Fix SetTransformer kwargs handling

bayesflow/networks/transformers/mab.py

@@ -3,7 +3,7 @@
 
 from bayesflow.networks import MLP
 from bayesflow.types import Tensor
-from bayesflow.utils import layer_kwargs
+from bayesflow.utils import layer_kwargs, filter_kwargs
 from bayesflow.utils.decorators import sanitize_input_shape
 from bayesflow.utils.serialization import serializable
 
@@ -111,7 +111,7 @@ def call(self, seq_x: Tensor, seq_y: Tensor, training: bool = False, **kwargs) -
         """
 
         h = self.input_projector(seq_x) + self.attention(
-            query=seq_x, key=seq_y, value=seq_y, training=training, **kwargs
+            query=seq_x, key=seq_y, value=seq_y, training=training, **filter_kwargs(kwargs, self.attention.call)
         )
         if self.ln_pre is not None:
             h = self.ln_pre(h, training=training)

bayesflow/networks/transformers/set_transformer.py

@@ -1,7 +1,7 @@
 import keras
 
 from bayesflow.types import Tensor
-from bayesflow.utils import check_lengths_same
+from bayesflow.utils import check_lengths_same, filter_kwargs
 from bayesflow.utils.serialization import serializable
 
 from ..summary_network import SummaryNetwork
@@ -147,7 +147,11 @@ def call(self, input_set: Tensor, training: bool = False, **kwargs) -> Tensor:
         out : Tensor
             Output of shape (batch_size, set_size, output_dim)
         """
-        summary = self.attention_blocks(input_set, training=training, **kwargs)
-        summary = self.pooling_by_attention(summary, training=training, **kwargs)
+        summary = self.attention_blocks(
+            input_set, training=training, **filter_kwargs(kwargs, self.attention_blocks.call)
+        )
+        summary = self.pooling_by_attention(
+            summary, training=training, **filter_kwargs(kwargs, self.pooling_by_attention.call)
+        )
         summary = self.output_projector(summary)
         return summary

tests/test_approximators/test_sample.py

@@ -1,3 +1,4 @@
+import pytest
 import keras
 from tests.utils import check_combination_simulator_adapter
 
@@ -16,3 +17,92 @@ def test_approximator_sample(approximator, simulator, batch_size, adapter):
     samples = approximator.sample(num_samples=2, conditions=data)
 
     assert isinstance(samples, dict)
+
+
+@pytest.mark.parametrize("inference_network_type", ["flow_matching", "diffusion_model"])
+@pytest.mark.parametrize("summary_network_type", ["none", "deep_set", "set_transformer", "time_series"])
+@pytest.mark.parametrize("method", ["euler", "rk45", "euler_maruyama"])
+def test_approximator_sample_with_integration_methods(
+    inference_network_type, summary_network_type, method, simulator, adapter
+):
+    """Test approximator sampling with different integration methods and summary networks.
+
+    Tests flow matching and diffusion models with different ODE/SDE solvers:
+    - euler, rk45: Available for both flow matching and diffusion models
+    - euler_maruyama: Only for diffusion models (stochastic)
+
+    Also tests with different summary network types.
+    """
+    batch_size = 8  # Use smaller batch size for faster tests
+    check_combination_simulator_adapter(simulator, adapter)
+
+    # Skip euler_maruyama for flow matching (deterministic model)
+    if inference_network_type == "flow_matching" and method == "euler_maruyama":
+        pytest.skip("euler_maruyama is only available for diffusion models")
+
+    # Create inference network based on type
+    if inference_network_type == "flow_matching":
+        from bayesflow.networks import FlowMatching, MLP
+
+        inference_network = FlowMatching(
+            subnet=MLP(widths=[32, 32]),
+            integrate_kwargs={"steps": 10},  # Use fewer steps for faster tests
+        )
+    elif inference_network_type == "diffusion_model":
+        from bayesflow.networks import DiffusionModel, MLP
+
+        inference_network = DiffusionModel(
+            subnet=MLP(widths=[32, 32]),
+            integrate_kwargs={"steps": 10},  # Use fewer steps for faster tests
+        )
+    else:
+        pytest.skip(f"Unsupported inference network type: {inference_network_type}")
+
+    # Create summary network based on type
+    summary_network = None
+    if summary_network_type != "none":
+        if summary_network_type == "deep_set":
+            from bayesflow.networks import DeepSet, MLP
+
+            summary_network = DeepSet(subnet=MLP(widths=[16, 16]))
+        elif summary_network_type == "set_transformer":
+            from bayesflow.networks import SetTransformer
+
+            summary_network = SetTransformer(embed_dims=[16, 16], mlp_widths=[16, 16])
+        elif summary_network_type == "time_series":
+            from bayesflow.networks import TimeSeriesNetwork
+
+            summary_network = TimeSeriesNetwork(subnet_kwargs={"widths": [16, 16]}, cell_type="lstm")
+        else:
+            pytest.skip(f"Unsupported summary network type: {summary_network_type}")
+
+        # Update adapter to include summary variables if summary network is present
+        from bayesflow import ContinuousApproximator
+
+        adapter = ContinuousApproximator.build_adapter(
+            inference_variables=["mean", "std"],
+            summary_variables=["x"],  # Use x as summary variable for testing
+        )
+
+    # Create approximator
+    from bayesflow import ContinuousApproximator
+
+    approximator = ContinuousApproximator(
+        adapter=adapter, inference_network=inference_network, summary_network=summary_network
+    )
+
+    # Generate test data
+    num_batches = 2  # Use fewer batches for faster tests
+    data = simulator.sample((num_batches * batch_size,))
+
+    # Build approximator
+    batch = adapter(data)
+    batch = keras.tree.map_structure(keras.ops.convert_to_tensor, batch)
+    batch_shapes = keras.tree.map_structure(keras.ops.shape, batch)
+    approximator.build(batch_shapes)
+
+    # Test sampling with the specified method
+    samples = approximator.sample(num_samples=2, conditions=data, method=method)
+
+    # Verify results
+    assert isinstance(samples, dict)