65bb3580e5cdd9adee17b5f80fba949550931271,test/test_tensorflow.py,MPITests,test_horovod_allreduce_grad,#MPITests#,321

Before Change

        hvd.init()
        size = hvd.size()

        with self.test_session(config=self.config) as session:
            // As of TensorFlow v1.9, gradients are not supported on
            // integer tensors
            dtypes = [tf.float32, tf.float64]
            dims = [1, 2, 3]
            for dtype, dim in itertools.product(dtypes, dims):
                with tf.device("/cpu:0"):
                    tf.set_random_seed(1234)
                    tensor = tf.random_uniform(
                        [5] * dim, -100, 100, dtype=dtype)
                    summed = hvd.allreduce(tensor, average=False)

                grad_ys = tf.ones([5] * dim)
                grad = tf.gradients(summed, tensor, grad_ys)[0]
                grad_out = session.run(grad)

                expected = np.ones([5] * dim) * size
                err = np.linalg.norm(expected - grad_out)
                self.assertLess(err, 0.00000001,
                                "gradient %s differs from expected %s, "
                                "error: %s" % (grad_out, expected, str(err)))

    def test_horovod_allgather(self):
        Test that the allgather correctly gathers 1D, 2D, 3D tensors.
        hvd.init()
        rank = hvd.rank()

After Change

            with tf.device("/cpu:0"):
                tf.set_random_seed(1234)
                if _executing_eagerly():
                    tensor = self.tfe.Variable(tf.random_uniform(
                        [5] * dim, -100, 100, dtype=dtype))
                    with tf.GradientTape() as tape:
                        summed = hvd.allreduce(tensor, average=False)
                else:
                    tensor = tf.random_uniform(
                        [5] * dim, -100, 100, dtype=dtype)
                    summed = hvd.allreduce(tensor, average=False)

Instances