overriding simulator outputs with auto-batched inputs

bayesflow/simulators/sequential_simulator.py

@@ -10,7 +10,7 @@
 class SequentialSimulator(Simulator):
     """Combines multiple simulators into one, sequentially."""
 
-    def __init__(self, simulators: Sequence[Simulator], expand_outputs: bool = True):
+    def __init__(self, simulators: Sequence[Simulator], expand_outputs: bool = True, replace_inputs: bool = True):
         """
         Initialize a SequentialSimulator.
 
@@ -22,10 +22,13 @@ def __init__(self, simulators: Sequence[Simulator], expand_outputs: bool = True)
         expand_outputs : bool, optional
             If True, 1D output arrays are expanded with an additional dimension at the end.
             Default is True.
+        replace_inputs : bool, optional
+            If True, **kwargs are auto-batched and replace simulator outputs.
         """
 
         self.simulators = simulators
         self.expand_outputs = expand_outputs
+        self.replace_inputs = replace_inputs
 
     @allow_batch_size
     def sample(self, batch_shape: Shape, **kwargs) -> dict[str, np.ndarray]:
@@ -53,6 +56,14 @@ def sample(self, batch_shape: Shape, **kwargs) -> dict[str, np.ndarray]:
         for simulator in self.simulators:
             data |= simulator.sample(batch_shape, **(kwargs | data))
 
+            if self.replace_inputs:
+                common_keys = set(data.keys()) & set(kwargs.keys())
+                for key in common_keys:
+                    value = kwargs.pop(key)
+                    if isinstance(data[key], np.ndarray):
+                        value = np.broadcast_to(value, data[key].shape)
+                    data[key] = value
+
         if self.expand_outputs:
             data = {
                 key: np.expand_dims(value, axis=-1) if np.ndim(value) == 1 else value for key, value in data.items()

tests/test_simulators/conftest.py

@@ -148,6 +148,35 @@ def two_moons_simulator(request):
     return request.getfixturevalue(request.param)
 
 
+@pytest.fixture()
+def composite_gaussian():
+    from bayesflow.simulators import make_simulator
+
+    def context():
+        n = np.random.randint(10, 100)
+        return dict(n=n)
+
+    def prior():
+        mu = np.random.normal(0, 1)
+        return dict(mu=mu)
+
+    def likelihood(mu, n):
+        y = np.random.normal(mu, 1, n)
+        return dict(y=y)
+
+    return make_simulator([prior, likelihood], meta_fn=context)
+
+
+@pytest.fixture()
+def fixed_n():
+    return 5
+
+
+@pytest.fixture()
+def fixed_mu():
+    return 100
+
+
 @pytest.fixture(
     params=[
         "bernoulli_glm",

tests/test_simulators/test_simulators.py

@@ -38,3 +38,12 @@ def test_sample(simulator, batch_size):
 
         # test batch randomness
         assert not np.allclose(value, value[0])
+
+
+def test_fixed_sample(composite_gaussian, batch_size, fixed_n, fixed_mu):
+    samples = composite_gaussian.sample((batch_size,), n=fixed_n, mu=fixed_mu)
+
+    assert samples["n"] == fixed_n
+    assert samples["mu"].shape == (batch_size, 1)
+    assert np.all(samples["mu"] == fixed_mu)
+    assert samples["y"].shape == (batch_size, fixed_n)