Implement AdvancedIncSubtensorExplicit

pytensor/tensor/subtensor.py

@@ -2738,10 +2738,42 @@ def is_bool_index(idx):
         assert node.outputs[0].ndim == len(res_shape)
         return [res_shape]
 
+    def _broadcast_indices(self, x, indices):
+        new_indices = []
+        x_dim = 0
+        for idx in indices:
+            if idx is None:
+                new_indices.append(idx)
+                continue
+            if isinstance(idx, slice):
+                x_dim += 1
+                new_indices.append(idx)
+                continue
+
+            # Check for boolean
+            if hasattr(idx, "dtype") and (idx.dtype == bool or idx.dtype == np.bool_):
+                x_dim += idx.ndim
+                new_indices.append(idx)
+                continue
+
+            # Integer array
+            if x_dim < x.ndim and x.shape[x_dim] == 1:
+                # Broadcast: replace with zeros
+                new_indices.append(np.zeros_like(idx))
+            else:
+                new_indices.append(idx)
+            x_dim += 1
+        return tuple(new_indices)
+
     def perform(self, node, inputs, out_):
         (out,) = out_
-        check_advanced_indexing_dimensions(inputs[0], inputs[1:])
-        rval = inputs[0].__getitem__(tuple(inputs[1:]))
+        x = inputs[0]
+        indices = inputs[1:]
+
+        indices = self._broadcast_indices(x, indices)
+
+        check_advanced_indexing_dimensions(x, indices)
+        rval = x.__getitem__(tuple(indices))
         # When there are no arrays, we are not actually doing advanced
         # indexing, so __getitem__ will not return a copy.
         # Since no view_map is set, we need to copy the returned value
@@ -2807,6 +2839,97 @@ def non_consecutive_adv_indexing(node: Apply) -> bool:
 advanced_subtensor = AdvancedSubtensor()
 
 
+class BatchedSliceType(Type):
+    def filter(self, x, strict=False, allow_downcast=None):
+        return x
+
+    def __str__(self):
+        return "BatchedSliceType"
+
+
+batched_slice_type = BatchedSliceType()
+
+
+class BatchedSlice(Op):
+    __props__ = ()
+
+    def make_node(self, start, stop, step):
+        return Apply(self, [start, stop, step], [batched_slice_type()])
+
+    def perform(self, node, inp, out_):
+        raise NotImplementedError("BatchedSlice is a placeholder")
+
+
+@_vectorize_node.register(MakeSlice)
+def vectorize_make_slice(op, node, *batched_inputs):
+    is_batched = False
+    for orig, batched in zip(node.inputs, batched_inputs):
+        if hasattr(batched.type, "ndim") and hasattr(orig.type, "ndim"):
+            if batched.type.ndim > orig.type.ndim:
+                is_batched = True
+                break
+
+    if is_batched:
+        return BatchedSlice().make_node(*batched_inputs)
+    return op.make_node(*batched_inputs)
+
+
+class AdvancedIncSubtensorExplicit(Op):
+    __props__ = ("structure", "set_instead_of_inc", "inplace", "ignore_duplicates")
+
+    def __init__(
+        self,
+        structure,
+        set_instead_of_inc=False,
+        inplace=False,
+        ignore_duplicates=False,
+    ):
+        self.structure = structure
+        self.set_instead_of_inc = set_instead_of_inc
+        self.inplace = inplace
+        self.ignore_duplicates = ignore_duplicates
+
+    def make_node(self, x, y, *inputs):
+        return Apply(self, [x, y, *inputs], [x.type()])
+
+    def perform(self, node, inputs, out_):
+        x, y, *flat_indices = inputs
+
+        indices = []
+        idx_ptr = 0
+        for kind in self.structure:
+            if kind == "slice":
+                (
+                    start_val,
+                    start_none,
+                    stop_val,
+                    stop_none,
+                    step_val,
+                    step_none,
+                ) = flat_indices[idx_ptr : idx_ptr + 6]
+                start = None if start_none else start_val
+                stop = None if stop_none else stop_val
+                step = None if step_none else step_val
+                indices.append(slice(start, stop, step))
+                idx_ptr += 6
+            else:
+                indices.append(flat_indices[idx_ptr])
+                idx_ptr += 1
+
+        (out,) = out_
+        if not self.inplace:
+            out[0] = x.copy()
+        else:
+            out[0] = x
+
+        if self.set_instead_of_inc:
+            out[0][tuple(indices)] = y
+        elif self.ignore_duplicates:
+            out[0][tuple(indices)] += y
+        else:
+            np.add.at(out[0], tuple(indices), y)
+
+
 @_vectorize_node.register(AdvancedSubtensor)
 def vectorize_advanced_subtensor(op: AdvancedSubtensor, node, *batch_inputs):
     x, *idxs = node.inputs
@@ -2967,9 +3090,130 @@ def non_consecutive_adv_indexing(node: Apply) -> bool:
 advanced_inc_subtensor = AdvancedIncSubtensor()
 advanced_set_subtensor = AdvancedIncSubtensor(set_instead_of_inc=True)
 advanced_inc_subtensor_nodup = AdvancedIncSubtensor(ignore_duplicates=True)
-advanced_set_subtensor_nodup = AdvancedIncSubtensor(
-    set_instead_of_inc=True, ignore_duplicates=True
-)
+
+
+@_vectorize_node.register(AdvancedIncSubtensor)
+def advanced_inc_subtensor_vectorize_node(op, node, *batched_inputs):
+    x = batched_inputs[0]
+    y = batched_inputs[1]
+    indices = batched_inputs[2:]
+
+    # Check if we have batched slices (BatchedSliceType)
+    has_batched_slices = any(isinstance(idx.type, BatchedSliceType) for idx in indices)
+
+    if has_batched_slices:
+        from pytensor.tensor.blockwise import Blockwise, safe_signature
+
+        structure = []
+        flat_inputs = []
+
+        # We need to construct inputs for AdvancedIncSubtensorExplicit
+        # x and y are passed as is (Blockwise handles them)
+
+        for idx in indices:
+            if isinstance(idx.type, BatchedSliceType):
+                structure.append("slice")
+                # Unwrap BatchedSlice
+                # idx is a Variable output of BatchedSlice node
+                bs_node = idx.owner
+                assert isinstance(bs_node.op, BatchedSlice)
+
+                for comp in bs_node.inputs:
+                    if isinstance(comp.type, NoneTypeT):
+                        # Pass dummy and True flag
+                        flat_inputs.append(tensor_constant(0, dtype="int8"))
+                        flat_inputs.append(tensor_constant(True, dtype="bool"))
+                    else:
+                        # Pass component and False flag
+                        flat_inputs.append(comp)
+                        flat_inputs.append(tensor_constant(False, dtype="bool"))
+            else:
+                structure.append("tensor")
+                flat_inputs.append(idx)
+
+        core_op = AdvancedIncSubtensorExplicit(
+            structure=tuple(structure),
+            set_instead_of_inc=op.set_instead_of_inc,
+            inplace=op.inplace,
+            ignore_duplicates=op.ignore_duplicates,
+        )
+
+        # Signature
+        # x: (n, m, ...), y: (n, m, ...), indices... -> (n, m, ...)
+
+        x_core_ndim = node.inputs[0].ndim
+        y_core_ndim = node.inputs[1].ndim
+
+        input_core_ndims = [x_core_ndim, y_core_ndim]
+
+        # For indices
+        for i, idx in enumerate(indices):
+            if isinstance(idx.type, BatchedSliceType):
+                # 6 components, all scalar (0-d) for slice parameters
+                input_core_ndims.extend([0] * 6)
+            else:
+                # Tensor index
+                # Core ndim is the ndim of the original index
+                input_core_ndims.append(node.inputs[2 + i].ndim)
+
+        output_core_ndims = [node.outputs[0].ndim]
+
+        signature = safe_signature(input_core_ndims, output_core_ndims)
+
+        return Blockwise(core_op, signature=signature).make_node(x, y, *flat_inputs)
+
+    x_orig = node.inputs[0]
+    x_batch_ndim = x.ndim - x_orig.ndim
+
+    y_orig = node.inputs[1]
+    y_batch_ndim = y.ndim - y_orig.ndim
+
+    batch_ndim = max(x_batch_ndim, y_batch_ndim)
+
+    if batch_ndim == 0:
+        return op.make_node(x, y, *indices)
+
+    if x_batch_ndim < batch_ndim:
+        # Broadcast x to match batch dimensions
+        # We assume the batch dimensions are the first batch_ndim dimensions
+        # and that y has them (since y_batch_ndim >= batch_ndim)
+        from pytensor.tensor import concatenate
+
+        batch_shape = y.shape[:batch_ndim]
+        full_shape = concatenate([batch_shape, x.shape])
+        x = alloc(x, *full_shape)
+
+    # Check if any index is batched
+    any_batched_index = False
+    for i, idx in enumerate(indices):
+        orig_idx = node.inputs[2 + i]
+        if (
+            hasattr(idx, "ndim")
+            and hasattr(orig_idx, "ndim")
+            and idx.ndim > orig_idx.ndim
+        ):
+            any_batched_index = True
+            break
+
+    if not any_batched_index:
+        # Simple case: prepend slice(None) for each batch dim
+        sl = make_slice(None, None, None)
+        new_indices = [sl] * batch_ndim + list(indices)
+        return op.make_node(x, y, *new_indices)
+
+    from pytensor.tensor import arange
+
+    batch_indices = []
+    for d in range(batch_ndim):
+        dim_len = x.shape[d]
+        idx = arange(dim_len)
+        pattern = ["x"] * batch_ndim
+        pattern[d] = 0
+        idx = idx.dimshuffle(pattern)
+        batch_indices.append(idx)
+
+    new_indices = batch_indices + list(indices)
+    return op.make_node(x, y, *new_indices)
 
 
 def take(a, indices, axis=None, mode="raise"):

tests/tensor/test_subtensor.py

@@ -17,7 +17,7 @@
 from pytensor.compile.mode import Mode
 from pytensor.configdefaults import config
 from pytensor.gradient import grad
-from pytensor.graph import Constant
+from pytensor.graph import Constant, vectorize_graph
 from pytensor.graph.basic import equal_computations
 from pytensor.graph.op import get_test_value
 from pytensor.graph.rewriting.utils import is_same_graph
@@ -3047,15 +3047,12 @@ def core_fn(x, start):
             (2,),
             False,
         ),
-        # (this is currently failing because PyTensor tries to vectorize the slice(None) operation,
-        # due to the exact same None constant being used there and in the np.newaxis)
         pytest.param(
             (lambda x, idx: x[:, idx, None]),
             "(7,5,3),(2)->(7,2,1,3)",
             (11, 7, 5, 3),
             (2,),
             False,
-            marks=pytest.mark.xfail(raises=NotImplementedError),
         ),
         (
             (lambda x, idx: x[:, idx, idx, :]),
@@ -3218,3 +3215,70 @@ def test_advanced_incsubtensor1(self, func, static_shape, gc, benchmark):
         )
         fn.vm.allow_gc = gc
         benchmark(fn, x_values)
+
+
+class TestAdvancedIncSubtensorVectorization:
+    def test_vectorize_advanced_inc_subtensor_slice(self):
+        # Regression test for vectorization of AdvancedIncSubtensor with slice inputs
+        x = matrix("x")
+        y = vector("y")
+        idx = as_tensor([0, 1])
+
+        # x[0:2, idx] = y
+        # This uses AdvancedIncSubtensor because of the vector index mixed with slice
+        z = set_subtensor(x[0:2, idx], y)
+
+        # Vectorize over a batch dimension
+        # batched_x: (B, N, M)
+        # batched_y: (B, 2)
+        batched_x = tensor3("bx")
+        batched_y = matrix("by")
+
+        out_batched = vectorize_graph(z, replace={x: batched_x, y: batched_y})
+
+        f = function([batched_x, batched_y], out_batched)
+
+        bx_val = np.zeros((2, 5, 5), dtype=config.floatX)
+        by_val = np.ones((2, 2), dtype=config.floatX)
+
+        res = f(bx_val, by_val)
+
+        # Verify result
+        # For each batch b:
+        # res[b, 0:2, [0, 1]] should be 1
+        assert np.all(res[:, 0:2, [0, 1]] == 1)
+        assert np.all(res[:, 2:, :] == 0)
+
+    def test_vectorize_advanced_inc_subtensor_batched_slice(self):
+        # Regression test for vectorization of AdvancedIncSubtensor with batched slice parameters
+        x = matrix("x")
+        s = lscalar("s")
+        # x[s:, [0, 0]] = 0
+        out = set_subtensor(x[s:, [0, 0]], 0)
+
+        # Vectorize s -> z (vector)
+        z = lvector("z")
+
+        out_batched = vectorize_graph(out, replace={s: z})
+
+        f = function([x, z], out_batched)
+
+        x_val = np.arange(12).reshape((4, 3)).astype(config.floatX)
+        z_val = np.array([1, 2], dtype="int64")
+
+        # For z=1: x[1:, [0,0]] = 0. Rows 1,2,3. Cols 0.
+        # For z=2: x[2:, [0,0]] = 0. Rows 2,3. Cols 0.
+
+        res = f(x_val, z_val)
+
+        # res shape: (2, 4, 3)
+        assert res.shape == (2, 4, 3)
+
+        expected_0 = x_val.copy()
+        expected_0[1:, [0, 0]] = 0
+
+        expected_1 = x_val.copy()
+        expected_1[2:, [0, 0]] = 0
+
+        assert np.allclose(res[0], expected_0)
+        assert np.allclose(res[1], expected_1)