Use RandomStream in truncate

Author: Ricardo

aeppl/truncation.py

@@ -4,7 +4,7 @@
 import aesara.tensor as at
 import aesara.tensor.random.basic as arb
 import numpy as np
-from aesara import scan, shared
+from aesara import scan
 from aesara.compile.builders import OpFromGraph
 from aesara.graph.basic import Node
 from aesara.graph.fg import FunctionGraph
@@ -15,6 +15,7 @@
 from aesara.scalar.basic import clip as scalar_clip
 from aesara.scan import until
 from aesara.tensor.elemwise import Elemwise
+from aesara.tensor.random import RandomStream
 from aesara.tensor.random.op import RandomVariable
 from aesara.tensor.var import TensorConstant, TensorVariable
 
@@ -188,7 +189,11 @@ def __str__(self):
 
 
 def truncate(
-    rv: TensorVariable, lower=None, upper=None, max_n_steps: int = 10_000, rng=None
+    rv: TensorVariable,
+    lower=None,
+    upper=None,
+    max_n_steps: int = 10_000,
+    srng: Optional[RandomStream] = None,
 ) -> Tuple[TensorVariable, Tuple[TensorVariable, TensorVariable]]:
     """Truncate a univariate `RandomVariable` between lower and upper.
 
@@ -218,13 +223,13 @@ def truncate(
     lower = at.as_tensor_variable(lower) if lower is not None else at.constant(-np.inf)
     upper = at.as_tensor_variable(upper) if upper is not None else at.constant(np.inf)
 
-    if rng is None:
-        rng = shared(np.random.RandomState(), borrow=True)
+    if srng is None:
+        srng = RandomStream()
 
     # Try to use specialized Op
     try:
         truncated_rv, updates = _truncated(
-            rv.owner.op, lower, upper, rng, *rv.owner.inputs[1:]
+            rv.owner.op, lower, upper, srng, *rv.owner.inputs[1:]
         )
         return truncated_rv, updates
     except NotImplementedError:
@@ -235,8 +240,8 @@ def truncate(
     # though it would not be necessary for the icdf OpFromGraph
     graph_inputs = [*rv.owner.inputs[1:], lower, upper]
     graph_inputs_ = [inp.type() for inp in graph_inputs]
-    *rv_inputs_, lower_, upper_ = graph_inputs_
-    rv_ = rv.owner.op.make_node(rng, *rv_inputs_).default_output()
+    size_, dtype_, *rv_inputs_, lower_, upper_ = graph_inputs_
+    rv_ = srng.gen(rv.owner.op, *rv_inputs_, size=size_, dtype=dtype_)
 
     # Try to use inverted cdf sampling
     try:
@@ -245,11 +250,10 @@ def truncate(
         lower_value = lower_ - 1 if rv.owner.op.dtype.startswith("int") else lower_
         cdf_lower_ = at.exp(logcdf(rv_, lower_value))
         cdf_upper_ = at.exp(logcdf(rv_, upper_))
-        uniform_ = at.random.uniform(
+        uniform_ = srng.uniform(
             cdf_lower_,
             cdf_upper_,
-            rng=rng,
-            size=rv_inputs_[0],
+            size=size_,
         )
         truncated_rv_ = icdf(rv_, uniform_)
         truncated_rv = TruncatedRV(
@@ -265,27 +269,23 @@ def truncate(
 
     # Fallback to rejection sampling
     # TODO: Handle potential broadcast by lower / upper
-    def loop_fn(truncated_rv, reject_draws, lower, upper, rng, *rv_inputs):
-        next_rng, new_truncated_rv = rv.owner.op.make_node(rng, *rv_inputs).outputs
+    def loop_fn(truncated_rv, reject_draws, lower, upper, size, dtype, *rv_inputs):
+        new_truncated_rv = srng.gen(rv.owner.op, *rv_inputs, size=size, dtype=dtype)  # type: ignore
         truncated_rv = at.set_subtensor(
             truncated_rv[reject_draws],
             new_truncated_rv[reject_draws],
         )
         reject_draws = at.or_((truncated_rv < lower), (truncated_rv > upper))
 
-        return (
-            (truncated_rv, reject_draws),
-            [(rng, next_rng)],
-            until(~at.any(reject_draws)),
-        )
+        return (truncated_rv, reject_draws), until(~at.any(reject_draws))
 
     (truncated_rv_, reject_draws_), updates = scan(
         loop_fn,
         outputs_info=[
             at.zeros_like(rv_),
             at.ones_like(rv_, dtype=bool),
         ],
-        non_sequences=[lower_, upper_, rng, *rv_inputs_],
+        non_sequences=[lower_, upper_, size_, dtype_, *rv_inputs_],
         n_steps=max_n_steps,
         strict=True,
     )
@@ -299,18 +299,28 @@ def loop_fn(truncated_rv, reject_draws, lower, upper, rng, *rv_inputs):
     truncated_rv = TruncatedRV(
         base_rv_op=rv.owner.op,
         inputs=graph_inputs_,
-        outputs=[truncated_rv_, tuple(updates.values())[0]],
+        # This will fail with `n_steps==1`, because in that case `Scan` won't return any updates
+        outputs=[truncated_rv_, rv_.owner.outputs[0], tuple(updates.values())[0]],
         inline=True,
     )(*graph_inputs)
-    updates = {truncated_rv.owner.inputs[-1]: truncated_rv.owner.outputs[-1]}
+    # TODO: Is the order of multiple shared variables determnistic?
+    assert truncated_rv.owner.inputs[-2] is rv_.owner.inputs[0]
+    updates = {
+        truncated_rv.owner.inputs[-2]: truncated_rv.owner.outputs[-2],
+        truncated_rv.owner.inputs[-1]: truncated_rv.owner.outputs[-1],
+    }
     return truncated_rv, updates
 
 
 @_logprob.register(TruncatedRV)
 def truncated_logprob(op, values, *inputs, **kwargs):
     (value,) = values
 
-    *rv_inputs, lower_bound, upper_bound, rng = inputs
+    # Rejection sample graph has two rngs
+    if len(op.shared_inputs) == 2:
+        *rv_inputs, lower_bound, upper_bound, _, rng = inputs
+    else:
+        *rv_inputs, lower_bound, upper_bound, rng = inputs
     rv_inputs = [rng, *rv_inputs]
 
     base_rv_op = op.base_rv_op
@@ -361,11 +371,11 @@ def truncated_logprob(op, values, *inputs, **kwargs):
 
 
 @_truncated.register(arb.UniformRV)
-def uniform_truncated(op, lower, upper, rng, size, dtype, lower_orig, upper_orig):
-    truncated_uniform = at.random.uniform(
+def uniform_truncated(op, lower, upper, srng, size, dtype, lower_orig, upper_orig):
+    truncated_uniform = srng.gen(
+        op,
         at.max((lower_orig, lower)),
         at.min((upper_orig, upper)),
-        rng=rng,
         size=size,
         dtype=dtype,
     )

tests/test_truncation.py

@@ -233,10 +233,10 @@ def _icdf_not_implemented(*args, **kwargs):
 def test_truncation_specialized_op():
     x = at.random.uniform(0, 10, name="x", size=100)
 
-    rng = aesara.shared(np.random.RandomState())
-    xt, _ = truncate(x, lower=5, upper=15, rng=rng)
+    srng = at.random.RandomStream()
+    xt, _ = truncate(x, lower=5, upper=15, srng=srng)
     assert isinstance(xt.owner.op, UniformRV)
-    assert xt.owner.inputs[0] is rng
+    assert xt.owner.inputs[0] is srng.updates()[0][0]
 
     lower_upper = at.stack(xt.owner.inputs[3:])
     assert np.all(lower_upper.eval() == [5, 10])
@@ -250,10 +250,10 @@ def test_truncation_continuous_random(op_type, lower, upper):
     normal_op = icdf_normal if op_type == "icdf" else rejection_normal
     x = normal_op(loc, scale, name="x", size=100)
 
-    rng = aesara.shared(np.random.RandomState())
-    xt, xt_update = truncate(x, lower=lower, upper=upper, rng=rng)
+    srng = at.random.RandomStream()
+    xt, xt_update = truncate(x, lower=lower, upper=upper, srng=srng)
     assert isinstance(xt.owner.op, TruncatedRV)
-    assert xt.owner.inputs[-1] is rng
+    assert xt.owner.inputs[-1] is srng.updates()[1 if op_type == "icdf" else 2][0]
     assert xt.type == x.type
 
     # Check that original op can be used on its own
@@ -275,7 +275,7 @@ def test_truncation_continuous_random(op_type, lower, upper):
     assert scipy.stats.cramervonmises(xt_draws.ravel(), ref_xt.cdf).pvalue > 0.001
 
     # Test max_n_steps
-    xt, xt_update = truncate(x, lower=lower, upper=upper, max_n_steps=1)
+    xt, xt_update = truncate(x, lower=lower, upper=upper, max_n_steps=2)
     xt_fn = aesara.function([], xt, updates=xt_update)
     if op_type == "icdf":
         xt_draws = xt_fn()