q, sigma, alpha):
   if float(alpha).is_integer():
-    return _compute_log_a_int(q, sigma, int(alpha), verbose)
+    return _compute_log_a_int(q, sigma, int(alpha))
   else:
-    return _compute_log_a_frac(q, sigma, alpha, verbose)
+    return _compute_log_a_frac(q, sigma, alpha)
 
 
 def _log_erfc(x):
@@ -173,14 +184,14 @@ def _log_erfc(x):
     #     erfc(x) ~ exp(-x^2-.5/x^2+.625/x^4)/(x*pi^.5)
     # To verify in Mathematica:
     #     Series[Log[Erfc[x]] + Log[x] + Log[Pi]/2 + x^2, {x, Infinity, 6}]
-    return (-np.log(np.pi) / 2 - np.log(x) - x**2 - .5 * x**-2 + .625 * x**-4 -
-            37. / 24. * x**-6 + 353. / 64. * x**-8)
+    return (-math.log(math.pi) / 2 - math.log(x) - x ** 2 - .5 * x ** -2 +
+            .625 * x ** -4 - 37. / 24. * x ** -6 + 353. / 64. * x ** -8)
   else:
-    return np.log(r)
+    return math.log(r)
 
 
 def _compute_zs(sigma, q):
-  z0 = sigma**2 * np.log(1. / q - 1) + .5
+  z0 = sigma ** 2 * math.log(1 / q - 1) + .5
   z1 = min(z0 - 2, z0 / 2)
   return z0, z1
 
@@ -191,40 +202,41 @@ def _compute_log_b0(sigma, q, alpha, z1):
   s, log_term, log_b0, k, sign, max_log_term = 0, 1., 0, 0, 1, -np.inf
   # Keep adding new terms until precision is no longer preserved.
   # Don't stop on the negative.
-  while k < alpha or (log_term > max_log_term - 36 and log_term > -30
-  ) or sign < 0.:
-    log_b1 = k * (k - 2 * z0) / (2 * sigma**2)
-    log_b2 = _log_erfc((k - z1) / (np.sqrt(2) * sigma))
+  while (k < alpha or (log_term > max_log_term - 36 and log_term > -30) or
+         sign < 0.):
+    log_b1 = k * (k - 2 * z0) / (2 * sigma ** 2)
+    log_b2 = _log_erfc((k - z1) / (math.sqrt(2) * sigma))
     log_term = log_b0 + log_b1 + log_b2
     max_log_term = max(max_log_term, log_term)
-    s += sign * np.exp(log_term)
+    s += sign * math.exp(log_term)
     k += 1
     # Maintain invariant: sign * exp(log_b0) = {-alpha choose k}
-    log_b0 += np.log(np.abs(-alpha - k + 1)) - np.log(k)
+    log_b0 += math.log(abs(-alpha - k + 1)) - math.log(k)
     sign *= -1
 
   if s == 0:  # May happen if all terms are < 1e-324.
     return -np.inf
-  if s < 0 or np.log(s) < max_log_term - 25:  # the series failed to converge
+  if s < 0 or math.log(s) < max_log_term - 25:  # The series failed to converge.
     return None
-  c = np.log(.5) - np.log(1 - q) * alpha
-  return c + np.log(s)
+  c = math.log(.5) - math.log(1 - q) * alpha
+  return c + math.log(s)
 
 
 def _bound_log_b1(sigma, q, alpha, z1):
-  log_c = _log_add(np.log(1 - q), np.log(q) + (2 * z1 - 1.) / (2 * sigma**2))
-  return np.log(.5) - log_c * alpha + _log_erfc(z1 / (2**.5 * sigma))
+  log_c = _log_add(math.log(1 - q),
+                   math.log(q) + (2 * z1 - 1.) / (2 * sigma ** 2))
+  return math.log(.5) - log_c * alpha + _log_erfc(z1 / (math.sqrt(2) * sigma))
 
 
-def bound_log_b(q, sigma, alpha, verbose=False):
+def _bound_log_b(q, sigma, alpha):
   """Compute a numerically stable bound on log(B_alpha)."""
   if q == 1.:  # If the sampling rate is 100%, A and B are symmetric.
-    return compute_log_a(q, sigma, alpha, verbose)
+    return _compute_log_a(q, sigma, alpha)
 
   z0, z1 = _compute_zs(sigma, q)
   log_b_bound = np.inf
 
-  # log_b1 cannot be less than its value at z0
+  # Puts a lower bound on B1: it cannot be less than its value at z0.
   log_lb_b1 = _bound_log_b1(sigma, q, alpha, z0)
 
   while z0 - z1 > 1e-3:
@@ -236,117 +248,150 @@ def bound_log_b(q, sigma, alpha, verbose=False):
     log_b1 = _bound_log_b1(sigma, q, alpha, m)
     log_b_bound = min(log_b_bound, _log_add(log_b0, log_b1))
     log_b_min_bound = _log_add(log_b0, log_lb_b1)
-    if log_b_bound < 0 or log_b_min_bound < 0 or log_b_bound > log_b_min_bound + .01:
+    if (log_b_bound < 0 or
+        log_b_min_bound < 0 or
+        log_b_bound > log_b_min_bound + .01):
       # If the bound is likely to be too loose, move z1 closer to z0 and repeat.
       z1 = m
     else:
       break
 
-  return np.float64(log_b_bound)
+  return log_b_bound
 
 
 def _log_bound_b_elementary(q, alpha):
-  return -np.log(1 - q) * alpha
+  return -math.log(1 - q) * alpha
 
 
-def _compute_delta(log_moments, eps):
-  """Compute delta for given log_moments and eps.
+def _compute_delta(orders, rdp, eps):
+  """Compute delta given an RDP curve and target epsilon.
 
   Args:
-    log_moments: the log moments of privacy loss, in the form of pairs
-      of (moment_order, log_moment)
-    eps: the target epsilon.
+    orders: An array (or a scalar) of orders.
+    rdp: A list (or a scalar) of RDP guarantees.
+    eps: The target epsilon.
+
   Returns:
-    delta, order
+    Pair of (delta, optimal_order).
+
+  Raises:
+    ValueError: If input is malformed.
+
   """
-  min_delta, opt_order = 1.0, float("NaN")
-  for moment_order, log_moment in log_moments:
-    if moment_order == 0:
-      continue
-    if math.isinf(log_moment) or math.isnan(log_moment):
-      sys.stderr.write("The %d-th order is inf or Nan\n" % moment_order)
-      continue
-    if log_moment < moment_order * eps:
-      delta = math.exp(log_moment - moment_order * eps)
-      if delta < min_delta:
-        min_delta, opt_order = delta, moment_order
-  return min_delta, opt_order
+  orders_vec = np.atleast_1d(orders)
+  rdp_vec = np.atleast_1d(rdp)
+
+  if len(orders_vec) != len(rdp_vec):
+    raise ValueError("Input lists must have the same length.")
+
+  deltas = np.exp((rdp_vec - eps) * (orders_vec - 1))
+  idx_opt = np.argmin(deltas)
+  return min(deltas[idx_opt], 1.), orders_vec[idx_opt]
 
 
-def _compute_eps(log_moments, delta):
-  """Compute epsilon for given log_moments and delta.
+def _compute_eps(orders, rdp, delta):
+  """Compute epsilon given an RDP curve and target delta.
 
   Args:
-    log_moments: the log moments of privacy loss, in the form of pairs
-      of (moment_order, log_moment)
-    delta: the target delta.
+    orders: An array (or a scalar) of orders.
+    rdp: A list (or a scalar) of RDP guarantees.
+    delta: The target delta.
+
   Returns:
-    epsilon, order
+    Pair of (eps, optimal_order).
+
+  Raises:
+    ValueError: If input is malformed.
+
   """
-  min_eps, opt_order = float("inf"), float("NaN")
-  for moment_order, log_moment in log_moments:
-    if moment_order == 0:
-      continue
-    if math.isinf(log_moment) or math.isnan(log_moment):
-      sys.stderr.write("The %d-th order is inf or Nan\n" % moment_order)
-      continue
-    eps = (log_moment - math.log(delta)) / moment_order
-    if eps < min_eps:
-      min_eps, opt_order = eps, moment_order
-  return min_eps, opt_order
+  orders_vec = np.atleast_1d(orders)
+  rdp_vec = np.atleast_1d(rdp)
 
+  if len(orders_vec) != len(rdp_vec):
+    raise ValueError("Input lists must have the same length.")
 
-def compute_log_moment(q, sigma, steps, alpha, verbose=False):
-  """Compute the log moment of Gaussian mechanism for given parameters.
+  eps = rdp_vec - math.log(delta) / (orders_vec - 1)
 
-  Args:
-    q: the sampling ratio.
-    sigma: the noise sigma.
-    steps: the number of steps.
-    alpha: the moment order.
-    verbose: if True, print out debug information.
-  Returns:
-    the log moment with type np.float64, could be np.inf.
-  """
-  log_moment_a = compute_log_a(q, sigma, alpha, verbose=verbose)
+  idx_opt = np.nanargmin(eps)  # Ignore NaNs
+  return eps[idx_opt], orders_vec[idx_opt]
+
+
+def _compute_rdp(q, sigma, steps, alpha):
+  log_moment_a = _compute_log_a(q, sigma, alpha - 1)
 
-  log_bound_b = _log_bound_b_elementary(q, alpha)  # does not require sigma
+  log_bound_b = _log_bound_b_elementary(q, alpha - 1)  # does not require sigma
 
   if log_bound_b < log_moment_a:
-    if verbose:
+    if FLAGS.rdp_verbose:
       print("Elementary bound suffices   : {} < {}".format(
           _log_print(log_bound_b), _log_print(log_moment_a)))
   else:
-    log_bound_b2 = bound_log_b(q, sigma, alpha, verbose=verbose)
-    if np.isnan(log_bound_b2):
-      if verbose:
+    log_bound_b2 = _bound_log_b(q, sigma, alpha - 1)
+    if math.isnan(log_bound_b2):
+      if FLAGS.rdp_verbose:
         print("B bound failed to converge")
     else:
-      if verbose and (log_bound_b2 < log_bound_b):
+      if FLAGS.rdp_verbose and (log_bound_b2 < log_bound_b):
         print("Elementary bound is stronger: {} < {}".format(
             _log_print(log_bound_b2), _log_print(log_bound_b)))
       log_bound_b = min(log_bound_b, log_bound_b2)
 
-  return max(log_moment_a, log_bound_b) * steps
+  return max(log_moment_a, log_bound_b) * steps / (alpha - 1)
+
+
+def compute_rdp(q, sigma, steps, orders):
+  """Compute RDP of Gaussian mechanism with sampling for given parameters.
+
+  Args:
+    q: The sampling ratio.
+    sigma: The noise sigma.
+    steps: The number of steps.
+    orders: An array (or a scalar) of RDP orders.
+
+  Returns:
+    The RDPs at all orders, can be np.inf.
+  """
 
+  if np.isscalar(orders):
+    return _compute_rdp(q, sigma, steps, orders)
+  else:
+    rdp = np.zeros_like(orders, dtype=float)
+    for i, order in enumerate(orders):
+      rdp[i] = _compute_rdp(q, sigma, steps, order)
+    return rdp
 
-def get_privacy_spent(log_moments, target_eps=None, target_delta=None):
-  """Compute delta (or eps) for given eps (or delta) from log moments.
+
+def get_privacy_spent(orders, rdp, target_eps=None, target_delta=None):
+  """Compute delta (or eps) for given eps (or delta) from the RDP curve.
 
   Args:
-    log_moments: array of (moment_order, log_moment) pairs.
-    target_eps: if not None, the epsilon for which we would like to compute
-      corresponding delta value.
-    target_delta: if not None, the delta for which we would like to compute
-      corresponding epsilon value. Exactly one of target_eps and target_delta
-      is None.
+    orders: An array (or a scalar) of RDP orders.
+    rdp: An array of RDP values.
+    target_eps: If not None, the epsilon for which we compute the corresponding
+              delta.
+    target_delta: If not None, the delta for which we compute the corresponding
+              epsilon. Exactly one of target_eps and target_delta must be None.
   Returns:
-    eps, delta, opt_order
+    eps, delta, opt_order.
   """
-  assert bool(target_eps is None) ^ bool(target_delta is None)
+  if target_eps is None and target_delta is None:
+    raise ValueError(
+        "Exactly one out of eps and delta must be None. (Both are).")
+
+  if target_eps is not None and target_delta is not None:
+    raise ValueError(
+        "Exactly one out of eps and delta must be None. (None is).")
+
   if target_eps is not None:
-    delta, opt_order = _compute_delta(log_moments, target_eps)
+    delta, opt_order = _compute_delta(orders, rdp, target_eps)
     return target_eps, delta, opt_order
   else:
-    eps, opt_order = _compute_eps(log_moments, target_delta)
+    eps, opt_order = _compute_eps(orders, rdp, target_delta)
     return eps, target_delta, opt_order
+
+
+def main(_): pass
+
+
+if __name__ == "__main__":
+  app.run(main)

research/differential_privacy/privacy_accountant/python/rdp_accountant_test.py

 # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. 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.
@@ -18,15 +19,17 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from absl.testing import absltest
 import numpy as np
 import mpmath as mp
-from absl.testing import absltest
 
 import rdp_accountant
 
 
 class TestGaussianMoments(absltest.TestCase):
-  # MULTI-PRECISION ROUTINES
+  ##############################
+  # MULTI-PRECISION ARITHMETIC #
+  ##############################
 
   def _pdf_gauss_mp(self, x, sigma, mean):
     return 1. / mp.sqrt(2. * sigma ** 2 * mp.pi) * mp.exp(-(x - mean)
@@ -85,88 +88,86 @@ class TestGaussianMoments(absltest.TestCase):
 
       print("B: numerically {} = {} + {}".format(b_numeric, b0_numeric,
                                                  b1_numeric))
-    return np.float64(b_numeric)
+    return float(b_numeric)
 
-  def _compute_log_moment_mp(self, q, sigma, order):
-    log_a_mp = np.float64(mp.log(self.compute_a_mp(sigma, q, order)))
-    log_b_mp = np.float64(mp.log(self.compute_b_mp(sigma, q, order)))
+  def _compute_rdp_mp(self, q, sigma, order):
+    log_a_mp = float(mp.log(self.compute_a_mp(sigma, q, order)))
+    log_b_mp = float(mp.log(self.compute_b_mp(sigma, q, order)))
     return log_a_mp, log_b_mp
 
   # TEST ROUTINES
-  def _almost_equal(self, a, b, rtol):
-    # Analogue of np.testing.assert_allclose(a, b, rtol).
-    self.assertBetween(a, b * (1 - rtol), b * (1 + rtol))
+  def _almost_equal(self, a, b, rtol, atol=0):
+    # Analogue of np.testing.assert_allclose(a, b, rtol, atol).
+    self.assertBetween(a, b * (1 - rtol) - atol, b * (1 + rtol) + atol)
 
   def _compare_bounds(self, q, sigma, order):
-    log_a_mp, log_b_mp = self._compute_log_moment_mp(q, sigma, order)
-    log_a = rdp_accountant.compute_log_a(q, sigma, order)
-    log_bound_b = rdp_accountant.bound_log_b(q, sigma, order)
+    log_a_mp, log_b_mp = self._compute_rdp_mp(q, sigma, order)
+    log_a = rdp_accountant._compute_log_a(q, sigma, order)
+    log_bound_b = rdp_accountant._bound_log_b(q, sigma, order)
 
     if log_a_mp < 1000 and log_a_mp > 1e-6:
       self._almost_equal(log_a, log_a_mp, rtol=1e-6)
-
     else:  # be more tolerant for _very_ large or small logarithms
-      if log_a_mp > 1e-12:
-        self._almost_equal(log_a, log_a_mp, rtol=1e-2)
-      else:
-        print("Bounds on A are too small to compare: {}, {}".format(
-            log_a, log_a_mp))
-    if np.isfinite(log_bound_b) and log_bound_b > 1e-12:
+      self._almost_equal(log_a, log_a_mp, rtol=1e-3, atol=1e-14)
+
+    if np.isfinite(log_bound_b):
       # Ignore divergence between the bound and exact value of B if
       # they don't matter anyway (bound on A is larger) or q > .5
       if log_bound_b > log_a and q <= .5:
-        self._almost_equal(log_b_mp, log_bound_b, rtol=1e-2)
+        self._almost_equal(log_b_mp, log_bound_b, rtol=1e-6, atol=1e-14)
 
     if np.isfinite(log_a_mp) and np.isfinite(log_b_mp):
       # We hypothesize that this assertion is always true; no proof yet.
       self.assertLessEqual(log_b_mp, log_a_mp + 1e-6)
 
-  def test_compute_log_moments(self):
-    log_moment = rdp_accountant.compute_log_moment(0.1, 2, 10, 4)
-    self.assertAlmostEqual(log_moment, 0.30948, places=5)
+  def test_compute_rdp(self):
+    rdp_scalar = rdp_accountant.compute_rdp(0.1, 2, 10, 5)
+    self.assertAlmostEqual(rdp_scalar, 0.07737, places=5)
+
+    rdp_vec = rdp_accountant.compute_rdp(0.01, 2.5, 50, [1.5, 2.5, 5, 50, 100])
+
+    correct = [0.00065, 0.001085, 0.00218075, 0.023846, 167.416307]
+    for i in range(len(rdp_vec)):
+      self.assertAlmostEqual(rdp_vec[i], correct[i], places=5)
+
+  def test_compare_with_mp(self):
+    # Compare the cheap computation with an expensive, multi-precision
+    # computation for a few parameters. Takes a few seconds.
 
     self._compare_bounds(q=.01, sigma=.1, order=.5)
     self._compare_bounds(q=.1, sigma=1., order=5)
     self._compare_bounds(q=.5, sigma=2., order=32.5)
 
-    # Compare the cheap computation with expensive, multi-precision
-    # computation for a few parameters. Takes about a minute.
-    # for q in (1e-6, .1, .999):
-    #   for sigma in (.1, 10.):
-    #     for order in (.5, 1., 1.5, 256.):
-    #       self._compare_bounds(q, sigma, order)
+    for q in (1e-6, .1, .999):
+      for sigma in (.1, 10., 100.):
+        for order in (1.01, 2, 255.9, 256):
+          self._compare_bounds(q, sigma, order)
 
   def test_get_privacy_spent(self):
-    orders = range(1, 33)
-    log_moments = []
-    for order in orders:
-      log_moment = rdp_accountant.compute_log_moment(0.01, 4, 10000, order)
-      log_moments.append((order, log_moment))
-    eps, delta, opt_order = rdp_accountant.get_privacy_spent(log_moments,
+    orders = range(2, 33)
+    rdp = rdp_accountant.compute_rdp(0.01, 4, 10000, orders)
+    eps, delta, opt_order = rdp_accountant.get_privacy_spent(orders, rdp,
                                                              target_delta=1e-5)
     self.assertAlmostEqual(eps, 1.258575, places=5)
-    self.assertEqual(opt_order, 19)
+    self.assertEqual(opt_order, 20)
 
-    eps, delta, _ = rdp_accountant.get_privacy_spent(log_moments,
+    eps, delta, _ = rdp_accountant.get_privacy_spent(orders, rdp,
                                                      target_eps=1.258575)
     self.assertAlmostEqual(delta, 1e-5)
 
   def test_compute_privacy_loss(self):
     parameters = [(0.01, 4, 10000), (0.1, 2, 100)]
     delta = 1e-5
-    orders = (
-    1, 1.25, 1.5, 1.75, 2., 2.5, 3., 4., 5., 6., 7., 8., 10., 12., 14.,
+    orders = (1.25, 1.5, 1.75, 2., 2.5, 3., 4., 5., 6., 7., 8., 10., 12., 14.,
               16., 20., 24., 28., 32., 64., 256.)
-    log_moments = []
-    for order in orders:
-      log_moment = 0
+
+    rdp = np.zeros_like(orders, dtype=float)
     for q, sigma, steps in parameters:
-        log_moment += rdp_accountant.compute_log_moment(q, sigma, steps, order)
-      log_moments.append((order, log_moment))
-    eps, delta, opt_order = rdp_accountant.get_privacy_spent(
-        log_moments, target_delta=delta)
+      rdp += rdp_accountant.compute_rdp(q, sigma, steps, orders)
+    eps, delta, opt_order = rdp_accountant.get_privacy_spent(orders, rdp,
+                                                             target_delta=delta)
     self.assertAlmostEqual(eps, 3.276237, places=5)
-    self.assertEqual(opt_order, 7)
+    self.assertEqual(opt_order, 8)
 
 
 if __name__ == "__main__":