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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
from commons import print_separator
from commons import initialize_distributed
import mpu
import torch
import sys
sys.path.append("../..")
def test_set_cuda_rng_state(tensor_model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing set_rng_state with size {} ...'.
format(tensor_model_parallel_size))
mpu.initialize_model_parallel(tensor_model_parallel_size)
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
size = 123
seed = 1234
get_accelerator().manual_seed(1234)
tensor = get_accelerator().FloatTensor(size)
# Get the state
rng_state = get_accelerator().get_rng_state()
rng_state_copy = rng_state.clone()
# Do some stuff.
for _ in range(5):
torch.randn(size, out=tensor)
result_1 = tensor.clone()
assert rng_state.sub(rng_state_copy).max() == 0
assert get_accelerator().get_rng_state().sub(rng_state_copy).max() > 0
# State should be different.
new_rng_state = get_accelerator().get_rng_state()
max_diff = new_rng_state.sub(rng_state).max()
print(' max diff in rng state (should be non-zero) on global rank {}: {}'.
format(torch.distributed.get_rank(), max_diff))
assert max_diff > 0
# Reset the rng state and do the same stuff.
mpu.random._set_cuda_rng_state(rng_state)
for _ in range(5):
torch.randn(size, out=tensor)
mpu.random._set_cuda_rng_state(rng_state)
for _ in range(5):
torch.randn(size, out=tensor)
result_2 = tensor.clone()
# Results should be the same
error = result_2.sub(result_1).abs().max()
print(' max error in generated tensors (should be zero) on '
'global rank {}: {}'.format(torch.distributed.get_rank(), error))
assert error < 1.0e-6
# Input state should have remained intact.
error = rng_state.sub(rng_state_copy).max()
print(' max error in rng state (should be zero) on global rank {}: {}'.
format(torch.distributed.get_rank(), error))
assert error == 0
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print('>> passed the test :-)')
def test_cuda_rng_tracker(tensor_model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing cuda rng tracker with size {} ...'.
format(tensor_model_parallel_size))
mpu.initialize_model_parallel(tensor_model_parallel_size)
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
seed_1 = 1234
seed_2 = 4321
size = [12, 21]
tensor = get_accelerator().FloatTensor(size)
# Set to seed_1 and generate two tensors.
get_accelerator().manual_seed(seed_1)
torch.randn(size, out=tensor)
target_11 = tensor.clone()
torch.randn(size, out=tensor)
target_12 = tensor.clone()
# Set to seed_2 and generate two tensors.
get_accelerator().manual_seed(seed_2)
torch.randn(size, out=tensor)
target_21 = tensor.clone()
torch.randn(size, out=tensor)
target_22 = tensor.clone()
# Now if we interleave seed_1 and seed_2,
# we should still get the same tensors
get_accelerator().manual_seed(seed_1)
mpu.get_cuda_rng_tracker().add('test', seed_2)
torch.randn(size, out=tensor)
result_11 = tensor.clone()
with mpu.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 mpu.get_cuda_rng_tracker().fork('test'):
torch.randn(size, out=tensor)
result_22 = tensor.clone()
diff = result_11.sub(result_21).abs().max()
diff = min(diff, result_12.sub(result_22).abs().max())
print(' max diff in generated tensors (should be non-zero) on '
'global rank {}: {}'.format(torch.distributed.get_rank(), diff))
assert diff > 1.0e-6
error = max(result_11.sub(target_11).abs().max(),
result_12.sub(target_12).abs().max())
error = max(error, result_21.sub(target_21).abs().max())
error = max(error, result_22.sub(target_22).abs().max())
print(' max error in generated tensors (should be zero) on '
'global rank {}: {}'.format(torch.distributed.get_rank(), error))
assert error < 1.0e-6
# Reset the tracker
mpu.get_cuda_rng_tracker().reset()
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print('>> passed the test :-)')
def test_model_parallel_cuda_manual_seed(tensor_model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing model parallel cuda manual seed with size {} ...'.
format(tensor_model_parallel_size))
mpu.initialize_model_parallel(tensor_model_parallel_size)
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
mpu.model_parallel_cuda_manual_seed(12345)
assert get_accelerator().initial_seed() == 12345
with mpu.get_cuda_rng_tracker().fork():
assert get_accelerator().initial_seed() == (12345 + 2718 +
mpu.get_tensor_model_parallel_rank())
# Reset the tracker
mpu.get_cuda_rng_tracker().reset()
# Reset groups
mpu.destroy_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print('>> passed the test :-)')
if __name__ == '__main__':
initialize_distributed()
world_size = torch.distributed.get_world_size()
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
print_separator('test set rng state')
test_set_cuda_rng_state(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
print_separator('test cuda rng tracker')
test_cuda_rng_tracker(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
print_separator('test model parallel cuda manual seed')
test_model_parallel_cuda_manual_seed(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
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