mirror of
https://github.com/saymrwulf/pytorch.git
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ee23944f46
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/33431 Some elementwise operators don't have shape and type inference specified for the output tensor: `BitwiseOr`, `BitwiseAnd`, `BitwiseXor`, `Not`, `Sign`. This change fixes this issue: - For `Not` and `Sign` operators, the output has the same type and shape as the input, so `IdenticalTypeAndShapeOfInput` function is used to specify that. - For bitwise operators created by `CAFFE2_SCHEMA_FOR_BINARY_BITWISE_OP` macro, the type and shape inference rules should be the same as for other binary element-wise operators, so `TensorInferenceFunction(ElementwiseOpShapeInference)` is used to specify that. Also some tests were modified to ensure that the shape and type are inferred (`ensure_outputs_are_inferred` parameter) Test Plan: ``` CAFFE2_ASSERT_SHAPEINFERENCE=1 buck test caffe2/caffe2/python/operator_test:elementwise_ops_test CAFFE2_ASSERT_SHAPEINFERENCE=1 buck test caffe2/caffe2/python/operator_test:math_ops_test ``` Note that the tests have to be executed with `CAFFE2_ASSERT_SHAPEINFERENCE=1` in order to fail upon shape inference failure. Reviewed By: idning Differential Revision: D19880164 fbshipit-source-id: 5d7902e045d79e5669e5e98dfb13a39711294939
54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from caffe2.python import core
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from hypothesis import given
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from hypothesis import strategies as st
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import caffe2.python.hypothesis_test_util as hu
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import caffe2.python.serialized_test.serialized_test_util as serial
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import numpy as np
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import unittest
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class TestMathOps(serial.SerializedTestCase):
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@given(X=hu.tensor(),
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exponent=st.floats(min_value=2.0, max_value=3.0),
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**hu.gcs)
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def test_elementwise_power(self, X, exponent, gc, dc):
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# negative integer raised with non-integer exponent is domain error
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X = np.abs(X)
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def powf(X):
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return (X ** exponent,)
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def powf_grad(g_out, outputs, fwd_inputs):
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return (exponent * (fwd_inputs[0] ** (exponent - 1)) * g_out,)
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op = core.CreateOperator(
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"Pow", ["X"], ["Y"], exponent=exponent)
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self.assertReferenceChecks(gc, op, [X], powf,
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output_to_grad="Y",
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grad_reference=powf_grad,
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ensure_outputs_are_inferred=True)
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@serial.given(X=hu.tensor(),
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exponent=st.floats(min_value=-3.0, max_value=3.0),
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**hu.gcs)
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def test_sign(self, X, exponent, gc, dc):
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def signf(X):
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return [np.sign(X)]
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op = core.CreateOperator(
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"Sign", ["X"], ["Y"])
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self.assertReferenceChecks(
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gc, op, [X], signf, ensure_outputs_are_inferred=True)
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self.assertDeviceChecks(dc, op, [X], [0])
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if __name__ == "__main__":
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unittest.main()
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