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Add rewrite for softplus(log(x)) -> log1p(x) #1452

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Add rewrite for softplus(log(x)) -> log1p(x) #1452

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9 changes: 8 additions & 1 deletion pytensor/tensor/rewriting/math.py
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Original file line number Diff line number Diff line change
Expand Up @@ -400,7 +400,7 @@ def local_exp_log(fgraph, node):


@register_specialize
@node_rewriter([exp, expm1])
@node_rewriter([exp, expm1, softplus])
def local_exp_log_nan_switch(fgraph, node):
# Rewrites of the kind exp(log...(x)) that require a `nan` switch
x = node.inputs[0]
Expand Down Expand Up @@ -453,6 +453,13 @@ def local_exp_log_nan_switch(fgraph, node):
new_out = switch(le(x, 0), neg(exp(x)), np.asarray(np.nan, old_out.dtype))
return [new_out]

# Case for softplus(log(x)) -> log1p(x)
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Nitpick I prefer to refer to it by log1pexp, which we have as an alias to softplus:

pytensor/pytensor/tensor/math.py

Line 2474 in ff98ab8

log1pexp = softplus

Also we can add a similar case for log1mexp?

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I tested this (with the code below) and it works fine in its domain [0, 1]. I will add it too.

if isinstance(prev_op, ps.Log) and isinstance(node_op, ps_math.Softplus):
x = x.owner.inputs[0]
old_out = node.outputs[0]
new_out = switch(ge(x, 0), log1p(x), np.asarray(np.nan, old_out.dtype))
return [new_out]


@register_canonicalize
@register_specialize
Expand Down
21 changes: 21 additions & 0 deletions tests/tensor/rewriting/test_math.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2010,6 +2010,27 @@ def test_exp_softplus(self, exp_op):
decimal=6,
)

def test_softplus_log(self):
# softplus(log(x)) -> log1p(x)
data_valid = np.random.random((4, 3)).astype("float32") * 2
data_valid[0, 0] = 0 # edge case
data_invalid = data_valid - 2

x = fmatrix()
f = function([x], softplus(log(x)), mode=self.mode)
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if you want you can check against the expected graph directly, something like

assert equal_computations(f.maker.fgraph.outputs, [pt.switch(x > 0, pt.log1p(x), np.asarray([[np.nan]], dtype="float32")])

Or something like that. This is not a request!

graph = f.maker.fgraph.toposort()
ops_graph = [
node
for node in graph
if isinstance(node.op, Elemwise)
and isinstance(node.op.scalar_op, ps.Log | ps.Exp | ps.Softplus)
]
assert len(ops_graph) == 0

expected = np.log1p(data_valid)
np.testing.assert_almost_equal(f(data_valid), expected)
assert np.all(np.isnan(f(data_invalid)))

@pytest.mark.parametrize(
["nested_expression", "expected_switches"],
[
Expand Down