Fix Blockwise vmap dispatch for no batch dimensions (#1705)

* Fix Blockwise vmap dispatch for no batch dimensions

Updates comments in funcify_Blockwise to avoid confusion about behaviour. Adds tests to verify correct behavior for these cases.

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Author: cetagostini

pytensor/link/mlx/dispatch/blockwise.py

@@ -6,30 +6,37 @@
 
 @mlx_funcify.register(Blockwise)
 def funcify_Blockwise(op: Blockwise, node, **kwargs):
-    # 2) Otherwise, get the core python function for this Blockwise
+    # Get the core python function for this Blockwise operation
     core_node = op._create_dummy_core_node(node.inputs)
     core_f = mlx_funcify(op.core_op, core_node)
 
-    # 3) Determine how many inputs correspond to batch dimensions
+    # Determine how many batch dimensions are present in the output
     n_batch = op.batch_ndim(node)
 
-    # 4) Handle case where no vectorization is needed
+    # If there are no batch dimensions, just return the core function
     if n_batch == 0:
         return core_f
 
-    # 5) Vectorize using mx.vmap over any batched inputs
+    # Build in_axes specification for mx.vmap
+    # Each input can be vectorized (axis=0) or static (axis=None)
     in_axes: list[int | None] = []
     for inp, sig in zip(node.inputs, op.inputs_sig):
         batch_ndim = inp.type.ndim - len(sig)
         if batch_ndim == 0:
+            # Input has no batch dimensions - treat as static
             in_axes.append(None)
             continue
 
         batch_bcast = inp.type.broadcastable[:batch_ndim]
         # If all batch dims are broadcastable (size 1), treat input as static
+        # Otherwise, vectorize over the first dimension (axis=0)
         in_axes.append(0 if not all(batch_bcast) else None)
 
+    # If all inputs are static (no actual vectorization needed), return core function
+    # This prevents calling mx.vmap with all-None in_axes, which would raise:
+    # "ValueError: At least one of in_axes must be non-None"
     if not any(axis == 0 for axis in in_axes):
         return core_f
 
+    # Apply mx.vmap to vectorize the core function over batch dimensions
     return mx.vmap(core_f, in_axes=tuple(in_axes))

tests/link/mlx/test_blockwise.py

@@ -25,3 +25,45 @@ def test_blockwise_conv1d():
 
     # assert isinstance(out.owner.op, Blockwise)
     compare_mlx_and_py([a, b], [out], test_values, must_be_device_array=True)
+
+
+def test_blockwise_no_batch_dimensions():
+    """Test that Blockwise returns the core function when there are no batch dimensions.
+
+    This verifies the fix for the vmap dispatcher issue where mx.vmap should not
+    be called when there are no batch dimensions to vectorize over.
+    """
+    rng = np.random.default_rng(42)
+
+    # Create a blockwise matmul with no batch dimensions (core operation only)
+    x = pt.matrix("x")
+    y = pt.matrix("y")
+
+    blockwise_matmul = Blockwise(Dot(), signature="(i,j),(j,k)->(i,k)")
+    z = blockwise_matmul(x, y)
+
+    x_test = rng.normal(size=(2, 3))
+    y_test = rng.normal(size=(3, 4))
+
+    compare_mlx_and_py([x, y], [z], [x_test, y_test], must_be_device_array=True)
+
+
+def test_blockwise_all_broadcastable_batch_dims():
+    """Test that Blockwise returns the core function when all batch dims are broadcastable.
+
+    When all batch dimensions are size-1 (broadcastable), vmap should not be called
+    since there's no actual vectorization needed.
+    """
+    rng = np.random.default_rng(43)
+
+    # Create inputs with size-1 batch dimensions
+    x = tensor("x", shape=(1, 2, 3))
+    y = tensor("y", shape=(1, 3, 4))
+
+    blockwise_matmul = Blockwise(Dot(), signature="(i,j),(j,k)->(i,k)")
+    z = blockwise_matmul(x, y)
+
+    x_test = rng.normal(size=(1, 2, 3))
+    y_test = rng.normal(size=(1, 3, 4))
+
+    compare_mlx_and_py([x, y], [z], [x_test, y_test], must_be_device_array=True)