From 52c92e15ae0c76b02b151f076095d671a3718bae Mon Sep 17 00:00:00 2001
From: chenyu <chenyu@fastmail.com>
Date: Thu, 30 Apr 2026 17:35:26 -0400
Subject: [PATCH] no replacement multinomial (#15995)
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* no replacement multinomial

Efraimidis–Spirakis

* num_samples == 1 can use fast path
---
 test/backend/test_randomness.py | 19 ++++++++++++++++++-
 tinygrad/tensor.py              | 18 +++++++++++++-----
 2 files changed, 31 insertions(+), 6 deletions(-)

diff --git a/test/backend/test_randomness.py b/test/backend/test_randomness.py
index 1a49f93cbc..5ac503b812 100644
--- a/test/backend/test_randomness.py
+++ b/test/backend/test_randomness.py
@@ -361,7 +361,7 @@ class TestRandomness(unittest.TestCase):
     _check_with_torch(w=[0.231, 0., 1., 0.5], num_samples=300, replacement=True)
     _check_with_torch(w=[[0.2, 0.8]], num_samples=300, replacement=True)  # 2D but only 1 row
     _check_with_torch(w=[[0.453, 0., 1., 0.81], [0.1, 0.8, 0., 0.1]], num_samples=300, replacement=True)
-    # no-replacement isn't supported, unless taking only one sample
+    # no-replacement
     w = [0.1, 0.9]
     self.assertRaises(AssertionError, lambda: Tensor(w).multinomial(100, replacement=False))
 
@@ -372,6 +372,23 @@ class TestRandomness(unittest.TestCase):
     torch_samples = [torch.tensor(w).multinomial(1, replacement=False).item() for _ in range(1000)]
     self.assertTrue(equal_distribution(lambda *_: Tensor(tiny_samples), lambda _: torch.tensor(torch_samples)))
 
+    w = list(range(32))
+    s1 = Tensor(w).multinomial(5, replacement=False).numpy()
+    self.assertEqual(len(set(s1.tolist())), 5)
+    s2 = Tensor(w).multinomial(5, replacement=False).numpy()
+    self.assertFalse(np.array_equal(s1, s2))
+    full = Tensor(w).multinomial(len(w), replacement=False).numpy()
+    self.assertEqual(sorted(full.tolist()), w)
+
+    w = [0.1, 0.2, 0.3, 0.4]
+    @TinyJit
+    def sample_three(): return Tensor(w).multinomial(3, replacement=False).realize()
+
+    tiny_draws = np.array([sample_three().numpy() for _ in range(1000)])
+    torch_draws = np.array([torch.tensor(w).multinomial(3, replacement=False).numpy() for _ in range(1000)])
+    for pos in range(3):
+      self.assertTrue(equal_distribution(lambda *_: Tensor(tiny_draws[:, pos]), lambda _: torch.tensor(torch_draws[:, pos])))
+
   @unittest.skip("this test is flaky")
   def test_multinomial_counterexample(self):
     tiny_res = Tensor([0.3, 0.6, 0.1]).multinomial(4000, replacement=True)
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 7f66912a2b..53efb55a4f 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -822,19 +822,27 @@ class Tensor(OpMixin):
     """
     Returns a tensor with `num_samples` indices sampled from a multinomial distribution weighted by `self`.
 
-    NOTE: `replacement=False` for `num_samples > 1` is not supported yet.
     ```python exec="true" source="above" session="tensor" result="python"
     Tensor.manual_seed(42)
     t = Tensor([1, 2, 3, 4])
     print(t.multinomial(20, replacement=True).numpy())
     ```
+    ```python exec="true" source="above" session="tensor" result="python"
+    Tensor.manual_seed(42)
+    t = Tensor([1, 2, 3, 4])
+    print(t.multinomial(3, replacement=False).numpy())
+    ```
     """
     assert 1 <= self.ndim <= 2 and num_samples > 0, f"{self.ndim=} must be 1 or 2 dim, {num_samples=} must be positive"
-    assert replacement or num_samples == 1, "no replacement only supports num_samples = 1"
     weight = self.unsqueeze(0) if self.ndim == 1 else self
-    cdf = (cw := weight.cumsum(1).float()) / cw[:, -1].unsqueeze(1)
-    unif_samples = Tensor.rand(num_samples, cdf.shape[0], 1).to(self.device)
-    indices = (unif_samples.expand((-1, -1, cdf.shape[1])) >= cdf).sum(2).permute((1, 0))
+    assert replacement or num_samples <= weight.shape[1], "no replacement samples must not exceed population size"
+    if replacement or num_samples == 1:
+      cdf = (cw := weight.cumsum(1).float()) / cw[:, -1].unsqueeze(1)
+      unif_samples = Tensor.rand(num_samples, cdf.shape[0], 1).to(self.device)
+      indices = (unif_samples.expand((-1, -1, cdf.shape[1])) >= cdf).sum(2).permute((1, 0))
+    else:
+      # Efraimidis–Spirakis
+      indices = (weight.rand_like(dtype=dtypes.float32).log2() / weight).topk(num_samples, dim=1)[1]
     return (indices.squeeze(0) if self.ndim == 1 else indices).cast(dtypes.int32)
 
   # ***** toposort and backward pass *****