feature: seeded random sampler

datastream/datastream.py

@@ -584,3 +584,29 @@ def test_last_batch():
         SequentialSampler(3),
     )
     assert list(map(len, datastream.data_loader(batch_size=2))) == [2, 1]
+
+
+def test_seeded_random_sampler():
+    dataset = Dataset.from_subscriptable(np.arange(100))
+    datastream = Datastream(dataset, sampler=StandardSampler(len(dataset), seed=1))
+
+    loader = datastream.data_loader(batch_size=1, collate_fn=tuple)
+    batches1 = [batch for batch in loader]
+    batches2 = [batch for batch in loader]
+    assert all(
+        batch1[0] == batch2[0]
+        for batch1, batch2 in zip(batches1, batches2)
+    )
+
+
+def test_unseeded_random_sampler():
+    dataset = Dataset.from_subscriptable(np.arange(100))
+    datastream = Datastream(dataset, sampler=StandardSampler(len(dataset)))
+
+    loader = datastream.data_loader(batch_size=1, collate_fn=tuple)
+    batches1 = [batch for batch in loader]
+    batches2 = [batch for batch in loader]
+    assert any(
+        batch1[0] != batch2[0]
+        for batch1, batch2 in zip(batches1, batches2)
+    )

datastream/samplers/standard_sampler.py

@@ -1,18 +1,26 @@
 from __future__ import annotations
 from pydantic import BaseModel
+from typing import Optional
 import torch
 
 
 class StandardSampler(BaseModel, torch.utils.data.Sampler):
     proportion: float
     replacement: bool
     sampler: torch.utils.data.WeightedRandomSampler
+    seed: Optional[int]
+    generator: Optional[torch.Generator]
 
     class Config:
         arbitrary_types_allowed = True
         allow_mutation = False
 
-    def __init__(self, length, proportion=1.0, replacement=False):
+    def __init__(self, length, proportion=1.0, replacement=False, seed=None):
+        if seed is not None:
+            generator = torch.Generator()
+            generator.manual_seed(seed)
+        else:
+            generator = None
         BaseModel.__init__(
             self,
             proportion=proportion,
@@ -21,13 +29,18 @@ def __init__(self, length, proportion=1.0, replacement=False):
                 torch.ones(length).double(),
                 num_samples=int(max(1, min(length, length * proportion))),
                 replacement=replacement,
-            )
+                generator=generator,
+            ),
+            seed=seed,
+            generator=generator,
         )
 
     def __len__(self):
         return len(self.sampler)
 
     def __iter__(self):
+        if self.generator is not None:
+            self.generator.manual_seed(self.seed)
         return iter(self.sampler)
 
     @property
@@ -51,6 +64,7 @@ def sample_proportion(self, proportion):
             len(self),
             proportion,
             self.replacement,
+            self.seed,
         )
         sampler.sampler.weights = self.sampler.weights
         return sampler