add a method to generate permutations

torchdata/stateful_dataloader/sampler.py

@@ -35,18 +35,24 @@ def __init__(self, sampler):
     def __iter__(self):
         return self
 
+    def _get_perm(self, replacement: bool, num_samples: int) -> List[int]:
+        if replacement:
+            return torch.randint(
+                high=self.n,
+                size=(num_samples,),
+                dtype=torch.int64,
+                generator=self.sampler.generator,
+            ).tolist()
+        else:
+            return torch.randperm(self.n, generator=self.sampler.generator).tolist()[:num_samples]
+
     def __next__(self):
         if self.replacement:
             num_full_chunks = self.num_samples // self.chunk_size
             remainder = self.num_samples % self.chunk_size
             if self.chunk_index < num_full_chunks:
-                if self.perm is None or not self.perm:
-                    self.perm = torch.randint(
-                        high=self.n,
-                        size=(self.chunk_size,),
-                        dtype=torch.int64,
-                        generator=self.sampler.generator,
-                    ).tolist()
+                if self.perm is None:
+                    self.perm = self._get_perm(self.replacement, self.chunk_size)
                     self.perm_index = 0
                 value = self.perm[self.perm_index]
                 self.perm_index += 1
@@ -56,13 +62,8 @@ def __next__(self):
                 self.yielded += 1
                 return value
             elif remainder > 0:
-                if self.perm is None or not self.perm:
-                    self.perm = torch.randint(
-                        high=self.n,
-                        size=(remainder,),
-                        dtype=torch.int64,
-                        generator=self.sampler.generator,
-                    ).tolist()
+                if self.perm is None:
+                    self.perm = self._get_perm(self.replacement, remainder)
                     self.perm_index = 0
                 value = self.perm[self.perm_index]
                 self.perm_index += 1
@@ -76,8 +77,8 @@ def __next__(self):
             num_full_perms = self.num_samples // self.n
             remainder = self.num_samples % self.n
             if self.chunk_index < num_full_perms:
-                if self.perm is None or not self.perm:
-                    self.perm = torch.randperm(self.n, generator=self.sampler.generator).tolist()
+                if self.perm is None:
+                    self.perm = self._get_perm(self.replacement, self.n)
                     self.perm_index = 0
                 value = self.perm[self.perm_index]
                 self.perm_index += 1
@@ -87,8 +88,8 @@ def __next__(self):
                 self.yielded += 1
                 return value
             elif remainder > 0:
-                if self.perm is None or not self.perm:
-                    self.perm = torch.randperm(self.n, generator=self.sampler.generator).tolist()[:remainder]
+                if self.perm is None:
+                    self.perm = self._get_perm(self.replacement, remainder)
                     self.perm_index = 0
                 value = self.perm[self.perm_index]
                 self.perm_index += 1