add multinomial operator

python/jittor/__init__.py

@@ -9,7 +9,7 @@
 # file 'LICENSE.txt', which is part of this source code package.
 # ***************************************************************
 
-__version__ = '1.3.6.8'
+__version__ = '1.3.6.9'
 from jittor_utils import lock
 with lock.lock_scope():
     ori_int = int

python/jittor/misc.py

@@ -2066,3 +2066,43 @@ The returned var has the same number of dimensions as the original var (x). The
     '''
     return x.getitem(((slice(None),)*dim)+(index,))
 jt.index_select = index_select
+
+def multinomial(weights: jt.Var, num_samples: int, replacement: bool=False) -> jt.Var:
+    ''' Returns a var where each row contains num_samples indices sampled from the multinomial probability distribution located in the corresponding row of input weights.
+
+    :param weights: the input probability.
+    :param num_samples: number of samples.
+    :param replacement: whether to draw with replacement or not.
+
+
+    Example::
+
+        weights = jt.float32([0, 10, 3, 0])
+        x = jt.multinomial(weights, 2)
+        assert jt.all_equal(x, [1, 2]) or jt.all_equal(x, [2, 1])
+        x = jt.multinomial(weights, 4, replacement=True)
+        assert x.shape == (4, )
+
+        weights = jt.float32([[0,0,2],[0,1,0], [0.5,0,0]])
+        x = jt.multinomial(weights, 1)
+        assert jt.all_equal(x, [[2],[1],[0]])
+
+    '''
+    if replacement:
+        cum_probs = jt.cumsum(weights)[..., None, :]
+        cum_probs_l = cum_probs[..., :-1]
+        cum_probs_r = cum_probs[..., 1:]
+        shape = weights.shape[:-1] + (num_samples, 1)
+        rand = jt.rand(shape) * cum_probs[..., :1, -1:]
+        one_hot = jt.logical_and(cum_probs_l < rand, rand <= cum_probs_r)
+        index = one_hot.index(one_hot.ndim - 1) + 1
+        return (one_hot * index).sum(-1)
+    else:
+        # A-Res algorithm
+        # Pavlos S. Efraimidis and Paul G. Spirakis, 2006, Weighted random sampling with a reservoir
+        assert num_samples <= weights.shape[-1], "num_samples larger than the input"
+        rand = jt.rand(weights.shape) ** (1/weights)
+        _, indices = jt.topk(rand.safe_clip(), num_samples)
+        return indices
+
+

python/jittor/src/ops/safe_clip_op.h

@@ -22,7 +22,7 @@ struct SafeClipOp : Op {
 
      */
     // @pybind(safe_clip)
-    SafeClipOp(Var* x, float64 left, float64 right);
+    SafeClipOp(Var* x, float64 left=-1e300, float64 right=1e300);
     
     const char* name() const override { return "safe_clip"; }
     VarPtr grad(Var* out, Var* dout, Var* v, int v_index) override;

python/jittor/test/test_misc_op.py

@@ -391,5 +391,16 @@ class TestOther(unittest.TestCase):
         y = jt.index_select(x, 1, indices)
         assert jt.all_equal(y, x[:, indices])
 
+    def test_multinorm(self):
+        weights = jt.float32([0, 10, 3, 0])
+        x = jt.multinomial(weights, 2)
+        assert jt.all_equal(x, [1, 2]) or jt.all_equal(x, [2, 1])
+        x = jt.multinomial(weights, 4, replacement=True)
+        assert x.shape == (4, )
+
+        weights = jt.float32([[0,0,2],[0,1,0], [0.5,0,0]])
+        x = jt.multinomial(weights, 1)
+        assert jt.all_equal(x, [[2],[1],[0]])
+
 if __name__ == "__main__":
     unittest.main()