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import torch
import megatron.core.parallel_state as ps
from tests.unit_tests.test_utilities import Utils
rank = Utils.rank
world_size = Utils.world_size
test_parallel_order = ['tp-cp-ep-dp-pp', 'tp-cp-pp-ep-dp']
@pytest.mark.parametrize('order', test_parallel_order)
@pytest.mark.flaky_in_dev
def test_initialize_and_destroy_model_parallel(order):
with pytest.raises(AssertionError):
assert ps.initialize_model_parallel(order=order)
Utils.initialize_distributed()
with pytest.raises(RuntimeError):
assert ps.initialize_model_parallel(tensor_model_parallel_size=2 * world_size, order=order)
with pytest.raises(RuntimeError):
assert ps.initialize_model_parallel(
pipeline_model_parallel_size=2 * world_size, order=order
)
with pytest.raises(RuntimeError):
assert ps.initialize_model_parallel(
pipeline_model_parallel_size=world_size,
tensor_model_parallel_size=world_size,
order=order,
)
with pytest.raises(RuntimeError):
assert ps.initialize_model_parallel(virtual_pipeline_model_parallel_size=2, order=order)
Utils.initialize_model_parallel(
tensor_model_parallel_size=2, pipeline_model_parallel_size=4, order=order
)
assert ps.model_parallel_is_initialized()
assert ps.get_model_parallel_group() is not None
assert ps.get_tensor_model_parallel_group() is not None
assert ps.get_pipeline_model_parallel_group() is not None
assert ps.get_data_parallel_group() is not None
assert ps.get_expert_model_parallel_group() is not None
assert ps.get_expert_tensor_parallel_group() is not None
assert ps.get_expert_data_parallel_group() is not None
assert ps.get_expert_tensor_model_pipeline_parallel_group() is not None
Utils.destroy_model_parallel()
assert ps._MODEL_PARALLEL_GROUP is None
@pytest.mark.parametrize('order', test_parallel_order)
def test_pipeline_parallel_initializations(order):
Utils.initialize_model_parallel(
tensor_model_parallel_size=2, pipeline_model_parallel_size=4, order=order
)
assert ps.get_pipeline_model_parallel_first_rank() == rank % 2
assert ps.get_data_parallel_src_rank() == rank
assert ps.get_pipeline_model_parallel_next_rank() == ((rank + 2) % world_size)
assert ps.get_pipeline_model_parallel_prev_rank() == ((rank - 2) % world_size)
Utils.destroy_model_parallel()
@pytest.mark.parametrize('order', test_parallel_order)
def test_data_parallel_initializations(order):
Utils.initialize_model_parallel(pipeline_model_parallel_size=world_size, order=order)
assert ps.get_data_parallel_src_rank() == rank
assert ps.get_data_parallel_world_size() == 1
assert ps.get_data_parallel_rank() == 0
Utils.destroy_model_parallel()
@pytest.mark.parametrize('order', test_parallel_order)
def test_tensor_model_parellel_world_size(order):
Utils.initialize_model_parallel(tensor_model_parallel_size=world_size, order=order)
assert ps.get_tensor_model_parallel_world_size() == world_size
ps.set_tensor_model_parallel_world_size(None)
assert ps.get_tensor_model_parallel_world_size() == world_size
Utils.destroy_model_parallel()
@pytest.mark.parametrize('order', test_parallel_order)
def test_expert_tensor_parellel_world_size(order):
Utils.initialize_model_parallel(expert_tensor_parallel_size=world_size, order=order)
assert ps.get_expert_tensor_parallel_world_size() == world_size
ps.set_expert_tensor_parallel_world_size(None)
assert ps.get_expert_tensor_parallel_world_size() == world_size
Utils.destroy_model_parallel()
@pytest.mark.parametrize('order', test_parallel_order)
def test_pipeline_model_parallel_world_size(order):
Utils.initialize_model_parallel(pipeline_model_parallel_size=world_size, order=order)
assert ps.get_pipeline_model_parallel_world_size() == world_size
ps.set_pipeline_model_parallel_world_size(None)
assert ps.get_pipeline_model_parallel_world_size() == world_size
Utils.destroy_model_parallel()
@pytest.mark.parametrize('order', test_parallel_order)
def test_tensor_model_parallel_rank(order):
Utils.initialize_model_parallel(tensor_model_parallel_size=world_size, order=order)
assert ps.get_tensor_model_parallel_rank() == rank
ps.set_tensor_model_parallel_rank(None)
assert ps.get_tensor_model_parallel_rank() == rank
Utils.destroy_model_parallel()
@pytest.mark.parametrize('order', test_parallel_order)
def test_moe_tensor_model_parellel_rank(order):
Utils.initialize_model_parallel(expert_tensor_parallel_size=world_size, order=order)
assert ps.get_expert_tensor_parallel_rank() == rank
ps.set_expert_tensor_parallel_rank(None)
assert ps.get_expert_tensor_parallel_rank() == rank
Utils.destroy_model_parallel()
@pytest.mark.parametrize('order', test_parallel_order)
def test_pipeline_model_parallel_rank(order):
Utils.initialize_model_parallel(pipeline_model_parallel_size=world_size, order=order)
assert ps.get_pipeline_model_parallel_rank() == rank
ps.set_pipeline_model_parallel_rank(None)
assert ps.get_pipeline_model_parallel_rank() == rank
Utils.destroy_model_parallel()
def test_context_parallel_rank():
Utils.initialize_model_parallel(context_parallel_size=world_size)
assert ps.get_context_parallel_rank() == rank
Utils.destroy_model_parallel()
def test_expert_model_parallel_rank():
Utils.initialize_model_parallel(expert_model_parallel_size=world_size)
assert ps.get_expert_model_parallel_rank() == rank
ps.set_expert_model_parallel_rank(None)
assert ps.get_expert_model_parallel_rank() == rank
Utils.destroy_model_parallel()
@pytest.mark.parametrize('order', test_parallel_order)
def test_is_pipeline_first_stage(order):
Utils.initialize_model_parallel(pipeline_model_parallel_size=world_size, order=order)
assert ps.is_pipeline_first_stage(ignore_virtual=False) == (rank == 0)
assert ps.is_pipeline_first_stage() == (rank == 0)
Utils.destroy_model_parallel()
@pytest.mark.parametrize('order', test_parallel_order)
def test_is_pipeline_last_stage(order):
Utils.initialize_model_parallel(pipeline_model_parallel_size=world_size, order=order)
assert ps.is_pipeline_last_stage(ignore_virtual=False) == (rank == world_size - 1)
assert ps.is_pipeline_last_stage() == (rank == world_size - 1)
Utils.destroy_model_parallel()
@pytest.mark.parametrize('order', test_parallel_order)
def test_virtual_pipeline_model_parallel_rank(order):
Utils.initialize_model_parallel(pipeline_model_parallel_size=world_size, order=order)
ps.set_virtual_pipeline_model_parallel_rank(rank)
assert ps.get_virtual_pipeline_model_parallel_rank() == rank
Utils.destroy_model_parallel()
@pytest.mark.parametrize('order', test_parallel_order)
def test_get_tensor_model_parallel_src_rank(order):
Utils.initialize_model_parallel(tensor_model_parallel_size=world_size, order=order)
assert ps.get_tensor_model_parallel_src_rank() == ((rank // world_size) * world_size)
Utils.destroy_model_parallel()
@pytest.mark.internal
@pytest.mark.parametrize(
'src_tp_pp, ep_size',
[
((1, 8), 1),
((2, 4), 1),
((4, 2), 1),
((8, 1), 1),
((4, 1), 2),
((1, 1), 8),
((1, 1), 2),
((2, 1), 4),
],
)
def test_different_initialize_order_consistency(src_tp_pp, ep_size):
Utils.initialize_model_parallel(
*src_tp_pp, expert_model_parallel_size=ep_size, order='tp-ep-dp-pp'
)
tp_rank = ps.get_tensor_model_parallel_rank()
dp_rank = ps.get_data_parallel_rank()
pp_rank = ps.get_pipeline_model_parallel_rank()
ep_rank = ps.get_expert_model_parallel_rank()
tp_g = torch.distributed.get_process_group_ranks(ps.get_tensor_model_parallel_group())
dp_g = torch.distributed.get_process_group_ranks(ps.get_data_parallel_group(False))
pp_g = torch.distributed.get_process_group_ranks(ps.get_pipeline_model_parallel_group())
dp_no_ep_g = torch.distributed.get_process_group_ranks(ps.get_expert_data_parallel_group())
cp_g = torch.distributed.get_process_group_ranks(ps.get_context_parallel_group())
mp_g = torch.distributed.get_process_group_ranks(ps.get_model_parallel_group())
tp_ep_g = torch.distributed.get_process_group_ranks(
ps.get_expert_tensor_and_model_parallel_group()
)
tp_dp_g = torch.distributed.get_process_group_ranks(
ps.get_tensor_and_data_parallel_group(False)
)
Utils.destroy_model_parallel()
Utils.initialize_model_parallel(
*src_tp_pp, expert_model_parallel_size=ep_size, order='tp-pp-ep-dp'
)
assert tp_rank == ps.get_tensor_model_parallel_rank()
assert dp_rank == ps.get_data_parallel_rank()
assert pp_rank == ps.get_pipeline_model_parallel_rank()
assert ep_rank == ps.get_expert_model_parallel_rank()
assert tp_g == torch.distributed.get_process_group_ranks(ps.get_tensor_model_parallel_group())
assert dp_g == torch.distributed.get_process_group_ranks(ps.get_data_parallel_group(False))
assert pp_g == torch.distributed.get_process_group_ranks(ps.get_pipeline_model_parallel_group())
assert dp_no_ep_g == torch.distributed.get_process_group_ranks(
ps.get_expert_data_parallel_group()
)
assert cp_g == torch.distributed.get_process_group_ranks(ps.get_context_parallel_group())
assert mp_g == torch.distributed.get_process_group_ranks(ps.get_model_parallel_group())
assert tp_ep_g == torch.distributed.get_process_group_ranks(
ps.get_expert_tensor_and_model_parallel_group()
)
assert tp_dp_g == torch.distributed.get_process_group_ranks(
ps.get_tensor_and_data_parallel_group(False)
)
Utils.destroy_model_parallel()
@pytest.mark.parametrize(
'src_tp_pp, ep_size',
[((1, 2), 1), ((1, 4), 1), ((2, 2), 1), ((1, 2), 2), ((1, 4), 2), ((2, 2), 2)],
)
@pytest.mark.flaky
@pytest.mark.flaky_in_dev
def test_different_initialize_order_unconsistency(src_tp_pp, ep_size):
Utils.initialize_model_parallel(
*src_tp_pp, expert_model_parallel_size=ep_size, order='tp-ep-dp-pp'
)
tp_g = torch.distributed.get_process_group_ranks(ps.get_tensor_model_parallel_group())
dp_g = torch.distributed.get_process_group_ranks(ps.get_data_parallel_group(False))
pp_g = torch.distributed.get_process_group_ranks(ps.get_pipeline_model_parallel_group())
cp_g = torch.distributed.get_process_group_ranks(ps.get_context_parallel_group())
amax_g = torch.distributed.get_process_group_ranks(ps.get_amax_reduction_group(False))
mp_g = torch.distributed.get_process_group_ranks(ps.get_model_parallel_group())
Utils.destroy_model_parallel()
Utils.initialize_model_parallel(
*src_tp_pp, expert_model_parallel_size=ep_size, order='tp-pp-ep-dp'
)
assert tp_g == torch.distributed.get_process_group_ranks(ps.get_tensor_model_parallel_group())
assert dp_g != torch.distributed.get_process_group_ranks(ps.get_data_parallel_group(False))
assert pp_g != torch.distributed.get_process_group_ranks(ps.get_pipeline_model_parallel_group())
assert cp_g == torch.distributed.get_process_group_ranks(ps.get_context_parallel_group())
assert amax_g != torch.distributed.get_process_group_ranks(ps.get_amax_reduction_group(False))
assert mp_g != torch.distributed.get_process_group_ranks(ps.get_model_parallel_group())
Utils.destroy_model_parallel()
@pytest.mark.internal
@pytest.mark.parametrize(
'nodes, num_gpu, tp, pp, cp, ep',
[
(1, 1, 1, 1, 1, 1),
(1, 8, 8, 1, 1, 1),
(1, 8, 2, 2, 1, 1),
(1, 8, 2, 4, 1, 1),
(3, 8, 8, 3, 1, 1),
(4, 8, 2, 4, 1, 1),
(8, 8, 8, 8, 1, 1),
(8, 8, 2, 1, 1, 4),
(8, 8, 2, 2, 2, 4),
(8, 8, 2, 1, 4, 8),
(8, 8, 2, 2, 2, 8),
(16, 8, 4, 8, 1, 1),
(16, 8, 4, 8, 1, 4),
(16, 8, 4, 8, 4, 1),
(16, 8, 8, 8, 1, 1),
(16, 8, 4, 8, 1, 1),
(16, 8, 8, 8, 1, 1),
(32, 8, 4, 8, 1, 1),
(32, 8, 8, 8, 1, 1),
(32, 8, 4, 8, 1, 4),
(32, 8, 8, 8, 4, 1),
(64, 8, 4, 2, 8, 8),
(64, 8, 4, 8, 1, 1),
(64, 8, 8, 8, 1, 1),
(96, 8, 4, 8, 1, 1),
(128, 8, 4, 2, 8, 8),
(128, 8, 4, 8, 1, 1),
(256, 8, 4, 8, 1, 1),
(316, 8, 4, 8, 1, 1),
(384, 8, 4, 8, 1, 1),
(512, 8, 4, 8, 1, 1),
(768, 8, 4, 8, 1, 1),
(1024, 8, 4, 8, 1, 1),
(1280, 8, 4, 8, 1, 1),
(1344, 8, 4, 8, 1, 1),
],
)
def test_rank_generator_for_tp_dp_pp(nodes, num_gpu, tp, pp, cp, ep):
def golden_rank_result_from_past_code(
world_size: int,
tensor_model_parallel_size: int = 1,
pipeline_model_parallel_size: int = 1,
context_parallel_size: int = 1,
expert_model_parallel_size: int = 1,
):
data_parallel_size: int = world_size // (
tensor_model_parallel_size * pipeline_model_parallel_size * context_parallel_size
)
num_tensor_model_parallel_groups: int = world_size // tensor_model_parallel_size
num_pipeline_model_parallel_groups: int = world_size // pipeline_model_parallel_size
dp_groups = []
dp_groups_with_cp = []
all_data_parallel_group_ranks_with_cp = []
for i in range(pipeline_model_parallel_size):
start_rank = i * num_pipeline_model_parallel_groups
end_rank = (i + 1) * num_pipeline_model_parallel_groups
for j in range(context_parallel_size * tensor_model_parallel_size):
ranks = range(
start_rank + j, end_rank, context_parallel_size * tensor_model_parallel_size
)
dp_groups.append(list(ranks))
for j in range(tensor_model_parallel_size):
ranks_with_cp = range(start_rank + j, end_rank, tensor_model_parallel_size)
all_data_parallel_group_ranks_with_cp.append(list(ranks_with_cp))
dp_groups_with_cp.append(list(ranks_with_cp))
cp_group = []
for i in range(pipeline_model_parallel_size):
for j in range(data_parallel_size):
start_rank = (
i * num_pipeline_model_parallel_groups
+ j * tensor_model_parallel_size * context_parallel_size
)
end_rank = (
i * num_pipeline_model_parallel_groups
+ (j + 1) * tensor_model_parallel_size * context_parallel_size
)
for k in range(tensor_model_parallel_size):
ranks = range(start_rank + k, end_rank, tensor_model_parallel_size)
cp_group.append(list(ranks))
mp_group = []
for i in range(data_parallel_size * context_parallel_size):
ranks = [
data_parallel_group_ranks_with_cp[i]
for data_parallel_group_ranks_with_cp in all_data_parallel_group_ranks_with_cp
]
mp_group.append(list(ranks))
tp_group = []
for i in range(num_tensor_model_parallel_groups):
ranks = range(i * tensor_model_parallel_size, (i + 1) * tensor_model_parallel_size)
tp_group.append(list(ranks))
pp_group = []
for i in range(num_pipeline_model_parallel_groups):
ranks = range(i, world_size, num_pipeline_model_parallel_groups)
pp_group.append(list(ranks))
tp_dp_group = []
tp_dp_cp_group = []
tensor_and_data_group_size_with_cp: int = (
tensor_model_parallel_size * data_parallel_size * context_parallel_size
)
num_tensor_and_data_groups_with_cp: int = world_size // tensor_and_data_group_size_with_cp
for i in range(num_tensor_and_data_groups_with_cp):
start_rank = i * tensor_and_data_group_size_with_cp
end_rank = start_rank + tensor_and_data_group_size_with_cp
ranks = range(start_rank, end_rank)
tp_dp_cp_group.append(list(ranks))
for j in range(context_parallel_size):
ranks = []
for k in range(data_parallel_size):
start_rank = (
i * tensor_and_data_group_size_with_cp
+ j * tensor_model_parallel_size
+ k * tensor_model_parallel_size * context_parallel_size
)
end_rank = start_rank + tensor_model_parallel_size
ranks = ranks + list(range(start_rank, end_rank))
tp_dp_group.append(list(ranks))
expert_tp_ep_group = []
expert_dp_group = []
expert_data_parallel_size = world_size // (
tensor_model_parallel_size * pipeline_model_parallel_size * expert_model_parallel_size
)
all_ranks = torch.arange(world_size).reshape(
(
pipeline_model_parallel_size,
expert_data_parallel_size,
expert_model_parallel_size,
tensor_model_parallel_size,
)
)
# (pp, dp, ep, tp) -> (pp*dp, ep*tp)
tp_ep_rearrange = torch.reshape(
all_ranks, (-1, expert_model_parallel_size * tensor_model_parallel_size)
)
num_tp_ep_groups = tp_ep_rearrange.shape[0]
for i in range(num_tp_ep_groups):
expert_tensor_and_model_parallel_ranks = tp_ep_rearrange[i].tolist()
expert_tp_ep_group.append(expert_tensor_and_model_parallel_ranks)
# (pp, dp, ep, tp) -> (pp*ep*tp, dp)
expert_dp_rearrange = torch.permute(all_ranks, (0, 2, 3, 1)).reshape(
-1, expert_data_parallel_size
)
num_expert_dp_groups = world_size // expert_data_parallel_size
for i in range(num_expert_dp_groups):
expert_dp_ranks = expert_dp_rearrange[i].tolist()
expert_dp_group.append(expert_dp_ranks)
return (
dp_groups,
dp_groups_with_cp,
cp_group,
mp_group,
tp_group,
pp_group,
tp_dp_group,
tp_dp_cp_group,
expert_tp_ep_group,
expert_dp_group,
)
world_size = nodes * num_gpu
dp = world_size // (tp * pp * cp)
expert_dp = world_size // (tp * ep * pp)
assert dp % ep == 0, f"dp size ({dp}) is not divisible by ep {ep} ."
assert (
world_size % (tp * pp * cp) == 0
), f"world_size ({world_size}) is not divisible by tp {tp} x pp {pp} x cp {cp}."
(
dp_groups,
dp_groups_with_cp,
cp_group,
mp_group,
tp_group,
pp_group,
tp_dp_group,
tp_dp_cp_group,
expert_tp_ep_group,
expert_dp_group,
) = golden_rank_result_from_past_code(
world_size=world_size,
tensor_model_parallel_size=tp,
pipeline_model_parallel_size=pp,
context_parallel_size=cp,
expert_model_parallel_size=ep,
)
rank_generator = ps.RankGenerator(tp=tp, ep=1, dp=dp, pp=pp, cp=cp, order="tp-cp-dp-pp")
expert_rank_generator = ps.RankGenerator(
tp=tp, ep=ep, dp=expert_dp, pp=pp, cp=1, order="tp-ep-dp-pp"
)
assert dp_groups == rank_generator.get_ranks(
"dp"
), f"{dp_groups} != {rank_generator.get_ranks('dp')}"
assert dp_groups_with_cp == rank_generator.get_ranks(
'dp-cp'
), f"{dp_groups_with_cp} != {rank_generator.get_ranks('dp-cp')}"
assert cp_group == rank_generator.get_ranks(
"cp"
), f"{cp_group} != {rank_generator.get_ranks('cp')}."
assert mp_group == rank_generator.get_ranks(
"tp-pp"
), f"{mp_group} != {rank_generator.get_ranks('tp-pp')}"
assert tp_group == rank_generator.get_ranks(
"tp"
), f"{tp_group} != {rank_generator.get_ranks('tp')}"
assert pp_group == rank_generator.get_ranks(
"pp"
), f"{pp_group} != {rank_generator.get_ranks('pp')}"
assert tp_dp_group == rank_generator.get_ranks(
"tp-dp"
), f"{tp_dp_group} != {rank_generator.get_ranks('tp-dp')}"
assert tp_dp_cp_group == rank_generator.get_ranks(
"tp-dp-cp"
), f"{tp_dp_cp_group} != {rank_generator.get_ranks('tp-dp-cp')}"
assert expert_tp_ep_group == expert_rank_generator.get_ranks(
"tp-ep"
), f"{expert_tp_ep_group} != {expert_rank_generator.get_ranks('tp-ep')}."
assert expert_dp_group == expert_rank_generator.get_ranks(
"dp"
), f"{expert_dp_group} != {expert_rank_generator.get_ranks('dp')}."
|