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Original file line number Diff line number Diff line change
Expand Up @@ -446,7 +446,18 @@ def type_inference(self):

@precondition(allow=VALUE)
def value_inference(self):
return np.asarray(np.reciprocal(self.x.val + self.epsilon.val))
x_val = self.x.val
epsilon_val = self.epsilon.val

# βœ… New check for unsupported int types
if np.issubdtype(x_val.dtype, np.integer):
raise ValueError(
f"CoreML 'inverse' op does not support integer types. "
f"Got input dtype: {x_val.dtype}. Please cast input to float32 or use float constants."
)

return np.asarray(np.reciprocal(x_val + epsilon_val))



@register_op
Expand Down
22 changes: 22 additions & 0 deletions coremltools/test/test_inverse_conversion.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,22 @@
import torch
import torch.nn as nn
import coremltools as ct

def test_inverse_with_int32_shape_input():
class Model(nn.Module):
def forward(self, x):
return 16 / x.shape[0] # int32 division

model = Model()
model.eval() # Make sure to silence the eval warning
inputs = (torch.randn(2, 3, 4),)
traced = torch.jit.trace(model, inputs)

try:
ct.convert(traced, inputs=[ct.TensorType(shape=(2, 3, 4))], convert_to="mlprogram")
except ValueError as e:
error_str = str(e).lower()
assert "inverse" in error_str
assert any(keyword in error_str for keyword in ["int32", "integer"])
else:
assert False, "Expected ValueError due to int32 input to inverse op"