Made all samplers operational with mini-batching

Author: papamarkou

eeyore/samplers/am.py

@@ -61,6 +61,9 @@ def set_recursive_cov(self, n, offset=0):
     def draw(self, x, y, savestate=False, offset=0):
         proposed = {key : None for key in self.keys}
 
+        if self.counter.num_batches != 1:
+            self.current['target_val'] = self.model.log_target(self.current['sample'].clone().detach(), x, y)
+
         randn_sample = torch.randn(self.model.num_params(), dtype=self.model.dtype, device=self.model.device)
         if (self.counter.idx + 1 - offset > self.t0):
             if torch.rand(1, dtype=self.model.dtype, device=self.model.device) < self.l:
@@ -76,7 +79,8 @@ def draw(self, x, y, savestate=False, offset=0):
 
         if torch.log(torch.rand(1, dtype=self.model.dtype, device=self.model.device)) < log_rate:
             self.current['sample'] = proposed['sample'].clone().detach()
-            self.current['target_val'] = proposed['target_val'].clone().detach()
+            if self.counter.num_batches == 1:
+                self.current['target_val'] = proposed['target_val'].clone().detach()
             self.current['accepted'] = 1
             if (self.counter.idx > 0):
                 self.num_accepted = self.num_accepted + 1

eeyore/samplers/hmc.py

@@ -126,6 +126,10 @@ def leapfrog(self, position0, momentum0, x, y):
     def draw(self, x, y, savestate=False):
         proposed = {key : None for key in self.keys}
 
+        if self.counter.num_batches != 1:
+            self.current['target_val'], self.current['grad_val'] = \
+                self.model.upto_grad_log_target(self.current['sample'].clone().detach(), x, y)
+
         proposed['sample'] = self.current['sample'].clone().detach()
         proposed['momentum'] = torch.randn(self.model.num_params(), dtype=self.model.dtype, device=self.model.device)
 
@@ -143,8 +147,9 @@ def draw(self, x, y, savestate=False):
 
         if torch.rand(1, dtype=self.model.dtype, device=self.model.device) < rate:
             self.current['sample'] = proposed['sample'].clone().detach()
-            self.current['target_val'] = proposed['target_val'].clone().detach()
-            self.current['grad_val'] = proposed['grad_val'].clone().detach()
+            if self.counter.num_batches == 1:
+                self.current['target_val'] = proposed['target_val'].clone().detach()
+                self.current['grad_val'] = proposed['grad_val'].clone().detach()
             self.current['accepted'] = 1
         else:
             self.model.set_params(self.current['sample'].clone().detach())

eeyore/samplers/mala.py

@@ -46,6 +46,10 @@ def set_kernel(self, state):
     def draw(self, x, y, savestate=False):
         proposed = {key : None for key in self.keys}
 
+        if self.counter.num_batches != 1:
+            self.current['target_val'], self.current['grad_val'] = \
+                self.model.upto_grad_log_target(self.current['sample'].clone().detach(), x, y)
+
         proposed['sample'] = self.kernel.sample()
 
         proposed['target_val'], proposed['grad_val'] = \
@@ -61,8 +65,9 @@ def draw(self, x, y, savestate=False):
 
         if torch.log(torch.rand(1, dtype=self.model.dtype, device=self.model.device)) < log_rate:
             self.current['sample'] = proposed['sample'].clone().detach()
-            self.current['target_val'] = proposed['target_val'].clone().detach()
-            self.current['grad_val'] = proposed['grad_val'].clone().detach()
+            if self.counter.num_batches == 1:
+                self.current['target_val'] = proposed['target_val'].clone().detach()
+                self.current['grad_val'] = proposed['grad_val'].clone().detach()
             self.current['accepted'] = 1
         else:
             self.model.set_params(self.current['sample'].clone().detach())

eeyore/samplers/ram.py

@@ -38,6 +38,9 @@ def set_all(self, theta, data=None, cov=None):
     def draw(self, x, y, savestate=False, offset=0):
         proposed = {key : None for key in self.keys}
 
+        if self.counter.num_batches != 1:
+            self.current['target_val'] = self.model.log_target(self.current['sample'].clone().detach(), x, y)
+
         randn_sample = torch.randn(self.model.num_params(), dtype=self.model.dtype, device=self.model.device)
         proposed['sample'] = self.current['sample'].clone().detach() + self.chol_cov @ randn_sample
         proposed['target_val'] = self.model.log_target(proposed['sample'].clone().detach(), x, y)
@@ -46,7 +49,8 @@ def draw(self, x, y, savestate=False, offset=0):
 
         if torch.log(torch.rand(1, dtype=self.model.dtype, device=self.model.device)) < log_rate:
             self.current['sample'] = proposed['sample'].clone().detach()
-            self.current['target_val'] = proposed['target_val'].clone().detach()
+            if self.counter.num_batches == 1:
+                self.current['target_val'] = proposed['target_val'].clone().detach()
             self.current['accepted'] = 1
         else:
             self.model.set_params(self.current['sample'].clone().detach())