caffe2/contrib/aten/aten_test.py

from caffe2.python import core
from hypothesis import given

import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np


class TestATen(hu.HypothesisTestCase):

    @given(inputs=hu.tensors(n=2), **hu.gcs)
    def test_add(self, inputs, gc, dc):
        op = core.CreateOperator(
            "ATen",
            ["X", "Y"],
            ["Z"],
            operator="add")

        def ref(X, Y):
            return [X + Y]
        self.assertReferenceChecks(gc, op, inputs, ref)

    @given(inputs=hu.tensors(n=2, dtype=np.float16), **hu.gcs_gpu_only)
    def test_add_half(self, inputs, gc, dc):
        op = core.CreateOperator(
            "ATen",
            ["X", "Y"],
            ["Z"],
            operator="add")

        def ref(X, Y):
            return [X + Y]
        self.assertReferenceChecks(gc, op, inputs, ref)

    @given(inputs=hu.tensors(n=1), **hu.gcs)
    def test_pow(self, inputs, gc, dc):
        op = core.CreateOperator(
            "ATen",
            ["S"],
            ["Z"],
            operator="pow", exponent=2.0)

        def ref(X):
            return [np.square(X)]

        self.assertReferenceChecks(gc, op, inputs, ref)

    @given(x=st.integers(min_value=2, max_value=8), **hu.gcs)
    def test_sort(self, x, gc, dc):
        inputs = [np.random.permutation(x)]
        op = core.CreateOperator(
            "ATen",
            ["S"],
            ["Z", "I"],
            operator="sort")

        def ref(X):
            return [np.sort(X), np.argsort(X)]
        self.assertReferenceChecks(gc, op, inputs, ref)

    @given(inputs=hu.tensors(n=1), **hu.gcs)
    def test_sum(self, inputs, gc, dc):
        op = core.CreateOperator(
            "ATen",
            ["S"],
            ["Z"],
            operator="sum")

        def ref(X):
            return [np.sum(X)]

        self.assertReferenceChecks(gc, op, inputs, ref)

    @given(**hu.gcs)
    def test_index_uint8(self, gc, dc):
        # Indexing with uint8 is deprecated, but we need to provide backward compatibility for some old models exported through ONNX
        op = core.CreateOperator(
            "ATen",
            ['self', 'mask'],
            ["Z"],
            operator="index")

        def ref(self, mask):
            return (self[mask.astype(np.bool_)],)

        tensor = np.random.randn(2, 3, 4).astype(np.float32)
        mask = np.array([[1, 0, 0], [1, 1, 0]]).astype(np.uint8)

        self.assertReferenceChecks(gc, op, [tensor, mask], ref)

    @given(**hu.gcs)
    def test_index_put(self, gc, dc):
        op = core.CreateOperator(
            "ATen",
            ['self', 'indices', 'values'],
            ["Z"],
            operator="index_put")

        def ref(self, indices, values):
            self[indices] = values
            return (self,)

        tensor = np.random.randn(3, 3).astype(np.float32)
        mask = np.array([[True, True, True], [True, False, False], [True, True, False]])
        values = np.random.randn(6).astype(np.float32)

        self.assertReferenceChecks(gc, op, [tensor, mask, values], ref)

    @given(**hu.gcs)
    def test_unique(self, gc, dc):
        op = core.CreateOperator(
            "ATen",
            ['self'],
            ["output"],
            sorted=True,
            return_inverse=True,
            # return_counts=False,
            operator="_unique")

        def ref(self):
            index, _ = np.unique(self, return_index=False, return_inverse=True, return_counts=False)
            return (index,)

        tensor = np.array([1, 2, 6, 4, 2, 3, 2])
        print(ref(tensor))
        self.assertReferenceChecks(gc, op, [tensor], ref)


if __name__ == "__main__":
    import unittest
    unittest.main()