65bb3580e5cdd9adee17b5f80fba949550931271,test/test_tensorflow.py,MPITests,test_horovod_broadcast,#MPITests#,509

Before Change

        if size == 1:
            return

        with self.test_session(config=self.config) as session:
            dtypes = [tf.uint8, tf.int8, tf.uint16, tf.int16,
                      tf.int32, tf.int64, tf.float16, tf.float32,
                      tf.float64, tf.bool]
            dims = [1, 2, 3]
            root_ranks = list(range(size))
            for dtype, dim, root_rank in itertools.product(dtypes, dims, root_ranks):
                tensor = tf.ones([17] * dim) * rank
                root_tensor = tf.ones([17] * dim) * root_rank
                if dtype == tf.bool:
                    tensor = tensor % 2
                    root_tensor = root_tensor % 2
                tensor = tf.cast(tensor, dtype=dtype)
                root_tensor = tf.cast(root_tensor, dtype=dtype)
                broadcasted_tensor = hvd.broadcast(tensor, root_rank)
                self.assertTrue(
                    session.run(tf.reduce_all(tf.equal(
                        tf.cast(root_tensor, tf.int32), tf.cast(broadcasted_tensor, tf.int32)))),
                    "hvd.broadcast produces incorrect broadcasted tensor")

    def test_horovod_broadcast_error(self):
        Test that the broadcast returns an error if any dimension besides
        the first is different among the tensors being broadcasted.
        hvd.init()

After Change

        dtypes = [tf.uint8, tf.int8, tf.uint16, tf.int16,
                  tf.int32, tf.int64, tf.float16, tf.float32,
                  tf.float64, tf.bool]
        dims = [1, 2, 3]
        root_ranks = list(range(size))
        for dtype, dim, root_rank in itertools.product(dtypes, dims, root_ranks):
            tensor = tf.ones([17] * dim) * rank
            root_tensor = tf.ones([17] * dim) * root_rank
            if dtype == tf.bool:
                tensor = tensor % 2
                root_tensor = root_tensor % 2
            tensor = tf.cast(tensor, dtype=dtype)
            root_tensor = tf.cast(root_tensor, dtype=dtype)
            broadcasted_tensor = hvd.broadcast(tensor, root_rank)
            self.assertTrue(
                self.evaluate(tf.reduce_all(tf.equal(
                    tf.cast(root_tensor, tf.int32), tf.cast(broadcasted_tensor, tf.int32)))),
                "hvd.broadcast produces incorrect broadcasted tensor")

    def test_horovod_broadcast_error(self):
        Test that the broadcast returns an error if any dimension besides
        the first is different among the tensors being broadcasted.
        hvd.init()

Instances