Update pf.py

mNSF/NSF/pf.py

@@ -25,20 +25,6 @@
 dtp = "float32"
 rng = np.random.default_rng()
 
-@tf.custom_gradient
-def checkpoint_grad(x):
-    y = tf.identity(x)
-    def grad(dy):
-        if tf.executing_eagerly():
-            with tf.GradientTape() as tape:
-                tape.watch(x)
-                y = tf.identity(x)
-            return tape.gradient(y, x, output_gradients=dy)
-        else:
-            return tf.gradients(y, x, dy)[0]
-    return y, grad
-
-
 class ProcessFactorization(tf.Module):
   def __init__(self, J, L, Z, lik="poi", chol = True, X=None, psd_kernel=tfk.MaternThreeHalves,
                nugget=1e-5, length_scale=0.1, disp="default",
@@ -252,8 +238,7 @@ def sample_latent_GP_funcs(self, X, S=1, kernel=None, mu_z=None, Kuu_chol=None,
     Kuf = kernel.matrix(self.Z, X) #LxMxN
     Kff_diag = kernel.apply(X, X, example_ndims=1)+self.nugget #LxN
     alpha_x = tfl.cholesky_solve(Kuu_chol, Kuf) #LxMxN
-    #mu_tilde = mu_x + tfl.matvec(alpha_x, self.delta-mu_z, transpose_a=True) #LxN
-    mu_tilde = checkpoint_grad(mu_x + tfl.matvec(alpha_x, self.delta-mu_z, transpose_a=True))
+    mu_tilde = mu_x + tfl.matvec(alpha_x, self.delta-mu_z, transpose_a=True) #LxN
     #compute the alpha(x_i)'(K_uu-Omega)alpha(x_i) term
     a_t_Kchol = tfl.matmul(alpha_x, Kuu_chol, transpose_a=True) #LxNxM
     aKa = tf.reduce_sum(tf.square(a_t_Kchol), axis=2) #LxN
@@ -269,8 +254,8 @@ def sample_predictive_mean(self, X, sz=1, S=1, kernel=None, mu_z=None, Kuu_chol=
     sz is a tensor of shape (N,1) of size factors.
     Typically sz would be the rowSums or rowMeans of the outcome matrix Y.
     """
-    F = checkpoint_grad(self.sample_latent_GP_funcs(X, S=S, kernel=kernel, mu_z=mu_z,
-                                                    Kuu_chol=Kuu_chol, chol=chol))
+    F = self.sample_latent_GP_funcs(X, S=S, kernel=kernel, mu_z=mu_z,
+                                    Kuu_chol=Kuu_chol, chol=chol) #SxLxN
     if self.nonneg:
       Lam = tfl.matrix_transpose(tfl.matmul(self.W, tf.exp(F))) #SxNxJ
       if self.lik=="gau":
@@ -335,7 +320,7 @@ def elbo_avg(self, X, Y, sz=1, S=1, Ntot=None, chol=True):
     #kl_terms is not affected by minibatching so use reduce_sum
     #print(1111)
     kl_term = tf.reduce_sum(self.eval_kl_term(mu_z, Kuu_chol))
-    Mu = checkpoint_grad(self.sample_predictive_mean(X, sz=sz, S=S, kernel=ker, mu_z=mu_z, Kuu_chol=Kuu_chol))
+    Mu = self.sample_predictive_mean(X, sz=sz, S=S, kernel=ker, mu_z=mu_z, Kuu_chol=Kuu_chol)
     eloglik = likelihoods.lik_to_distr(self.lik, Mu, self.disp).log_prob(Y)
     return J*tf.reduce_mean(eloglik) - kl_term/Ntot