Generalize and simplify local_reduce_join

Author: ricardoV94

pytensor/tensor/rewriting/math.py

@@ -1626,68 +1626,54 @@ def local_reduce_chain(fgraph, node):
 @node_rewriter([CAReduce])
 def local_reduce_join(fgraph, node):
     """
-    CAReduce{scalar.op}(Join(axis=0, a, b), axis=0) -> Elemwise{scalar.op}(a, b)
+    CAReduce{scalar.op}(Join(axis=x, a, b), axis=x) -> Elemwise{scalar.op}(a, b)
 
-    Notes
-    -----
-    Supported scalar.op are Maximum, Minimum in some cases and Add and Mul in
-    all cases.
-
-    Currently we must reduce on axis 0. It is probably extensible to the case
-    where we join and reduce on the same set of axis.
+    When a, b have a dim length of 1 along the join axis
 
     """
-    if node.inputs[0].owner and isinstance(node.inputs[0].owner.op, Join):
-        join_node = node.inputs[0].owner
-        if extract_constant(join_node.inputs[0], only_process_constants=True) != 0:
-            return
+    if not (node.inputs[0].owner and isinstance(node.inputs[0].owner.op, Join)):
+        return None
 
-        if isinstance(node.op.scalar_op, ps.ScalarMaximum | ps.ScalarMinimum):
-            # Support only 2 inputs for now
-            if len(join_node.inputs) != 3:
-                return
-        elif not isinstance(node.op.scalar_op, ps.Add | ps.Mul):
-            return
-        elif len(join_node.inputs) <= 2:
-            # This is a useless join that should get removed by another rewrite?
-            return
+    [joined_out] = node.inputs
+    joined_node = joined_out.owner
+    join_axis_tensor, *joined_inputs = joined_node.inputs
 
-        new_inp = []
-        for inp in join_node.inputs[1:]:
-            inp = inp.owner
-            if not inp:
-                return
-            if not isinstance(inp.op, DimShuffle) or inp.op.new_order != (
-                "x",
-                *range(inp.inputs[0].ndim),
-            ):
-                return
-            new_inp.append(inp.inputs[0])
-        ret = Elemwise(node.op.scalar_op)(*new_inp)
+    n_joined_inputs = len(joined_inputs)
+    if n_joined_inputs < 2:
+        # Let some other rewrite get rid of this useless Join
+        return None
+    if n_joined_inputs > 2 and not hasattr(node.op.scalar_op, "nfunc_variadic"):
+        # We don't rewrite if a single Elemwise cannot take all inputs at once
+        return None
 
-        if ret.dtype != node.outputs[0].dtype:
-            # The reduction do something about the dtype.
-            return
+    if not isinstance(join_axis_tensor, Constant):
+        return None
+    join_axis = join_axis_tensor.data
 
-        reduce_axis = node.op.axis
-        if reduce_axis is None:
-            reduce_axis = tuple(range(node.inputs[0].ndim))
+    # Check whether reduction happens on joined axis
+    reduce_op = node.op
+    reduce_axis = reduce_op.axis
+    if reduce_axis is None:
+        if joined_out.type.ndim > 1:
+            return None
+    elif reduce_axis != (join_axis,):
+        return None
 
-        if len(reduce_axis) != 1 or 0 not in reduce_axis:
-            return
+    # Check all inputs are broadcastable along the join axis and squeeze those dims away
+    new_inp = []
+    for inp in joined_inputs:
+        if not inp.type.broadcastable[join_axis]:
+            return None
+        # Let other rewrites clean up useless expand_dims
+        new_inp.append(inp.squeeze(join_axis))
 
-        # We add the new check late to don't add extra warning.
-        try:
-            join_axis = get_underlying_scalar_constant_value(
-                join_node.inputs[0], only_process_constants=True
-            )
+    ret = Elemwise(node.op.scalar_op)(*new_inp)
 
-            if join_axis != reduce_axis[0]:
-                return
-        except NotScalarConstantError:
-            return
+    if ret.dtype != node.outputs[0].dtype:
+        # The reduction do something about the dtype.
+        return None
 
-        return [ret]
+    return [ret]
 
 
 @register_infer_shape

tests/tensor/rewriting/test_math.py

@@ -3231,7 +3231,7 @@ def test_local_prod_of_div(self):
 class TestLocalReduce:
     def setup_method(self):
         self.mode = get_default_mode().including(
-            "canonicalize", "specialize", "uncanonicalize", "local_max_and_argmax"
+            "canonicalize", "specialize", "uncanonicalize"
         )
 
     def test_local_reduce_broadcast_all_0(self):
@@ -3304,62 +3304,92 @@ def test_local_reduce_broadcast_some_1(self):
                 isinstance(node.op, CAReduce) for node in f.maker.fgraph.toposort()
             )
 
-    def test_local_reduce_join(self):
+
+class TestReduceJoin:
+    def setup_method(self):
+        self.mode = get_default_mode().including("canonicalize", "specialize")
+
+    @pytest.mark.parametrize(
+        "op, nin", [(pt_sum, 3), (pt_max, 2), (pt_min, 2), (prod, 3)]
+    )
+    def test_local_reduce_join(self, op, nin):
         vx = matrix()
         vy = matrix()
         vz = matrix()
         x = np.asarray([[1, 0], [3, 4]], dtype=config.floatX)
         y = np.asarray([[4, 0], [2, 1]], dtype=config.floatX)
         z = np.asarray([[5, 0], [1, 2]], dtype=config.floatX)
-        # Test different reduction scalar operation
-        for out, res in [
-            (pt_max((vx, vy), 0), np.max((x, y), 0)),
-            (pt_min((vx, vy), 0), np.min((x, y), 0)),
-            (pt_sum((vx, vy, vz), 0), np.sum((x, y, z), 0)),
-            (prod((vx, vy, vz), 0), np.prod((x, y, z), 0)),
-            (prod((vx, vy.T, vz), 0), np.prod((x, y.T, z), 0)),
-        ]:
-            f = function([vx, vy, vz], out, on_unused_input="ignore", mode=self.mode)
-            assert (f(x, y, z) == res).all(), out
-            topo = f.maker.fgraph.toposort()
-            assert len(topo) <= 2, out
-            assert isinstance(topo[-1].op, Elemwise), out
 
+        inputs = (vx, vy, vz)[:nin]
+        test_values = (x, y, z)[:nin]
+
+        out = op(inputs, axis=0)
+        f = function(inputs, out, mode=self.mode)
+        np.testing.assert_allclose(
+            f(*test_values), getattr(np, op.__name__)(test_values, axis=0)
+        )
+        topo = f.maker.fgraph.toposort()
+        assert len(topo) <= 2
+        assert isinstance(topo[-1].op, Elemwise)
+
+    def test_type(self):
         # Test different axis for the join and the reduction
         # We must force the dtype, of otherwise, this tests will fail
         # on 32 bit systems
         A = shared(np.array([1, 2, 3, 4, 5], dtype="int64"))
 
         f = function([], pt_sum(pt.stack([A, A]), axis=0), mode=self.mode)
-        utt.assert_allclose(f(), [2, 4, 6, 8, 10])
+        np.testing.assert_allclose(f(), [2, 4, 6, 8, 10])
         topo = f.maker.fgraph.toposort()
         assert isinstance(topo[-1].op, Elemwise)
 
         # Test a case that was bugged in a old PyTensor bug
         f = function([], pt_sum(pt.stack([A, A]), axis=1), mode=self.mode)
 
-        utt.assert_allclose(f(), [15, 15])
+        np.testing.assert_allclose(f(), [15, 15])
         topo = f.maker.fgraph.toposort()
         assert not isinstance(topo[-1].op, Elemwise)
 
         # This case could be rewritten
         A = shared(np.array([1, 2, 3, 4, 5]).reshape(5, 1))
         f = function([], pt_sum(pt.concatenate((A, A), axis=1), axis=1), mode=self.mode)
-        utt.assert_allclose(f(), [2, 4, 6, 8, 10])
+        np.testing.assert_allclose(f(), [2, 4, 6, 8, 10])
         topo = f.maker.fgraph.toposort()
         assert not isinstance(topo[-1].op, Elemwise)
 
         A = shared(np.array([1, 2, 3, 4, 5]).reshape(5, 1))
         f = function([], pt_sum(pt.concatenate((A, A), axis=1), axis=0), mode=self.mode)
-        utt.assert_allclose(f(), [15, 15])
+        np.testing.assert_allclose(f(), [15, 15])
         topo = f.maker.fgraph.toposort()
         assert not isinstance(topo[-1].op, Elemwise)
 
+    def test_not_supported_axis_none(self):
         # Test that the rewrite does not crash in one case where it
         # is not applied.  Reported at
         # https://groups.google.com/d/topic/theano-users/EDgyCU00fFA/discussion
+        vx = matrix()
+        vy = matrix()
+        vz = matrix()
+        x = np.asarray([[1, 0], [3, 4]], dtype=config.floatX)
+        y = np.asarray([[4, 0], [2, 1]], dtype=config.floatX)
+        z = np.asarray([[5, 0], [1, 2]], dtype=config.floatX)
+
         out = pt_sum([vx, vy, vz], axis=None)
-        f = function([vx, vy, vz], out)
+        f = function([vx, vy, vz], out, mode=self.mode)
+        np.testing.assert_allclose(f(x, y, z), np.sum([x, y, z]))
+
+    def test_not_supported_unequal_shapes(self):
+        # Not the same shape along the join axis
+        vx = matrix(shape=(1, 3))
+        vy = matrix(shape=(2, 3))
+        x = np.asarray([[1, 0, 1]], dtype=config.floatX)
+        y = np.asarray([[4, 0, 1], [2, 1, 1]], dtype=config.floatX)
+        out = pt_sum(join(0, vx, vy), axis=0)
+
+        f = function([vx, vy], out, mode=self.mode)
+        np.testing.assert_allclose(
+            f(x, y), np.sum(np.concatenate([x, y], axis=0), axis=0)
+        )
 
 
 def test_local_useless_adds():