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Fix clamped_relu's value_inference for a negative beta #2797

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Fix clamped_relu's value_inference for a negative beta #2797

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11 changes: 8 additions & 3 deletions coremltools/converters/mil/mil/ops/defs/iOS15/activation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -77,9 +77,14 @@ class clamped_relu(activation_with_alpha_and_beta):

@precondition(allow=VALUE)
def value_inference(self):
x = np.minimum(np.maximum(self.x.val, 0), self.beta.val)
y = np.minimum(np.minimum(self.x.val, 0) * self.alpha.val, self.beta.val)
return x + y
# Splitting into a positive and a negative half and adding them back together
# only works when at most one half is non-zero, which stops being true once
# beta is negative and clamps both of them. Apply the documented formula
# directly instead.
return np.minimum(
np.where(self.x.val >= 0, self.x.val, self.x.val * self.alpha.val),
self.beta.val,
)


@register_op
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14 changes: 14 additions & 0 deletions coremltools/converters/mil/mil/ops/tests/iOS14/test_activation.py
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Expand Up @@ -55,6 +55,20 @@ def test_builder_eval(self):
y = np.minimum(np.minimum(x_val, 0) * 2.0, 1.0)
np.testing.assert_allclose(x + y, v.val, atol=1e-04, rtol=1e-05)

@ssa_fn
def test_builder_eval_negative_beta(self):
"""
Constant folding must agree with the runtime contract
``f(x) = min((x >= 0 ? x : alpha * x), beta)`` for a negative beta too.
The alpha / beta combinations here match the ones the neural network backend
test for this layer already covers (test_numpy_nn_layers.test_clamped_relu_cpu).
"""
x_val = np.arange(-20, 20, dtype=np.float32)
for alpha, beta in itertools.product([0.0, 2.0, -3.0], [7.0, -8.0]):
v = mb.clamped_relu(x=x_val, alpha=alpha, beta=beta)
expected = np.minimum(beta, np.where(x_val >= 0, x_val, x_val * alpha))
np.testing.assert_allclose(expected, v.val, atol=1e-04, rtol=1e-05)

@pytest.mark.parametrize(
"compute_unit, backend, dim, alpha, beta",
itertools.product(compute_units, backends, [2, 4, 8], [2.0, 3.0], [4.0, 5.0]),
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