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| import logging | |
| import torch | |
| from torch.testing._internal import common_utils | |
| logging.getLogger("torch").setLevel(logging.WARNING) | |
| from apex.transformer import parallel_state | |
| from apex.transformer import tensor_parallel | |
| from apex.transformer.testing.distributed_test_base import NcclDistributedTestBase | |
| from apex.transformer.testing.distributed_test_base import UccDistributedTestBase | |
| logging.getLogger("apex").setLevel(logging.WARNING) | |
| class TransformerRandomTestBase: | |
| def test_set_cuda_rng_state(self): | |
| for tensor_model_parallel_world_size in range(1, self.world_size + 1): | |
| if self.world_size % tensor_model_parallel_world_size: | |
| continue | |
| msg = f"tensor_model_parallel_world_size: {tensor_model_parallel_world_size}" | |
| parallel_state.initialize_model_parallel( | |
| tensor_model_parallel_size_=tensor_model_parallel_world_size | |
| ) | |
| size, seed = 123, 1234 | |
| torch.cuda.manual_seed(seed) | |
| tensor = torch.cuda.FloatTensor(size) | |
| rng_state = torch.cuda.get_rng_state() | |
| rng_state_clone = rng_state.clone() | |
| for _ in range(5): | |
| torch.randn(size, out=tensor) | |
| result_1 = tensor.clone() | |
| self.assertEqual(rng_state.sub(rng_state_clone).max(), 0, msg=msg) | |
| self.assertGreater( | |
| torch.cuda.get_rng_state().sub(rng_state_clone).max(), 0, | |
| msg=msg, | |
| ) | |
| new_rng_state = torch.cuda.get_rng_state() | |
| self.assertGreater(new_rng_state.sub(rng_state).max(), 0, msg=msg) | |
| tensor_parallel.random._set_cuda_rng_state(rng_state) | |
| for _ in range(5): | |
| torch.randn(size, out=tensor) | |
| tensor_parallel.random._set_cuda_rng_state(rng_state) | |
| for _ in range(5): | |
| torch.randn(size, out=tensor) | |
| result_2 = tensor.clone() | |
| self.assertEqual(result_2, result_1, msg=msg) | |
| self.assertEqual(rng_state.sub(rng_state_clone).max(), 0, msg=msg) | |
| parallel_state.destroy_model_parallel() | |
| def test_cuda_rng_tracker(self): | |
| for tensor_model_parallel_world_size in range(1, self.world_size + 1): | |
| if self.world_size % tensor_model_parallel_world_size: | |
| continue | |
| msg = f"tensor_model_parallel_world_size: {tensor_model_parallel_world_size}" | |
| parallel_state.initialize_model_parallel( | |
| tensor_model_parallel_size_=tensor_model_parallel_world_size | |
| ) | |
| seed_1, seed_2, size = 1234, 4321, [12, 21] | |
| tensor = torch.cuda.FloatTensor(size) | |
| torch.cuda.manual_seed(seed_1) | |
| torch.randn(size, out=tensor) | |
| target_11 = tensor.clone() | |
| torch.randn(size, out=tensor) | |
| target_12 = tensor.clone() | |
| torch.cuda.manual_seed(seed_2) | |
| torch.randn(size, out=tensor) | |
| targt_21 = tensor.clone() | |
| torch.randn(size, out=tensor) | |
| target_22 = tensor.clone() | |
| torch.cuda.manual_seed(seed_1) | |
| tensor_parallel.random.get_cuda_rng_tracker().add("test", seed_2) | |
| torch.randn(size, out=tensor) | |
| result_11 = tensor.clone() | |
| with tensor_parallel.random.get_cuda_rng_tracker().fork("test"): | |
| torch.randn(size, out=tensor) | |
| result_21 = tensor.clone() | |
| torch.randn(size, out=tensor) | |
| result_12 = tensor.clone() | |
| with tensor_parallel.random.get_cuda_rng_tracker().fork("test"): | |
| torch.randn(size, out=tensor) | |
| result_22 = tensor.clone() | |
| self.assertEqual(target_11, result_11, msg=msg) | |
| self.assertEqual(target_12, result_12, msg=msg) | |
| self.assertEqual(targt_21, result_21, msg=msg) | |
| self.assertEqual(target_22, result_22, msg=msg) | |
| self.assertNotEqual(result_11, result_21, msg=msg) | |
| self.assertNotEqual(result_21, result_22, msg=msg) | |
| tensor_parallel.random.get_cuda_rng_tracker().reset() | |
| parallel_state.destroy_model_parallel() | |
| class NcclTransformerRandomTest(TransformerRandomTestBase, NcclDistributedTestBase): pass | |
| class UccTransformerRandomTest(TransformerRandomTestBase, UccDistributedTestBase): pass | |
| if __name__ == "__main__": | |
| common_utils.run_tests() | |