add combine_terms option to exact MLL

gpytorch/distributions/multivariate_normal.py

@@ -142,7 +142,7 @@ def lazy_covariance_matrix(self):
         else:
             return lazify(super().covariance_matrix)
 
-    def log_prob(self, value):
+    def log_prob(self, value, combine_terms=True):
         if settings.fast_computations.log_prob.off():
             return super().log_prob(value)
 
@@ -167,9 +167,13 @@ def log_prob(self, value):
         # Get log determininant and first part of quadratic form
         covar = covar.evaluate_kernel()
         inv_quad, logdet = covar.inv_quad_logdet(inv_quad_rhs=diff.unsqueeze(-1), logdet=True)
+        norm_const = diff.size(-1) * math.log(2 * math.pi)
+        split_terms = [inv_quad, logdet, norm_const]
 
-        res = -0.5 * sum([inv_quad, logdet, diff.size(-1) * math.log(2 * math.pi)])
-        return res
+        if combine_terms:
+            return -0.5 * sum(split_terms)
+        else:
+            return [-0.5 * term for term in split_terms]
 
     def rsample(self, sample_shape=torch.Size(), base_samples=None):
         covar = self.lazy_covariance_matrix

gpytorch/mlls/exact_marginal_log_likelihood.py

@@ -1,5 +1,7 @@
 #!/usr/bin/env python3
 
+import torch
+
 from ..distributions import MultivariateNormal
 from ..likelihoods import _GaussianLikelihoodBase
 from .marginal_log_likelihood import MarginalLogLikelihood
@@ -59,9 +61,17 @@ def forward(self, function_dist, target, *params):
 
         # Get the log prob of the marginal distribution
         output = self.likelihood(function_dist, *params)
-        res = output.log_prob(target)
-        res = self._add_other_terms(res, params)
+        res = output.log_prob(target, combine_terms=self.combine_terms)
 
         # Scale by the amount of data we have
         num_data = function_dist.event_shape.numel()
-        return res.div_(num_data)
+
+        if self.combine_terms:
+            res = self._add_other_terms(res, params)
+            return res.div(num_data)
+        else:
+            norm_const = res[-1]
+            other_terms = torch.zeros_like(norm_const)
+            other_terms = self._add_other_terms(other_terms, params)
+            res.append(other_terms)
+            return [term.div(num_data) for term in res]

gpytorch/mlls/marginal_log_likelihood.py

@@ -25,7 +25,7 @@ class MarginalLogLikelihood(Module):
     these functions must be negated for optimization).
     """
 
-    def __init__(self, likelihood, model):
+    def __init__(self, likelihood, model, combine_terms=True):
         super(MarginalLogLikelihood, self).__init__()
         if not isinstance(model, GP):
             raise RuntimeError(
@@ -35,6 +35,7 @@ def __init__(self, likelihood, model):
             )
         self.likelihood = likelihood
         self.model = model
+        self.combine_terms = combine_terms
 
     def forward(self, output, target, **kwargs):
         r"""