Removed length from the csprng API.

src/liberate/csprng/csprng.py

@@ -222,23 +222,18 @@ def generate_nonce(self, seed):
         # nonce is 64bits.
         return self.generate_initial_bytes(8, seed=None)
 
-    def randbytes(self, shares=None, repeats=0, length=None, reshape=False):
+    def randbytes(self, shares=None, repeats=0, reshape=False):
         # Generates (shares_i + repeats) X length random bytes.
         if shares is None:
             shares = self.shares
 
-        if length is None:
-            L = self.L
-        else:
-            L = length // 16
-
         # Set the target states.
         target_states = []
         for devi in range(self.num_devices):
             start_channel = self.shares[devi] - shares[devi]
             end_channel = self.shares[devi] + repeats
             device_states = self.channeled_states[devi][
-                start_channel:end_channel, :L, :
+                start_channel:end_channel, :, :
             ]
             target_states.append(device_states.view(-1, 16))
 
@@ -247,22 +242,17 @@ def randbytes(self, shares=None, repeats=0, length=None, reshape=False):
 
         # If not reshape, flatten.
         if reshape:
-            random_bytes = [rb.view(-1, L, 16) for rb in random_bytes]
+            random_bytes = [rb.view(-1, self.L, 16) for rb in random_bytes]
 
         return random_bytes
 
-    def randint(self, amax=3, shift=0, repeats=0, length=None):
+    def randint(self, amax=3, shift=0, repeats=0):
         # The default values are for generating the same uniform ternary
         # arrays in all GPUs.
 
         if not isinstance(amax, (list, tuple)):
             amax = [[amax] for share in self.shares]
 
-        if length is None:
-            L = self.L
-        else:
-            L = length // 4
-
         # Calculate shares.
         # If repeats are greater than 0, those channels are
         # subtracted from shares.
@@ -278,7 +268,7 @@ def randint(self, amax=3, shift=0, repeats=0, length=None):
             start_channel = self.shares[devi] - shares[devi]
             end_channel = self.shares[devi] + repeats
             device_states = self.channeled_states[devi][
-                start_channel:end_channel, :L, :
+                start_channel:end_channel, :, :
             ]
             target_states.append(device_states)
 
@@ -289,24 +279,19 @@ def randint(self, amax=3, shift=0, repeats=0, length=None):
 
         return rand_int
 
-    def discrete_gaussian(self, non_repeats=0, repeats=1, length=None):
+    def discrete_gaussian(self, non_repeats=0, repeats=1):
         if not isinstance(non_repeats, (list, tuple)):
             shares = [non_repeats] * self.num_devices
         else:
             shares = non_repeats
 
-        if length is None:
-            L = self.L
-        else:
-            L = length // 4
-
         # Set the target states.
         target_states = []
         for devi in range(self.num_devices):
             start_channel = self.shares[devi] - shares[devi]
             end_channel = self.shares[devi] + repeats
             device_states = self.channeled_states[devi][
-                start_channel:end_channel, :L, :
+                start_channel:end_channel, :, :
             ]
             target_states.append(device_states.view(-1, 16))
 
@@ -327,6 +312,9 @@ def randround(self, coef):
         """Randomly round coef. Coef must be a double tensor.
         coef must reside in the fist GPU in the GPUs list"""
 
+        # The following slicing is OK, since we're using only the first
+        # contiguous stream of states.
+        # It will not make the target state strided.
         L = self.num_coefs // 16
         rand_bytes = chacha20_cuda.chacha20((self.states[0][:L],), self.inc)[
             0