def test_build_randomized_omega(self):
artemis_update = ArtemisUpdate(self.params, self.workers)
artemis_update.workers_sub_set = self.workers
artemis_update.value_to_compress = torch.FloatTensor(
[i for i in range(0, 100, 10)])
# We initilize omega_k with two values (as if we are at iteration 2)
artemis_update.omega_k = [[
torch.FloatTensor([i for i in range(0, 100, 10)]),
torch.FloatTensor([i for i in range(10)])
],
[
torch.FloatTensor(
[i for i in range(0, 20, 2)]),
torch.FloatTensor([i for i in range(10)])
]]
nb_try = 1
# We want to check that we have two different quantization of the value to compress.
# But in quantization there is some randomness, and thus vectors can some time be identical.
# We carry out five try, it after that there are still equal we consider that it is uncorrect.
artemis_update.build_randomized_omega(self.cost_models)
self.assertEqual(
len(artemis_update.omega), 2,
"The number of compressed value kept on central server must be equal to 2."
)
while (nb_try < 5 and torch.all(artemis_update.omega[0].eq(
artemis_update.omega[1]))):
artemis_update.build_randomized_omega(self.cost_models)
nb_try += 1
self.assertTrue(
nb_try < 5,
"After 5 try, the two different quantizations are still identical."
)
self.assertTrue(len(artemis_update.omega_k) == 3)
def test_send_back_global_informations_and_update(self):
artemis_update = ArtemisUpdate(self.params, self.workers)
self.workers[0].idx_last_update = 1
self.workers[1].idx_last_update = 1
artemis_update.workers_sub_set = [
(self.workers[i], self.cost_models[i])
for i in range(self.params.nb_devices)
]
artemis_update.omega_k = [[
torch.FloatTensor([0, 50]),
torch.FloatTensor([0, 10])
], [torch.FloatTensor([2, 4]),
torch.FloatTensor([10, 20])]]
nb_try = 1
artemis_update.step = 1 / 10
artemis_update.send_back_global_informations_and_update(
self.cost_models)
while (nb_try < 5 and torch.all(
artemis_update.workers[0].local_update.model_param.eq(
artemis_update.workers[1].local_update.model_param))):
self.workers[0].idx_last_update = 1
self.workers[1].idx_last_update = 1
artemis_update.send_back_global_informations_and_update(
self.cost_models)
nb_try += 1
self.assertFalse(
torch.all(artemis_update.workers[0].local_update.model_param.eq(
artemis_update.workers[1].local_update.model_param)),
"The models on workers are expected to be different.")
self.assertTrue(
self.workers[0].idx_last_update == 2
and self.workers[1].idx_last_update == 2,
"Index of last participation of each worker should be updated to 2"
)